nils-herrmann/clean_scirepeval huggingface dataset:
THE DANGER OF NOT LISTENING : HOW BROAD-BASED BUSINESS PARTICIPATION IN GOVERNMENT DESIGN OF REGULATIONS CAN INCREASE COMPLIANCE AND BENEFIT SOCIETY

Firms in emerging economies exhibit dangerously low compliance with government regulations aimed at protecting society from the negative externalities of their operations. Weak government enforcement contributes to this lack of compliance, but we argue that the limited legitimacy of government and its regulations also plays a critical role, especially among firms without insider access to key policy makers. We ground our theory development in the procedural justice and deliberative democracy literatures to show how a positive effect on regulatory compliance of participation by a firm in the regulatory design process is mediated by the firm’s view of government legitimacy. Importantly, government itself also plays a pivotal role in this process, as the effect of firm participation on compliance can actually become negative if firms see government as unresponsive to their input. We find empirical support for our theory in analysis of a large-scale survey of firms in the rapidly changing emerging economy of Vietnam.
Sequence to sequence learning for unconstrained scene text recognition

In this work we present a state-of-the-art approach for unconstrained natural scene text recognition. We propose a cascade approach that incorporates a convolutional neural network (CNN) architecture followed by a long short term memory model (LSTM). The CNN learns visual features for the characters and uses them with a softmax layer to detect sequence of characters. While the CNN gives very good recognition results, it does not model relation between characters, hence gives rise to false positive and false negative cases (confusing characters due to visual similarities like"g"and"9", or confusing background patches with characters; either removing existing characters or adding non-existing ones) To alleviate these problems we leverage recent developments in LSTM architectures to encode contextual information. We show that the LSTM can dramatically reduce such errors and achieve state-of-the-art accuracy in the task of unconstrained natural scene text recognition. Moreover we manually remove all occurrences of the words that exist in the test set from our training set to test whether our approach will generalize to unseen data. We use the ICDAR 13 test set for evaluation and compare the results with the state of the art approaches [11, 18]. We finally present an application of the work in the domain of for traffic monitoring.
Envelope-Tracking Two-Stage Power Amplifier With Dual-Mode Supply Modulator for LTE Applications

This paper presents an envelope tracking power amplifier (ET PA) using a dual-mode supply modulator for handset application. The supply modulator has a combined structure with a linear amplifier and a switching amplifier. The dual-mode supply modulator operates in high-power mode and low-power mode by providing the supply voltage of the linear amplifier of 5 and 2.5 V, respectively. For 1.74-GHz long-term evolution signal with 10-MHz bandwidth, 6.44-dB peak-to-average power ratio, and 16-quadrature amplitude modulation, the ET PA delivers a power-added efficiency (PAE) of 39.8%, an evolved universal terrestrial radio access adjacent channel leakage ratio (E-UTRAACLR) of - 35.7 dBc, and an error vector magnitude (EVM) of 3.81% at an average output power of 27 dBm. The ET PA also delivers a PAE of 22.6% at an average output power of 18 dBm, which is 6.6% higher than that of the conventional ET PA and 13.1% higher than that of the standalone PA. The supply modulator is connected to a drive stage and a power stage of the PA simultaneously for further enhanced efficiency at a power back-off region. The dual-mode two-stage ET PA delivers a PAE of 38.1%, an E-UTRAACLR of - 32.9 dBc, and an EVM of 4.74% at an average output power of 27 dBm. The dual-mode two-stage ET PA also delivers a PAE of 26.3% at an average output power of 18 dBm, which is 7.8% higher than that of the conventional two-stage ET PA and 16.7% higher than that of the standalone PA.
Finding local communities in protein networks

Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf , allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent, making our application useful for biologists who wish to investigate functional modules that a particular protein is a part of.
Snitches, Trolls, and Social Norms: Unpacking Perceptions of Social Media Use for Crime Prevention

