nils-herrmann/clean_scirepeval huggingface dataset:
Enhancing Suricata intrusion detection system for cyber security in SCADA networks

Industrial Control and SCADA (Supervisory Control and Data Acquisition) networks control critical infrastructure such as power plants, nuclear facilities, and water supply systems. These systems are increasingly the target of cyber attacks by threat actors of different kinds, with successful attacks having the potential to cause damage, cost and injury/loss of life. As a result, there is a strong need for enhanced tools to detect cyber threats in SCADA networks. This paper makes a number of contributions to advance research in this area. First, we study the level of support for SCADA protocols in well-known open source intrusion detection systems (IDS). Second, we select a specific IDS, Suricata, and enhance it to include support for detecting threats against SCADA systems running the EtherNet/IP (ENIP) industrial control protocol. Finally, we conduct a traffic-based study to evaluate the performance of the new ENIP module in Suricata - analyzing its performance in low performance hardware systems.
FPGA implementation of image steganography using Haar DWT and modified LSB techniques

Security places an important role in communication applications for secure data transfers. Image Steganography is one of the most reliable technique in encryption and decryption of an image (hidden) inside other image (cover) such way that only cover image is visible. In this paper frequency domain Image Steganography using DWT and Modified LSB technique is proposed. The proposed approach uses DWT to convert spatial domain information to frequency domain information. The LL band is used for further Image Steganographic process. The image is decoded using inverse LSB. Since the LL band is used for encoding and decoding purpose, memory requirement of the design is less for hardware implementation. Also this will increase the operating frequency of the architecture. The proposed technique obtains high PSNR for both stegano and recovered hidden image.
A Review on Object Manipulation Techniques in Virtual Environments and their Performance Metrics

The aim of this paper is to classify and characterize the existing interaction methods regarding object manipulation in virtual reality environment.  Over the past few decades, the field of virtual reality has gone through rapid advances and various virtual object manipulation techniques have been developed. However, an established classification of the existing object manipulation techniques does not seem available at this point. A systematic review of the advances during the past three decades and a classification of the existing object manipulation techniques based on it would help researchers prospect the future of human-machine interaction in virtual environments and further formulate future research directions for virtual object manipulation.  We collected and examined studies that are related to virtual reality and virtual object manipulation. The collected studies were reviewed in chronological order, and virtual object manipulation techniques were identified. Then, the existing virtual object manipulation techniques were sorted into categories according to similarities and each category was characterized. Additionally, this study reviewed the existing performance metrics for evaluating virtual object manipulation techniques.  This paper will aid in ameliorating the understanding of the existing virtual object manipulation techniques, and furthermore, offer directions to the future researchers in this field.
PERSIANN-CDR Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies

asHouri, Hsu, soroosHian, and braitHwaite— Center for Hydrometeorology and Remote Sensing, Henry Samueli School of Engineering, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California; Knapp and neLson—NOAA/National Climatic Data Center, Asheville, North Carolina; CeCiL—Global Science & Technology, Inc., Asheville, North Carolina; prat—Cooperative Institute for Climate and Satellites, North Carolina State University, and NOAA/National Climatic Data Center, Asheville, North Carolina CORRESPONDING AUTHOR: Hamed Ashouri, Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697 E-mail:
Short Chemical Ischemia Triggers Phosphorylation of eIF2α and Death of SH-SY5Y Cells but not Proteasome Stress and Heat Shock Protein Response in both SH-SY5Y and T98G Cells

Both translation arrest and proteasome stress associated with accumulation of ubiquitin-conjugated protein aggregates were considered as a cause of delayed neuronal death after transient global brain ischemia; however, exact mechanisms as well as possible relationships are not fully understood. The aim of this study was to compare the effect of chemical ischemia and proteasome stress on cellular stress responses and viability of neuroblastoma SH-SY5Y and glioblastoma T98G cells. Chemical ischemia was induced by transient treatment of the cells with sodium azide in combination with 2-deoxyglucose. Proteasome stress was induced by treatment of the cells with bortezomib. Treatment of SH-SY5Y cells with sodium azide/2-deoxyglucose for 15 min was associated with cell death observed 24 h after treatment, while glioblastoma T98G cells were resistant to the same treatment. Treatment of both SH-SY5Y and T98G cells with bortezomib was associated with cell death, accumulation of ubiquitin-conjugated proteins, and increased expression of Hsp70. These typical cellular responses to proteasome stress, observed also after transient global brain ischemia, were not observed after chemical ischemia. Finally, chemical ischemia, but not proteasome stress, was in SH-SY5Y cells associated with increased phosphorylation of eIF2α, another typical cellular response triggered after transient global brain ischemia. Our results showed that short chemical ischemia of SH-SY5Y cells is not sufficient to induce both proteasome stress associated with accumulation of ubiquitin-conjugated proteins and stress response at the level of heat shock proteins despite induction of cell death and eIF2α phosphorylation.
Structural Brain MRI Segmentation Using Machine Learning Technique

