nils-herrmann/clean_scirepeval huggingface dataset:
Paper in Business Analytics Feature Selection using LASSO

Which are the most relevant attributes to describe a response variable? This is one of the first question a researcher need to ask himself while analyzing a dataset, and the answer is not trivial. This research paper aims to explain and discuss the use of the LASSO method to address the feature selection task. Feature selection is a crucial and challenging task in the statistical modeling field, there are many studies that try to optimize and standardize this process for any kind of data, but this is not an easy thing to do. In the first chapter an introduction of feature selection task and the LASSO method are presented. In the second chapter we will apply the LASSO feature selection property to a Linear Regression problem, and the results of the analysis on a real dataset will be shown. Finally, in the third chapter the same analysis is repeated on a Generalized Linear Model in particular a Logistic Regression Model for a high-dimensional dataset. In the same chapter the findings of the scientific study of J.Chen and Z.Chen  are presented.
Large-scale visual sentiment ontology and detectors using adjective noun pairs

We address the challenge of sentiment analysis from visual content. In contrast to existing methods which infer sentiment or emotion directly from visual low-level features, we propose a novel approach based on understanding of the visual concepts that are strongly related to sentiments. Our key contribution is two-fold: first, we present a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP). Second, we propose SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image. The VSO and SentiBank are distinct from existing work and will open a gate towards various applications enabled by automatic sentiment analysis. Experiments on detecting sentiment of image tweets demonstrate significant improvement in detection accuracy when comparing the proposed SentiBank based predictors with the text-based approaches. The effort also leads to a large publicly available resource consisting of a visual sentiment ontology, a large detector library, and the training/testing benchmark for visual sentiment analysis.
Is the deconvolution layer the same as a convolutional layer?

1 In our CVPR 2016 paper , we proposed a novel network architecture to perform single image super­resolution (SR). Most existing convolutional neural network (CNN) based super­resolution methods [10,11] first upsample the image using a bicubic interpolation, then apply a convolutional network. We will refer to these types of networks as high­resolution (HR) networks because the images are upsampled first. Instead, we feed the low­resolution (LR) input directly to a sub­pixel CNN as shown in Fig.1 :  An illustration of the ESCPN framework where r denotes the upscaling ratio. Let denote the upscaling ratio ­ e.g if the input LR image is then the output HR image will be r 1 × 1. We then output number of channels instead of one high­resolution (HR) image and use periodic r × r r 2 shuffling to recreate the HR image. The exact details about how our efficient sub­pixel convolutional layer works can be found in the paper. We will refer to our network as a LR network. In this note, we want to focus on two aspects related to two questions most people asked us at CVPR when they saw this network. Firstly, how can channels magically become a HR image? And secondly, r 2 why are convolution in LR space a better choice? These are actually the key questions we tried to answer in the paper, but we were not able to go into as much depth and clarity as we would've liked given the page limit. To better answer these questions, we first discuss the relationships between the deconvolution layer in the form of the transposed convolution layer, the sub­pixel convolutional layer and our efficient sub­pixel convolutional layer, which we'll go through in Sec. 1 and Sec. 2. We will refer to our efficient sub­pixel convolutional layer as a convolutional layer in LR space to distinguish it from the common sub­pixel convolutional layer . We will then show that for a fixed computational budget and complexity, a network with convolutions exclusively in LR space has more representation power at the same speed than a network that first upsamples the input in HR space.
The Complete Exosome Workflow Solution: From Isolation to Characterization of RNA Cargo

Exosomes are small (30-150 nm) vesicles containing unique RNA and protein cargo, secreted by all cell types in culture. They are also found in abundance in body fluids including blood, saliva, and urine. At the moment, the mechanism of exosome formation, the makeup of the cargo, biological pathways, and resulting functions are incompletely understood. One of their most intriguing roles is intercellular communication--exosomes function as the messengers, delivering various effector or signaling macromolecules between specific cells. There is an exponentially growing need to dissect structure and the function of exosomes and utilize them for development of minimally invasive diagnostics and therapeutics. Critical to further our understanding of exosomes is the development of reagents, tools, and protocols for their isolation, characterization, and analysis of their RNA and protein contents. Here we describe a complete exosome workflow solution, starting from fast and efficient extraction of exosomes from cell culture media and serum to isolation of RNA followed by characterization of exosomal RNA content using qRT-PCR and next-generation sequencing techniques. Effectiveness of this workflow is exemplified by analysis of the RNA content of exosomes derived from HeLa cell culture media and human serum, using Ion Torrent PGM as a sequencing platform.
Content-based image retrieval: approaches and trends of the new age

