nils-herrmann/clean_scirepeval huggingface dataset:
Development of a network intrusion detection system using Apache Hadoop and Spark

Cyber attacks on network communication are executed against companies, governments, and even individuals. A number of these attacks is drastically increased over the last decade. Nowadays, protecting private data, latest research data, etc. is a crucial problem. Therefore, developing an intelligent system to detect the attacks is required. In this paper, we propose an anomaly based network intrusion detection system. The system is capable to analyze huge datasets in a short period of time. We utilized 90.9 GB of a real network packet dataset provided by the Information Security Centre of Excellence at the University of New Brunswick. The system analyzes the packet capture files of this dataset in the environment by using Apache Hadoop and Spark. An approach to implement the system is based on Hive SQL and unsupervised learning algorithms. The accuracy of the proposed detection system is 86.2% with 13% of the false positive rate. These results are promising to detect attacks in real-time.
Framework to Detect Malicious Transactions in Database System

Every organization is associated with huge amount of information which is more valuable. Data is important and so it should be consistent, accurate and correct. Today many approaches are used to protect the data as well as networks from attackers (attacks like SQLIA, Brute-force attack). One way to make data more secure is using Intrusion Detection System (IDS). Many researches are done in this intrusion detection field but it mainly concentrated on networks and operating system. This approach is for database so that it will prevent the data loss, maintain consistency and accuracy. Database security research is concerned about the protection of database from unauthorized access. The unauthorized access may be in the form of execution of malicious transaction and this may lead to break the integrity of the system. Banking is one of the sectors which are suffering from million dollars losses only because of this unauthorized activities and malicious transactions. So, it is today&apos;s demand to detect malicious transactions and also to provide some recommendation. In this paper, we provided the detection system for the real-world problem of intrusion detection in the banking system and we are going to give some preventive measures to avoid or reduce future attacks. In order to detect malicious transactions, we used data mining algorithm for framing a data dependency miner for our banking database IDS. Our approach extracts the read-write dependency rules and then these rules are used to check whether the coming transaction is malicious or not.
A Reliability-Augmented Particle Filter for Magnetic Fingerprinting Based Indoor Localization on Smartphone

Using magnetic field data as fingerprints for smartphone indoor positioning has become popular in recent years. Particle filter is often used to improve accuracy. However, most of existing particle filter based approaches either are heavily affected by motion estimation errors, which result in unreliable systems, or impose strong restrictions on smartphone such as fixed phone orientation, which are not practical for real-life use. In this paper, we present a novel indoor positioning system for smartphones, which is built on our proposed reliability-augmented particle filter. We create several innovations on the motion model, the measurement model, and the resampling model to enhance the basic particle filter. To minimize errors in motion estimation and improve the robustness of the basic particle filter, we propose a dynamic step length estimation algorithm and a heuristic particle resampling algorithm. We use a hybrid measurement model, combining a new magnetic fingerprinting model and the existing magnitude fingerprinting model, to improve system performance, and importantly avoid calibrating magnetometers for different smartphones. In addition, we propose an adaptive sampling algorithm to reduce computation overhead, which in turn improves overall usability tremendously. Finally, we also analyze the “Kidnapped Robot Problem” and present a practical solution. We conduct comprehensive experimental studies, and the results show that our system achieves an accuracy of 1~2 m on average in a large building.
Image Quality Assessment for Color Correction Based on Color Contrast Similarity and Color Value Difference

Color correction plays an important role in the image processing field. But substantial research on the assessment of color correction is still insufficient. In this paper, we present an image quality assessment metric for color correction. It assesses the color consistency between the reference and target/result images of color correction according to their color contrast similarity and color value difference. Both the average difference and difference span are considered during the assessment. To compensate for the scene difference between the reference and target/result images of color correction, we propose to use an image registration algorithm to build their matching relationship, upon which a matching image is built. The matching image has the same scene as the target image and the same color feature as the reference image, and thus the matching image is regarded as the real reference image of our color correction assessment. Furthermore, we combine a confidence map of the matching image and a saliency map of the target/result image as a weighting map for assessment, which helps to improve the consistency between the objective and subjective assessment results. The experimental results show that our color correction assessment metric has better correlation, accuracy, and monotonicity with users’ subjective scores than 19 state-of-the-art metrics.
Constructing Category Hierarchies for Visual Recognition

