nils-herrmann/clean_scirepeval huggingface dataset:
SNORTRAN : An Optimizing Compiler for Snort Rules

We developed an optimizing compiler for intrusion detection rules popularized by an open-source Snort Network Intrusion Detection System (www.snort.org). While Snort and Snort-like rules are usually thought of as a list of independent patterns to be tested in a sequential order, we demonstrate that common compilation techniques are directly applicable to Snort rule sets and are able to produce high-performance matching engines. SNORTRAN combines several compilation techniques, including cost-optimized decision trees, pattern matching precompilation, and string set clustering. Although all these techniques have been used before in other domain-specific languages, we believe their synthesis in SNORTRAN is original and unique.  Snort [RO99] is a popular open-source Network Intrusion Detection System (NIDS). Snort is controlled by a set of pattern/action rules residing in a configuration file of a specific format. Due to Snort’s popularity, Snort-like rules are accepted by several other NIDS [FSTM, HANK]. In this paper we describe an optimizing compiler for Snort rule sets called SNORTRAN that incorporates ideas of pattern matching compilation based on cost-optimized decision trees [DKP92, KS88] with setwise string search algorithms popularized by recent research in highperformance NIDS detection engines [FV01, CC01, GJMP]. The two main design goals were performance and compatibility with the original Snort rule interpreter. The primary application area for NIDS is monitoring IP traffic inside and outside of firewalls, looking for unusual activities that can be attributed to external attacks or internal misuse. Most NIDS are designed to handle T1/partial T3 traffic, but as the number of the known vulnerabilities grows and more and more weight is given to internal misuse monitoring on high-throughput networks (100Mbps/1Gbps), it gets harder to keep up with the traffic without dropping too many packets to make detection ineffective. Throwing hardware at the problem is not always possible because of growing maintenance and support costs, let alone the fact that the problem of making multi-unit system work in realistic environment is as hard as the original performance problem. Bottlenecks of the detection process were identified by many researchers and practitioners [FV01, ND02, GJMP], and several approaches were proposed [FV01, CC01]. Our benchmarking supported the performance analysis made by M. Fisk and G. Varghese [FV01], adding some interesting findings on worst-case behavior of setwise string search algorithms in practice. Traditionally, NIDS are designed around a packet grabber (system-specific or libcap) getting traffic packets off the wire, combined with preprocessors, packet decoders, and a detection engine looking for a static set of signatures loaded from a rule file at system startup. Snort [SNORT] and
Improvement Strategies for Agile Processes: A SWOT Analysis Approach

Agile software development methodologies have been greeted with enthusiasm by many software developers, yet their widespread adoption has also resulted in closer examination of their strengths and weaknesses. While analyses and evaluations abound, the need still remains for an objective and systematic appraisal of Agile processes specifically aimed at defining strategies for their improvement. We provide a review of the strengths and weaknesses identified in Agile processes, based on which a strengths- weaknesses-opportunities-threats (SWOT) analysis of the processes is performed. We suggest this type of analysis as a useful tool for highlighting and addressing the problem issues in Agile processes, since the results can be used as improvement strategies.
The Vegetation Outlook ( VegOut ) : A New Method for Predicting Vegetation Seasonal Greenness

The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of “historical patterns” (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate–vegetation interactions and ocean–climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation’s response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regionallevel vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing–based approaches that monitor “current” vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions.
Cache-Friendly Streaming Bitrate Adaptation by Congestion Feedback in ICN

HTTP adaptive streaming is an attractive solution to the explosion of multimedia content consumption over the Internet, which has recently been introduced to information-centric networking in the form of DASH over CCN. In this paper, we enhance the performance of such design by taking advantage of congestion feedback available in ICN networks. By means of utility fairness optimization framework, we improve the adaptation logic in terms of fairness and stability of the multimedia bitrate delivered to content consumers. Interestingly, we find that such fairness and stability have a very positive impact on caching, making streaming adaptation highly friendly to the ubiquitous in-network caches of the ICN architectures.
Business Functions Ontology and its Application in Semantic Business Process Modelling

Current business process modelling tools support neither restricting names nor using ontologies to describe process artefacts. This lack results in creating non-consistent process models which are difficult to understand, compare, evaluate and re-use, etc. Within this article we argue that the Business Functions Ontology (BFO) developed within the SUPER project may be effectively used while modelling processes as a mean for annotating them and thus help to avoid some of the above mentioned problems. We show the BFO structure as well as an example of its practical application within a tool for business process development.
Enhancement of Forensic Computing Investigations through Memory Forensic Techniques

