nils-herrmann/clean_scirepeval huggingface dataset:
Detecting botnet by anomalous traffic

Botnets can cause significant security threat and huge loss to organizations, and are difficult to discover their existence. Therefore they have become one of the most severe threats on the Internet. The core component of botnets is their command and control channel. Botnets often use IRC (Internet Relay Chat) as a communication channel through which the botmaster can control the bots to launch attacks or propagate more infections. In this paper, anomaly score based botnet detection is proposed to identify the botnet activities by using the similarity measurement and the periodic characteristics of botnets. To improve the detection rate, the proposed system employs two-level correlation relating the set of hosts with same anomaly behaviors. The proposed method can differentiate the malicious network traffic generated by infected hosts (bots) from that by normal IRC clients, even in a network with only a very small number of bots. The experiment results show that, regardless the size of the botnet in a network, the proposed approach efficiently detects abnormal IRC traffic and identifies botnet activities.
Adaptive Fastest Path Computation on a Road Network: A Traffic Mining Approach

Efficient fastest path computation in the presence of varying speed conditions on a large scale road network is an essential problem in modern navigation systems. Factors affecting road speed, such as weather, time of day, and vehicle type, need to be considered in order to select fast routes that match current driving conditions. Most existing systems compute fastest paths based on road Euclidean distance and a small set of predefined road speeds. However, “History is often the best teacher”. Historical traffic data or driving patterns are often more useful than the simple Euclidean distance-based computation because people must have good reasons to choose these routes, e.g., they may want to avoid those that pass through high crime areas at night or that likely encounter accidents, road construction, or traffic jams. In this paper, we present an adaptive fastest path algorithm capable of efficiently accounting for important driving and speed patterns mined from a large set of traffic data. The algorithm is based on the following observations: (1) The hierarchy of roads can be used to partition the road network into areas, and different path pre-computation strategies can be used at the area level, (2) we can limit our route search strategy to edges and path segments that are actually frequently traveled in the data, and (3) drivers usually traverse the road network through the largest roads available given the distance of the trip, except if there are small roads with a significant speed advantage over the large ones. Through an extensive experimental evaluation on real road networks we show that our algorithm provides desirable (short and well-supported) routes, and that it is significantly faster than competing methods.
VIBRATION ANALYSIS OF PLANETARY GEAR SYSTEM

In this work a dynamic model of a planetary gear transmission is developed to study the sensitivity of the natural frequencies and vibration modes to system parameters in perturbed situation. Parameters under consideration include component masses ,moment of inertia , mesh and support stiff nesses .The model admits three planar degree of freedom for planets ,sun, ring, and carrier. Vibration modes are classified into translational, rotational and planet modes .Well-defined modal properties of tuned (cyclically symmetric) planetary gears for linear ,time-invariant case are used to calculate eigensensitivities and express them in simple formulae .These formulae provide efficient mean to determine the sensitivity to stiffness ,mass and inertia parameters in perturbed situation.
Particle Swarm Optimization: Technique, System and Challenges

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO. On the other hand, basic PSO is more appropriate to process static, simple optimization problem. Modification PSO is developed for solving the basic PSO problem. The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO. The application can show which one the modified or variant PSO that haven’t been made and which one the modified or variant PSO that will be developed.
An active learning-enabled annotation system for clinical named entity recognition

Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain.



In this study, we developed the first AL-enabled annotation system for clinical named entity recognition (NER) with a novel AL algorithm. Besides the simulation study to evaluate the novel AL algorithm, we further conducted user studies with two nurses using this system to assess the performance of AL in real world annotation processes for building clinical NER models.



The simulation results show that the novel AL algorithm outperformed traditional AL algorithm and random sampling. However, the user study tells a different story that AL methods did not always perform better than random sampling for different users.



We found that the increased information content of actively selected sentences is strongly offset by the increased time required to annotate them. Moreover, the annotation time was not considered in the querying algorithms. Our future work includes developing better AL algorithms with the estimation of annotation time and evaluating the system with larger number of users.
A content-constrained spatial (CCS) model for layout analysis of mathematical expressions

