nils-herrmann/clean_scirepeval huggingface dataset:
Search Engine Optimization to Increase Website

Search engine optimization [SEO] is often about making modifications to parts of the website. When viewed individually, these changes might seem like incremental improvements, but when combined with optimization technique, they could have noticeable impact on website’s user experience and performance in search results. SEO requires considerable time, professional communicators should progressively apply these lessons in sequence presented in this paper and should keep up to date with frequently changing ranking algorithms and with the associated changing practices of the search engine optimization professionals. Search engine rankings are shaped by three classes of key logic participants namely, 1.Business logic, 2.Professional communicators, 3.End user logic. By using these key logics the optimization technique makes it easier for the users to search their web contents. It focuses only on general web search engine and the deliver lesson that professional communication can readily implement without any specialized technique. The key concepts introduces a theoretical framework for this to search engine optimization, describes how the approach was used to implement the three classes of stakeholders to shape the whole framework because it is easier for the audiences to find their web-content and websites through search engines. Search engine users of course hold the attention economy’s key commodity, their own attention, and confer it not only among the sites of contending web content creators but also among the search engine themselves, thereby compelling search engines to try to better accommodate users’ interests that search engines serve up among their top results.
Effect of ammonium or nitrate nutrition on net photosynthesis, growth, and activity of the enzymes nitrate reductase and glutamine synthetase in blueberry, raspberry and strawberry

Blueberry, raspberry and strawberry may have evolved strategies for survival due to the different soil conditions available in their natural environment. Since this might be reflected in their response to rhizosphere pH and N form supplied, investigations were carried out in order to compare effects of nitrate and ammonium nutrition (the latter at two different pH regimes) on growth, CO2 gas exchange, and on the activity of key enzymes of the nitrogen metabolism of these plant species. Highbush blueberry (Vaccinium corymbosum L. cv. 13–16–A), raspberry (Rubus idaeus L. cv. Zeva II) and strawberry (Fragaria × ananassa Duch. cv. Senga Sengana) were grown in 10 L black polyethylene pots in quartz sand with and without 1% CaCO3 (w: v), respectively. Nutrient solutions supplied contained nitrate (6 mM) or ammonium (6 mM) as the sole nitrogen source. Compared with strawberries fed with nitrate nitrogen, supply of ammonium nitrogen caused a decrease in net photosynthesis and dry matter production when plants were grown in quartz sand without added CaCO3. In contrast, net photosynthesis and dry matter production increased in blueberries fed with ammonium nitrogen, while dry matter production of raspberries was not affected by the N form supplied. In quartz sand with CaCO3, ammonium nutrition caused less deleterious effects on strawberries, and net photosynthesis in raspberries increased as compared to plants grown in quartz sand without CaCO3 addition. Activity of nitrate reductase (NR) was low in blueberries and could only be detected in the roots of plants supplied with nitrate nitrogen. In contrast, NR activity was high in leaves, but low in roots of raspberry and strawberry plants. Ammonium nutrition caused a decrease in NR level in leaves. Activity of glutamine synthetase (GS) was high in leaves but lower in roots of blueberry, raspberry and strawberry plants. The GS level was not significantly affected by the nitrogen source supplied. The effects of nitrate or ammonium nitrogen on net photosynthesis, growth, and activity of enzymes in blueberry, raspberry and strawberry cultivars appear to reflect their different adaptability to soil pH and N form due to the conditions of their natural environment.
A Basic Study of Image Processing and Its Application Areas

Digital Image processing has become popular and rapidly growing area of application under Computer Science. A basic study of image processing and its application areas are carried out in this paper. Each of these applications may be unique from others. To illustrate the basic concepts of image processing various reviews had done in this paper. The main two applications of digital image processing are discussed below. Firstly pictorial information can be improved for human perception, secondly for autonomous machine perception and efficient storage processing using image data. Digital image can be represented using set of digital values called pixels. Pixel value represent opacities, colors, gray levels, heights etc. Digitization causes a digital image to become an approximation of a real scene. To process an image some operations are applied on image. This paper discusses about the basic aspects of image processing .Image Acquisition means sensing an image .Image Enhancement means improvement in appearance of image .Image Restoration to restore an image .Image Compression to reduce the amount of data of an image to reduce size .This class of technique also include extraction/selection procedures .The importance applications of image processing include Artistic effects ,Bio-medical ,Industrial Inspection ,Geographic Information system ,Law Enforcement, Human Computer interface such as Face Recognition and Gesture recognition.
Treatment of Fournier's Gangrene with Combination of Vacuum-Assisted Closure Therapy, Hyperbaric Oxygen Therapy, and Protective Colostomy

