nils-herrmann/clean_scirepeval huggingface dataset:
A Dual Path Deep Network for Single Image Super-Resolution Reconstruction

Super-resolution reconstruction based on deep learning has come a long way since the first proposed method in 2015. Numerous methods have been developed for this task using deep learning approaches. Among these methods, residual deep learning algorithms have shown better performance. Although all early proposed deep learning based super-resolution frameworks used bicubic upsampled versions of low resolution images as the main input, most of the current ones use the low resolution images directly by adding up-sampling layers to their networks. In this work, we propose a new method by using both low resolution and bicubic upsampled images as the inputs to our network. The final results confirm that decreasing the depth of the network in lower resolution space and adding the bicubic path lead to almost similar results to those of the deeper networks in terms of PSNR and SSIM, yet making the network computationally inexpensive and more efficient.
Sequences, Items And Latent Links: Recommendation With Consumed Item Packs

Recommenders personalize the web content by typically using collaborative filtering to relate users (or items) based on explicit feedback, e.g., ratings. The difficulty of collecting this feedback has recently motivated to consider implicit feedback (e.g., item consumption along with the corresponding time). In this paper, we introduce the notion of consumed item pack (CIP) which enables to link users (or items) based on their implicit analogous consumption behavior. Our proposal is generic, and we show that it captures three novel implicit recommenders: a user-based (CIP-U), an item-based (CIP-I), and a word embedding-based (DEEPCIP), as well as a state-ofart technique using implicit feedback (FISM). We show that our recommenders handle incremental updates incorporating freshly consumed items. We demonstrate that all three recommenders provide a recommendation quality that is competitive with stateof-the-art ones, including one incorporating both explicit and implicit feedback.
Active learning of expressive linkage rules using genetic programming

A central problem in the context of the Web of Linked Data as well as in data integration in general is to identify entities in different data sources that describe the same real-world object. Many existing methods for matching entities rely on explicit linkage rules, which specify the conditions which must hold true for two entities in order to be interlinked. As writing good linkage rules by hand is a non-trivial problem, the burden to generate links between data sources is still high. In order to reduce the effort and expertise required to write linkage rules, we present the ActiveGenLink algorithm which combines genetic programming and active learning to generate expressive linkage rules interactively. The ActiveGenLink algorithm automates the generation of linkage rules and only requires the user to confirm or decline a number of link candidates. ActiveGenLink uses a query strategy which minimizes user involvement by selecting link candidates which yield a high information gain. Our evaluation shows that ActiveGenLink is capable of generating high quality linkage rules based on labeling a small number of candidate links and that our query strategy for selecting the link candidates outperforms the query-by-vote-entropy baseline.
Nuclei segmentation for computer-aided diagnosis of breast cancer

Breast cancer is the most common cancer among women. The effectiveness of treatment depends on early detection of the disease. Computer-aided diagnosis plays an increasingly important role in this field. Particularly, digital pathology has recently become of interest to a growing number of scientists. This work reports on advances in computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. The task at hand is to classify those as either benign or malignant. We propose a robust segmentation procedure giving satisfactory nuclei separation even when they are densely clustered in the image. Firstly, we determine centers of the nuclei using conditional erosion. The erosion is performed on a binary mask obtained with the use of adaptive thresholding in grayscale and clustering in a color space. Then, we use the multi-label fast marching algorithm initialized with the centers to obtain the final segmentation. A set of 84 features extracted from the nuclei is used in the classification by three different classifiers. The approach was tested on 450 microscopic images of fine needle biopsies obtained from patients of the Regional Hospital in Zielona Góra, Poland. The classification accuracy presented in this paper reaches 100%, which shows that a medical decision support system based on our method would provide accurate diagnostic information.
Textural Features for Image Classification

Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on graytone spatial dependancies, and illustrates their application in categoryidentification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
Interactive artistic rendering

We present an algorithm for rendering subdivision surface models of complex scenes in a variety of artistic styles using an interactively editable particle system. The algorithm is suitable for modeling artistic techniques explicitly by the user, or automatically by the system. Our approach can simulate a large number of artistic effects due to the fact that almost any type of mark made on paper or canvas can be imitated. Any of our artistic effects is customizable by the user through a particle editing interface. The algorithm maintains complete frame-to-frame coherence, a characteristic required for good animation, and runs at interactive rates on current computer graphics workstations. CR Categories: I.3.0 [Computer Graphics]: General; I.3.6 [Computer Graphics]: Methodology and Techniques.
NoSQL Systems for Big Data Management

