nils-herrmann/clean_scirepeval huggingface dataset:
Applications of Convolutional Neural Networks

In recent years, deep learning has been used extensively in a wide range of fields. In deep learning, Convolutional Neural Networks are found to give the most accurate results in solving real world problems. In this paper, we give a comprehensive summary of the applications of CNN in computer vision and natural language processing. We delineate how CNN is used in computer vision, mainly in face recognition, scene labelling, image classification, action recognition, human pose estimation and document analysis. Further, we describe how CNN is used in the field of speech recognition and text classification for natural language processing. We compare CNN with other methods to solve the same problem and explain why CNN is better than other methods.  Deep Learning, Convolutional Neural Networks, Computer Vision, Natural Language
Adaptive Boosting for Synthetic Aperture Radar Automatic Target Recognition

We propose a novel automatic target recognition (ATR) system for classification of three types of ground vehicles in the MSTAR public release database. First, each MSTAR image chip is pre-processed by extracting fine and raw feature sets, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) network as the base learner. Since the RBF network is a binary classifier, we decompose our multiclass problem into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF network for each binary problem into a code word, which is then “decoded” as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature.
Robust line fitting using LMedS clustering

The authors propose a robust clustering method based on the Least Median of Squares principle. This is a general-purpose method, and is efficient for finding a majority structure using dictionary sorting and a smallest square region enclosing for a set of data points. In addition, the authors propose a robust line fitting algorithm using this method. The fitting problem is solved through a dual transform of a pair of points selected from data space to a parameter space, and then clustering the mapped points. Moreover, a function to identify outliers based on the size of the square region converging in the parameter space is defined, and is used to distinguish between normal values and outliers. The proposed method enables robust line fitting for data points found in images or in files. The resulting line statistically satisfies the least median condition. Furthermore, line fitting to groups of points with a high outlier percentage is also possible. Here, outliers and normal values are removed in sequence. The validity of the proposed method has been shown based on the results of simulation experiments.
Secure Border Gateway Protocol (S-BGP)

The Border Gateway Protocol (BGP), which is used to distribute routing information between autonomous systems (ASes), is a critical component of the Internet's routing infrastructure. It is highly vulnerable to a variety of malicious attacks, due to the lack of a secure means of verifying the authenticity and legitimacy of BGP control traffic. This paper describes a secure, scalable, deployable architecture (S-BGP) for an authorization and authentication system that addresses most of the security problems associated with BGP. The paper discusses the vulnerabilities and security requirements associated with BGP, describes the S-BGP countermeasures, and explains how they address these vulnerabilities and requirements. In addition, this paper provides a comparison of this architecture to other approaches that have been proposed, analyzes the performance implications of the proposed countermeasures, and addresses operational issues.
Effective Web Log Mining and Online Navigational Pattern Prediction: A Survey

The web has become the world's largest repository of knowledge. Web usage mining is the process of discovering knowledge from the interactions generated by the user in the form of access logs, cookies, and user sessions data. Web Mining consists of three different categories, namely Web Content Mining, Web Structure Mining, and Web Usage Mining (is the process of discovering knowledge from the interaction generated by the users in the form of access logs, browser logs, proxy-server logs, user session data, cookies). Accurate web log mining results and efficient online navigational pattern prediction are undeniably crucial for tuning up websites and consequently helping in visitors’ retention. Like any other data mining task, web log mining starts with data cleaning and preparation and it ends up discovering some hidden knowledge which cannot be extracted using conventional methods. After applying web mining on web sessions we will get navigation patterns which are important for web users such that appropriate actions can be adopted. Due to huge data in web, discovery of patterns and there analysis for further improvement in website becomes a real time necessity. The main focus of this paper is using of hybrid prediction engine to classify users on the basis of discovered patterns from web logs. Our proposed framework is to overcome the problem arise due to using of any single algorithm, we will give results based on comparison of two different algorithms like Longest Common Sequence (LCS) algorithm and Frequent Pattern (Growth) algorithm.  Web Usage Mining, Navigation Pattern, Frequent Pattern (Growth) Algorithm. ________________________________________________________________________________________________________
Multiple causes of difficulty in insight: the case of the nine-dot problem.

