nils-herrmann/clean_scirepeval huggingface dataset:
TPD: Travel Prediction-based Data Forwarding for light-traffic vehicular networks

This paper proposes Travel Prediction-based Data forwarding (TPD), tailored and optimized for multihop vehicle-to-vehicle communications. The previous schemes forward data packets mostly utilizing statistical information about road network traffic, which becomes much less accurate when vehicles travel in a light-traffic vehicular network. In this light-traffic vehicular network, highly dynamic vehicle mobility can introduce a large variance for the traffic statistics used in the data forwarding process. However, with the popularity of GPS navigation systems, vehicle trajectories become available and can be utilized to significantly reduce this uncertainty in the road traffic statistics. Our TPD takes advantage of these vehicle trajectories for a better data forwarding in light-traffic vehicular networks. Our idea is that with the trajectory information of vehicles in a target road network, a vehicle encounter graph is constructed to predict vehicle encounter events (i.e., timing for two vehicles to exchange data packets in communication range). With this encounter graph, TPD optimizes data forwarding process for minimal data delivery delay under a specific delivery ratio threshold. Through extensive simulations, we demonstrate that our TPD significantly outperforms existing legacy schemes in a variety of road network settings.
Stereo image feature matching using Harris corner detection algorithm

Stereo image matching is one of the research areas in computer vision. In stereo image matching, technological developments advances from area based matching techniques to the feature based matching techniques. In this paper, we present a Harris corner detection algorithm for stereo image feature matching. This is an intensity based feature matching algorithm and it controls the strong and weak corners with the help of threshold value. Generally image processing algorithms are simulated in software but to do hardware co-simulation, here the model based design is implemented in Xilinx System Generator. Further the architecture is synthesized on the Xilinx Virtex - 5 FPGA. Simulation results are included in this paper to verify the performance of proposed system. Complexity level is minimized in model based design than that of script level design.
Real-world Noisy Image Denoising: A New Benchmark

Most of previous image denoising methods focus on additive white Gaussian noise (AWGN). However,the real-world noisy image denoising problem with the advancing of the computer vision techiniques. In order to promote the study on this problem while implementing the concurrent real-world image denoising datasets, we construct a new benchmark dataset which contains comprehensive real-world noisy images of different natural scenes. These images are captured by different cameras under different camera settings. We evaluate the different denoising methods on our new dataset as well as previous datasets. Extensive experimental results demonstrate that the recently proposed methods designed specifically for realistic noise removal based on sparse or low rank theories achieve better denoising performance and are more robust than other competing methods, and the newly proposed dataset is more challenging. The constructed dataset of real photographs is publicly available at https://github.com/csjunxu/PolyUDataset for researchers to investigate new real-world image denoising methods. We will add more analysis on the noise statistics in the real photographs of our new dataset in the next version of this article.
Phloem-Transported Cytokinin Regulates Polar Auxin Transport and Maintains Vascular Pattern in the Root Meristem

Cytokinin phytohormones regulate a variety of developmental processes in the root such as meristem size, vascular pattern, and root architecture . Long-distance transport of cytokinin is supported by the discovery of cytokinins in xylem and phloem sap [4] and by grafting experiments between wild-type and cytokinin biosynthesis mutants . Acropetal transport of cytokinin (toward the shoot apex) has also been implicated in the control of shoot branching [6]. However, neither the mode of transport nor a developmental role has been shown for basipetal transport of cytokinin (toward the root apex). In this paper, we combine the use of a new technology that blocks symplastic connections in the phloem with a novel approach to visualize radiolabeled hormones in planta to examine the basipetal transport of cytokinin. We show that this occurs through symplastic connections in the phloem. The reduction of cytokinin levels in the phloem leads to a destabilization of the root vascular pattern in a manner similar to mutants affected in auxin transport or cytokinin signaling [7]. Together, our results demonstrate a role for long-distance basipetal transport of cytokinin in controlling polar auxin transport and maintaining the vascular pattern in the root meristem.
Shaping Information Security Behaviors Related to Social Engineering Attacks