The advent of Big Data created a need for out-of-the-box horizontal scalability for data management systems. This ushered in an array of choices for Big Data management under the umbrella term NoSQL. In this paper, we provide a taxonomy and unified perspective on NoSQL systems. Using this perspective, we compare and contrast various NoSQL systems using multiple facets including system architecture, data model, query language, client API, scalability, and availability. We group current NoSQL systems into seven broad categories:  Table-type/Column, Document, Graph, Native XML, Native Object, and Hybrid databases. We also describe application scenarios for each category to help the reader in choosing an appropriate NoSQL system for a given application. We conclude the paper by indicating future research directions.
Hempseed as a nutritional resource: An overview

The seed of Cannabis sativa L. has been an important source of nutrition for thousands of years in Old World cultures. Non-drug varieties of Cannabis, commonly referred to as hemp, have not been studied extensively for their nutritional potential in recent years, nor has hempseed been utilized to any great extent by the industrial processes and food markets that have developed during the 20th century. Technically a nut, hempseed typically contains over 30% oil and about 25% protein, with considerable amounts of dietary fiber, vitamins and minerals. Hempseed oil is over 80% in polyunsaturated fatty acids (PUFAs), and is an exceptionally rich source of the two essential fatty acids (EFAs) linoleic acid (18:2 omega-6) and alpha-linolenic acid (18:3 omega-3). The omega-6 to omega-3 ratio (n6/n3) in hempseed oil is normally between 2:1 and 3:1, which is considered to be optimal for human health. In addition, the biological metabolites of the two EFAs, gamma-linolenic acid (18:3 omega-6; ‘GLA’) and stearidonic acid (18:4 omega-3; ‘SDA’), are also present in hempseed oil. The two main proteins in hempseed are edestin and albumin. Both of these high-quality storage proteins are easily digested and contain nutritionally significant amounts of all essential amino acids. In addition, hempseed has exceptionally high levels of the amino acid arginine. Hempseed has been used to treat various disorders for thousands of years in traditional oriental medicine. Recent clinical trials have identified hempseed oil as a functional food, and animal feeding studies demonstrate the long-standing utility of hempseed as an important food resource.
Calibration of the Computer Science and Applications, Inc. accelerometer.

We established accelerometer count ranges for the Computer Science and Applications, Inc. (CSA) activity monitor corresponding to commonly employed MET categories.



Data were obtained from 50 adults (25 males, 25 females) during treadmill exercise at three different speeds (4.8, 6.4, and 9.7 km x h(-1)).



Activity counts and steady-state oxygen consumption were highly correlated (r = 0.88), and count ranges corresponding to light, moderate, hard, and very hard intensity levels were < or = 1951, 1952-5724, 5725-9498, > or = 9499 cnts x min(-1), respectively. A model to predict energy expenditure from activity counts and body mass was developed using data from a random sample of 35 subjects (r2 = 0.82, SEE = 1.40 kcal x min(-1)). Cross validation with data from the remaining 15 subjects revealed no significant differences between actual and predicted energy expenditure at any treadmill speed (SEE = 0.50-1.40 kcal x min(-1)).



These data provide a template on which patterns of activity can be classified into intensity levels using the CSA accelerometer.
Business-to-business e-commerce adoption: An empirical investigation of business factors

We develop an overarching model to explain the adoption of business-to-business e-commerce using five business factors: external environment, organizational context, decision-maker’s characteristics, technology context, and organizational learning. Data collected from IS executives was analyzed using logistic regression. Our model has a good fit with over 77 % variance explained. Findings indicate that the existence of informal interest groups, and the tolerant attitude of decision makers towards negative information about business-to-business e-commerce have significant influence on the adoption decision. In addition, our results indicate that price intensity and perceived barriers negatively affect the adoption decision.
The Ontological Foundation of REA Enterprise Information Systems

Philosophers have studied ontologies for centuries in their search for a systematic explanation of existence: “What kind of things exist?” Recently, ontologies have emerged as a major research topic in the fields of artificial intelligence and knowledge management where they address the content issue: “What kind of things should we represent?” The answer to that question differs with the scope of the ontology. Ontologies that are subject-independent are called upper-level ontologies, and they attempt to define concepts that are shared by all domains, such as time and space. Domain ontologies, on the other hand, attempt to define the things that are relevant to a specific application domain. Both types of ontologies are becoming increasingly important in the era of the Internet where consistent and machine-readable semantic definitions of economic phenomena become the language of e-commerce. In this paper, we propose the conceptual accounting framework of the Resource-Event-Agent (REA) model of McCarthy (1982) as an enterprise domain ontology, and we build upon the initial ontology work of Geerts and McCarthy (2000) which explored REA with respect to the ontological categorizations of John Sowa (1999). Because of its conceptual modeling heritage, REA already resembles an established ontology in many declarative (categories) and procedural (axioms) respects, and we also propose here to extend formally that framework both (1) vertically in terms of entrepreneurial logic (value chains) and workflow detail, and (2) horizontally in terms of type and commitment images of enterprise economic phenomena. A strong emphasis throughout the paper is given to the microeconomic foundations of the category definitions.
Recent Developments on Direct Relative Orientation