Theories of insight problems are often tested by formulating hypotheses about the particular difficulties of individual insight problems. Such evaluations often implicitly assume that there is a single difficulty. We argue that the quantitatively small effects of many studies arise because the difficulty of many insight problems is determined by multiple factors, so the removal of 1 factor has limited effect on the solution rate. Difficulties can reside either in problem perception, in prior knowledge, or in the processing of the problem information. We support this multiple factors perspective through 3 experiments on the 9-dot problem (N.R.F. Maier, 1930). Our results lead to a significant reformulation of the classical hypothesis as to why this problem is difficult. The results have general implications for our understanding of insight problem solving and for the interpretation of data from studies that aim to evaluate hypotheses about the sources of difficulty of particular insight problems.
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network

The inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-aided diagnosis methods. In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process. MDNet includes an image model and a language model. The image model is proposed to enhance multi-scale feature ensembles and utilization efficiency. The language model, integrated with our improved attention mechanism, aims to read and explore discriminative image feature descriptions from reports to learn a direct mapping from sentence words to image pixels. The overall network is trained end-to-end by using our developed optimization strategy. Based on a pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we conduct sufficient experiments to demonstrate that MDNet outperforms comparative baselines. The proposed image model obtains state-of-the-art performance on two CIFAR datasets as well.
The Impact of Computer Self Efficacy and Technology Acceptance Model on Behavioral Intention in Internet Banking System

The aim of this paper is to determine acceptance of internet banking systems among potential young users, specifically future marketers, who significantly affect the continuous usage of internet banking service. It attempted to examine the impact of Computer Self Efficacy (CSE) and extended Technology Acceptance Model (TAM) on the behavioral intention (BI) to use the internet banking systems. Measure of CSE was based on the Self Service Technology, as proposed by Compaeu and Higgins. A technology acceptance model for internet banking system was developed based on the modified version of TAM to examine the effects of Perceived Usefulness (PU), Perceived Ease of use (PE) and Perceived Credibility (PC) of extended TAM on the BI to use the internet banking systems. PU and PE are the established dimensions of classical TAM and Perceived credibility (PC) is the additional dimension to be included in the conceptual model of this study. Data were obtained from 222 undergraduates marketing students in a Malaysia’s public university. The finding indicated that CSE, PU, PE and PC of extended TAM were determinants of users’ acceptance of internet banking systems. PU, PE and PC were significantly affected BI, and respondents’ perceived credibility of the internet banking system had the strongest impact on their intention to use the system. This research validated that PU, PE and PC of the extended TAM were good predictors in understanding individual responses to information technology systems. The result of this study highlighted that issues of privacy and security of PC are important in the study of information systems acceptance, suggesting that internet banking providers need to address these issues effectively to convince potential users to use internet banking service. This study also validated the critical role of CSE in predicting individual responses to information technology systems. The finding unveiled that indirect relationship existed between CSE and BI through PU, PE and PC of TAM.
Association between angiotensin-converting-enzyme gene polymorphism and failure of renoprotective therapy

Polymorphism in the gene for angiotensin-converting enzyme (ACE), especially the DD genotype, is associated with risk for cardiovascular disease. Glomerulosclerosis has similarities to atherosclerosis, and we looked at ACE gene polymorphism in patients with kidney disease who were in a trial of long-term therapy with an ACE inhibitor or a beta-blocker.



81 patients with non-diabetic renal disease had been entered into a randomised comparison of oral atenolol or enalapril to prevent progressive decline in renal function. The dose was titrated to a goal diastolic blood pressure of 10 mm Hg below baseline and/or below 95 mm Hg. The mean (SE) age was 50 (1) years, and the group included 49 men. Their renal function had been monitored over 3-4 years. We have looked at their ACE genotype, which we assessed with PCR.



27 patients had the II genotype, 37 were ID, and 17 were DD. 11 patients were lost to follow-up over 1-3 years. The decline of glomerular filtration rate over the years was significantly steeper in the DD group than in the ID and the II groups (p = 0.02; means -3.79, -1.37, and -1.12 mL/min per year, respectively). The DD patients treated with enalapril fared as equally a bad course as the DD patients treated with atenolol. Neither drug lowered the degree of proteinuria in the DD group.



