nils-herrmann/clean_scirepeval huggingface dataset:
A longitudinal study of follow predictors on twitter

Follower count is important to Twitter users: it can indicate popularity and prestige. Yet, holistically, little is understood about what factors -- like social behavior, message content, and network structure - lead to more followers. Such information could help technologists design and build tools that help users grow their audiences. In this paper, we study 507 Twitter users and a half-million of their tweets over 15 months. Marrying a longitudinal approach with a negative binomial auto-regression model, we find that variables for message content, social behavior, and network structure should be given equal consideration when predicting link formations on Twitter. To our knowledge, this is the first longitudinal study of follow predictors, and the first to show that the relative contributions of social behavior and mes-sage content are just as impactful as factors related to social network structure for predicting growth of online social networks. We conclude with practical and theoretical implications for designing social media technologies.
Secondary DNA transfer of biological substances under varying test conditions.

This research investigates factors that may influence the secondary transfer of DNA. These include the type of biological substance deposited, the nature of the primary and secondary substrate, moisture content of the deposit and type of contact between the surfaces. Results showed that secondary transfer is significantly affected by both the type of primary substrate and the moisture (wetness) of the biological sample. Porous substrates and/or dry samples diminished transfer (with on average only 0.36% of biological material being transferred from one site to another), whereas non-porous substrates and/or wet samples facilitated transfer events (approximately 50-95% of biological material was transferred from one site to another). Further, the type of secondary substrate also influenced transfer rate, with porous surfaces, absorbing transferred biological substances more readily than non-porous ones. No significant differences were observed among the biological substances tested (pure DNA, blood and saliva). Friction contact between the two substrates significantly enhanced secondary transfer compared to either passive or pressure contact. These preliminary results will assist in developing general assumptions when estimating probability of a secondary DNA transfer event under simple conditions.
Heuristic-based Approach for Phishing Site Detection Using URL Features

Damage caused by phishing attacks that target personal user information is increasing. Phishing involves sending an email to a user or inducing a phishing page to steal a user’s personal information. This type of attack can be detected by blacklist-based detection techniques; however, these methods have some disadvantages and the numbers of victims have therefore continued to increase. In this paper, we propose a heuristic-based phishing detection technique that uses uniform resource locator (URL) features. We identified features that phishing site URLs contain. The proposed method employs those features for phishing detection. The technique was evaluated with a dataset of 3,000 phishing site URLs and 3,000 legitimate site URLs. The results demonstrate that the proposed technique can detect more than 98.23% of phishing sites. phishing sites, URL-based features, heuristic, machine learning
Spatial and Spatio-Temporal Multidimensional Data Modelling: A Survey

Data warehouse store and provide access to large volume of historical data supporting the strategic decisions of organisations. Data warehouse is based on a multidimensional model which allow to express user’s needs for supporting the decision making process. Since it is estimated that 80% of data used for decision making has a spatial or location component, spatial data have been widely integrated in Data Warehouses and in OLAP systems. Extending a multidimensional data model by the inclusion of spatial data provides a concise and organised spatial data warehouse representation. This paper aims to provide a comprehensive review of literature on developed and suggested spatial and spatio-temporel multidimensional models. A benchmarking study of the proposed models is presented. Several evaluation criteria’s are used to identify the existence of trends as well as potential needs for further investigations.  Data warehouse, Spatial data, Multidimensional modelling, Temporal data
Data mining a diabetic data warehouse

Diabetes is a major health problem in the United States. There is a long history of diabetic registries and databases with systematically collected patient information. We examine one such diabetic data warehouse, showing a method of applying data mining techniques, and some of the data issues, analysis problems, and results. The diabetic data warehouse is from a large integrated health care system in the New Orleans area with 30,383 diabetic patients. Methods for translating a complex relational database with time series and sequencing information to a flat file suitable for data mining are challenging. We discuss two variables in detail, a comorbidity index and the HgbA1c, a measure of glycemic control related to outcomes. We used the classification tree approach in Classification and Regression Trees (CART) with a binary target variable of HgbA1c >9.5 and 10 predictors: age, sex, emergency department visits, office visits, comorbidity index, dyslipidemia, hypertension, cardiovascular disease, retinopathy, end-stage renal disease. Unexpectedly, the most important variable associated with bad glycemic control is younger age, not the comorbiditity index or whether patients have related diseases. If we want to target diabetics with bad HgbA1c values, the odds of finding them is 3.2 times as high in those <65 years of age than those older. Data mining can discover novel associations that are useful to clinicians and administrators [corrected].
Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research