The last decade has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Likewise, digital imagery has expanded its horizon in many directions, resulting in an explosion in the volume of image data required to be organized. In this paper, we discuss some of the key contributions in the current decade related to image retrieval and automated image annotation, spanning 120 references. We also discuss some of the key challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. We conclude with a study on the trends in volume and impact of publications in the field with respect to venues/journals and sub-topics.
Using Scalable Video Coding for Dynamic Adaptive Streaming over HTTP in mobile environments

Dynamic Adaptive Streaming over HTTP (DASH) is a convenient approach to transfer videos in an adaptive and dynamic way to the user. As a consequence, this system provides high bandwidth flexibility and is especially suitable for mobile use cases where the bandwidth variations are tremendous. In this paper we have integrated the Scalable Video Coding (SVC) extensions of the Advanced Video Coding (AVC) standard into the recently ratified MPEG-DASH standard. Furthermore, we have evaluated our solution under restricted conditions using bandwidth traces from mobile environments and compared it with an improved version of our MPEG-DASH implementation using AVC as well as major industry solutions.
A Fuzzy Logic Controller for Autonomous Wheeled Vehicles

Autonomous vehicles have potential applications in many fields, such as replacing humans in hazardous environments, conducting military missions, and performing routine tasks for industry. Driving ground vehicles is an area where human performance has proven to be reliable. Drivers typically respond quickly to sudden changes in their environment. While other control techniques may be used to control a vehicle, fuzzy logic has certain advantages in this area; one of them is its ability to incorporate human knowledge and experience, via language, into relationships among the given quantities. Fuzzy logic controllers for autonomous vehicles have been successfully applied to address various (and sometimes simultaneous) navigational issues,including: • reaching a static target (Baturone, et al., 2004, Boada, et al., 2005, El Hajjaji & Bentalba, 2003, Maeda et al., 1991, Chen & Ozguner, 2005), • tracking moving targets (Ollero, et al., 2001), • maintaining stable vehicular velocity (Holzmann et al., 1998, Nobe & Wang, 2001), • following a lane or a wall (Rosa & Garcia-Alegre, 1990, Hessburg & Tomizuka, 1994, Peng & Tomizuka, 1993) and, • avoiding collision with static and dynamic obstacles (Baturone, et al., 2004, Murphy, 2001, Godjevac, et al., 2001, Seraji, 2005, Lee & Wang, 1994, Wheeler & Shoureshi, 1994, Ye & Wang, 2001). Several researchers combined fuzzy logic controllers with various learning techniques, such as: • supervised learning method (Godjevac, 2001), • evolutionary method (Hoffman, 2001, Kim, et al., 2001), • neural network (Pasquier, et al., 2001, Cang, et al., 2003), • reinforcement learning (Dai, et al., 2005) and, • optimization methods (Hong, 1997, Sanchez, et al., 1999).
Specifying Services for ITIL Service Management

The Information Technology Infrastructure Library (ITIL) is a collection of best practices for the management of IT services. ITIL helps organizations to become aware of the business value their IT services provide to internal and external stakeholders. Understanding this value is crucial to the definition of service level agreements (SLA) between an IT department and its stakeholders. However, it is not ITIL's objective to define how this value is to be elicited from stakeholders. This creates an opportunity for the use of RE methods in businesses. This paper describes the main principles of ITIL service management and illustrates how the SEAM RE method can contribute to the definition of an SLA by modeling the service provided by an IT department, the stakeholders of this service and the value the stakeholders expect from this service. A real industrial example is presented and analyzed.
Ontologies: principles, methods and applications

We observe that disparate backgrounds, languages, tools, and techniques are a major barrier to e ective communication among people, organisations, and/or software systems. We show how the development and implementation of an explicit account of a shared understanding (i.e. an `ontology') in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, inter-operability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purposes they serve. We outline a methodology for developing and evaluating ontologies, rst discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing de nitions. We then consider the bene ts of and describe, a more formal approach. We re-visit the scoping phase, and discuss the role of formal languages and techniques in the speci cation, implementation and evaluation of ontologies. Finally, we review the state of the art and practice in this emerging eld, considering various case studies, software tools for ontology development, key research issues and future prospects. AIAI-TR-191 Ontologies Page i
Increasing pre-kindergarten early literacy skills in children with developmental disabilities and delays.