Fournier's gangrene is a rare process which affects soft tissue in the genital and perirectal area. It can also progress to all different stages of sepsis, and abdominal compartment syndrome can be one of its complications. Two patients in septic shock due to Fournier gangrene were admitted to the Intensive Care Unit of Emergency Department. In both cases, infection started from the scrotum and the necrosis quickly involved genitals, perineal, and inguinal regions. Patients were treated with surgical debridement, protective colostomy, hyperbaric oxygen therapy, and broad-spectrum antibacterial chemotherapy. Vacuum-assisted closure (VAC) therapy was applied to the wound with the aim to clean, decontaminate, and avoid abdominal compartmental syndrome development. Both patients survived and were discharged from Intensive Care Unit after hyperbaric oxygen therapy cycles and abdominal closure.
The neural basis of unconditional love

Functional neuroimaging studies have shown that romantic love and maternal love are mediated by regions specific to each, as well as overlapping regions in the brain's reward system. Nothing is known yet regarding the neural underpinnings of unconditional love. The main goal of this functional magnetic resonance imaging study was to identify the brain regions supporting this form of love. Participants were scanned during a control condition and an experimental condition. In the control condition, participants were instructed to simply look at a series of pictures depicting individuals with intellectual disabilities. In the experimental condition, participants were instructed to feel unconditional love towards the individuals depicted in a series of similar pictures. Significant loci of activation were found, in the experimental condition compared with the control condition, in the middle insula, superior parietal lobule, right periaqueductal gray, right globus pallidus (medial), right caudate nucleus (dorsal head), left ventral tegmental area and left rostro-dorsal anterior cingulate cortex. These results suggest that unconditional love is mediated by a distinct neural network relative to that mediating other emotions. This network contains cerebral structures known to be involved in romantic love or maternal love. Some of these structures represent key components of the brain's reward system.
Information-Maximization Clustering Based on Squared-Loss Mutual Information

Information-maximization clustering learns a probabilistic classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments is maximized. A notable advantage of this approach is that it involves only continuous optimization of model parameters, which is substantially simpler than discrete optimization of cluster assignments. However, existing methods still involve nonconvex optimization problems, and therefore finding a good local optimal solution is not straightforward in practice. In this letter, we propose an alternative information-maximization clustering method based on a squared-loss variant of mutual information. This novel approach gives a clustering solution analytically in a computationally efficient way via kernel eigenvalue decomposition. Furthermore, we provide a practical model selection procedure that allows us to objectively optimize tuning parameters included in the kernel function. Through experiments, we demonstrate the usefulness of the proposed approach.
3DTI Amphitheater: Towards 3DTI Broadcasting

Amphitheater is a live broadcasting system for dissemination of 3DTI (3D Tele-immersive) content. The virtual environment constructed by the system mimics an amphitheater in the real world, where performers interact with each other in the central circular stage, and the audience is placed in virtual seats that surround the stage. Users of the Amphitheater can be geographically dispersed and the streams created by the performer sites are disseminated in a P2P network among the participants. To deal with the high bandwidth demand and strict latency bound of the service, we identify the hierarchical priority of streams in construction of the content dissemination forest. Result shows that the Amphitheater outperforms prior 3DTI systems by boosting the application QoS by a factor of 2.8 while sustaining the same hundred-scale audience group.
The virtual showcase

Intuitive access to information in habitual real-world environments is a challenge for information technology. An important question is how can we enhance established and well-functioning everyday environments rather than replace them by virtual environments (VEs)? Augmented reality (AR) technology has a lot of potential in this respect because it augments real-world environments with computer-generated imagery. Today, most AR systems use see-through head-mounted displays, which share most of the disadvantages of other head-attached display devices.
How deep learning works -The geometry of deep learning

Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective. In this paper we draw a geometric picture of the deep learning system by finding its analogies with two existing geometric structures, the geometry of quantum computations and the geometry of the diffeomorphic template matching. In this framework, we give the geometric structures of different deep learning systems including convolutional neural networks, residual networks, recursive neural networks, recurrent neural networks and the equilibrium prapagation framework. We can also analysis the relationship between the geometrical structures and their performance of different networks in an algorithmic level so that the geometric framework may guide the design of the structures and algorithms of deep learning systems.
Grammar-based Genetic Programming: a survey

Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP). Practical grammar-based GP systems first appeared in the mid 1990s, and have subsequently become an important strand in GP research and applications. We trace their subsequent rise, surveying the various grammar-based formalisms that have been used in GP and discussing the contributions they have made to the progress of GP. We illustrate these contributions with a range of applications of grammar-based GP, showing how grammar formalisms contributed to the solutions of these problems. We briefly discuss the likely future development of grammar-based GP systems, and conclude with a brief summary of the field.
An Empirically-Derived Question Taxonomy for Task-Oriented Tutorial Dialogue

Devising an expressive question taxonomy is a central problem in question generation. Through examination of a corpus of human-human taskoriented tutoring, we have found that existing question taxonomies do not capture all of the tutorial questions present in this form of tutoring. We propose a hierarchical question classification scheme for tutorial questions in which the top level corresponds to the tutor’s goal and the second level corresponds to the question type. The application of this hierarchical classification scheme to a corpus of keyboard-to-keyboard tutoring of introductory computer science yielded high inter-rater reliability, suggesting that such a scheme is appropriate for classifying tutor questions in design-oriented tutoring. We discuss numerous open issues that are highlighted by the current analysis.
PCG-Based Game Design Patterns

People enjoy encounters with generative software, but rarely are they encouraged to interact with, understand or engage with it. In this paper we define the term PCG-based game, and explain how this concept follows on from the idea of an AI-based game. We look at existing examples of games which foreground their AI, put forward a methodology for designing PCG-based games, describe some example case study designs for PCG-based games, and describe lessons learned during this process of sketching and developing ideas.
A cascaded long short-term memory (LSTM) driven generic visual question answering (VQA)

A cascaded long short-term memory (LSTM) architecture with discriminant feature learning is proposed for the task of question answering on real world images. The proposed LSTM architecture jointly learns visual features and parts of speech (POS) tags of question words or tokens. Also, dimensionality of deep visual features is reduced by applying Principal Component Analysis (PCA) technique. In this manner, the proposed question answering model captures the generic pattern of question for a given context of image which is just not constricted within the training dataset. Empirical outcome shows that this kind of approach significantly improves the accuracy. It is believed that this kind of generic learning is a step towards a real-world visual question answering (VQA) system which will perform well for all possible forms of open-ended natural language queries.
Re-ranking Person Re-identification with k-Reciprocal Encoding

When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance. The final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method.
SPEA2: Improving the strength pareto evolutionary algorithm

The Strength Pareto Evolutionary Algorithm (SPEA) (Zitzle r and Thiele 1999) is a relatively recent technique for finding or approximatin g the Pareto-optimal set for multiobjective optimization problems. In different st udies (Zitzler and Thiele 1999; Zitzler, Deb, and Thiele 2000) SPEA has shown very good performance in comparison to other multiobjective evolutionary algorith ms, and therefore it has been a point of reference in various recent investigations, e.g., (Corne, Knowles, and Oates 2000). Furthermore, it has been used in different a pplic tions, e.g., (Lahanas, Milickovic, Baltas, and Zamboglou 2001). In this pap er, an improved version, namely SPEA2, is proposed, which incorporates in cont rast o its predecessor a fine-grained fitness assignment strategy, a density estima tion technique, and an enhanced archive truncation method. The comparison of SPEA 2 with SPEA and two other modern elitist methods, PESA and NSGA-II, on diffe rent test problems yields promising results.
Oral lichen planus and oral lichenoid lesions; a critical appraisal with emphasis on the diagnostic aspects.

Oral lichen planus (OLP) has a prevalence of approximately 1%. The etiopathogenesis is poorly understood. The annual malignant transformation is less than 0.5%. There are no effective means to either predict or to prevent such event. Oral lesions may occur that to some extent look like lichen planus but lacking the characteristic features of OLP, or that are indistinguishable from OLP clinically but having a distinct cause, e.g. amalgam restoration associated. Such lesions are referred to as oral lichenoid lesions (OLLs). The management of OLP and the various OLLs may be different. Therefore, accurate diagnosis should be aimed at.
Performance evaluation of hybrid ACE-PTS PAPR reduction techniques