This paper presents a novel version of the five-point relative orientation algorithm given in Nistér (2004). The name of the algorithm arises from the fact that it can operate even on the minimal five point correspondences required for a finite number of solutions to relative orientation. For the minimal five correspondences the algorithm returns up to ten real solutions. The algorithm can also operate on many points. Like the previous version of the five-point algorithm, our method can operate correctly even in the face of critical surfaces, including planar and ruled quadric scenes. The paper presents comparisons with other direct methods, including the previously developed five-point method, two different six-point methods, the seven-point method, and the eight-point method. It is shown that the five-point method is superior in most cases among the direct methods. The new version of the algorithm was developed from the perspective of algebraic geometry and is presented in the context of computing a Gröbner basis. The constraints are formulated in terms of polynomial equations in the entries of the fundamental matrix. The polynomial equations generate an algebraic ideal for which a Gröbner basis is computed. The Gröbner basis is used to compute the action matrix for multiplication by a single variable monomial. The eigenvectors of the action matrix give the solutions for all the variables and thereby also relative orientation. Using a Gröbner basis makes the solution clear and easy to explain.
Detailed 3D Representations for Object Recognition and Modeling

Geometric 3D reasoning at the level of objects has received renewed attention recently in the context of visual scene understanding. The level of geometric detail, however, is typically limited to qualitative representations or coarse boxes. This is linked to the fact that today's object class detectors are tuned toward robust 2D matching rather than accurate 3D geometry, encouraged by bounding-box-based benchmarks such as Pascal VOC. In this paper, we revisit ideas from the early days of computer vision, namely, detailed, 3D geometric object class representations for recognition. These representations can recover geometrically far more accurate object hypotheses than just bounding boxes, including continuous estimates of object pose and 3D wireframes with relative 3D positions of object parts. In combination with robust techniques for shape description and inference, we outperform state-of-the-art results in monocular 3D pose estimation. In a series of experiments, we analyze our approach in detail and demonstrate novel applications enabled by such an object class representation, such as fine-grained categorization of cars and bicycles, according to their 3D geometry, and ultrawide baseline matching.
Stock-out Prediction in Multi-echelon Networks

In multi-echelon inventory systems the performance of a given node is affected by events that occur at many other nodes and in many other time periods. For example, a supply disruption upstream will have an effect on downstream, customer-facing nodes several periods later as the disruption "cascades" through the system. There is very little research on stock-out prediction in single-echelon systems and (to the best of our knowledge) none on multi-echelon systems. However, in real the world, it is clear that there is significant interest in techniques for this sort of stock-out prediction. Therefore, our research aims to fill this gap by using deep neural networks (DNN) to predict stock-outs in multi-echelon supply chains.
Accurate indoor navigation using Smartphone , Bluetooth Low Energy and Visual Tags

Every moment of our day takes place in a location, identified by geographical coordinates which connect physical and digital world. For that reason it’s important to know the user position: it enables new location-related services and give to the people the opportunity to find their way in complex environments such as hospitals. The best approach to realize an indoor navigation system is to exploit the potential of the smartphone in some way. Different techniques were proposed in scientific literature, but the topic is still matter of research. In this paper we propose a smartphone-based indoor navigation system which use both Bluetooth Low Energy(BLE) and a visual-tags system. A BLE beacons system is deployed in the environment to segment a big area into smaller areas and to perform a rough background localization. An accurate indoor navigation is performed using the camera to decode a visual-tags system deployed onto the room’s floor.
Transanal endoscopic total mesorectal excision: technical aspects of approaching the mesorectal plane from below—a preliminary report

