nils-herrmann/clean_scirepeval huggingface dataset:
Complementarity of Informational and Transactional IT systems in Generating IT-based Business Value

Different IT asset classes generate business value consistent with the strategic goal of that asset class. While transactional IT systems contribute to process efficiencies, informational IT systems generate insights and contribute to informational benefits. We argue that the complementary interactions between these two classes of system make up a new system which is capable of generating firm-level business value. The new system supports effectiveness and efficiency as well as informed and intelligent processes. We use systems theory to conceptualise the complementary interactions and synergy between Business Analytics (BA) systems and Customer Relationship Management (CRM) systems as two examples of informational and transactional IT systems, respectively. The synergy between BA systems and CRM systems will give rise to BA-enabled CRM systems with emergent properties. BA-enabled CRM systems are capable of contributing to significant business value. This paper concludes by calling for further empirical studies to examine the impact of BA systems, CRM systems and emergent BA-enabled CRM systems on business value.
Verilog-HDL based implementation of a fuzzy logic controller for embedded systems

Fuzzy logic has the advantage of modelling complex, non-linear problems linguistically rather than mathematically with satisfying increasing demand of higher performance and sophistication of embedded systems. This paper describes design of a fuzzy logic controller using Verilog-HDL language with different approaches showing which elements of language should be used for most optimized synthesis. Different algorithms for defuzzification process are also implemented and synthesized to check which method is most efficient and uses less hardware with higher computational speed. The development of a controller is carried out using ISE Design Suite 14.2 of Xilinx.
Voice Recognition using Dynamic Time Warping and Mel-Frequency Cepstral Coefficients Algorithms

Voice recognition is an important and active research area of the recent years. This research aims to build a system for voice recognition using dynamic time wrapping algorithm, by comparing the voice signal of the speaker with pre-stored voice signals in the database, and extracting the main features of the speaker voice signal using Mel-frequency cepstral coefficients, which is one of the most important factors in achieving high recognition accuracy.
“ GrowCut ”-Interactive Multi-Label ND Image Segmentation By Cellular Automata

In this paper we describe a novel algorithm for interactive multilabel segmentation of N-dimensional images. Given a small number of user-labelled pixels, the rest of the image is segmented automatically by a Cellular Automaton. The process is iterative, as the automaton labels the image, user can observe the segmentation evolution and guide the algorithm with human input where the segmentation is difficult to compute. In the areas, where the segmentation is reliably computed automatically no additional user effort is required. Results of segmenting generic photos and medical images are presented. Our experiments show that modest user effort is required for segmentation of moderately hard images.
End-To-End Multi-Modal Sensors Fusion System For Urban Automated Driving

In this paper, we present a novel framework for urban automated driving based 1 on multi-modal sensors; LiDAR and Camera. Environment perception through 2 sensors fusion is key to successful deployment of automated driving systems, 3 especially in complex urban areas. Our hypothesis is that a well designed deep 4 neural network is able to end-to-end learn a driving policy that fuses LiDAR and 5 Camera sensory input, achieving the best out of both. In order to improve the 6 generalization and robustness of the learned policy, semantic segmentation on 7 camera is applied, in addition to applying our new LiDAR post processing method; 8 Polar Grid Mapping (PGM). The system is evaluated on the recently released urban 9 car simulator, CARLA. The evaluation is measured according to the generalization 10 performance from one environment to another. The experimental results show that 11 the best performance is achieved by fusing the PGM and semantic segmentation.
Guided data repair

In this paper we present GDR, a Guided Data Repair framework that incorporates user feedback in the cleaning process to enhance and accelerate existing automatic repair techniques while minimizing user involvement. GDR consults the user on the updates that are most likely to be beneficial in improving data quality. GDR also uses machine learning methods to identify and apply the correct updates directly to the database without the actual involvement of the user on these specific updates. To rank potential updates for consultation by the user, we first group these repairs and quantify the utility of each group using the decision-theory concept of value of information (VOI). We then apply active learning to order updates within a group based on their ability to improve the learned model. User feedback is used to repair the database and to adaptively refine the training set for the model. We empirically evaluate GDR on a real-world dataset and show significant improvement in data quality using our user guided repairing process. We also, assess the trade-off between the user efforts and the resulting data quality.
A survey of exemplar-based texture synthesis