Orthogonal Frequency Division Multiplexing (OFDM) is an attractive technique for wireless communication over frequency-selective fading channels. OFDM suffers from high Peak-to-Average Power Ratio (PAPR), which limits OFDM usage and reduces the efficiency of High Power Amplifier (HPA) or badly degrades BER. Many PAPR reduction techniques have been proposed in the literature. PAPR reduction techniques can be classified into blind receiver and non-blind receiver techniques. Active Constellation Extension (ACE) is one of the best blind receiver techniques. While, Partial Transmit Sequence (PTS) can work as blind / non-blind technique. PTS has a great PAPR reduction gain on the expense of increasing computational complexity. In this paper we combine PTS with ACE in four possible ways to be suitable for blind receiver applications with better performance than conventional methods (i.e. PTS and ACE). Results show that ACE-PTS scheme is the best among others. Expectedly, any hybrid technique has computational complexity larger than that of its components. However, ACE-PTS can be used to achieve the same performance as that of PTS or worthy better, with less number of subblocks (i.e. with less computational complexity) especially in low order modulation techniques (e.g. 4-QAM and 16-QAM). Results show that ACE-PTS with V=8 can perform similar to or better than PTS with V=10 in 16-QAM or 4-QAM, respectively, with 74% and 40.5% reduction in required numbers of additions and multiplications, respectively.
Topological fingerprints reveal protein-ligand binding mechanism

Protein-ligand binding is a fundamental biological process that is paramount to many other biological processes, such as signal transduction, metabolic pathways, enzyme construction, cell secretion, gene expression, etc. Accurate prediction of protein-ligand binding affinities is vital to rational drug design and the understanding of protein-ligand binding and binding induced function. Existing binding affinity prediction methods are inundated with geometric detail and involve excessively high dimensions, which undermines their predictive power for massive binding data. Topology provides an ultimate level of abstraction and thus incurs too much reduction in geometric information. Persistent homology embeds geometric information into topological invariants and bridges the gap between complex geometry and abstract topology. However, it over simplifies biological information. This work introduces element specific persistent homology (ESPH) to retain crucial biological information during topological simplification. The combination of ESPH and machine learning gives rise to one of the most efficient and powerful tools for revealing protein-ligand binding mechanism and for predicting binding affinities.
An efficient and secure mobile payment protocol for restricted connectivity scenarios in vehicular ad hoc network

0140-3664/$ see front matter 2011  A doi:10.1016/j.comcom.2011.09.003 ⇑ Corresponding author.  E-mail address: liwenmin02@hotmail.com (W. Li). Value-added applications in vehicular ad hoc network (VANET) come with the emergence of electronic trading. The restricted connectivity scenario in VANET, where the vehicle cannot communicate directly with the bank for authentication due to the lack of internet access, opens up new security challenges. Hence a secure payment protocol, which meets the additional requirements associated with VANET, is a must. In this paper, we propose an efficient and secure payment protocol that aims at the restricted connectivity scenario in VANET. The protocol applies self-certified key agreement to establish symmetric keys, which can be integrated with the payment phase. Thus both the computational cost and communication cost can be reduced. Moreover, the protocol can achieve fair exchange, user anonymity and payment security.
GMD-Based Hybrid Precoding For Millimeter-Wave Massive MIMO Systems

Hybrid precoding can significantly reduce the number of required radio frequency (RF) chains and relieve the huge energy consumption in mmWave massive MIMO systems, thus attracting much interests from academic and industry. However, most existing hybrid precoding schemes are based on singula r value decomposition (SVD). Due to the very different subchannel signal-to-noise ratios (SNRs) after SVD, complica ted bit allocations is usually required to match the sub-channel SN Rs. To solve this problem, we propose a geometric mean decompositi on (GMD)-based hybrid precoding scheme to avoid the complicat ed bit allocation. Its basic idea is to seek a pair of analog and digital precoding matrices that are sufficiently close to the optimal unconstrained GMD precoding matrix. Specifically, we desig n the analog (digital) precoding matrix while keeping the digital (analog) precoding matrix fixed. Further, the principle of basis pursuit is utilized in the design of analog precoding matrix, while we obtain the digital precoding matrix by projecting the GMD operation on the digital precoding matrix. Simulation results verify that the proposed GMD-based hybird precoding scheme outperforms conventional SVD-based hybrid precoding sche mes and achieves much better bit error rate (BER) performance wi th low complexity.
Numerical modelling of an electromagnetic actuator for vacuum contactors

In this paper, the authors aim to present the modelling, simulation and experimentation of an electromagnetic actuator used in the construction of vacuum contactors. In order to validate the numerical results obtained by computer simulation was performed and an experimental model. The role of the electromagnetic actuator is to achieve movement of the movable electrical contacts of the contactor with certain speeds and precise trajectories. The advantages of the electromagnetic actuator presented in this paper are the following: it is able to perform a large number of mechanical cycles (at least than one million), with high frequency, with high holding force in the closed position, through reducing air gaps and technology leakage flux, low power consumption, enrolling in a compact volume, reliable and low cost manufacturing. Using finite element simulation software, it was modelated and optimized an electromagnetic actuator with high performance, reliable, inexpensive that can be easily used in the construction of the Vacuum Contactors.