nils-herrmann/clean_scirepeval huggingface dataset:
A multi-scene deep learning model for image aesthetic evaluation

Aesthetic evaluation of images has attracted a lot of research interests recently. Previous work focused on extracting handcrafted image features or generic image descriptors to build statistical model for aesthetic evaluation. However, the effectiveness of these approaches is limited by researchers' understanding on the aesthetic rules. In this paper, we present a Multi-Scene Deep Learning Model (MSDLM) to enable automatic aesthetic feature learning. This deep learning model achieves better results because it improves performance on some major problems, including limited data amount and categories, scenes dependent evaluation, unbalanced dataset, noise data etc. Major innovations are as follows. (1) We design a scene convolutional layer consist of multi-group descriptors in the network elaborately so that the model has a comprehensive learning capacity for image aesthetic. (2) We design a pre-training procedure to initialize our model. Through pre-training the multi-group descriptors discriminatively, our model can extract specific aesthetic features for various scenes, and reduce the impact of noise data when building the model. Experimental results show that our approach significantly outperforms existing methods on two benchmark datasets.  Deep learning, Image aesthetic, Multi-scene deep learning model, Pre-training
Computational microscopic imaging for malaria parasite detection: a systematic review.

Malaria, being an epidemic disease, demands its rapid and accurate diagnosis for proper intervention. Microscopic image-based characterization of erythrocytes plays an integral role in screening of malaria parasites. In practice, microscopic evaluation of blood smear image is the gold standard for malaria diagnosis; where the pathologist visually examines the stained slide under the light microscope. This visual inspection is subjective, error-prone and time consuming. In order to address such issues, computational microscopic imaging methods have been given importance in recent times in the field of digital pathology. Recently, such quantitative microscopic techniques have rapidly evolved for abnormal erythrocyte detection, segmentation and semi/fully automated classification by minimizing such diagnostic errors for computerized malaria detection. The aim of this paper is to present a review on enhancement, segmentation, microscopic feature extraction and computer-aided classification for malaria parasite detection.
The Service Dominant Strategy Canvas: Towards Networked Business Models

Service orientation, customer focus and collaboration between firms are profoundly changing the way of doing business. Marketing scholars are the first academics to conceptualize these changes under a new mindset, known as the Service Dominant Logic. However, management constructs are needed to apply this mindset to the business environment. Therefore, we have developed a new conceptual model of a Service Dominant Strategy with a visual representation in the form of a canvas. Our model is constructed by integrating current definitions of a Service Dominant strategy and by confronting them with traditional strategies. The model facilitates the design of Service Dominant strategies by answering the questions associated with fifteen elements. Experimental application of our approach in several industry domains shows the importance of both strategic level design and Service Dominant thinking.
Clinical performance of porcelain laminate veneers: outcomes of the aesthetic pre-evaluative temporary (APT) technique.

This article evaluates the long-term clinical performance of porcelain laminate veneers bonded to teeth prepared with the use of an additive mock-up and aesthetic pre-evaluative temporary (APT) technique over a 12-year period. Sixty-six patients were restored with 580 porcelain laminate veneers. The technique, used for diagnosis, esthetic design, tooth preparation, and provisional restoration fabrication, was based on the APT protocol. The influence of several factors on the durability of veneers was analyzed according to pre- and postoperative parameters. With utilization of the APT restoration, over 80% of tooth preparations were confined to the dental enamel. Over 12 years, 42 laminate veneers failed, but when the preparations were limited to the enamel, the failure rate resulting from debonding and microleakage decreased to 0%. Porcelain laminate veneers presented a successful clinical performance in terms of marginal adaptation, discoloration, gingival recession, secondary caries, postoperative sensitivity, and satisfaction with restoration shade at the end of 12 years. The APT technique facilitated diagnosis, communication, and preparation, providing predictability for the restorative treatment. Limiting the preparation depth to the enamel surface significantly increases the performance of porcelain laminate veneers.
Reconstruction of 3D virtual buildings from 2D architectural floor plans

Visualizing architectural designs with t,he aid of Virtual Reality (VR) technology is becoming a common task among architects, design engineers, contractors and customers due to a more realistic resemblance to the real look of the final constructions. In order to preview virtual buildings, one needs to reconstruct a VR model from the two-dimensional architectural drawings. In the conventional approach, a large amount of manual tasks are necessary while using some common 3D authoring tools avaliable in the market. This gives us the incentive to examine the process of modelling and reconstruction of 2D designs for use in practical applications. In this paper, we identify the main tasks in the conventional reconstruction process and give a semi-aut,omated solution that aids in the process of ext,rusion and modelling of large building complexes.
School Leadership and Cyberbullying—A Multilevel Analysis