Exemplar-based texture synthesis is the process of generating, from an input sample, new texture images of arbitrary size and which are perceptually equivalent to the sample. The two main approaches are statisticsbased methods and patch re-arrangement methods. In the first class, a texture is characterized by a statistical signature; then, a random sampling conditioned to this signature produces genuinely different texture images. The second class boils down to a clever “copy-paste” procedure, which stitches together large regions of the sample. Hybrid methods try to combines ideas from both approaches to avoid their hurdles. Current methods, including the recent CNN approaches, are able to produce impressive synthesis on various kinds of textures. Nevertheless, most real textures are organized at multiple scales, with global structures revealed at coarse scales and highly varying details at finer ones. Thus, when confronted with large natural images of textures the results of state-of-the-art methods degrade rapidly.
Quantify customer perception using natural language reviews

This project focuses on the multi-label classification of product (BeerAdvocate dataset) and service (Yelp restaurants) reviews. We found Recurrent Neural Networks (RNN) with Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) to be the most suitable Neural Network architecture for unstructured natural language content like reviews. We achieved validation accuracies of 56-58% with LSTM and CNN, against best-performing baselines 55% (from Support Vector Machines) and 47% (from the Random Forest approach). This is in line with similar best in class performance by neural networks in the RNN [5],[6] and CNN [13],[14] families. The typical skew towards higher scores in reviews has been removed, and equal numbers of reviews corresponding to each label were considered. While our architecture works far superiorly on binary classification tasks (82% accuracy), we have focused on 5-label classification since that is more representative of the actual application we target (predicting quantified summary of reviews).
Vulvar squamous cell carcinoma treated using the Mohs technique: case and assessment of treatment strategies.

Although it is the most common malignancy of the vulva, squamous cell carcinoma (SCC) of the vulva is an uncommon tumor. Unlike most cutaneous SCC, vulvar SCC is not associated with chronic sun exposure. Invasive SCC of the vulva may arise de novo; may occur in association with chronic inflammatory lesions such as lichen sclerosis, hidradenitis suppurativa, Darier-White disease, and lichen planus; or may result from malignant transformation of human papillomavirus (HPV) infection. Vulvar SCC arising in association with a chronic inflammatory condition or de novo tends to have more aggressive clinical behavior than those that result from HPV infection. We present this case to heighten awareness of the most current recommended treatment algorithms for this uncommon gynecologic tumor and to underscore the role that Mohs micrographic surgery (MMS) can play in such therapy.
Generic Application-Level Protocol Analyzer and its Language

The Shield project relied on application protocol analyzers to detect potential exploits of application vulnerabilities. We present the design of a second-generation generic application-level protocol analyzer (GAPA) that encompasses a domain-specific language and the associated run-time. We designed GAPA to satisfy three important goals: safety, real-time analysis and response, and rapid development of analyzers. We have found that these goals are relevant for many network monitors that implement protocol analysis. Therefore, we built GAPA to be readily integrated into tools such as Ethereal as well as Shield. GAPA preserves safety through the use of a memorysafe language for both message parsing and analysis, and through various techniques to reduce the amount of state maintained in order to avoid denial-of-service attacks. To support online analysis, the GAPA runtime uses a streamprocessing model with incremental parsing. In order to speed protocol development, GAPA uses a syntax similar to many protocol RFCs and other specifications, and incorporates many common protocol analysis tasks as built-in abstractions. We have specified 10 commonly used protocols in the GAPA language and found it expressive and easy to use. We measured our GAPA prototype and found that it can handle an enterprise client HTTP workload at up to 60 Mbps, sufficient performance for many end-host firewall/IDS scenarios. At the same time, the trusted code base of GAPA is an order of magnitude smaller than Ethereal.
Directional asymmetries in human smooth pursuit eye movements.

Humans make smooth pursuit eye movements to bring the image of a moving object onto the fovea. Although pursuit accuracy is critical to prevent motion blur, the eye often falls behind the target. Previous studies suggest that pursuit accuracy differs between motion directions. Here, we systematically assess asymmetries in smooth pursuit.



In experiment 1, binocular eye movements were recorded while observers (n = 20) tracked a small spot of light moving along one of four cardinal or diagonal axes across a featureless background. We analyzed pursuit latency, acceleration, peak velocity, gain, and catch-up saccade latency, number, and amplitude. In experiment 2 (n = 22), we examined the effects of spatial location and constrained stimulus motion within the upper or lower visual field.