Behavioral addiction research has been particularly flourishing over the last two decades. However, recent publications have suggested that nearly all daily life activities might lead to a genuine addiction.  In this article, we discuss how the use of atheoretical and confirmatory research approaches may result in the identification of an unlimited list of "new" behavioral addictions.  Both methodological and theoretical shortcomings of these studies were discussed.  We suggested that studies overpathologizing daily life activities are likely to prompt a dismissive appraisal of behavioral addiction research. Consequently, we proposed several roadmaps for future research in the field, centrally highlighting the need for longer tenable behavioral addiction research that shifts from a mere criteria-based approach toward an approach focusing on the psychological processes involved.
3D distance fields: a survey of techniques and applications

A distance field is a representation where, at each point within the field, we know the distance from that point to the closest point on any object within the domain. In addition to distance, other properties may be derived from the distance field, such as the direction to the surface, and when the distance field is signed, we may also determine if the point is internal or external to objects within the domain. The distance field has been found to be a useful construction within the areas of computer vision, physics, and computer graphics. This paper serves as an exposition of methods for the production of distance fields, and a review of alternative representations and applications of distance fields. In the course of this paper, we present various methods from all three of the above areas, and we answer pertinent questions such as How accurate are these methods compared to each other? How simple are they to implement?, and What is the complexity and runtime of such methods?.
Setting Lower Bounds on Truthfulness

We present general techniques for proving inapproximability results for several paradigmatic truthful multidimensional mechanism design problems. In particular, we demonstrate the strength of our techniques by exhibiting a lower bound of 2 − 1 m for the scheduling problem with m unrelated machines (formulated as a mechanism design problem in the seminal paper of Nisan and Ronen on Algorithmic Mechanism Design). Our lower bound applies to truthful randomized mechanisms, regardless of any computational assumptions on the running time of these mechanisms. Moreover, it holds even for the wider class of truthfulness-in-expectation mechanisms. This lower bound nearly matches the known 1.58606 randomized truthful upper bound for the case of two machines (a non-truthful FPTAS exists). Recently, Daskalakis and Weinberg  show that there is a polynomial-time 2approximately optimal Bayesian mechanism for makespan minimization for unrelated machines. We complement this result by showing an appropriate lower bound of 1.25 for deterministic incentive compatible Bayesian mechanisms. We then show an application of our techniques to the workload-minimization problem in networks. We prove our lower bounds for this problem in the inter-domain routing setting presented by Feigenbaum, Papadimitriou, Sami, and Shenker. Finally, we discuss several notions of non-utilitarian fairness (Max-Min fairness, Min-Max fairness, and envy minimization) and show how our techniques can be used to prove lower bounds for these notions. No lower bounds for truthful mechanisms in multidimensional probabilistic settings were previously known.1 ∗Computer Science Department, Technion, Haifa, Israel. ahumu@yahoo.com. †School of Computer Science and Engineering, Hebrew University, Jerusalem,  The current paper supersedes " Lower Bounds on Truthfulness" that appeared as an extended abstract in the Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA07), pages 1143-1152, 2007. The current version includes a new lower bound result for Bayesian Incentive compatible Mechanisms.
A Survey On Video Forgery Detection

The Digital Forgeries though not visibly identifiable to human perception it may alter or meddle with underlying natural statistics of digital content. Tampering involves fiddling with video content in order to cause damage or make unauthorized alteration/modification. Tampering detection in video is cumbersome compared to image when considering the properties of the video. Tampering impacts need to be studied and the applied technique/method is used to establish the factual information for legal course in judiciary. In this paper we give an overview of the prior literature and challenges involved in video forgery detection where passive approach is found.
Decade-bandwidth planar balun using CPW-to-slotline transition for UHF applications

This paper presents an improved broadband balun using a coplanar-waveguide(CPW)-to-slotline field transformation. It operates at very wide frequency, and is of compact size since it does not depend on a resonant structure. The measured results show a passband of 200 MHz to 2 GHz, insertion loss less than 0.75 dB and a size of 20 mm × 14 mm. The amplitude imbalance is approximately 0.3 dB and the phase imbalance is less than 6o over the entire operation range.
Mutual Information-based multi-label feature selection using interaction information