Our data show that patients with the DD genotype are resistant to commonly advocated renoprotective therapy.
BLEU Deconstructed: Designing a Better MT Evaluation Metric

BLEU is the de facto standard automatic evaluation metric in machine translation. While BLEU is undeniably useful, it has a number of limitations. Although it works well for large documents and multiple references, it is unreliable at the sentence or sub-sentence levels, and with a single reference. In this paper, we propose new variants of BLEU which address these limitations, resulting in a more flexible metric which is not only more reliable, but also allows for more accurate discriminative training. Our best metric has better correlation with human judgements than standard BLEU, despite using a simpler formulation. Moreover, these improvements carry over to a system tuned for our new metric.
Real time drowsiness detection using eye blink monitoring

According to analysis reports on road accidents of recent years, it's renowned that the main cause of road accidents resulting in deaths, severe injuries and monetary losses, is due to a drowsy or a sleepy driver. Drowsy state may be caused by lack of sleep, medication, drugs or driving continuously for long time period. An increase rate of roadside accidents caused due to drowsiness during driving indicates a need of a system that detects such state of a driver and alerts him prior to the occurrence of any accident. During the recent years, many researchers have shown interest in drowsiness detection. Their approaches basically monitor either physiological or behavioral characteristics related to the driver or the measures related to the vehicle being used. A literature survey summarizing some of the recent techniques proposed in this area is provided. To deal with this problem we propose an eye blink monitoring algorithm that uses eye feature points to determine the open or closed state of the eye and activate an alarm if the driver is drowsy. Detailed experimental findings are also presented to highlight the strengths and weaknesses of our technique. An accuracy of 94% has been recorded for the proposed methodology.
/ Subscribe Network Designs for Multiplayer Games ( Experimentation and Deployment Paper )

Massively multiplayer online games (MMOGs), which are typically supported by large distributed systems, require a scalable, low latency messaging middleware that supports the location-based semantics and the loosely coupled interaction of multiplayer games components. In this experimentation paper, we present three different pub/sub-driven designs for a MMOG networking engine that account for the highly interactive and massive nature of these games. Each design uses not only different pub/sub approaches (from topic-based to content-based) but also serves varying degrees of responsibilities. In particular, some of them integrate game functionality, such as interest management, into the network engine. We implement, evaluate, and compare our proposed designs in the MMOG prototype Mammoth. Our real-world results show the viability of pub/sub while at the same time highlighting clear trade-offs between the different designs used, especially in the number and frequency of the various message types, such as subscriptions.
Transgender medicine- transitioning transgender children to adulthood

There has been an increasing prevalence of individuals presenting for treatment of gender dysphoria over the past several years. This growing population includes transgender children referred to pediatric clinics. Transgender children meeting diagnostic criteria for gender dysphoria, with supportive mental health care, may be treated with gonadotropin releasing hormone (GnRH) agonists and cross sex hormones. The treatment for these children requires ongoing maintenance and monitoring and therefore follow-up in the adult care setting. As is the case with other conditions that require long term treatment, these youth need a formal transition from pediatric to adult clinical care. The period of transition of care is critical and if not executed properly exposes the adolescent to risks of complications, and loss of follow-up. The proper clinical transfer is especially important for transgender youth in the setting of increased psychosocial and mental health issues. The transition of care for transgender adolescents has not been formally studied but given a growing population seeking treatment for gender dysphoria, it will need to be systematically assessed to determine the best outcomes to measure.
pigeo: A Python Geotagging Tool

We present pigeo, a Python geolocation prediction tool that predicts a location for a given text input or Twitter user. We discuss the design, implementation and application of pigeo, and empirically evaluate it. pigeo is able to geolocate informal text and is a very useful tool for users who require a free and easy-to-use, yet accurate geolocation service based on pre-trained models. Additionally, users can train their own models easily using pigeo’s API.
Basis Set Exchange: A Community Database for Computational Sciences

Basis sets are some of the most important input data for computational models in the chemistry, materials, biology, and other science domains that utilize computational quantum mechanics methods. Providing a shared, Web-accessible environment where researchers can not only download basis sets in their required format but browse the data, contribute new basis sets, and ultimately curate and manage the data as a community will facilitate growth of this resource and encourage sharing both data and knowledge. We describe the Basis Set Exchange (BSE), a Web portal that provides advanced browsing and download capabilities, facilities for contributing basis set data, and an environment that incorporates tools to foster development and interaction of communities. The BSE leverages and enables continued development of the basis set library originally assembled at the Environmental Molecular Sciences Laboratory.
What Will Your Future Child Look Like? Modeling and Synthesis of Hereditary Patterns of Facial Dynamics

Analysis of kinship from facial images or videos is an important problem. Prior machine learning and computer vision studies approach kinship analysis as a verification or recognition task. In this paper, first time in the literature, we propose a kinship synthesis framework, which generates smile videos of (probable) children from the smile videos of parents. While the appearance of a child’s smile is learned using a convolutional encoder-decoder network, another neural network models the dynamics of the corresponding smile. The smile video of the estimated child is synthesized by the combined use of appearance and dynamics models. In order to validate our results, we perform kinship verification experiments using videos of real parents and estimated children generated by our framework. The results show that generated videos of children achieve higher correct verification rates than those of real children. Our results also indicate that the use of generated videos together with the real ones in the training of kinship verification models, increases the accuracy, suggesting that such videos can be used as a synthetic dataset.
TectoMT: Modular NLP Framework