Pursuit was significantly faster (higher acceleration, peak velocity, and gain) and smoother (fewer and later catch-up saccades) in response to downward versus upward motion in both the upper and the lower visual fields. Pursuit was also more accurate and smoother in response to horizontal versus vertical motion.  Our study is the first to report a consistent up-down asymmetry in human adults, regardless of visual field. Our findings suggest that pursuit asymmetries are adaptive responses to the requirements of the visual context: preferred motion directions (horizontal and downward) are more critical to our survival than nonpreferred ones.
Algorithmic Authority: The Case of Bitcoin

In this paper, we propose a new concept for understanding the role of algorithms in daily life: algorithmic authority. Algorithmic authority is the legitimate power of algorithms to direct human action and to impact which information is considered true. We use this concept to examine the culture of users of Bit coin, a crypto-currency and payment platform. Through Bit coin, we explore what it means to trust in algorithms. Our study utilizes interview and survey data. We found that Bit coin users prefer algorithmic authority to the authority of conventional institutions, which they see as untrustworthy. However, we argue that Bit coin users do not have blind faith in algorithms, rather, they acknowledge the need for mediating algorithmic authority with human judgment. We examine the tension between members of the Bit coin community who would prefer to integrate Bit coin with existing institutions and those who would prefer to resist integration.
Comparative Analysis and Simulation of Different CMOS Full Adders Using Cadence in 90 nm Technology

This paper analyzes & compares four adders with different logic styles (Conventional, transmission gate, 14 transistors & GDI based technique) for transistor count, power dissipation, delay and power delay product. It is performed in virtuoso platform, using Cadence tool with available GPDK - 90nm kit. The width of NMOS and PMOS is set at 120nm and 240nm respectively. Transmission gate full adder has sheer advantage of high speed but consumes more power. GDI full adder gives reduced voltage swing not being able to pass logic 1 and logic 0 completely showing degraded output. Transmission gate full adder shows better performance in terms of delay (0.417530 ns), whereas 14T full adder shows better performance in terms of all three aspects.
Large-scale behavioral targeting with a social twist

Behavioral targeting (BT) is a widely used technique for online advertising. It leverages information collected on an individual's web-browsing behavior, such as page views, search queries and ad clicks, to select the ads most relevant to user to display. With the proliferation of social networks, it is possible to relate the behavior of individuals and their social connections. Although the similarity among connected individuals are well established (i.e., homophily), it is still not clear whether and how we can leverage the activities of one's friends for behavioral targeting; whether forecasts derived from such social information are more accurate than standard behavioral targeting models. In this paper, we strive to answer these questions by evaluating the predictive power of social data across 60 consumer domains on a large online network of over 180 million users in a period of two and a half months. To our best knowledge, this is the most comprehensive study of social data in the context of behavioral targeting on such an unprecedented scale. Our analysis offers interesting insights into the value of social data for developing the next generation of targeting services.
Mechanical ventilation and the total artificial heart: optimal ventilator trigger to avoid post-operative autocycling - a case series and literature review

Many patients with end-stage cardiomyopathy are now being implanted with Total Artificial Hearts (TAHs). We have observed individual cases of post-operative mechanical ventilator autocycling with a flow trigger, and subsequent loss of autocycling after switching to a pressure trigger. These observations prompted us to do a retrospective review of all TAH devices placed at our institution between August 2007 and May 2009. We found that in the immediate post-operative period following TAH placement, autocycling was present in 50% (5/10) of cases. There was immediate cessation of autocycling in all patients after being changed from a flow trigger of 2 L/minute to a pressure trigger of 2 cm H2O. The autocycling group was found to have significantly higher CVP values than the non-autocycling group (P = 0.012). Our data suggest that mechanical ventilator autocycling may be resolved or prevented by the use of a pressure trigger rather than a flow trigger setting in patients with TAHs who require mechanical ventilation.
Aspect and Ratings Inference with Aspect Ratings: Supervised Generative Models for Mining Hotel Reviews

Today, a large volume of hotel reviews is available on many websites, such as TripAdvisor (http://www.tripadvisor.com) and Orbitz (http://www.orbitz.com). A typical review contains an overall rating and several aspect ratings along with text. The rating is perceived as an abstraction of reviewers’ satisfaction in terms of points. Although the amount of reviews having aspect ratings is growing, there are plenty of reviews including only an overall rating. Extracting aspect-specific opinions hidden in these reviews can help users quickly digest them without actually reading through them. The task mainly consists of two parts: aspect identification and rating inference. Most existing studies cannot utilize aspect ratings which are becoming abundant in the last few years. In this paper, we propose two topic models which explicitly model aspect ratings as observed variables to improve the performance of aspect rating inference over unrated reviews. Specifically, we consider sentiment distributions in the aspect level, which generate sentiment words and aspect ratings. The experiment results show our approaches outperform other existing methods on the data set crawled from TripAdvisor.
Theory of Ontological Engineering