Multi-label feature selection is regarded as one of the most promising techniques that can be used to maximize the efficacy and efficiency of multi-label classification. However, because multi-label feature selection algorithms must consider multiple labels concurrently, the task is more difficult than singlelabel feature selection tasks. In this paper, we propose the Mutual Information-based multi-label feature selection method using interaction information. This method is naturally able to measure dependencies among multiple variables. To develop an efficient multi-label feature selection method, we derive theoretical bounds for the interaction information. Empirical studies indicate that our proposed multi-label feature selection method discovers effective feature subsets for multi-label classification problems.
An Empirical Study of the Impact of Two Antipatterns, Blob and Spaghetti Code, on Program Comprehension

Antipatterns are "poor" solutions to recurring design problems which are conjectured in the literature to make object-oriented systems harder to maintain. However, little quantitative evidence exists to support this conjecture. We performed an empirical study to investigate whether the occurrence of antipatterns does indeed affect the understandability of systems by developers during comprehension and maintenance tasks. We designed and conducted three experiments, with 24 subjects each, to collect data on the performance of developers on basic tasks related to program comprehension and assessed the impact of two antipatterns and of their combinations: Blob and Spaghetti Code. We measured the developers’ performance with: (1) the NASA task load index for their effort, (2) the time that they spent performing their tasks, and, (3) their percentages of correct answers. Collected data show that the occurrence of one antipattern does not significantly decrease developers’ performance while the combination of two antipatterns impedes significantly developers. We conclude that developers can cope with one antipattern but that combinations of antipatterns should be avoided possibly through detection and refactorings.
Introduction to Automata Theory, Languages and Computation

Let n be the pumping-lemma constant (note this n is unrelated to the n that is a local variable in the definition of the language L). Pick w = 0^n10^n. Then when we write w = xyz, we know that |xy| <= n, and therefore y consists of only 0's. Thus, xz, which must be in L if L is regular, consists of fewer than n 0's, followed by a 1 and exactly n 0's. That string is not in L, so we contradict the assumption that L is regular.
Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation

The applicability of computer vision to real paintings and artworks has been rarely investigated, even though a vast heritage would greatly benefit from techniques which can understand and process data from the artistic domain. This is partially due to the small amount of annotated artistic data, which is not even comparable to that of natural images captured by cameras. In this paper, we propose a semantic-aware architecture which can translate artworks to photo-realistic visualizations, thus reducing the gap between visual features of artistic and realistic data. Our architecture can generate natural images by retrieving and learning details from real photos through a similarity matching strategy which leverages a weakly-supervised semantic understanding of the scene. Experimental results show that the proposed technique leads to increased realism and to a reduction in domain shift, which improves the performance of pre-trained architectures for classification, detection, and segmentation. Code will be made publicly available.
A 5G MIMO Antenna Manufactured by 3-D Printing Method

In this letter, a multiple-input–multiple-output (MIMO) antenna for 5G base stations is manufactured utilizing three-dimensional (3-D) printing technology. A high-resolution printer based on 3-D stereo lithography is induced to ensure the printing accuracy. The printing process of the proposed antenna with a size of 27.2 × 27.2 × 17 mm3 can be completed within half an hour. The printed sample is made of a kind of photosensitive resin, so all of its surfaces are metalized as the conductive surfaces of the antenna using a metal plating method. The characteristics of the 3-D printed antenna are investigated. The bandwidth of the antenna is measured to be 3.5–5.1 GHz. The antenna is composed of two orthogonal polarized dipoles. In the bandwidth, both the voltage standing-wave ratios of the two dipoles are below 1.5, and the isolation between the two dipoles is better than 18 dB. The far-field radiation patterns of the antenna in both the horizontal and vertical planes are measured in an anechoic chamber. All the characteristics of the proposed antenna are also simulated using High Frequency Structure Simulator. The measured and simulated results are in good accordance.
Scaling Up Mixed Workloads: A Battle of Data Freshness, Flexibility, and Scheduling

The common “one size does not fit all” paradigm isolates transactional and analytical workloads into separate, specialized database systems. Operational data is periodically replicated to a data warehouse for analytics. Competitiveness of enterprises today, however, depends on real-time reporting on operational data, necessitating an integration of transactional and analytical processing in a single database system. The mixed workload should be able to query and modify common data in a shared schema. The database needs to provide performance guarantees for transactional workloads, and, at the same time, efficiently evaluate complex analytical queries. In this paper, we share our analysis of the performance of two main-memory databases that support mixed workloads, SAP HANA and HyPer, while evaluating the mixed workload CHbenCHmark. By examining their similarities and differences, we identify the factors that affect performance while scaling the number of concurrent transactional and analytical clients. The three main factors are (a) data freshness, i.e., how recent is the data processed by analytical queries, (b) flexibility, i.e., restricting transactional features in order to increase optimization choices and enhance performance, and (c) scheduling, i.e., how the mixed workload utilizes resources. Specifically for scheduling, we show that the absence of workload management under cases of high concurrency leads to analytical workloads overwhelming the system and severely hurting the performance of transactional workloads.
Working with young people: the impact of mental health awareness programmes in schools in the UK and Canada