Laparoscopic total mesorectal excision (TME) for low rectal cancer can be technically challenging. This report describes our initial experience with a hybrid laparoscopic and transanal endoscopic technique for TME in low rectal cancer. Between December 2012 and October 2013, we identified patients with rectal cancer < 5 cm from the anorectal junction (ARJ) who underwent laparoscopic-assisted TME with a transanal minimally invasive surgery (TAMIS) technique. A standardized stepwise approach was used in all patients. Resection specimens were examined for completeness and measurement of margins. Preoperative magnetic resonance imaging (MRI) characteristics and short-term postoperative outcomes were examined. All values are mean ± standard deviation. Ten patients (8 males; median age: 60.5 (range 36–70) years) were included. On initial MRI, all tumors were T2 or T3, mean tumor height from the ARJ was 28.9 ± 12.2 mm, mean circumferential resection margin was 5.3 ± 3.1 mm , and the mean angle between the anal canal and the levator ani was 83.9° ± 9.7°. All patients had had preoperative chemoradiotherapy, TME via TAMIS, and distal anastomosis. There were no intraoperative complications, anastomotic leaks, or 30-day mortality. The pathologic quality of all mesorectal specimens was excellent. The distal resection margin was 19.4 ± 10.4 mm, the mean circumferential resection margin was 13.8 ± 5.1 mm, and the median lymph node harvest was 10.5 (range 5–15) nodes. A combined laparoscopic and transanal approach can achieve a safe and oncologically complete TME dissection for low rectal tumors. This approach may improve clinical outcomes in these technically difficult cases, but larger prospective studies are needed.
DualBlink: A Wearable Device to Continuously Detect, Track, and Actuate Blinking For Alleviating Dry Eyes and Computer Vision Syndrome

Increased visual attention, such as during computer use leads to less blinking, which can cause dry eyes—the leading cause of computer vision syndrome. As people spend more time looking at screens on mobile and desktop devices, computer vision syndrome is becoming epidemic in today's population, leading to blurry vision, fatigue, and a reduced quality of life.
 One way to alleviate dry eyes is increased blinking. In this paper, we present a series of glasses-mounted devices that track the wearer's blink rate and, upon absent blinks, trigger blinks through actuation: light flashes, physical taps, and small puffs of air near the eye. We conducted a user study to evaluate the effectiveness of our devices and found that air puff and physical tap actuations result in a 36% increase in participants’ average blink rate. Air puff thereby struck the best compromise between effective blink actuations and low distraction ratings from participants. In a follow-up study, we found that high intensity, short puffs near the eye were most effective in triggering blinks while receiving only low-rated distraction and invasiveness ratings from participants. We conclude this paper with two miniaturized and self-contained DualBlink prototypes, one integrated into the frame of a pair of glasses and the other one as a clip-on for existing glasses. We believe that DualBlink can serve as an always-available and viable option to treat computer vision syndrome in the future.
Complex hole-filling algorithm for 3D models

In this paper, we propose a new and simple method for filling complex holes in surfaces. To fill a hole, locally uniform points are added to the hole by creating contour curves inside the boundary edges of the hole. A set of contour curves is created by shortening the flow of the boundary edges of the hole. The Delaunay triangulation method in a local area is applied for creating new meshes. The direction of the shortening flow is changed to satisfy the convergence of the curve shortening flow. It enables the filling of a complex hole, such as a hole with an island and a hole with highly curved boundary edges. In addition, the method can be used to fill a hole by preserving the sharp features of the model.
Evolutionary Cost-sensitive Extreme Learning Machine and Subspace Extension

Conventional extreme learning machines solve a Moore-Penrose generalized inverse of hidden layer activated matrix and analytically determine the output weights to achieve generalized performance, by assuming the same loss from different types of misclassification. The assumption may not hold in cost-sensitive recognition tasks, such as face recognition based access control system, where misclassifying a stranger as a family member and allowed to enter the house may result in more serious disaster than misclassifying a family member as a stranger and not allowed to enter. Though recent cost-sensitive learning can reduce the total loss with a given cost matrix that quantifies how severe one type of mistake against another type of mistake, in many realistic cases the cost matrix is unknown to users. Motivated by these concerns, this paper proposes an evolutionary cost-sensitive extreme learning machine (ECSELM), with the following merits: 1) to our best knowledge, it is the first proposal of cost-sensitive ELM; 2) it well addresses the open issue of how to define the cost matrix in cost-sensitive learning tasks; 3) an evolutionary backtracking search algorithm is induced for adaptive cost matrix optimization. Extensively, an ECSLDA method is generalized by coupling with cost-sensitive subspace learning. Experiments in a variety of cost-sensitive tasks well demonstrate the efficiency and effectiveness of the proposed approaches, specifically, 5%~10% improvements in classification are obtained on several datasets compared with ELMs; the computational efficiency is also 10 times faster than cost-sensitive subspace learning methods.