In this paper, we describe how people perceive the use of social media to support crime prevention in their communities. Based on survey and interview data from residents in high- and low-crime neighborhoods in Chicago, we found that African Americans, people from high-crime neighborhoods, and people with low levels of trust in local police are less likely to view social media as an appropriate tool to support citizens or the police in local crime prevention efforts. Residents' concerns include information getting into the wrong hands, trolls, and being perceived as a snitch. Despite concerns with usage, citizens also viewed social media as a tool that can supplement in-person crime prevention efforts and facilitate relationship-building and information sharing. We discuss the complexities of hyper-local usage of social media to combat crime by describing the social and historical contexts in which these tools exist.
Local Descriptor for Robust Place Recognition Using LiDAR Intensity

Place recognition is a challenging problem in mobile robotics, especially in unstructured environments or under viewpoint and illumination changes. Most LiDAR-based methods rely on geometrical features to overcome such challenges, as generally scene geometry is invariant to these changes, but tend to affect camera-based solutions significantly. Compared to cameras, however, LiDARs lack the strong and descriptive appearance information that imaging can provide. To combine the benefits of geometry and appearance, we propose coupling the conventional geometric information from the LiDAR with its calibrated intensity return. This strategy extracts extremely useful information in the form of a new descriptor design, coined ISHOT, outperforming popular state-of-the-art geometric-only descriptors by significant margin in our local descriptor evaluation. To complete the framework, we furthermore develop a probabilistic keypoint voting place recognition algorithm, leveraging the new descriptor and yielding sublinear place recognition performance. The efficacy of our approach is validated in challenging global localization experiments in large-scale built-up and unstructured environments.
Delta-sigma modulation in fractional-N frequency synthesis

This paper describes a delta-sigma ( A-Z ) modulation and fractional-S frequency division technique to perform indirect digital frequency synthesis based on the use of a phaselocked loop (PLL). The use of 1 Y modulation concepts results in a beneficial noise shaping of the phase noise (jitter) introduced by fractional-S division. The technique has the potential to provide low phase noise, fast settling time, and reduced impact of spurious frequencies when compared with existing fractional--Y PLL techniques. Phase Detector Loop Filter vco
Using Simulation to Model Customer Behavior in the Context of Customer Lifetime Value Estimation

T article illustrates how simulation can be used in the classroom for modeling customer behavior in the context of customer lifetime value estimation. Operations research instructors could use this exercise to introduce multiperiod spreadsheet simulation models in a business setting that is of great importance in practice, and the simulation approach to teaching this subject could be of interest also to marketing and accounting instructors. At Babson College, the spreadsheet simulation exercise is part of an integrated one-case teaching day of the marketing, accounting, and operations research disciplines in the full-time MBA program, but the exercise is directly transferable to stand-alone courses as well. In our experience, students have felt empowered by the ability to incorporate their ideas about customer behavior directly into customer lifetime value models, and have appreciated the ease with which simulation enables them to obtain intuition about the sensitivity of their estimates to different assumptions.
An improved sampling-based DBSCAN for large spatial databases

Spatial data clustering is one of the important data mining techniques for extracting knowledge from large amount of spatial data collected in various applications, such as remote sensing, GIS, computer cartography, environmental assessment and planning, etc. Several useful and popular spatial data clustering algorithms have been proposed in the past decade. DBSCAN is one of them, which can discover clusters of any arbitrary shape and can handle the noise points effectively. However, DBSCAN requires large volume of memory support because it operates on the entire database. This paper presents an improved sampling-based DBSCAN which can cluster large-scale spatial databases effectively. Experimental results included to establish that the proposed sampling-based DBSCAN outperforms DBSCAN as well as its other counterparts, in terms of execution time, without losing the quality of clustering.
dbrec - Music Recommendations Using DBpedia

This paper describes the theoretical background and the implementation of dbrec, a music recommendation system built on top of DBpedia, offering recommendations for more than 39,000 bands and solo artists. We discuss the various challenges and lessons learnt while building it, providing relevant insights for people developing applications consuming Linked Data. Furthermore, we provide a user-centric evaluation of the system, notably by comparing it to last.fm.
A complete fuzzy decision tree technique

In this paper, a new method of fuzzy decision trees called soft decision trees (SDT) is presented. This method combines tree growing and pruning, to determine the structure of the soft decision tree, with re4tting and back4tting, to improve its generalization capabilities. The method is explained and motivated and its behavior is 4rst analyzed empirically on 3 large databases in terms of classi4cation error rate, model complexity and CPU time. A comparative study on 11 standard UCI Repository databases then shows that the soft decision trees produced by this method are signi4cantly more accurate than standard decision trees. Moreover, a global model variance study shows a much lower variance for soft decision trees than for standard trees as a direct cause of the improved accuracy.
On Semantic Algebra: A Denotational Mathematics for Natural Language Comprehension and Cognitive Computing