Segmenting brain MR scans could be highly beneficial for diagnosing, treating and evaluating the progress of specific diseases. Up to this point, manual segmentation, performed by experts, is the conventional method in hospitals and clinical environments. Although manual segmentation is accurate, it is time consuming, expensive and might not be reliable. Many non-automatic and semi automatic methods have been proposed in the literature in order to segment MR brain images, but the level of accuracy is not sufficiently comparable with the one of manual. The aim of this project is to implement and make a preliminary evaluation of a method based on machine learning technique for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) of brain MR scans using images available within the open MICCAI grand challenge (MRBrainS13). The proposed method employs supervised artificial neural network based auto-context algorithm, exploiting intensity-based, spatial-based and shape model-based level set segmentation results as features of the network. The obtained average results based on Dice similarity index were 96.98%, 95.35%, 80.95%, 88.36% and 84.71% for intracranial volume, brain (WM + GM), CSF, WM and GM respectively. This method achieved competitive results with considerably shorter required training time on MRBrainsS13 challenge.
Job engagement, job satisfaction, and contrasting associations with person-job fit.

Forms of well-being vary in their activation as well as valence, differing in respect of energy-related arousal in addition to whether they are negative or positive. Those differences suggest the need to refine traditional assumptions that poor person-job fit causes lower well-being. More activated forms of well-being were proposed to be associated with poorer, rather than better, want-actual fit, since greater motivation raises wanted levels of job features and may thus reduce fit with actual levels. As predicted, activated well-being (illustrated by job engagement) and more quiescent well-being (here, job satisfaction) were found to be associated with poor fit in opposite directions--positively and negatively, respectively. Theories and organizational practices need to accommodate the partly contrasting implications of different forms of well-being.
MIDAS: Model-Independent Training Data Selection Under Cost Constraints

In general, as the amount of training data is increased, a deep learning model gains a higher training accuracy. To assign labels to training data for use in supervised learning, human resources are required, which incur temporal and economic costs. Therefore, if a sufficient amount of training data cannot be constructed owing to existing cost constraints, it becomes necessary to select the training data that can maximize the accuracy of the deep learning model with only a limited amount of training data. However, although conventional studies on such training data selections take into consideration the training data labeling cost, the selection cost required in the training data selection process is not taken into consideration, which is a problem. Therefore, with the consideration of the selection cost constraint in addition to the data labeling cost constraint, we introduce a training data selection problem and propose novel algorithms to solve it. The advantage of the proposed algorithms is that they can be applied to any network model or data model of deep learning. The performance was verified through experiments using various network models and data.
Reinforcement Learning and Causal Models

This chapter reviews the diverse roles that causal knowledge plays in reinforcement learning. The first half of the chapter contrasts a “model-free” system that learns to repeat actions that lead to reward with a “model-based” system that learns a probabilistic causal model of the environment which it then uses to plan action sequences. Evidence suggests that these two systems coexist in the brain, both competing and cooperating with each other. The interplay of two systems allows the brain to negotiate a balance between cognitively cheap but inaccurate model-free algorithms and accurate but expensive model-based algorithms. The second half of the chapter reviews research on hidden state inference in reinforcement learning. The problem of inferring hidden states can be construed in terms of inferring the latent causes that give rise to sensory data and rewards. Because hidden state inference affects both model-based and model-free reinforcement learning, causal knowledge impinges upon both systems.
Reinforcement Learning for UAV Attitude Control

Autopilot systems are typically composed of an “inner loop” providing stability and control, whereas an “outer loop” is responsible for mission-level objectives, such as way-point navigation. Autopilot systems for unmanned aerial vehicles are predominately implemented using Proportional-Integral-Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. However, more sophisticated control is required to operate in unpredictable and harsh environments. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL), which has had success in other applications, such as robotics. Yet previous work has focused primarily on using RL at the mission-level controller. In this work, we investigate the performance and accuracy of the inner control loop providing attitude control when using intelligent flight control systems trained with state-of-the-art RL algorithms—Deep Deterministic Policy Gradient, Trust Region Policy Optimization, and Proximal Policy Optimization. To investigate these unknowns, we first developed an open source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL. We then used our environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.
REVERSE OSMOSIS IN DESALINATION PLANTS