Class hierarchies are commonly used to reduce the complexity of the classification problem. This is crucial when dealing with a large number of categories. In this work, we evaluate class hierarchies currently constructed for visual recognition. We show that top-down as well as bottom-up approaches, which are commonly used to automatically construct hierarchies, incorporate assumptions about the separability of classes. Those assumptions do not hold for visual recognition of a large number of object categories. We therefore propose a modification which is appropriate for most top-down approaches. It allows to construct class hierarchies that postpone decisions in the presence of uncertainty and thus provide higher recognition accuracy. We also compare our method to a one-against-all approach and show how to control the speed-foraccuracy trade-off with our method. For the experimental evaluation, we use the Caltech-256 visual object classes dataset and compare to stateof-the-art methods.
Adaptive eye gaze patterns in interactions with human and artificial agents

Efficient collaborations between interacting agents, be they humans, virtual or embodied agents, require mutual recognition of the goal, appropriate sequencing and coordination of each agent's behavior with others, and making predictions from and about the likely behavior of others. Moment-by-moment eye gaze plays an important role in such interaction and collaboration. In light of this, we used a novel experimental paradigm to systematically investigate gaze patterns in both human-human and human-agent interactions. Participants in the study were asked to interact with either another human or an embodied agent in a joint attention task. Fine-grained multimodal behavioral data were recorded including eye movement data, speech, first-person view video, which were then analyzed to discover various behavioral patterns. Those patterns show that human participants are highly sensitive to momentary multimodal behaviors generated by the social partner (either another human or an artificial agent) and they rapidly adapt their gaze behaviors accordingly. Our results from this data-driven approach provide new findings for understanding micro-behaviors in human-human communication which will be critical for the design of artificial agents that can generate human-like gaze behaviors and engage in multimodal interactions with humans.
Optimal Placement and Configuration of Roadside Units in Vehicular Networks

In this paper, we propose a novel optimization framework for Roadside Unit (RSU) deployment and configuration in a vehicular network. We formulate the problem of placement of RSUs and selecting their configurations (e.g. power level, types of antenna and wired/wireless back haul network connectivity) as a linear program. The objective function is to minimize the total cost to deploy and maintain the network of RSU's. A user specified constraint on the minimum coverage provided by the RSU is also incorporated into the optimization framework. Further, the framework also supports the option of specifying selected regions of higher importance such as locations of frequently occurring accidents and incorporating constraints requiring stricter coverage in those areas. Simulation results are presented to demonstrate the feasibility of deployment on the campus map of Southern Methodist University (SMU). The efficiency and scalability of the optimization procedure for large scale problems are also studied and results shows that optimization over an area with the size of Cambridge, Massachusetts is completed in under 2 minutes. Finally, the effects of variation in several key parameters on the resulting design are studied.
Ac2-26 Induces IKKβ Degradation Through Chaperone-Mediated Autophagy Via HSPB1 in NCM-Treated Microglia

Annexin A1 (ANXA1) is an endogenous protein with potent anti-inflammatory properties in the brain. Although ANXA1 has been predominantly studied for its binding to formyl peptide receptors (FPRs) on plasma membranes, little is known regarding whether this protein has an anti-inflammatory effect in the cytosol. Here, we investigated the mechanism by which the ANXA1 peptide Ac2-26 decreases high TNF-α production and IKKβ activity, which was caused by oxygen glucose deprivation/reperfusion (OGD/R)-induced neuronal conditioned medium (NCM) in microglia. We found that exogenous Ac2-26 crosses into the cytoplasm of microglia and inhibits both gene expression and protein secretion of TNF-α. Ac2-26 also causes a decrease in IKKβ protein but not IKKβ mRNA, and this effect is inverted by lysosome inhibitor NH4CL. Furthermore, we demonstrate that Ac2-26 induces IKKβ accumulation in lysosomes and that lysosomal-associated membrane protein 2A (LAMP-2A), not LC-3, is enhanced in microglia exposed to Ac2-26. We hypothesize that Ac2-26 mediates IKKβ degradation in lysosomes through chaperone-mediated autophagy (CMA). Interestingly, ANXA1 in the cytoplasm does not interact with IKKβ but with HSPB1, and Ac2-26 promotes HSPB1 binding to IKKβ. Furthermore, both ANXA1 and HSPB1 can interact with Hsc70 and LAMP-2A, but IKKβ only associates with LAMP-2A. Downregulation of HSPB1 or LAMP-2A reverses the degradation of IKKβ induced by Ac2-26. Taken together, these findings define an essential role of exogenous Ac2-26 in microglia and demonstrate that Ac2-26 is associated with HSPB1 and promotes HSPB1 binding to IKKβ, which is degraded by CMA, thereby reducing TNF-α expression.
Distributed robust control of spatially interconnected systems with parameter uncertainty

This paper addresses the problem of robust control of spatially interconnected systems (SISs) subject to both exogenous disturbances and time-varying norm-bounded parameter uncertainties. A sufficient condition to the well-posedness, stability and attractiveness of an SIS with respect to all admissible uncertainties is presented in terms of a set of matrix inequalities. For the synthesis of distributed dynamic output feedback controllers, the nominal system is expanded in its output space so as to eliminate the uncertainties in the system matrices. In reference to the standard distributed scaled H∞ control, the synthesis of distributed robust H∞ controllers is proposed based on the system model in the expanded space. Numerical examples validate the effectiveness of the method.
Endothelium-restricted overexpression of human endothelin-1 causes vascular remodeling and endothelial dysfunction.