Semantics is composed meaning of language expressions and perceptions at the levels of words, phrase, sentence, paragraph, and essay. The basic unit of formal semantics is a concept as a formal model of words in a natural language. Cognitive linguistics focuses on cognitive semantics and its interaction with underlying syntactic structures. A denotational mathematics known as semantic algebra is presented for rigorous semantics manipulations in cognitive linguistics. Semantic algebra deals with formal semantics of linguistics by a general mathematical model of semantics and a set of algebraic operators. On the basis of semantic algebra, language semantics may not only be deductively analyzed based on syntactic structures from the top down, but also be synthetically composed by the algebraic semantic operators from the bottom up at different levels of language units. Semantic algebra enables a wide range of applications in cognitive informatics, cognitive linguistics, computational linguistics, semantic computing, cognitive computing, machine learning, computing with words, as well as natural language analysis, synthesis, and comprehension.
Inferring causal impact using Bayesian structural time-series models

An important problem in econometrics and marketing is to infer the causal impact that a designed market intervention has exerted on an outcome metric over time. In order to allocate a given budget optimally, for example, an advertiser must determine the incremental contributions that different advertising campaigns have made to web searches, product installs, or sales. This paper proposes to infer causal impact on the basis of a diffusion-regression state-space model that predicts the counterfactual market response that would have occurred had no intervention taken place. In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of variation, including the time-varying influence of contemporaneous covariates, i.e., synthetic controls. Using a Markov chain Monte Carlo algorithm for posterior inference, we illustrate the statistical properties of our approach on synthetic data. We then demonstrate its practical utility by evaluating the effect of an online advertising campaign on search-related site visits. We discuss the strengths and limitations of our approach in improving the accuracy of causal attribution, power analyses, and principled budget allocation.
Linear AC LED driver with the multi-level structure and variable current regulator

This paper proposes a linear AC LED driver for LED lighting applications. The proposed circuit is small in size because the circuit structure consists of only semiconductors and resistors without any reactors and electrolytic capacitors. The current bypass circuit which is connected in parallel to the LED string consists of single MOSFET, single zener diode and two resistors. The MOSFET is operated in an active state by a self-bias circuit. Thus, an external controller and high voltage gate drivers are not required. The proposed circuit is experimentally validated by using a 7 W prototype. From the experimental results, the THD of input current is 2.1% and the power factor is 0.999. In addition, the simulation loss analysis demonstrates an efficiency of 87% for a 7 W prototype.
MiniCPS: A Toolkit for Security Research on CPS Networks

In recent years, tremendous effort has been spent to modernizing communication infrastructure in Cyber-Physical Systems (CPS) such as Industrial Control Systems (ICS) and related Supervisory Control and Data Acquisition (SCADA) systems. While a great amount of research has been conducted on network security of office and home networks, recently the security of CPS and related systems has gained increased attention. Unfortunately, real-world CPS are often not open to security researchers, and as a result very few reference physical-layer processes, control systems and communication topologies are available.
 In this work, we present MiniCPS, a toolkit intended to alleviate this problem. The goal of MiniCPS is to create an extensible, reproducible research environment for network communications, control systems, and physical-layer interactions in CPS. Instead of focusing on a customized simulation settings for specific subsystems, the main goal is to establish a framework to connect together real CPS soft- and hardware, simulation scripts for such components, and physical-layer simulation engines. MiniCPS builds on Mininet to provide lightweight real-time network emulation, and extends Mininet with tools to simulate typical CPS components such as programmable logic controllers, which use industrial protocols (eg. EtherNet/IP, Modbus/TCP). To capture physical-layer interactions, MiniCPS defines a simple API to connect to physical-layer simulations. We demonstrate applications of MiniCPS in two example scenarios, and show how MiniCPS can be used to develop attacks and defenses that are directly applicable to real systems.
RCS Reduction of Ridged Waveguide Slot Antenna Array Using EBG Radar Absorbing Material