This paper will explore the process of desalination by reverse osmosis (RO) and the benefits that it can contribute to society. RO may offer a sustainable solution to the water crisis, a global problem that is not going away without severe interference and innovation. This paper will go into depth on the processes involved with RO and how contaminants are removed from sea-water. Additionally, the use of significant pressures to force water through the semipermeable membranes, which only allow water to pass through them, will be investigated. Throughout the paper, the topics of environmental and economic sustainability will be covered. Subsequently, the two primary methods of desalination, RO and multi-stage flash distillation (MSF), will be compared. It will become clear that RO is a better method of desalination when compared to MSF. This paper will study examples of RO in action, including; the Carlsbad Plant, the Sorek Plant, and applications beyond the potable water industry. It will be shown that The Claude "Bud" Lewis Carlsbad Desalination Plant (Carlsbad), located in San Diego, California is a vital resource in the water economy of the area. The impact of the Sorek Plant, located in Tel Aviv, Israel will also be explained. Both plants produce millions of gallons of fresh, drinkable water and are vital resources for the people that live there.
Nomophobia: Dependency on virtual environments or social phobia?

a Laboratory of Panic and Respiration (LABPR), Institute of Psychiatry (IPUB), University Federal of Rio de Janeiro (UFRJ) and National Institute of Science Technology Translational Medicine (INCT-TM) (CNPq), Av. Venceslau Brás, 71 – Botafogo, Rio de Janeiro CEP 22290-140, Brazil b Psychiatry and Mental Health Center of Medical Sciences, University Federal Fluminense (UFF), Brazil c Laboratory of Thanatology and Psychometrics, University Federal Fluminense (UFF), Brazil d Mental Health, Laboratory of Panic and Respiration (LABPR), Institute of Psychiatry (IPUB), University Federal of Rio de Janeiro (UFRJ) and National Institute of Science Technology Translational Medicine (INCT-TM) (CNPq), Av. Venceslau Brás, 71 – Botafogo, Rio de Janeiro CEP 22290-140, Brazil e Institute of Psychiatry, School of Medicine, Laboratory of Panic and Respiration (LABPR), University Federal of Rio de Janeiro (UFRJ) and National Institute of Science Technology Translational Medicine (INCT-TM) (CNPq), Av. Venceslau Brás, 71 – Botafogo, Rio de Janeiro CEP 22290-140, Brazil
A Review on Architectures and Communications Technologies for Wearable Health-Monitoring Systems

Nowadays society is demanding more and more smart healthcare services that allow monitoring patient status in a non-invasive way, anywhere and anytime. Thus, healthcare applications are currently facing important challenges guided by the u-health (ubiquitous health) and p-health (pervasive health) paradigms. New emerging technologies can be combined with other widely deployed ones to develop such next-generation healthcare systems. The main objective of this paper is to review and provide more details on the work presented in "LOBIN: E-Textile and Wireless-Sensor-Network-Based Platform for Healthcare Monitoring in Future Hospital Environments", published in the IEEE Transactions on Information Technology in Biomedicine, as well as to extend and update the comparison with other similar systems. As a result, the paper discusses the main advantages and disadvantages of using different architectures and communications technologies to develop wearable systems for pervasive healthcare applications.
Performance-based Pricing Models in Online Advertising : Cost per Click versus Cost per Action

The multibillion-dollar online advertising industry continues to debate whether to use the CPC (cost per click) or CPA (cost per action) pricing model as an industry standard. This article applies the economic framework of incentive contracts to study how these pricing models can lead to risk sharing between the publisher and the advertiser and incentivize them to make e orts that improve the performance of online ads. We nd that, compared to the CPC model, the CPA model can better incentivize the publisher to make e orts that can improve the purchase rate. However, the CPA model can cause an adverse selection problem: the winning advertiser tends to have a lower pro t margin under the CPA model than under the CPC model. We identify the conditions under which the CPA model leads to higher publisher (or advertiser) payo s than the CPC model. Whether publishers (or advertisers) prefer the CPA model over the CPC model depends on the advertisers' risk aversion, uncertainty in the product market, and the presence of advertisers with low immediate sales ratios. Our ndings indicate a con ict of interest between publishers and advertisers in their preferences for these two pricing models. We further consider which pricing model o ers greater social welfare.
The Waterfall Model in Large-Scale Development

Waterfall development is still a widely used way of working in software development companies. Many problems have been reported related to the model. Commonly accepted problems are for example to cope with change and that defects all too often are detected too late in the software development process. However, many of the problems mentioned in literature are based on beliefs and experiences, and not on empirical evidence. To address this research gap, we compare the problems in literature with the results of a case study at Ericsson AB in Sweden, investigating issues in the waterfall model. The case study aims at validating or contradicting the beliefs of what the problems are in waterfall development through empirical research.
Data Breaches, Phishing, or Malware?: Understanding the Risks of Stolen Credentials