The use of memory forensic techniques has the potential to enhance computer forensic investigations. The analysis of digital evidence is facing several key challenges; an increase in electronic devices, network connections and bandwidth, the use of anti-forensic technologies and the development of network centric applications and technologies has lead to less potential evidence stored on static media and increased amounts of data stored off-system. Memory forensic techniques have the potential to overcome these issues in forensic analysis. While much of the current research in memory forensics has been focussed on low-level data, there is a need for research to extract high-level data from physical memory as a means of providing forensic investigators with greater insight into a target system. This paper outlines the need for further research into memory forensic techniques. In particular it stresses the need for methods and techniques for understanding context on a system and also as a means of augmenting other data sources to provide a more complete and efficient searching of investigations.
Infanticide and neonaticide: a review of 40 years of research literature on incidence and causes.

The prevailing public view on women who kill their babies is that they are either monsters or psychotic, or both. The psychiatric and legal communities recognize that the issue is not as simply dichotomous as this. Evidence suggests that there are important distinctions to be drawn between different types of baby deaths and that this may have implications for identification, punishment, and/or treatment of potential and actual perpetrators. This article reviews and summarizes research, incidence statistics, and judicial and clinical outcomes ranging over four decades of work and sets out various ways forward in the study and prevention of infant murder.
Performance Analysis of a Two-Diode model of PV cell for PV based generation in MATLAB

The increase in concern for carbon emission and reduction in natural resources for conventional power generation, the renewable energy based generation such as Wind, Photovoltaic (PV), and Fuel cell has gained importance. Out of which the PV based generation has gained significance due to availability of abundant sunlight. As the Solar power conversion is a low efficient conversion process, accurate and reliable, modeling of solar cell is important. Due to the non-linear nature of diode based PV model, the accurate design of PV cell is a difficult task. A built-in model of PV cell is available in Simscape, Simelectronics library, Matlab. The equivalent circuit parameters have to be computed from data sheet and incorporated into the model. However it acts as a stiff source when implemented with a MPPT controller. Henceforth, to overcome this drawback, in this paper a two-diode model of PV cell is implemented in Matlab Simulink with reduced four required parameters along with similar configuration of the built-in model. This model allows incorporation of MPPT controller. The I-V and P-V characteristics of these two models are investigated under different insolation levels. A PV based generation system feeding a DC load is designed and investigated using these two models and further implemented with MPPT based on P&O technique.
Self-adaptive differential evolution algorithm using population size reduction and three strategies

Many real-world optimization problems are large-scale in nature. In order to solve these problems, an optimization algorithm is required that is able to apply a global search regardless of the problems’ particularities. This paper proposes a self-adaptive differential evolution algorithm, called jDElscop, for solving large-scale optimization problems with continuous variables. The proposed algorithm employs three strategies and a population size reduction mechanism. The performance of the jDElscop algorithm is evaluated on a set of benchmark problems provided for the Special Issue on the Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. Non-parametric statistical procedures were performed for multiple comparisons between the proposed algorithm and three well-known algorithms from literature. The results show that the jDElscop algorithm can deal with large-scale continuous optimization effectively. It also behaves significantly better than other three algorithms used in the comparison, in most cases.
Anomaly Detection for imbalanced datasets with Deep Generative Models

Many important data analysis applications present with severely imbalanced datasets with respect to the target variable. A typical example is medical image analysis, where positive samples are scarce, while performance is commonly estimated against the correct detection of these positive examples. We approach this challenge by formulating the problem as anomaly detection with generative models. We train a generative model without supervision on the ‘negative’ (common) datapoints and use this model to estimate the likelihood of unseen data. A successful model allows us to detect the ‘positive’ case as low likelihood datapoints. In this position paper, we present the use of state-of-the-art deep generative models (GAN and VAE) for the estimation of a likelihood of the data. Our results show that on the one hand both GANs and VAEs are able to separate the ‘positive’ and ‘negative’ samples in the MNIST case. On the other hand, for the NLST case, neither GANs nor VAEs were able to capture the complexity of the data and discriminate anomalies at the level that this task requires. These results show that even though there are a number of successes presented in the literature for using generative models in similar applications, there remain further challenges for broad successful implementation.
Cranial strains and malocclusion: a rationale for a new diagnostic and treatment approach.