Two hundred and nine children receiving early childhood special education services for developmental disabilities or delays who also had behavioral, social, or attentional difficulties were included in a study of an intervention to increase school readiness, including early literacy skills. Results showed that the intervention had a significant positive effect on children's literacy skills from baseline to the end of summer before the start of kindergarten (d=.14). The intervention also had significant indirect effects on teacher ratings of children's literacy skills during the fall of their kindergarten year (β=.09). Additionally, when scores were compared to standard benchmarks, a greater percentage of the children who received the intervention moved from being at risk for reading difficulties to having low risk. Overall, this study demonstrates that a school readiness intervention delivered prior to the start of kindergarten may help increase children's early literacy skills.
01 05 03 2 v 2 1 5 N ov 2 00 1 Quantum Digital Signatures

We present a quantum digital signature scheme whose security is based on fundamental principles of quantum physics. It allows a sender (Alice) to sign a message in such a way that the signature can be validated by a number of different people, and all will agree either that the message came from Alice or that it has been tampered with. To accomplish this task, each recipient of the message must have a copy of Alice’s “public key,” which is a set of quantum states whose exact identity is known only to Alice. Quantum public keys are more difficult to deal with than classical public keys: for instance, only a limited number of copies can be in circulation, or the scheme becomes insecure. However, in exchange for this price, we achieve unconditionally secure digital signatures. Sending an m-bit message uses up O(m) quantum bits for each recipient of the public key. We briefly discuss how to securely distribute quantum public keys, and show the signature scheme is absolutely secure using one method of key distribution. The protocol provides a model for importing the ideas of classical public key cryptography into the quantum world.
Optimal Degrees of Synaptic Connectivity

Synaptic connectivity varies widely across neuronal types. Cerebellar granule cells receive five orders of magnitude fewer inputs than the Purkinje cells they innervate, and cerebellum-like circuits, including the insect mushroom body, also exhibit large divergences in connectivity. In contrast, the number of inputs per neuron in cerebral cortex is more uniform and large. We investigate how the dimension of a representation formed by a population of neurons depends on how many inputs each neuron receives and what this implies for learning associations. Our theory predicts that the dimensions of the cerebellar granule-cell and Drosophila Kenyon-cell representations are maximized at degrees of synaptic connectivity that match those observed anatomically, showing that sparse connectivity is sometimes superior to dense connectivity. When input synapses are subject to supervised plasticity, however, dense wiring becomes advantageous, suggesting that the type of plasticity exhibited by a set of synapses is a major determinant of connection density.
The International Caries Detection and Assessment System (ICDAS): an integrated system for measuring dental caries.

This paper describes early findings of evaluations of the International Caries Detection and Assessment System (ICDAS) conducted by the Detroit Center for Research on Oral Health Disparities (DCR-OHD). The lack of consistency among the contemporary criteria systems limits the comparability of outcomes measured in epidemiological and clinical studies. The ICDAS criteria were developed by an international team of caries researchers to integrate several new criteria systems into one standard system for caries detection and assessment. Using ICDAS in the DCR-OHD cohort study, dental examiners first determined whether a clean and dry tooth surface is sound, sealed, restored, crowned, or missing. Afterwards, the examiners classified the carious status of each tooth surface using a seven-point ordinal scale ranging from sound to extensive cavitation. Histological examination of extracted teeth found increased likelihood of carious demineralization in dentin as the ICDAS codes increased in severity. The criteria were also found to have discriminatory validity in analyses of social, behavioral and dietary factors associated with dental caries. The reliability of six examiners to classify tooth surfaces by their ICDAS carious status ranged between good to excellent (kappa coefficients ranged between 0.59 and 0.82). While further work is still needed to define caries activity, validate the criteria and their reliability in assessing dental caries on smooth surfaces, and develop a classification system for assessing preventive and restorative treatment needs, this early evaluation of the ICDAS platform has found that the system is practical; has content validity, correlational validity with histological examination of pits and fissures in extracted teeth; and discriminatory validity.
Flexible and Fine-Grained Attribute-Based Data Storage in Cloud Computing