The persistent and disabling nature of psychiatric stigma has led to the establishment of global programmes to challenge the negative stereotypes and discriminatory responses that generate social disability, but these initiatives are rarely evaluated. This study compares the effectiveness of school-based interventions with young people aged 14-16 aimed at increasing mental health literacy and challenging negative stereotypes associated with severe mental illness in sites in Canada and the UK. In both countries, short educational sessions were delivered involving a facilitator with direct experience of mental illness. Students in Canada at baseline (N=1501) were significantly more aware than those in the UK (N=635) that schizophrenia is not a split personality, that mental illness is prevalent and that it is a myth that people with schizophrenia are any more likely to be violent than members of the general population. Both the UK and Canada programmes had a favourable impact on students factual recall and reported attitudes at first follow-up. The cumulative proportion of students expressing no social distance across 4 key items improved over time in both the UK (N=512) and Canada (N=634). Therefore, this study shows that short educational workshops can produce positive change in young people’s views of mental illness. However, more robust evaluations are needed to assess the long-term impact of both short and integrated whole school approaches to increasing mental health literacy across the globe.
Case-control studies: research in reverse

Epidemiologists benefit greatly from having case-control study designs in their research armamentarium. Case-control studies can yield important scientific findings with relatively little time, money, and effort compared with other study designs. This seemingly quick road to research results entices many newly trained epidemiologists. Indeed, investigators implement case-control studies more frequently than any other analytical epidemiological study. Unfortunately, case-control designs also tend to be more susceptible to biases than other comparative studies. Although easier to do, they are also easier to do wrong. Five main notions guide investigators who do, or readers who assess, case-control studies. First, investigators must explicitly define the criteria for diagnosis of a case and any eligibility criteria used for selection. Second, controls should come from the same population as the cases, and their selection should be independent of the exposures of interest. Third, investigators should blind the data gatherers to the case or control status of participants or, if impossible, at least blind them to the main hypothesis of the study. Fourth, data gatherers need to be thoroughly trained to elicit exposure in a similar manner from cases and controls; they should use memory aids to facilitate and balance recall between cases and controls. Finally, investigators should address confounding in case-control studies, either in the design stage or with analytical techniques. Devotion of meticulous attention to these points enhances the validity of the results and bolsters the reader's confidence in the findings.
Efficient String Matching: An Aid to Bibliographic Search

This paper describes a simple, efficient algorithm to locate all occurrences of any of a finite number of keywords in a string of text. The algorithm consists of constructing a finite state pattern matching machine from the keywords and then using the pattern matching machine to process the text string in a single pass. Construction of the pattern matching machine takes time proportional to the sum of the lengths of the keywords. The number of state transitions made by the pattern matching machine in processing the text string is independent of the number of keywords. The algorithm has been used to improve the speed of a library bibliographic search program by a factor of 5 to 10.
Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

0957-4174/$ see front matter 2008  Corresponding author.  E-mail addresses: mcastron@utk.edu (M. CastroJeong), mjeong@utk.edu (M.-K. Jeong),  Most literature on short-term traffic flow forecasting focused mainly on normal, or non-incident, conditions and, hence, limited their applicability when traffic flow forecasting is most needed, i.e., incident and atypical conditions. Accurate prediction of short-term traffic flow under atypical conditions, such as vehicular crashes, inclement weather, work zone, and holidays, is crucial to effective and proactive traffic management systems in the context of intelligent transportation systems (ITS) and, more specifically, dynamic traffic assignment (DTA). To this end, this paper presents an application of a supervised statistical learning technique called Online Support Vector machine for Regression, or OL-SVR, for the prediction of short-term freeway traffic flow under both typical and atypical conditions. The OL-SVR model is compared with three well-known prediction models including Gaussian maximum likelihood (GML), Holt exponential smoothing, and artificial neural net models. The resultant performance comparisons suggest that GML, which relies heavily on the recurring characteristics of day-to-day traffic, performs slightly better than other models under typical traffic conditions, as demonstrated by previous studies. Yet OL-SVR is the best performer under non-recurring atypical traffic conditions. It appears that for deployed ITS systems that gear toward timely response to real-world atypical and incident situations, OL-SVR may be a better tool than GML.