Cyberbullying is a relatively new form of bullying, with both similarities and differences to traditional bullying. While earlier research has examined associations between school-contextual characteristics and traditional bullying, fewer studies have focused on the links to students' involvement in cyberbullying behavior. The aim of the present study is to assess whether school-contextual conditions in terms of teachers' ratings of the school leadership are associated with the occurrence of cyberbullying victimization and perpetration among students. The data are derived from two separate data collections performed in 2016: The Stockholm School Survey conducted among students in the second grade of upper secondary school (ages 17-18 years) in Stockholm municipality, and the Stockholm Teacher Survey which was carried out among teachers in the same schools. The data include information from 6067 students distributed across 58 schools, linked with school-contextual information based on reports from 1251 teachers. Cyberbullying victimization and perpetration are measured by students' self-reports. Teachers' ratings of the school leadership are captured by an index based on 10 items; the mean value of this index was aggregated to the school level. Results from binary logistic multilevel regression models show that high teacher ratings of the school leadership are associated with less cyberbullying victimization and perpetration. We conclude that a strong school leadership potentially prevents cyberbullying behavior among students.
Hamsa: fast signature generation for zero-day polymorphic worms with provable attack resilience

Zero-day polymorphic worms pose a serious threat to the security of Internet infrastructures. Given their rapid propagation, it is crucial to detect them at edge networks and automatically generate signatures in the early stages of infection. Most existing approaches for automatic signature generation need host information and are thus not applicable for deployment on high-speed network links. In this paper, we propose Hamsa, a network-based automated signature generation system for polymorphic worms which is fast, noise-tolerant and attack-resilient. Essentially, we propose a realistic model to analyze the invariant content of polymorphic worms which allows us to make analytical attack-resilience guarantees for the signature generation algorithm. Evaluation based on a range of polymorphic worms and polymorphic engines demonstrates that Hamsa significantly outperforms Polygraph (J. Newsome et al., 2005) in terms of efficiency, accuracy, and attack resilience
A hybrid and learning agent architecture for network intrusion detection

Learning is an effective way for automating the adaptation of systems to their environment. This ability is especially relevant in dynamic environments as computer networks where new intrusions are constantly emerging, most of them having similarities and occurring frequently. Traditional intrusion detection systems still have limitations of adaptability because they are just able to detect intrusions previously set in system design. This paper proposes HyLAA a software agent architecture that combines case-based reasoning, reactive behavior and learning. Through its learning mechanism, HyLAA can adapt itself to its environment and identify new intrusions not previously specified in system design. This is done by learning new reactive rules by observing recurrent good solutions to the same perception from the casebased reasoning system, which will be stored in the agent knowledge base. The effectiveness of HyLAA to detect intrusions using case-based reasoning behavior, the accuracy of the classifier learned by the learning component and both the performance and effectiveness of HyLAA to detect intrusions using hybrid behavior with learning and without learning were evaluated, respectively, by conducting four experiments. In the first experiment, HyLAA exhibited good effectiveness to detect intrusions. In the second experiment the classifiers learned by the learning component presented high accuracy. Both the hybrid agent behavior with learning and without learning (third and fourth experiment, respectively) presented greater effectiveness and a balance between performance and effectiveness, but only the hybrid behavior showed better effectiveness and performance as long as the agent learns.
SSD-Assisted Backup and Recovery for Database Systems

Backup and recovery is an important feature of database systems since it protects data against unexpected hardware and software failures. Database systems can provide data safety and reliability by creating a backup and restoring the backup from a failure. Database administrators can use backup/recovery tools that are provided with database systems or backup/recovery methods with operating systems. However, the existing tools perform time-consuming jobs and the existing methods may negatively affect run-time performance during normal operation even though high-performance SSDs are used. In this paper, we present an SSD-assisted backup/recovery scheme for database systems. In our scheme, we extend the out-of-place update characteristics of flash-based SSDs for backup/recovery operations. To this end, we exploit the resources (e.g., flash translation layer and DRAM cache with supercapacitors) inside SSDs, and we call our SSD with new backup/recovery features BR-SSD. We design and implement the backup/recovery functionality in the Samsung enterprise-class SSD (i.e., SM843Tn) for more realistic systems. Furthermore, we conduct a case study of BR-SSDs in replicated database systems and modify MySQL with replication to integrate BR-SSDs. The experimental result demonstrates that our scheme provides fast recovery while it does not negatively affect the run-time performance during normal operation.
Episodic and semantic memory in mild cognitive impairment