Endothelin (ET)-1 is a potent vasoconstrictor that contributes to vascular remodeling in hypertension and other cardiovascular diseases. Endogenous ET-1 is produced predominantly by vascular endothelial cells. To directly test the role of endothelium-derived ET-1 in cardiovascular pathophysiology, we specifically targeted expression of the human preproET-1 gene to the endothelium by using the Tie-2 promoter in C57BL/6 mice.



Ten-week-old male C57BL/6 transgenic (TG) and nontransgenic (wild type; WT) littermates were studied. TG mice exhibited 3-fold higher vascular tissue ET-1 mRNA and 7-fold higher ET-1 plasma levels than did WT mice but no significant elevation in blood pressure. Despite the absence of significant blood pressure elevation, TG mice exhibited marked hypertrophic remodeling and oxidant excess-dependent endothelial dysfunction of resistance vessels, altered ET-1 and ET-3 vascular responses, and significant increases in ET(B) expression compared with WT littermates. Moreover, TG mice generated significantly higher oxidative stress, possibly through increased activity and expression of vascular NAD(P)H oxidase than did their WT counterparts.



In this new murine model of endothelium-restricted human preproET-1 overexpression, ET-1 caused structural remodeling and endothelial dysfunction of resistance vessels, consistent with a direct nonhemodynamic effect of ET-1 on the vasculature, at least in part through the activation of vascular NAD(P)H oxidase.
Quantitative salivary gland scintigraphy.

Uptake of 99mTc-pertechnetate in salivary glands reflects intact salivary gland parenchyma. However, no standardized protocol for an accurate quantification of parenchymal function has been established so far.



In this paper we report on a validated acquisition protocol supplying a normal database for standardized quantitative salivary gland scintigraphy.



The major advantage of salivary gland scintigraphy, as compared to other imaging modalities, is that both parenchymal function and excretion fraction of all four major salivary glands (i.e., parotid and submandibular glands) can be simultaneously quantified with a single intravenous injection.



Quantitative salivary gland scintigraphy is demonstrated to be a suitable imaging modality for research applications in evaluating the effects of radioprotective drugs on salivary glands. Salivary gland scintigraphy is easy to perform, reproducible and well-tolerated by the patient.
Short-segment posterior instrumentation combined with anterior spondylodesis using an autologous rib graft in thoracolumbar burst fractures

There are very few data concerning the outcome after short-segment posterior stabilization and anterior spondylodesis with rib grafts in patients suffering from unstable thoracolumbar burst fractures. We have therefore investigated the clinical and radiographic outcome after posterior bisegmental instrumentation and monosegmental anterior spondylodesis using an autologous rib graft for unstable thoracolumbar burst fractures.



This was a retrospective analysis of 32 consecutive patients at a single center. The monosegmental Cobb angle was measured preoperatively, postoperatively, then 6 and 12 months postoperatively, and also after implant removal. Anterior vertebral fusion was graded on conventional radiographs according to the criteria proposed by Molinari.



Segmental kyphosis at the fracture site was corrected from a median of -20° (95% CI: -21.2 to -18.8) to -1.0° (95% CI: -2.7 to 0.7) postoperatively. 1 year after surgery, the segmental angle had decreased by a median of 2.0° (95% CI: 0.2 to 2.8). The spondylodesis fused in all patients, which was evident from incorporation and remodeling of the rib grafts. The median correction loss after implant removal was 0.0° (95% CI: -0.5 to 0.5). 26 of the 32 patients reported having no back complaints at the last follow-up (2 years postoperatively). 1 patient suffered from intercostal neuralgia, and 5 patients reported mild to moderate back pain.