Today, few companies would manage to continuously stay competitive without the proper utilization of information technology (IT). This has increased companies’ dependency of IT and created new threats that need to be addressed to mitigate risks to daily business operations. A large extent of these IT-related threats includes hackers attempting to gain unauthorized access to internal computer networks by exploiting vulnerabilities in the behaviors of employees. A common way to exploit human vulnerabilities is to deceive and manipulate employees through the use of social engineering. Although researchers have attempted to understand social engineering, there is a lack of empirical research capturing multilevel factors explaining what drives employees’ existing behaviors and how these behaviors can be improved. This is addressed in this thesis. The contribution of this thesis includes (i) an instrument to measure security behaviors and its multilevel determinants, (ii) identification of multilevel variables that significantly influence employees’ intent for behavior change, (iii) identification of what behavioral governance factors that lay the foundation for behavior change, (iv) identification that national culture has a significant effect on how organizations cope with behavioral information security threats, and (v) a strategy to ensure adequate information security behaviors throughout an organization. This thesis is a composite thesis of eight papers. Paper 1 describes the instrument measuring multilevel determinants. Paper 2 and 3 describes how security knowledge is established in organizations, and the effect on employee information security awareness. In Paper 4 the root cause of employees’ intention to change their behaviors and resist social engineering is described. Paper 5 and 8 describes how the instrument to measure social engineering security behaviors was developed and validated through scenariobased surveys and phishing experiments. Paper 6 and 7 describes experiments performed to understand reason to why employees fall for social engineering. Finally, paper 2, 5 and 6 examines the moderating effect of national culture.
SEQUENTIAL MINIMAL OPTIMIZATION IN SUPPORT VECTOR MACHINE

Computer based medical decision support system (MDSS) can be useful for the physicians with its fast and accurate decision making process. Predicting the existence of heart disease accurately, results in saving life of patients followed by proper treatment. The main objective of our paper is to present a MDSS for heart disease classification based on sequential minimal optimization (SMO) technique in support vector machine (SVM). In this we illustrated the UCI machine learning repository data of Cleveland heart disease database; we trained SVM by using SMO technique. Training a SVM requires the solution of a very large QP optimization problem..SMO algorithm breaks this large optimization problem into small sub-problems. Both the training and testing phases give the accuracy on each record. The results proved that the MDSS is able to carry out heart disease diagnosis accurately in fast way and on a large dataset it shown good ability of prediction.
Therapeutic agents for the treatment of cognitive dysfunction syndrome in senior dogs

With increasing age, dogs develop a form of neurodegenerative disease which has many similarities to age related cognitive impairment and Alzheimer's disease in humans. A decline in learning and memory can be demonstrated in dogs beginning as young as 7 years of age using a variety of neuropsychological tests. However, clinical cases of cognitive dysfunction syndrome are seldom identified until the age of 11 years or older. This is likely due to the fact that the owners are relying on clinical observations such as house-soiling, sleep-wake cycles and disorientation, rather than tests of learning and memory. On the other hand, dogs that are trained to more exacting tasks such as guide dogs for the visually impaired, or bomb detection and agility trained dogs might be noticed to have a decline in performance at a much earlier age. Through the use of standardized neuropsychological testing protocols, a number of drugs, natural products and supplement formulations have been developed for use in dogs with cognitive dysfunction and, in some cases clinical trials have validated their efficacy. Furthermore, the testing of products currently licensed and in the pipeline for the treatment of cognitive decline and Alzheimer's in humans, may provide additional therapeutic agents for the treatment of senior dogs, as well as provide insight as to the potential for the efficacy of these compounds in humans. This review will examine those products that are now marketed along with some that might be considered for use in senior dogs with cognitive dysfunction as well as the research that has been used to validate the efficacy (or lack thereof) of these compounds.
Systemic mastocytosis: bone marrow involvement assessed by Tc-99m MDP scintigraphy and magnetic resonance imaging.

Mastocytosis is a rare, heterogeneous disease of complex etiology, characterized by a marked increase in mast cell density in the skin, bone marrow, liver, spleen, gastrointestinal mucosa and lymph nodes. The most frequent site of organ involvement is the skin. Cutaneous lesions include urticaria pigmentosa, mastocytoma, diffuse and erythematous cutaneous mastocytosis, and telangiectasia macularis eruptiva perstans. Human mast cells originate from CD34 progenitors, under the influence of stem cell factor (SCF); a substantial number of patients exhibit activating mutations in c-kit, the receptor for SCF. Mast cells can synthesize a variety of cytokines that could affect the skeletal system, increasing perforating bone resorption and leading to osteoporosis. The coexistence of hematologic disorders, such as myeloproliferative or myelodysplastic syndromes, or of lymphoreticular malignancies, is common. Compared with radiographs, Tc-99m methylenediphosphonate (MDP) scintigraphy is better able to show the widespread skeletal involvement in patients with diffuse disease. T1-weighted MR imaging is a sensitive technique for detecting marrow abnormalities in patients with systemic mastocytosis, showing several different patterns of marrow involvement. We report the imaging findings a 36-year old male with well-documented urticaria pigmentosa. In order to evaluate mastocytic bone marrow involvement, 99mTc-MDP scintigraphy, T1-weighted spin echo and short tau inversion recovery MRI at 1.0 T, were performed. Both scan findings were consistent with marrow hyperactivity. Thus, the combined use of bone scan and MRI may be useful in order to recognize marrow involvement in suspected systemic mastocytosis, perhaps avoiding bone biopsy.
The use of guided tissue regeneration techniques among endodontists: a web-based survey.