This letter investigates the application of EBG radar absorbing material (RAM) to asymmetric ridged waveguide slot antenna array to reduce its backward RCS. The EBG RAM is based on the mushroom-like EBG structure loaded with lumped resistances. A ridged waveguide slot antenna array with 4 times 10 slot elements was designed and built, part of the metal ground plane of the array is replaced with this EBG RAM. The measured results show that performance of the antenna array is preserved when EBG RAM is used. At working frequency the backward RCS of antenna array with EBG RAM has dramatically reduced than that of the common antenna array.
Exploiting Visibility Information in Surface Reconstruction to Preserve Weakly Supported Surfaces

We present a novel method for 3D surface reconstruction from an input cloud of 3D points augmented with visibility information. We observe that it is possible to reconstruct surfaces that do not contain input points. Instead of modeling the surface from input points, we model free space from visibility information of the input points. The complement of the modeled free space is considered full space. The surface occurs at interface between the free and the full space. We show that under certain conditions a part of the full space surrounded by the free space must contain a real object also when the real object does not contain any input points; that is, an occluder reveals itself through occlusion. Our key contribution is the proposal of a new interface classifier that can also detect the occluder interface just from the visibility of input points. We use the interface classifier to modify the state-of-the-art surface reconstruction method so that it gains the ability to reconstruct weakly supported surfaces. We evaluate proposed method on datasets augmented with different levels of noise, undersampling, and amount of outliers. We show that the proposed method outperforms other methods in accuracy and ability to reconstruct weakly supported surfaces.
Building an Efficient Put-Intensive Key-Value Store with Skip-Tree

Multi-component based Log-Structured Merge-tree (LSM-tree) has been becoming one of the mainstream indexes. LSM-tree adopts component-by-component KV item flowing down mechanism to push each KV item from one smaller component to the adjacent larger component during compaction procedures until the KV items reach the largest component. This process incurs significant write amplification and limits the write throughput. In this paper, we propose one multi-component Skip-tree to aggressively push the KV items to the non-adjacent larger components via skipping some components and then make the KV items’ top-down move more efficient. We develop adaptive and reliable KV item movements among components. By reducing the number of steps during the flowing process from memory-resident component to the disk-resident largest component, Skip-tree can effectively reduce the write amplification and thus improve the system throughput. We design and implement one high performance key-value store, named SkipStore, based on Skip-tree. The experiments demonstrate that SkipStore outperforms the state-of-the-art open-sourced system RocksDB in Facebook by 66.5 percent under HDD and 61 percent under SSD.
A fair method for resetting the target in interrupted one-day cricket matches

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Guided Local Search for the Three-Dimensional Bin-Packing Problem

The three-dimensional bin packing problem is the problem of orthogonally packing a set of boxes into a minimum number of three-dimensional bins. In this paper we present a heuristic algorithm based on Guided Local Search (GLS). Starting with an upper bound on the number of bins obtained by a greedy heuristic, the presented algorithm iteratively decreases the number of bins, each time searching for a feasible packing of the boxes using GLS. The process terminates when a given time limit has been reached or the upper bound matches a precomputed lower bound. The algorithm can also be applied to two-dimensional bin packing problems by having a constant depth for all boxes and bins. Computational experiments are reported for two-and three-dimensional instances with up to 200 boxes, and the results are compared with those obtained by heuristics and exact methods from the literature.
An Approach of a Decision Support and Home Monitoring System for Patients with Neurological Disorders using Internet of Things Concepts

-The Internet of Things and information and CommunicationTechnologies applied in development of health care systems have reached an evolutionary process. This paper presentsthe development of an integrated intelligent system for Parkinson’s disease Screening.The Decision Support and Home Monitoring System aredesigned to assist and support physicians in diagnosis, home monitoring, medical treatment, medical prescriptions, rehabilitation and progress of his patients with Parkinson’s disease. The system will be extended in future research for other Neurological Disorders.This paper has an interdisciplinary character and includes areas such as e-Health, Internet of Things, Information and Communication Technologyand Artificial Intelligence with their application in medical domain.  -Health Monitoring, ExpertSystem, Internet of Things, Neurological Disorders, sensors, Artificial Intelligence.