Little is known about episodic and semantic memory in the early predementia stage of Alzheimer's disease (AD), which is referred to as mild cognitive impairment (MCI). To explore person knowledge, item recognition and spatial associative memory, we designed the Face Place Test (FPT). A total of 75 subjects participated: 22 patients with early AD, 24 with MCI and 29 matched controls. As predicted, AD patients showed significant deficits in person naming, item recognition and recall of spatial location (placing). Surprisingly, subjects with MCI were also impaired on all components. There was no significant difference between AD and MCI except on the placing component. Analysis of the relationship between semantic (naming) and episodic (recognition and placing) components of the FPT revealed a significant association between the two episodic tasks, but not between episodic and semantic performance. Patients with MCI show deficits of episodic and semantic memory. The extent of impairment suggests dysfunction beyond the medial temporal lobe. The FPT might form the basis of a sensitive early indicator of AD.
A Deep Learning Framework for Smart Street Cleaning

Conventional street cleaning methods include street sweepers going to various spots in the city and manually verifying if the street needs cleaning and taking action if required. However, this method is not optimized and demands a huge investment in terms of time and money. This paper introduces an automated framework which addresses street cleaning problem in a better way by making use of modern equipment with cameras and computational techniques to analyze, find and efficiently schedule clean-up crews for the areas requiring more attention. Deep learning-based neural network techniques can be used to achieve better accuracy and performance for object detection and classification than conventional machine learning algorithms for large volume of images. The proposed framework for street cleaning leverages the deep learning algorithm pipeline to analyze the street photographs and determines if the streets are dirty by detecting litter objects. The pipeline further determines the the degree to which the streets are littered by classifying the litter objects detected in earlier stages. The framework also provides information on the cleanliness status of the streets on a dashboard updated in real-time. Such framework can prove effective in reducing resource consumption and overall operational cost involved in street cleaning.
Ensemble of jointly trained deep neural network-based acoustic models for reverberant speech recognition

Distant speech recognition is a challenge, particularly due to the corruption of speech signals by reverberation caused by large distances between the speaker and microphone. In order to cope with a wide range of reverberations in real-world situations, we present novel approaches for acoustic modeling including an ensemble of deep neural networks (DNNs) and an ensemble of jointly trained DNNs. First, multiple DNNs are established, each of which corresponds to a different reverberation time 60 (RT60) in a setup step. Also, each model in the ensemble of DNN acoustic models is further jointly trained, including both feature mapping and acoustic modeling, where the feature mapping is designed for the dereverberation as a front-end. In a testing phase, the two most likely DNNs are chosen from the DNN ensemble using maximum a posteriori (MAP) probabilities, computed in an online fashion by using maximum likelihood (ML)-based blind RT60 estimation and then the posterior probability outputs from two DNNs are combined using the ML-based weights as a simple average. Extensive experiments demonstrate that the proposed approach leads to substantial improvements in speech recognition accuracy over the conventional DNN baseline systems under diverse reverberant conditions.
Management of trust in the e-marketplace: the role of the buyer's experience in building trust

The e-marketplace, or broker-managed online market, is one of today’s most profitable e-business models. In these marketplaces, buyers routinely engage with businesses and individual sellers with whom they have had little or no prior interaction, making trust one of the most important issues in the e-marketplaces. Therefore, a clear understanding of the trust building process in the e-marketplace is important for the success in the market. In this study, we analyze the process by which e-marketplace customers develop trust in both a market-maker and sellers. Additionally, this analysis provides a theoretical framework to identify the antecedents of trust. In the framework, we suggest that the impact of trust on transaction intention is moderated by the customer’s previous experiences in the e-marketplace. The theoretical model presented here was tested on survey and transaction data collected from 692 respondents. The results indicate that customers value the importance of trust in the market-maker and sellers differentially by the level of transaction experience. It is also shown that market-maker’s characteristics (reputation, website’s usability and security) and seller’s characteristics (expertise) play an important role in the formation and development of trust toward a market-maker and sellers, respectively. Journal of Information Technology (2007) 22, 119–132. doi:10.1057/palgrave.jit.2000095 Published online 23 January 2007
The Thought Worlds of Marketing and Sales : Which Differences Make a Difference ?