I n our last article 1 we described the external features which characterize the cranial and facial structures of the cranial strains known as hyperflexion and hyperextension. To understand how these strains develop we have to examine the anatomical relations underlying all cranial patterns. Each strain represent a variation on a theme. By studying the features in common, it is possible to account for the facial and dental consequences of these variations. The key is the spheno-basilar symphysis and the displacements which can take place between the occiput and the sphenoid at that suture. In hyperflexion there is shortening of the cranium in an antero-posterior direction with a subsequent upward buckling of the spheno-basilar symphysis . In children, where the cartilage of the joint has not ossified, a v-shaped wedge can be seen occasionally on the lateral skull radiograph . Figure (3a) is of the cranial base seen from a vertex viewpoint. By leaving out the temporal bones the connection between the centrally placed spheno-basilar symphysis and the peripheral structures of the cranium can be seen more easily. Sutherland realized that the cranium could be divided into quadrants (Figure 3b) centered on the spheno-basilar symphysis and that what happens in each quadrant is directly influenced by the spheno-basilar symphysis. He noted that accompanying the vertical changes at the symphysis there are various lateral displacements. As the peripheral structures move laterally, this is known as external rotation. If they move closer to the midline, this is called internal rotation. It is not unusual to have one side of the face externally rotated and the other side internally rotated (Figure 4a). This can have a significant effect in the mouth, giving rise to asymmetries . This shows a palatal view of the maxilla with the left posterior dentition externally rotated and the right buccal posterior segment internally rotated, reflecting the internal rotation of the whole right side of the face. This can be seen in hyperflexion but also other strains. With this background, it is now appropriate to examine in detail the cranial strain known as hyperflexion. As its name implies, it is brought about by an exaggeration of the flexion/ extension movement of the cranium into flexion. Rhythmic movement of the cranium continues despite the displacement into flexion, but it does so more readily into flexion than extension. As the skull is shortened in an antero-posterior plane, it is widened laterally. Figures 3a and 3b. 3a: cranial base from a vertex view (temporal bones left out). 3b: Sutherland’s quadrants imposed on cranial base.  Skull Radiograph of Hyperflexion patient. Note V-shaped wedge at superior border of the spheno-basillar symphysis.  Reprinted from Orthopedic Gnathology, Hockel,  With permission from Quintessence Publishing Co.
The utilization of forensic science and criminal profiling for capturing serial killers.

Movies and nightly television shows appear to emphasize highly efficient regimens in forensic science and criminal investigative analysis (profiling) that result in capturing serial killers and other perpetrators of homicide. Although some of the shows are apocryphal and unrealistic, they reflect major advancements that have been made in the fields of forensic science and criminal psychology during the past two decades that have helped police capture serial killers. Some of the advancements are outlined in this paper. In a study of 200 serial killers, we examined the variables that led to police focusing their attention on specific suspects. We developed 12 categories that describe how serial killers come to the attention of the police. The results of the present study indicate that most serial killers are captured as a result of citizens and surviving victims contributing information that resulted in police investigations that led to an arrest. The role of forensic science appears to be important in convicting the perpetrator, but not necessarily in identifying the perpetrator.
Body uneasiness, eating disorders, and muscle dysmorphia in individuals who overexercise.

This study aimed to investigate exercise dependence, body and eating concerns of active individuals in relation to muscle dysmorphia (MD) and eating disorders (EDs). One hundred and thirty-four active individuals (86 men, 48 women) and 20 ED women were divided into 4 groups according to their sex and the difference between their actual and desired body weight (group A: men who wanted to gain weight; group B: men who wanted to lose weight; group C: women who wanted to lose weight; group D: ED women). The Eating Disorder Inventory 2, Body Uneasiness Test, and Muscle Dysmorphia Inventory questionnaires were administered. All women desired to reduce their body weight, whereas 55% of men wanted to increase their muscle mass, also using anabolic steroids and food integrators. All groups showed a similar use of diuretics and laxatives (range 10-21%). The findings highlighted the presence of minor body image disorders in groups B and C. Conversely, body image was remarkably altered in groups D and A. Recognizing main MD traits, physical trainers should recommend further psychological counseling. Information should also be provided to gym clients regarding the health risks associated with purgative behaviors, use of steroids, and abuse of food supplements.
Anti-periodic Solutions of Inertial Neural Networks with Time Delays