Short-segment posterior instrumentation and anterior spondylodesis using an autologous rib graft resulted in sufficient correction of posttraumatic segmental kyphosis. There was no clinically relevant correction loss, and the majority of patients had no back complaints at the 2-year follow-up.
Rational Extremism : Understanding Terrorism in the Twenty-first Century

In the last decade, the field of international relations has undergone a revolution in conflict studies. Where earlier approaches attempted to identify the attributes of individuals, states, and systems that produced conflict, the “rationalist approach to war” now explains violence as the product of private information with incentives to misrepresent, problems of credible commitment, and issue indivisibilities. In this new approach, war is understood as a bargaining failure that leaves both sides worse off than had they been able to negotiate an efficient solution. This rationalist framework has proven remarkably general—being applied to civil wars, ethnic conflicts, and interstate wars—and fruitful in understanding not only the causes of war but also war termination and conflict management. Interstate war is no longer seen as sui generis, but as a particular form within a single, integrated theory of conflict. This rationalist approach to war may at first appear to be mute in the face of the terrorist attacks of September 11, 2001. Civilian targets were attacked “out of the blue.” The terrorists did not issue prior demands. A theory premised on bargaining, therefore, would seem ill-suited to explaining such violence. Yet, as I hope to show, extremist terrorism can be rational and strategic. A rationalist approach also yields insights into the nature and strategy of terrorism and offers some general guidelines that targets should consider in response, including the importance of a multilateral coalition as a means of committing the target to a moderate military strategy. Analytically, and more centrally for this essay, extremist terrorism reveals a silence at the heart of the current rationalist approach to war even as it suggests a potentially fruitful way of extending the basic model. In extant models, the distribution of capabilities and, thus, the range of acceptable bargains are exogenous,
Brain computer interface: A review

A brain-computer interface (BCI), also referred to as a mind-machine interface (MMI) or a brain-machine interface (BMI), provides a non-muscular channel of communication between the human brain and a computer system. With the advancements in low-cost electronics and computer interface equipment, as well as the need to serve people suffering from disabilities of neuromuscular disorders, a new field of research has emerged by understanding different functions of the brain. The electroencephalogram (EEG) is an electrical activity generated by brain structures and recorded from the scalp surface through electrodes. Researchers primarily rely on EEG to characterise the brain activity, because it can be recorded non-invasively by using portable equipment. The EEG or the brain activity can be used in real time to control external devices via a complete BCI system. A typical BCI scheme generally consists of a data acquisition system, pre-processing of the acquired signals, feature extraction process, classification of the features, post-processing of the classifier output, and finally the control interface and device controller. The post-processed output signals are translated into appropriate commands so as to control output devices, with several applications such as robotic arms, video games, wheelchair etc.
Novel wideband high gain Right Hand Circular Polarized (RHCP) 2×4 Array Antenna at L-Band for data link applications

In this paper, a novel design for wideband high gain Right Hand Circular Polarized (RHCP) 2×4 array antenna at L-Band is presented for data link application. The proposed antenna used for long range highly maneuvering vehicles like missiles which needs high gain and RHCP to provide sufficient link margins. Orthogonal feeding is used for first patch and second patch is electromagnetically coupled to enhance impedance bandwidth. Sequential rotation technique is implemented to improve the Axial Ratio (AR) bandwidth. The antenna is simulated using commercially available software (CST microwave studio). The realized antenna reported 20.2% VSWR bandwidth, 14.64% AR bandwidth. The obtained gain is more than 14.5dBi, El & Az beamwidth of 44° and 20° over frequency band respectively.
Analysis of Visitor Behaviour inside the Museum : An Empirical Study

The purpose of this study is to analyse behaviour and usage models within the museum space. Time, attention and the capacity to retain information and knowledge are “scarce resources” during the visit. These factors have not been thoroughly analysed, especially as far as their implications in determining the possibility of interaction between visitor, exhibits and display are concerned. A better knowledge and comprehension of these factors can allow a museum to intervene in different phases of the life cycle of a display (front-end evaluation, formative evaluation, summative evaluation). Observation can be considered a useful methodology to help museums and researchers in the evaluation process. A further objective of the study is to verify the correspondence degree between the exhibition the curators have in mind, and the actual interpretation of the occasional visitor. Information that arise from this methodology approach provide museums with valuable insights for museological and museographical planning as well as for marketing strategies.
A survey on recommendation system with collaborative filtering using big data

The large amount of increase in information available over the internet has created a greatest challenge in searching useful information. As a result an intelligent approach such as recommendation system is used that can recommend everything from movies, books, music, restaurant, news and jokes that can efficiently retrieve useful information from web. CF is primary approach of any RS. But only CF cannot provide enough scalability and accuracy. In our work we present a model that combines RS method such as CF with big data technique such as association rule mining. The main focus of this paper is to provide a scalable and robust recommendation system that can provide good accuracy. In our work, we have proposed to conduct a personalized movie recommendation by considering user's past behavior.