This paper proposes a content-constrained spatial (CCS) model to recover the mathematical layout (M-layout, or MLme) of an mathematical expression (ME) from its font setting layout (F-layout, or FLme). The M-layout can be used for content analysis applications such as ME based indexing and retrieval of documents. The first of the two-step process is to divide a compounded ME into blocks based on explicit mathematical structure primitives such as fraction lines, radical signs, fence, etc. Subscripts and superscripts within a block are resolved by probabilistic inference of their likelihood based on a global optimization model. The dual peak distributions of the features to capture the relative position between sibling blocks as super/subscript call for a sampling based non-parametric probability distribution estimation method to resolve their ambiguity. The notion of spatial constraint indicators is proposed to reduce the search space while improving the prediction performance. The proposed scheme is tested using the InftyCDB data set to achieve the F1 score of 0.98.
Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange

In the study, we discussed the ARCH/GARCH family models and enhanced them with artificial neural networks to evaluate the volatility of daily returns for 23.10.1987–22.02.2008 period in Istanbul Stock Exchange. We proposed ANN-APGARCH model to increase the forecasting performance of APGARCH model. The ANN-extended versions of the obtained GARCH models improved forecast results. It is noteworthy that daily returns in the ISE show strong volatility clustering, asymmetry and nonlinearity characteristics.
Fish-oil fatty acid supplementation in active rheumatoid arthritis. A double-blinded, controlled, crossover study.

to determine the efficacy of fish-oil dietary supplements in active rheumatoid arthritis and their effect on neutrophil leukotriene levels.



nonrandomized, double-blinded, placebo-controlled, crossover trial with 14-week treatment periods and 4-week washout periods.



academic medical center, referral-based rheumatology clinic.



forty volunteers with active, definite, or classical rheumatoid arthritis. Five patients dropped out, and two were removed for noncompliance.



treatment with nonsteroidal anti-inflammatory drugs, slow-acting antirheumatic drugs, and prednisone was continued. Twenty-one patients began with a daily dosage of 2.7 g of eicosapaentanic acid and 1.8 g of docosahexenoic acid given in 15 MAX-EPA capsules (R.P. Scherer, Clearwater, Florida), and 19 began with identical-appearing placebos. The background diet was unchanged.



the following results favored fish oil placebo after 14 weeks: mean time to onset of fatigue improved by 156 minutes (95% confidence interval, 1.2 to 311.0 minutes), and number of tender joints decreased by 3.5 (95% Cl, -6.0 to -1.0). Other clinical measures favored fish oil as well but did reach statistical significance. Neutrophil leukotriene B4 production was correlated with the decrease in number of tender joints (Spearman rank correlation r=0.53; p less than 0.05). There were no statistically significant differences in hemoglobin level, sedimentation rate, or presence of rheumatoid factor or in patient-reported adverse effects. An effect from the fish oil persisted beyond the 4-week washout period.



fish-oil ingestion results in subjective alleviation of active rheumatoid arthritis and reduction in neutrophil leukotriene B4 production. Further studies are needed to elucidate mechanisms of action and optimal dose and duration of fish-oil supplementation.
Minimizing Efforts in Validating Crowd Answers

In recent years, crowdsourcing has become essential in a wide range of Web applications. One of the biggest challenges of crowdsourcing is the quality of crowd answers as workers have wide-ranging levels of expertise and the worker community may contain faulty workers. Although various techniques for quality control have been proposed, a post-processing phase in which crowd answers are validated is still required. Validation is typically conducted by experts, whose availability is limited and who incur high costs. Therefore, we develop a probabilistic model that helps to identify the most beneficial validation questions in terms of both, improvement of result correctness and detection of faulty workers. Our approach allows us to guide the expert's work by collecting input on the most problematic cases, thereby achieving a set of high quality answers even if the expert does not validate the complete answer set. Our comprehensive evaluation using both real-world and synthetic datasets demonstrates that our techniques save up to 50% of expert efforts compared to baseline methods when striving for perfect result correctness. In absolute terms, for most cases, we achieve close to perfect correctness after expert input has been sought for only 20\% of the questions.
TrustRank: a Cold-Start tolerant recommender system