The purpose of this study was to determine factors and clinical situations that influence an endodontist's decision to use guided tissue regeneration (GTR) techniques during endodontic root-end surgery.



An invitation to participate in a web-based survey was e-mailed to 3,750 members of the American Association of Endodontists. Data were collected from 1,129 participants, representing a 30.1% completion rate. The number of questions varied from 3 to 11 depending on individual responses.



40.7% of respondents who perform root-end surgeries also use GTR techniques. The clinical situation in which GTR techniques are used most often is for transosseous lesions. Barrier membranes and bone replacement grafts are each used by more than 85% of respondents using GTR techniques. Insufficient training and insufficient evidence in support of its use were selected as the predominant reasons for not using GTR techniques at 42.4% and 32%, respectively.



Although over 40% of respondents are currently using GTR techniques in conjunction with their root-end surgeries, a majority of those who do not use GTR indicated they would consider using these techniques with better evidence and available training.
Pearls and pitfalls of allergy diagnostic testing: report from the American College of Allergy, Asthma and Immunology/American Academy of Allergy, Asthma and Immunology Specific IgE Test Task Force.

The intended purpose of this monograph is to provide a general overview of allergy diagnostics for health care professionals who care for patients with allergic disease. For a more comprehensive review of allergy diagnostic testing, readers can refer to the Allergy Diagnostic Practice Parameters. A key message is that a positive allergy test result (skin or blood) indicates only the presence of allergen specific IgE (called sensitization). It does not necessarily mean clinical allergy (ie, allergic symptoms with exposure). It is important for this reason that the allergy evaluation be based on the patient's history and directed by a health care professional with sufficient understanding of allergy diagnostic testing to use the information obtained from his/her evaluation of the patient to determine (1) what allergy diagnostic tests to order, (2) how to interpret the allergy diagnostic test results, and (3) how to use the information obtained from the allergy evaluation to develop an appropriate therapeutic treatment plan.
Integrating TAM and TOE Frameworks and Expanding their Characteristic Constructs for E-Commerce Adoption by SMEs

Electronic Commerce has increased the global reach of small and medium scale enterprises (SMEs); its acceptance as an IT infrastructure depends on the users’ conscious assessment of the influencing constructs as depicted in Technology Acceptance Model (TAM), Theory of Reasoned Action (TRA), Theory of Planned Behaviour (TPB), and Technology-Organization-Environment (T-O-E) model. The original TAM assumes the constructs of perceived usefulness (PU) and perceived ease of use (PEOU); TPB perceived behavioural control and subjective norms; and T-O-E firm’s size, consumer readiness, trading partners’ readiness, competitive pressure, and scope of business operation. This paper reviewed and synthesized the constructs of these models and proposed an improved TAM through T-O-E. The improved TAM and T-O-E integrated more constructs than the original TAM, T-O-E, TPB, and IDT, leading to eighteen propositions to promote and facilitate future research, and to guide explanation and prediction of IT adoption in an organized system. The integrated constructscompany mission, individual difference factors, perceived trust, and perceived service quality improve existing knowledge on EC acceptance and provide bases for informed decision(s).
Model-based Pricing for Machine Learning in a Data Marketplace

Data analytics using machine learning (ML) has become ubiquitous in science, business intelligence, journalism and many other domains. While a lot of work focuses on reducing the training cost, inference runtime and storage cost of ML models, little work studies how to reduce the cost of data acquisition, which potentially leads to a loss of sellers’ revenue and buyers’ affordability and efficiency. In this paper, we propose a model-based pricing (MBP) framework, which instead of pricing the data, directly prices ML model instances. We first formally describe the desired properties of the MBP framework, with a focus on avoiding arbitrage. Next, we show a concrete realization of the MBP framework via a noise injection approach, which provably satisfies the desired formal properties. Based on the proposed framework, we then provide algorithmic solutions on how the seller can assign prices to models under different market scenarios (such as to maximize revenue). Finally, we conduct extensive experiments, which validate that the MBP framework can provide high revenue to the seller, high affordability to the buyer, and also operate on low runtime cost.
Merging knowledge bases in different languages