In this paper, we present the first longitudinal measurement study of the underground ecosystem fueling credential theft and assess the risk it poses to millions of users. Over the course of March, 2016--March, 2017, we identify 788,000 potential victims of off-the-shelf keyloggers; 12.4 million potential victims of phishing kits; and 1.9 billion usernames and passwords exposed via data breaches and traded on blackmarket forums. Using this dataset, we explore to what degree the stolen passwords---which originate from thousands of online services---enable an attacker to obtain a victim's valid email credentials---and thus complete control of their online identity due to transitive trust. Drawing upon Google as a case study, we find 7--25% of exposed passwords match a victim's Google account. For these accounts, we show how hardening authentication mechanisms to include additional risk signals such as a user's historical geolocations and device profiles helps to mitigate the risk of hijacking. Beyond these risk metrics, we delve into the global reach of the miscreants involved in credential theft and the blackhat tools they rely on. We observe a remarkable lack of external pressure on bad actors, with phishing kit playbooks and keylogger capabilities remaining largely unchanged since the mid-2000s.
Analysis of the differences in whole-genome expression related to asthma and obesity.

Concomitant obesity significantly impairs asthma control. Obese asthmatics show more severe symptoms and an increased use of medications.



The primary aim of the study was to identify genes that are differentially expressed in the peripheral blood of asthmatic patients with obesity, asthmatic patients with normal body mass, and obese patients without asthma. Secondly, we investigated whether the analysis of gene expression in peripheral blood may be helpful in the differential diagnosis of obese patients who present with symptoms similar to asthma.



The study group included 15 patients with asthma (9 obese and 6 normal-weight patients), while the control group-13 obese patients in whom asthma was excluded. The analysis of whole-genome expression was performed on RNA samples isolated from peripheral blood.



The comparison of gene expression profiles between asthmatic patients with obesity and those with normal body mass revealed a significant difference in 6 genes. The comparison of the expression between controls and normal-weight patients with asthma showed a significant difference in 23 genes. The analysis of genes with a different expression revealed a group of transcripts that may be related to an increased body mass (PI3, LOC100008589, RPS6KA3, LOC441763, IFIT1, and LOC100133565). Based on gene expression results, a prediction model was constructed, which allowed to correctly classify 92% of obese controls and 89% of obese asthmatic patients, resulting in the overall accuracy of the model of 90.9%.



The results of our study showed significant differences in gene expression between obese asthmatic patients compared with asthmatic patients with normal body mass as well as in obese patients without asthma compared with asthmatic patients with normal body mass.
Balanced Crystalloids versus Saline in Noncritically Ill Adults.

Comparative clinical effects of balanced crystalloids and saline are uncertain, particularly in noncritically ill patients cared for outside an intensive care unit (ICU).



We conducted a single-center, pragmatic, multiple-crossover trial comparing balanced crystalloids (lactated Ringer's solution or Plasma-Lyte A) with saline among adults who were treated with intravenous crystalloids in the emergency department and were subsequently hospitalized outside an ICU. The type of crystalloid that was administered in the emergency department was assigned to each patient on the basis of calendar month, with the entire emergency department crossing over between balanced crystalloids and saline monthly during the 16-month trial. The primary outcome was hospital-free days (days alive after discharge before day 28). Secondary outcomes included major adverse kidney events within 30 days - a composite of death from any cause, new renal-replacement therapy, or persistent renal dysfunction (defined as an elevation of the creatinine level to ≥200% of baseline) - all censored at hospital discharge or 30 days, whichever occurred first.



A total of 13,347 patients were enrolled, with a median crystalloid volume administered in the emergency department of 1079 ml and 88.3% of the patients exclusively receiving the assigned crystalloid. The number of hospital-free days did not differ between the balanced-crystalloids and saline groups (median, 25 days in each group; adjusted odds ratio with balanced crystalloids, 0.98; 95% confidence interval [CI], 0.92 to 1.04; P=0.41). Balanced crystalloids resulted in a lower incidence of major adverse kidney events within 30 days than saline (4.7% vs. 5.6%; adjusted odds ratio, 0.82; 95% CI, 0.70 to 0.95; P=0.01).



Among noncritically ill adults treated with intravenous fluids in the emergency department, there was no difference in hospital-free days between treatment with balanced crystalloids and treatment with saline. (Funded by the Vanderbilt Institute for Clinical and Translational Research and others; SALT-ED ClinicalTrials.gov number, NCT02614040 .).