The explosive growth of the World Wide Web leads to the fast advancing development of e-commerce techniques. Recommender systems, which use personalised information filtering techniques to generate a set of items suitable to a given user, have received considerable attention. Userand item-based algorithms are two popular techniques for the design of recommender systems. These two algorithms are known to have Cold-Start problems, i.e., they are unable to effectively handle Cold-Start users who have an extremely limited number of purchase records. In this paper, we develop TrustRank, a novel recommender system which handles the Cold-Start problem by leveraging the user-trust networks which are commonly available for e-commerce applications. A user-trust network is formed by friendships or trust relationships that users specify among them. While it is straightforward to conjecture that a user-trust network is helpful for improving the accuracy of recommendations, a key challenge for using user-trust network to facilitate Cold-Start users is that these users also tend to have a very limited number of trust relationships. To address this challenge, we propose a pre-processing propagation of the Cold-Start users’ trust network. In particular, by applying the personalised PageRank algorithm, we expand the friends of a given user to include others with similar purchase records to his/her original friends. To make this propagation algorithm scalable to a large amount of users, as required by real-world recommender systems, we devise an iterative computation algorithm of the original personalised TrustRank which can incrementally compute trust vectors for Cold-Start users. We conduct extensive experiments to demonstrate the consistently improvement provided by our proposed algorithm over the existing recommender algorithms on the accuracy of recommendations for Cold-Start users.
Addressing MIST (Metabolites in Safety Testing): bioanalytical approaches to address metabolite exposures in humans and animals.

Recent regulatory guidance suggests that drug metabolites identified in human plasma should be present at equal or greater levels in at least one of the animal species used in safety assessments (MIST). Often synthetic standards for the metabolites do not exist, thus this has introduced multiple challenges regarding the quantitative comparison of metabolites between human and animals. Various bioanalytical approaches are described to evaluate the exposure of metabolites in animal vs. human. A simple LC/MS/MS peak area ratio comparison approach is the most facile and applicable approach to make a first assessment of whether metabolite exposures in animals exceed that in humans. In most cases, this measurement is sufficient to demonstrate that an animal toxicology study of the parent drug has covered the safety of the human metabolites. Methods whereby quantitation of metabolites can be done in the absence of chemically synthesized authentic standards are also described. Only in rare cases, where an actual exposure measurement of a metabolite is needed, will a validated or qualified method requiring a synthetic standard be needed. The rigor of the bioanalysis is increased accordingly based on the results of animal:human ratio measurements. This data driven bioanalysis strategy to address MIST issues within standard drug development processes is described.
"Help Me Pull That Cursor" A Collaborative Interactive Floor Enhancing Community Interaction

In this paper we describe the development, experiments and evaluation of the iFloor, an interactive floor prototype installed at the local central municipality library. The primary purpose of the iFloor prototype is to support and stimulate community interaction between collocated people. The context of the library demands that any user can walk up and use the prototype without any devices or prior introduction. To achieve this, the iFloor proposes innovative interaction (modes/paradigms/patterns) for floor surfaces through the means of video tracking. Browsing and selecting content is done in a collaborative process and mobile phones are used for posting messages onto the floor. The iFloor highlights topics on social issues of ubiquitous computing environments in public spaces, and provides an example of how to exploit human spatial movements, positions and arrangements in interaction with computers.
Vacuum/Compression Valving (VCV) Using Parrafin-Wax on a Centrifugal Microfluidic CD Platform

This paper introduces novel vacuum/compression valves (VCVs) utilizing paraffin wax. A VCV is implemented by sealing the venting channel/hole with wax plugs (for normally-closed valve), or to be sealed by wax (for normally-open valve), and is activated by localized heating on the CD surface. We demonstrate that the VCV provides the advantages of avoiding unnecessary heating of the sample/reagents in the diagnostic process, allowing for vacuum sealing of the CD, and clear separation of the paraffin wax from the sample/reagents in the microfluidic process. As a proof of concept, the microfluidic processes of liquid flow switching and liquid metering is demonstrated with the VCV. Results show that the VCV lowers the required spinning frequency to perform the microfluidic processes with high accuracy and ease of control.
Automated Social Skills Trainer

Social skills training is a well-established method to decrease human anxiety and discomfort in social interaction, and acquire social skills. In this paper, we attempt to automate the process of social skills training by developing a dialogue system named "automated social skills trainer," which provides social skills training through human-computer interaction. The system includes a virtual avatar that recognizes user speech and language information and gives feedback to users to improve their social skills. Its design is based on conventional social skills training performed by human participants, including defining target skills, modeling, role-play, feedback, reinforcement, and homework. An experimental evaluation measuring the relationship between social skill and speech and language features shows that these features have a relationship with autistic traits. Additional experiments measuring the effect of performing social skills training with the proposed application show that most participants improve their skill by using the system for 50 minutes.
Commensal cuckoo: secure group partitioning for large-scale services

We present commensal cuckoo,* a secure group partitioning scheme for large-scale systems that maintains the correctness of many small groups, despite a Byzantine adversary that controls a constant (global) fraction of all nodes. In particular, the adversary is allowed to repeatedly rejoin faulty nodes to the system in an arbitrary adaptive manner, e.g., to collocate them in the same group. Commensal cuckoo addresses serious practical limitations of the state-ofthe- art scheme, the cuckoo rule of Awerbuch and Scheideler, tolerating 32x--41x more faulty nodes with groups as small as 64 nodes (as compared to the hundreds required by the cuckoo rule). Secure group partitioning is a key component of highly-scalable, reliable systems such as Byzantine faulttolerant distributed hash tables (DHTs).
Alternative consent models for biobanks: the new Spanish law on biomedical research.