Recently, different systems which learn to populate and extend a knowledge base (KB) from the web in different languages have been presented. Although a large set of concepts should be learnt independently from the language used to read, there are facts which are expected to be more easily gathered in local language (e.g., culture or geography). A system that merges KBs learnt in different languages will benefit from the complementary information as long as common beliefs are identified, as well as from redundancy present in web pages written in different languages. In this paper, we deal with the problem of identifying equivalent beliefs (or concepts) across language specific KBs, assuming that they share the same ontology of categories and relations. In a case study with two KBs independently learnt from different inputs, namely web pages written in English and web pages written in Portuguese respectively, we report on the results of two methodologies: an approach based on personalized PageRank and an inference technique to find out common relevant paths through the KBs. The proposed inference technique efficiently identifies relevant paths, outperforming the baseline (a dictionary-based classifier) in the vast majority of tested categories.
Utilizing Knowledge Bases in Text-centric Information Retrieval

General-purpose knowledge bases are increasingly growing in terms of depth (content) and width (coverage). Moreover, algorithms for entity linking and entity retrieval have improved tremendously in the past years. These developments give rise to a new line of research that exploits and combines these developments for the purposes of text-centric information retrieval applications. This tutorial focuses on a) how to retrieve a set of entities for an ad-hoc query, or more broadly, assessing relevance of KB elements for the information need, b) how to annotate text with such elements, and c) how to use this information to assess the relevance of text. We discuss different kinds of information available in a knowledge graph and how to leverage each most effectively.
 We start the tutorial with a brief overview of different types of knowledge bases, their structure and information contained in popular general-purpose and domain-specific knowledge bases. In particular, we focus on the representation of entity-centric information in the knowledge base through names, terms, relations, and type taxonomies. Next, we will provide a recap on ad-hoc object retrieval from knowledge graphs as well as entity linking and retrieval. This is essential technology, which the remainder of the tutorial builds on. Next we will cover essential components within successful entity linking systems, including the collection of entity name information and techniques for disambiguation with contextual entity mentions. We will present the details of four previously proposed systems that successfully leverage knowledge bases to improve ad-hoc document retrieval. These systems combine the notion of entity retrieval and semantic search on one hand, with text retrieval models and entity linking on the other. Finally, we also touch on entity aspects and links in the knowledge graph as it can help to understand the entities' context.
 This tutorial is the first to compile, summarize, and disseminate progress in this emerging area and we provide both an overview of state-of-the-art methods and outline open research problems to encourage new contributions.
Effective worm detection for various scan techniques

In recent years, the threats and damages caused by active worms have become more and more serious. In order to reduce the loss caused by fastspreading active worms, an effective detection mechanism to quickly detect worms is desired. In this paper, we first explore various scan strategies used by worms on finding vulnerable hosts. We show that targeted worms spread much faster than random scan worms. We then present a generic worm detection architecture to monitor malicious worm activities. We propose and evaluate our detection mechanism called Victim Number Based Algorithm. We show that our detection algorithm is effective and able to detect worm events before 2% of vulnerable hosts are infected for most scenarios. Furthermore, in order to reduce false alarms, we propose an integrated approach using multiple parameters as indicators to detect worm events. The results suggest that our integrated approach can differentiate worm attacks from DDoS attacks and benign scans.
client2vec: Towards Systematic Baselines for Banking Applications

The workflow of data scientists normally involves potentially inefficient processes such as data mining, feature engineering and model selection. Recent research has focused on automating this workflow, partly or in its entirety, to improve productivity. We choose the former approach and in this paper share our experience in designing the client2vec: an internal library to rapidly build baselines for banking applications. Client2vec uses marginalized stacked denoising autoencoders on current account transactions data to create vector embeddings which represent the behaviors of our clients. These representations can then be used in, and optimized against, a variety of tasks such as client segmentation, profiling and targeting. Here we detail how we selected the algorithmic machinery of client2vec and the data it works on and present experimental results on several business cases.
The FIGO classification of causes of abnormal uterine bleeding in the reproductive years.

At this juncture, clinical management, education for medical providers, and the design and interpretation of clinical trials have been hampered by the absence of a consensus system for nomenclature for the description of symptoms as well as classification of causes or potential causes of abnormal uterine bleeding (AUB). To address this issue, the Fédération Internationale de Gynécologie et d'Obstétrique (FIGO) has designed the PALM-COEIN (Polyp, Adenomyosis, Leiomyoma, Malignancy and Hyperplasia, Coagulopathy, Ovulatory Disorders, Endometrial Disorders, Iatrogenic Causes, and Not Classified) classification system for causes of AUB in the reproductive years.