The literature is divided on whether differences between the thought worlds of marketing and sales are deleterious or beneficial. This article empirically investigates various facets of thought-world differences and their effects on various outcomes. It confirms that, in general, differences hamper the cooperation between marketing and sales, which leads to a lower market performance of the business unit. However, some facets of thought-world differences enhance the market performance of the business unit through a direct effect that outweighs the negative effect mediated by the quality of cooperation between marketing and sales. Market performance is enhanced if one side plays the customers’ advocate while the other plays the products’ advocate. Market performance is also enhanced if one side plays the advocate of short-term considerations while the other plays the advocate of long-term considerations. In contrast, differences between marketing and sales in regard to product knowledge and interpersonal skills are deleterious to market performance. Thus, the kind of difference makes a difference.
Open Source Big Data Analytics Frameworks Written in Scala

Frameworks for big data arguably began with Google's use of MapReduce. Since then, a huge amount of progress has been made in the development of big data frameworks, many of which have been released as open source. Further to increase portability and ease of set-up, many are coded in a Java Virtual Machine (JVM) based language, e.g., Java or Scala. In addition, processing of big data involves the flow of data, and of course, the processing of data as it flows. This computational paradigm is a natural for functional programming. Furthermore, the map, reduce and combiner have analogs in functional programming. There has been a trend in the last few years toward developing open source big data frameworks written in Scala to support big data analytics. Scala is a modern JVM language that supports both object-oriented and functional programming paradigms.
Feature combination strategies for saliency-based visual attention systems

acin ral eais a000 Abstract. Bottom-up or saliency-based visual attention allows primates to detect nonspecific conspicuous targets in cluttered scenes. A classical metaphor, derived from electrophysiological and psychophysical studies, describes attention as a rapidly shiftable ‘‘spotlight.’’ We use a model that reproduces the attentional scan paths of this spotlight. Simple multi-scale ‘‘feature maps’’ detect local spatial discontinuities in intensity, color, and orientation, and are combined into a unique ‘‘master’’ or ‘‘saliency’’ map. The saliency map is sequentially scanned, in order of decreasing saliency, by the focus of attention. We here study the problem of combining feature maps, from different visual modalities (such as color and orientation), into a unique saliency map. Four combination strategies are compared using three databases of natural color images: (1) Simple normalized summation, (2) linear combination with learned weights, (3) global nonlinear normalization followed by summation, and (4) local nonlinear competition between salient locations followed by summation. Performance was measured as the number of false detections before the most salient target was found. Strategy (1) always yielded poorest performance and (2) best performance, with a threefold to eightfold improvement in time to find a salient target. However, (2) yielded specialized systems with poor generalization. Interestingly, strategy (4) and its simplified, computationally efficient approximation (3) yielded significantly better performance than (1), with up to fourfold improvement, while preserving generality.
Advanced laser techniques for filler-induced complications.

The increasing use of injectable fillers has been increasing the occurrence of disfiguring anaerobic infection or granulomas. This study presents two types of laser-assisted evacuation of filler material and inflammatory and necrotic tissue that were used to treat disfiguring facial nodules after different types of gel fillers.



Infectious lesions after hydrogels were drained using a lithium triborate laser at 532 nm, with subsequent removal of infected gel and pus (laser assisted evacuation). Granuloma after gels containing microparticles were treated using an 808-nm diode laser using intralesional laser technique. The latter melted and liquefied the organic and synthetic components of the granulomas, facilitating subsequent evacuation. Both lasers had an easily controllable thin laser beam, which enabled the physician to control tissue damage and minimize discomfort and pain.



All 20 patients experienced reduction or complete resolution, the latter increasing with repeated treatments.



Laser-assisted treatment offers a successful solution for patients who have been suffering from disfiguring nodules from injected fillers-often for many years. The procedure broadens the range of treatment options in cases of untoward reactions to fillers, in line with surgical removal but with lower morbidity and less cosmetic disfigurement.
Subpixel Multiplexing Method for 3D Lenticular Display

Three-dimensional display technologies based on lenticular sheet overlaid onto spatial light modulator screen have been studied for decades. However, the quality of these displays still suffers from insufficient number of views and zone-jumping between views. We present herein a subpixel multiplexing method in this paper. We propose to split mapping and alignment into two separate tasks, processed in parallel threads. Alignment thread deals with the task of computing the geometrical relationship between lenticular sheet and Liquid Crystal Display (LCD) panel for multiplexing. Afterwards, we conduct the multiplexing procedure through a box-constrained integer least squares algorithm. After multiplexing, each subpixel aggregated on the lenticular sheet is a multiplexing one that mixes up a number of subpixels in local region on the LCD plane. As a result, we multiplex subpixels on the synthetic image up to 27 views with a resolution of 1080 × 1920 and the rendering speed is 73.34 frames per second (fps).