Ontology research has embarked in knowledge base community, spread over the web technology community by semantic web movement. Ontological engineering is the discipline that deals with development and maintenance of ontologies. It was introduced in computer science in early nineties; as a result immense content of this discipline is available in literature. But, because of its huge extent and specialization, an exhaustive effort should be made to understand the said area. Some studies are available that are for comparative reasons or for review of the state of the art, but these address one or other aspect like comparison of methodologies or evaluation approaches. So the current study, after a comprehensive review and analysis, presents the compilation, as theory of ontological engineering that addresses all aspects from definition to evaluation, but in a concise manner with all-inclusive contents.
A practical evaluation of popular volume rendering algorithms

This paper evaluates and compares four volume renderi algorithms that have become rather popular for rendering datase described on uniform rectilinear grids: raycasting, splatting shear-warp, and hardware-assisted 3D texture-mapping. In ord to assess both the strengths and the weaknesses of these algori in a wide variety of scenarios, a set of real-life benchmark datase with different characteristics was carefully selected. In the rende ing, all algorithm-independent image synthesis parameters, su as viewing matrix, transfer functions, and optical model, were ke constant to enable a fair comparison of the rendering results. Bo image quality and computational complexity were evaluated a compared, with the aim of providing both researchers and prac tioners with guidelines on which algorithm is most suited in whic scenario. Our analysis also indicates the current weaknesses each algorithm’s pipeline, and possible solutions to these as w as pointers for future research are offered.
A Framework for Computing the Privacy Scores of Users in Online Social Networks

A large body of work has been devoted to address corporate-scale privacy concerns related to social networks. Most of this work focuses on how to share social networks owned by organizations without revealing the identities or the sensitive relationships of the users involved. Not much attention has been given to the privacy risk of users posed by their daily information-sharing activities.
 In this article, we approach the privacy issues raised in online social networks from the individual users’ viewpoint: we propose a framework to compute the privacy score of a user. This score indicates the user’s potential risk caused by his or her participation in the network. Our definition of privacy score satisfies the following intuitive properties: the more sensitive information a user discloses, the higher his or her privacy risk. Also, the more visible the disclosed information becomes in the network, the higher the privacy risk. We develop mathematical models to estimate both sensitivity and visibility of the information. We apply our methods to synthetic and real-world data and demonstrate their efficacy and practical utility.
Multi-objective UAV mission planning using evolutionary computation

This investigation develops an innovative algorithm for multiple autonomous unmanned aerial vehicle (UAV) mission routing. The concept of a UAV Swarm Routing Problem (SRP) as a new combinatorics problem, is developed as a variant of the Vehicle Routing Problem with Time Windows (VRPTW). Solutions of SRP problem model result in route assignments per vehicle that successfully track to all targets, on time, within distance constraints. A complexity analysis and multi-objective formulation of the VRPTW indicates the necessity of a stochastic solution approach leading to a multi-objective evolutionary algorithm. A full problem definition of the SRP as well as a multi-objective formulation parallels that of the VRPTW method. Benchmark problems for the VRPTW are modified in order to create SRP benchmarks. The solutions show the SRP solutions are comparable or better than the same VRPTW solutions, while also representing a more realistic UAV swarm routing solution.
BSR and BHPR guidelines for the management of giant cell arteritis.

Definition of GCA (TA) . Is a chronic vasculitis of large and medium vessels. . Leads to granulomatous inflammation histologically. . Predominantly affects the cranial branches of arteries arising from the arch of the aorta. . Incidence is reported as 2.2/10 000 patient-years in the UK  and between 7 and 29/100 000 in population age >50 years in Europe. . Incidence rates appear higher in northern climates.
The regulation of immune tolerance by FOXP3

The proper restraint of the destructive potential of the immune system is essential for maintaining health. Regulatory T (Treg) cells ensure immune homeostasis through their defining ability to suppress the activation and function of other leukocytes. The expression of the transcription factor forkhead box protein P3 (FOXP3) is a well-recognized characteristic of Treg cells, and FOXP3 is centrally involved in the establishment and maintenance of the Treg cell phenotype. In this Review, we summarize how the expression and activity of FOXP3 are regulated across multiple layers by diverse factors. The therapeutic implications of these topics for cancer and autoimmunity are also discussed.