This article provides an overview of recent contributions to the debate on the ethical use of previously collected biobank samples, as well as a country report about how this issue has been regulated in Spain by means of the new Biomedical Research Act, enacted in the summer of 2007. By contrasting the Spanish legal situation with the wider discourse of international bioethics, we identify and discuss a general trend moving from the traditional requirements of informed consent towards new models more favourable to research in a post-genomic context.
Overview and open issues on penetration test

Several studies regarding security testing for corporate environments, networks, and systems were developed in the past years. Therefore, to understand how methodologies and tools for security testing have evolved is an important task. One of the reasons for this evolution is due to penetration test, also known as Pentest. The main objective of this work is to provide an overview on Pentest, showing its application scenarios, models, methodologies, and tools from published papers. Thereby, this work may help researchers and people that work with security to understand the aspects and existing solutions related to Pentest. A systematic mapping study was conducted, with an initial gathering of 1145 papers, represented by 1090 distinct papers that have been evaluated. At the end, 54 primary studies were selected to be analyzed in a quantitative and qualitative way. As a result, we classified the tools and models that are used on Pentest. We also show the main scenarios in which these tools and methodologies are applied to. Finally, we present some open issues and research opportunities on Pentest.
Deciphering word-of-mouth in social media: Text-based metrics of consumer reviews

Enabled by Web 2.0 technologies, social media provide an unparalleled platform for consumers to share their product experiences and opinions through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics influence consumer purchases and product sales. By integrating marketing theories with text mining techniques, we propose a set of novel measures that focus on sentiment divergence in consumer product reviews. To test the validity of these metrics, we conduct an empirical study based on data from Amazon.com and BN.com (Barnes & Noble). The results demonstrate significant effects of our proposed measures on product sales. This effect is not fully captured by nontextual review measures such as numerical ratings. Furthermore, in capturing the sales effect of review content, our divergence metrics are shown to be superior to and more appropriate than some commonly used textual measures the literature. The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research. From a managerial perspective, our results suggest that firms should pay special attention to textual content information when managing social media and, more importantly, focus on the right measures.
Intranasal adminsitration of oxytocin in postnatal depression: implications for psychodynamic psychotherapy from a randomized double-blind pilot study

Oxytocin is a neuropeptide that is active in the central nervous system and is generally considered to be involved in prosocial behaviors and feelings. In light of its documented positive effect on maternal behavior, we designed a study to ascertain whether oxytocin exerts any therapeutic effects on depressive symptoms in women affected by maternal postnatal depression. A group of 16 mothers were recruited in a randomized double-blind study: the women agreed to take part in a brief course of psychoanalytic psychotherapy (12 sessions, once a week) while also being administered, during the 12-weeks period, a daily dose of intranasal oxytocin (or a placebo). The pre-treatment evaluation also included a personality assessment of the major primary-process emotional command systems described by Panksepp () and a semi-quantitative assessment by the therapist of the mother's depressive symptoms and of her personality. No significant effect on depressive symptomatology was found following the administration of oxytocin (as compared to a placebo) during the period of psychotherapy. Nevertheless, a personality trait evaluation of the mothers, conducted in our overall sample group, showed a decrease in the narcissistic trait only within the group who took oxytocin. The depressive (dysphoric) trait was in fact significantly affected by psychotherapy (this effect was only present in the placebo group so it may reflect a positive placebo effect enhancing the favorable influence of psychotherapy on depressive symptoms) but not in the presence of oxytocin. Therefore, the neuropeptide would appear to play some role in the modulation of cerebral functions involved in the self-centered (narcissistic) dimension of the suffering that can occur with postnatal depression. Based on these results, there was support for our hypothesis that what is generally defined as postnatal depression may include disturbances of narcissistic affective balance, and oxytocin supplementation can counteract that type of affective disturbance. The resulting improvements in well-being, reflected in better self-centering in post-partuent mothers, may in turn facilitate better interpersonal acceptance of (and interactions with) the child and thereby, improved recognition of the child's needs.