In the present paper we describe TectoMT, a multi-purpose open-source NLP framework. It allows for fast and efficient development of NLP applications by exploiting a wide range of software modules already integrated in TectoMT, such as tools for sentence segmentation, tokenization, morphological analysis, POS tagging, shallow and deep syntax parsing, named entity recognition, anaphora resolution, tree-to-tree translation, natural language generation, word-level alignment of parallel corpora, and other tasks. One of the most complex applications of TectoMT is the English-Czech machine translation system with transfer on deep syntactic (tectogrammatical) layer. Several modules are available also for other languages (German, Russian, Arabic). Where possible, modules are implemented in a language-independent way, so they can be reused in many applications.
The concurrent application of lean production and ERP: Towards an ERP-based lean implementation process

Lean production and enterprise resource planning systems are often quoted as being the two most important strategies for achieving competitive advantage in today’s global manufacturing environments. Though IT has traditionally been viewed as a contributor to waste within lean production, we suggest that modern developments in IT and the onset of hybrid ‘‘push-pull’’ production control mechanisms have allowed ERP and lean approaches to converge towards a state where ERP systems can in fact be used to support the deployment of lean practices. This paper analyses typical lean and ERP implementation processes contained within the scientific literature, and by further examining a concurrent implementation process in real-time, we develop and propose a process for ERP-based lean implementations. Our findings suggest that the implementation of a contemporary ERP system can act as a catalyst for the application of lean production practices.  E-mail address: daryl.j.powell@ntnu.no (D. Powell).
Tenidap is neuroprotective in a pilocarpine rat model of temporal lobe epilepsy.

Tenidap is a liposoluble non-steroidal anti-inflammatory drug that is easily distributed in the central nervous system and also inhibits the production and activity of cyclooxygenase-2 (COX-2) and cytokines in vitro. This study aimed to evaluate the neuroprotective effect of tenidap in a pilocarpine rat model of temporal lobe epilepsy (TLE).



Tenidap was administered daily at 10 mg/kg for 10 days following pilocarpine-induced status epilepticus (SE) in male Wistar rats after which prolonged generalized seizures resulted in TLE. After tenidap treatment, spontaneous recurrent seizures (SRSs) were recorded by video monitoring (for 7 hours per day for 14 days). The frequency and severity of the SRSs were observed. Histological and immunocytochemical analyses were used to evaluate the neuroprotective effect of tenidap and detect COX-2 expression, which may be associated with neuronal death.



There were 46.88 ± 10.70 survival neurons in tenidap-SE group, while there were 27.60 ± 5.18 survival neurons in saline-SE group at -2.4 mm field in the CA3 area. There were 37.75 ± 8.78 survival neurons in tenidap-SE group, while there were 33.40 ± 8.14 survival neurons in saline-SE group at -2.4 mm field in the CA1 area. Tenidap treatment significantly reduced neuronal damage in the CA3 area (P < 0.05) and slightly reduced damage in the CA1 area. Tenidap markedly inhibited COX-2 expression in the hippocampus, especially in the CA3 area.



Tenidap conferred neuroprotection to the CA3 area in a pilocarpine-induced rat model of TLE by inhibiting COX-2 expression.
Effective Audience Extension in Online Advertising

In digital advertising, advertisers want to reach the right audience over media channels such as display, mobile, video, or social at the appropriate cost. The right audience for an advertiser consists of existing customers as well as valuable prospects, those that can potentially be turned into future customers. Identifying valuable prospects is called the audience extension problem because advertisers find new customers by extending the desirable criteria for their starting point, which is their existing audience or customers. The complexity of the audience extension problem stems from the difficulty of defining desirable criteria objectively, the number of desirable criteria (such as similarity, diversity, performance) to simultaneously satisfy, and the expected runtime (a few minutes) to find a solution over billions of cookie-based users. In this paper, we formally define the audience extension problem, propose an algorithm that extends a given audience set efficiently under multiple desirable criteria, and experimentally validate its performance. Instead of iterating over individual users, the algorithm takes in Boolean rules that define the seed audience and returns a new set of Boolean rules that corresponds to the extended audience that satisfy the multiple criteria.