With the development of cloud computing, outsourcing data to cloud server attracts lots of attentions. To guarantee the security and achieve flexibly fine-grained file access control, attribute based encryption (ABE) was proposed and used in cloud storage system. However, user revocation is the primary issue in ABE schemes. In this article, we provide a ciphertext-policy attribute based encryption (CP-ABE) scheme with efficient user revocation for cloud storage system. The issue of user revocation can be solved efficiently by introducing the concept of user group. When any user leaves, the group manager will update users’ private keys except for those who have been revoked. Additionally, CP-ABE scheme has heavy computation cost, as it grows linearly with the complexity for the access structure. To reduce the computation cost, we outsource high computation load to cloud service providers without leaking file content and secret keys. Notably, our scheme can withstand collusion attack performed by revoked users cooperating with existing users. We prove the security of our scheme under the divisible computation Diffie-Hellman assumption. The result of our experiment shows computation cost for local devices is relatively low and can be constant. Our scheme is suitable for resource constrained devices.
Low voltage sub-nanosecond pulsed current driver IC for high-resolution LIDAR applications

This paper introduces a new low voltage sub-nanosecond monolithic pulsed current driver for light detection and ranging (LIDAR) applications. Unique architecture based on a controlled current source and Vernier activation sequence, combined with a monolithic implementation that allows operation with low input voltage levels, high-resolution pulse width and sub-nanosecond rise and fall times. An on-chip low voltage pulsed driver sub-nanosecond prototype has been implemented in a TS 0.18-μm 5V-gated power management process. It incorporates an integrated wide range sesnseFET based current sensor and a rail-to-rail comparator for current regulation. To characterize the avalanche capabilities of the integrated lateral MOSFET power devices required for the driver IC, a separate line of investigation has been carried out. Several lateral diffused MOS (LDMOS) power devices have been custom designed and experimentally evaluated for a life-cycle performance characterization. Post-layout analysis of the power driver IC is in a good agreement with the theoretical predictions. For a 5V input voltage, rise and fall times of the laser pulse light output are on the order of hundreds of picoseconds, with currents up to 5A. To validate the concept of high-resolution pulse width generation and short fall time, a discrete prototype has been constructed and experimentally tested.
Psychometric properties of a Korean version of the summary of diabetes self-care activities measure.

The summary of diabetes self-care activities (SDSCA) questionnaire is one of the most widely used self-report instruments for measuring diabetes self-management in adults.



This study aimed to examine the psychometric properties of a Korean version of the SDSCA questionnaire.



The 11-item English version of the SDSCA was translated into Korean following the standard translation methodology. The questionnaire was administered to 208 patients with type 2 diabetes. Exploratory and confirmatory factor analyses (EFA and CFA) were carried out for construct validity. Content validity index (CVI), internal consistency and a diabetes management self-efficacy scale (DMSES) were assessed.



The CVI of a Korean version of the SDSCA was .83. The EFA yielded a 9-item measure with a four factor solution with the same labels for original scales. The results of CFA showed the goodness of fit in the 9-item Korean SDSCA version (SDSCA-K). The internal consistency of SDSCA-K was moderate (Cronbach's α=.69) and the positive correlation between the SDSCA-K and the DMSES was identified.



The current study provides the initial psychometric properties of SDSCA-K modified to 9 items and supports SDSCA-K as a reliable and valid measure of diabetes self-management in Korean patients.
Photorealistic Video Super Resolution

With the advent of perceptual loss functions, new possibilities in super-resolution have emerged, and we currently have models that successfully generate near-photorealistic high-resolution images from their low-resolution observations. Up to now, however, such approaches have been exclusively limited to single image super-resolution. The application of perceptual loss functions on video processing still entails several challenges, mostly related to the lack of temporal consistency of the generated images, i.e., flickering artifacts. In this work, we present a novel adversarial recurrent network for video upscaling that is able to produce realistic textures in a temporally consistent way. The proposed architecture naturally leverages information from previous frames due to its recurrent architecture, i.e. the input to the generator is composed of the low-resolution image and, additionally, the warped output of the network at the previous step. We also propose an additional loss function to further reinforce temporal consistency in the generated sequences. The experimental validation of our algorithm shows the effectiveness of our approach which obtains competitive samples in terms of perceptual quality with improved temporal consistency.
Research on continuous auditing: A bibliometric analysis

This paper presents the results of a bibliometric study about the evolution of research on Continuous Auditing. This study has as main motivation to find reasons for the very slow evolvement of research on this topic. In addition, Continuous Auditing is one of the features of the emerging concept of Continuous Assurance. Thus, considering that Continuous Assurance represents numerous advantages for the organizational performance, this study also intends to understand if there is a relation between the evolution of research on Continuous Auditing and the still very low maturity levels of continuous assurance solutions. This study shows that the number of publications is considerably reduced and that the slow growth of research on Continuous Auditing may be contributing to the lack of maturity of Continuous Assurance.