In this paper, the exponential stability of anti-periodic solutions for inertial neural networks with time delays is investigated. First, by properly chosen variable substitution the system is transformed to first order differential equation. Second, some sufficient conditions which can ensure the existence and exponential stability of anti-periodic solutions for the system are obtained by using Lyapunov method and uniformly converges. Finally, an example is given to illustrate the effectiveness of the results.
Geometric Adaptive Tracking Control of a Quadrotor UAV on SE ( 3 ) for Agile Maneuvers

This paper presents nonlinear tracking control systems for a quadrotor unmanned aerial vehicle under the influence of uncertainties. Assuming that there exist unstructured disturbances in the translational dynamics and the attitude dynamics, a geometric nonlinear adaptive controller is developed directly on the special Euclidean group. In particular, a new form of an adaptive control term is proposed to guarantee stability while compensating the effects of uncertainties in quadrotor dynamics. A rigorous mathematical stability proof is given. The desirable features are illustrated by numerical example and experimental results of aggressive maneuvers.
A Program Plagiarism Detection Model Based on Information Distance and Clustering

Plagiarism in students programming assignment submissions causes considerable difficulties for course designers. Efficient detection of plagiarism in programming assignments of students is important to the educational procedure. This paper proposes a metric, based on information distance, to measure similarity between two programs. Furthermore, clustering analysis, based on shared near neighbors, is applied in order to provide more beneficial and detailed information about the program plagiarism. Experimental results demonstrate that our software has clear advantages over other plagiarism detection systems and it is quite beneficial to teachers to get rid of time-consuming and toilsome tasks.  Program plagiarism, Detection, Information distance, Clustering
Cystocele repair by transobturator four arms mesh: monocentric experience of first 123 patients

The aims of the study were to evaluate the per- and post-operative complications and outcomes after cystocele repair with transobturator mesh. A retrospective continuous series study was conducted over a period of 3 years. Clinical evaluation was up to 1 year with additional telephonic interview performed after 34 months on average. When stress urinary incontinence (SUI) was associated with the cystocele, it was treated with the same mesh. One hundred twenty-three patients were treated for cystocele. Per-operative complications occurred in six patients. After 1 year, erosion rate was 6.5%, and only three cystoceles recurred. After treatment of SUI with the same mesh, 87.7% restored continence. Overall patient’s satisfaction rate was 93.5%. Treatment of cystocele using transobturator four arms mesh appears to reduce the risk of recurrence at 1 year, along with high rate of patient’s satisfaction. The transobturator path of the prosthesis arms seems devoid of serious per- and post-operative risks and allows restoring continence when SUI is present.
Optimal Trajectory Control of LLC Resonant Converters for LED PWM Dimming

In this paper, a novel PWM dimming solution with the optimal trajectory control for a multichannel constant current (MC3) LLC resonant LED driver is proposed. When PWM dimming is on, the LLC resonant converter operates under the full-load condition. The LED intensity is controlled by the ratio between the on-time and off-time of the PWM dimming signal. To eliminate the dynamic oscillations when the MC3 LLC starts to work from the idle status, the switching pattern is optimized based on the graphic state-trajectory analysis. Thus, the full-load steady state is tracked within the minimum time. Moreover, under low dimming conditions, the LED intensity can be controlled more precisely. Finally, the optimal PWM dimming approach is verified on a 200 W, 4-channel MC3 LLC LED driver prototype.
Security and Enforcement in Spectrum Sharing

When different stakeholders share a common resource, such as the case in spectrum sharing, security and enforcement become critical considerations that affect the welfare of all stakeholders. Recent advances in radio spectrum access technologies, such as cognitive radios, have made spectrum sharing a viable option for significantly improving spectrum utilization efficiency. However, those technologies have also contributed to exacerbating the difficult problems of security and enforcement. In this paper, we review some of the critical security and privacy threats that impact spectrum sharing. We propose a taxonomy for classifying the various threats, and describe representative examples for each threat category. We also discuss threat countermeasures and enforcement techniques, which are discussed in the context of two different approaches: ex ante (preventive) and ex post (punitive) enforcement.