nils-herrmann/clean_scirepeval huggingface dataset:
A Simple Laboratory Environment for Real-World Offensive Security Education

In recent years cybersecurity has gained prominence as a field of expertise and the relevant practical skills are in high demand. To reduce the cost and amount of dedicated hardware required to set up a cybersecurity lab to teach those skills, several virtualization and outsourcing approaches were developed but the resulting setup has often increased in total complexity, hampering adoption. In this paper we present a very simple (and therefore highly scalable) setup that incorporates state-of-the-art industry tools. We also describe a structured set of lab assignments developed for this setup that build one on top of the other to cover the material of a semester-long Cybersecurity course taught at Boston University. We explore alternative lab architectures, discuss other existing sets of lab assignments and present some ideas for further improvement.
A carrier-based PWM with synchronous switching technique for a vienna rectifier

This paper proposes a carrier-based pulse width modulation (CB-PWM) method with synchronous switching technique for a Vienna rectifier. In this paper, a Vienna rectifier is one of the 3-level converter topologies. It is similar to a 3-level T-type topology the used back-to-back switches. When CB-PWM switching method is used, a Vienna rectifier is operated with six PWM signals. On the other hand, when the back-to-back switches are synchronized, PWM signals can be reduced to three from six. However, the synchronous switching method has a problem that the current distortion around zero-crossing point is worse than one of the conventional CB-PWM switching method. To improve current distortions, this paper proposes a reactive current injection technique. The performance and effectiveness of the proposed synchronous switching method are verified by simulation with a 5-kW Vienna rectifier.
Filipino Americans and racism: A multiple mediation model of coping.

Although the literature on Asian Americans and racism has been emerging, few studies have examined how coping influences one's encounters with racism. To advance the literature, the present study focused on the psychological impact of Filipino Americans' experiences with racism and the role of coping as a mediator using a community-based sample of adults (N = 199). Two multiple mediation models were used to examine the mediating effects of active, avoidance, support-seeking, and forbearance coping on the relationship between perceived racism and psychological distress and self-esteem, respectively. Separate analyses were also conducted for men and women given differences in coping utilization. For men, a bootstrap procedure indicated that active, support-seeking, and avoidance coping were mediators of the relationship between perceived racism and psychological distress. Active coping was negatively associated with psychological distress, whereas both support seeking and avoidance were positively associated with psychological distress. A second bootstrap procedure for men indicated that active and avoidance coping mediated the relationship between perceived racism and self-esteem such that active coping was positively associated with self-esteem, and avoidance was negatively associated with self-esteem. For women, only avoidance coping had a significant mediating effect that was associated with elevations in psychological distress and decreases in self-esteem. The results highlight the importance of examining the efficacy of specific coping responses to racism and the need to differentiate between the experiences of men and women.
Holoprosencephaly: a developmental field defect.

Holoprosencephaly refers to a spectrum of craniofacial malformations including cyclopia, ethmocephaly, cebocephaly, and premaxillary agenesis. Etiologic heterogeneity is well documented. Chromosomal, genetic, and teratogenic factors have been implicated. Recognition of holoprosencephaly as a developmental field defect stresses the importance of close scrutiny of relatives for mild forms such as single median incisor, hypotelorism, bifid uvula, or pituitary deficiency.
Simulation of conductive and radiated emissions from a wiper motor according to CISPR 25 standard

This paper proposes a high-frequency dynamic circuit network model of a DC motor for predicting conductive and radiated emissions in low-voltage automotive applications, and discusses a study in which this model was examined. The proposed model is based on a behavioral approach. The methodology for testing various motors together with their filters and optimization of overall system performance by achieving minima of emissions is introduced.
Octopus-Inspired Innovative Suction Cups

Octopus show great adhesion capabilities thanks to their suckers covering their ventral side of their arms. Starting from biological investigation, we identified preliminary specifications for the design of innovative artificial suction cups, which could be used in the field of soft robotics. The main features of the biological sucker are maintained as leading criteria for the choice of the actuation technology and mechanism. In this preliminary work, we focused on the imitation of the functionality of the specific muscle bundles which generate suction to obtain adhesion. Dielectric Elastomers Actuators (DEA) were identified as a suitable solution. A study on materials and manufacturing techniques was made. Different possible solutions in the use of DEA are also
Geometric phases and robotic locomotion

Robotic locomotion is based in a variety of instances upon cyclic changes in the shape of a robot mechanism. Certain variations in shape exploit the constrained nature of a robot's interaction with its environment to generate net motion. This is true for legged robots, snakelike robots, and wheeled mobile robots undertaking maneuvers such as parallel parking. In this paper we explore the use of tools from diierential geometry to model and analyze this class of locomotion mechanisms in a uniied way. In particular, we describe locomotion in terms of the geometric phase associated with a connection on a principal bundle, and address issues such as controllability and choice of gait. We also provide an introduction to the basic mathematical concepts which we require and apply the theory to numerous example systems. 1.  The term \locomotion" refers to autonomous movement from place to place. Robotic locomotion employs a variety of mechanisms. Though most of today's mobile robots have wheels or legs, other classes of robots|such as hyper-redundant, snakelike robots|are becoming increasingly popular. Traditionally, distinct methods of locomotion have been studied separately, and most approaches have relied on the speciic properties of a given system to analyze its locomotive capabilities. Despite their diierences in approach, however, most robotic locomotion systems share certain common features. Many involve motion in either two or three dimensional Euclidean space. Land based robots almost always rely upon interaction with the environment to achieve motion. These robots push, slide, and roll along the ground. A variety of systems, furthermore, rely on periodic control inputs to achieve motion in certain directions. The use of periodic inputs to generate motion is particularly intriguing since it is the rectiication of these inputs which characterizes locomotion. By changing the basic shape of a robot in a cyclic fashion, net motion in some direction can be
Biocompatibility analysis of chemomechanical caries removal material Papacárie on cultured fibroblasts and subcutaneous tissue.

Chemomechanical caries removal allies an atraumatic technique with antimicrobiotic characteristics, minimizing painful stimuli and maximally preserving healthy dental structures. The purpose of this study was to compare the cytotoxic effects of papain-based gel (Papacarie) and another caries-removing substance, Carisolv, to a nontreatment control on cultured fibroblasts in vitro and the biocompatibility in subcutaneous tissue in vivo. The cytotoxicity analysis was performed on fibroblast cultures (NIH-3T3) after 0-, 4-, 8-, and 12-hour exposure (cell viability assay) and after 1-, 3-, 5-, and 7-day exposure (survival assay). In the in vivo study, the 2 compounds were introduced into polyethylene tubes that were implanted into subcutaneous tissues of rats. After 1, 7, 14, 30, and 60 days, tissue samples were examined histologically. Cell viability did not differ between the 2 experimental groups. The control group, however, showed significantly higher percentage viability. There were no differences in cell survival between the control and experimental groups. The histological analysis revealed a moderate inflammatory response at 2 and 7 days and a mild response at 15 days, becoming almost imperceptible by 30 and 60 days in both experimental groups. The 2 tested substances exhibited acceptable biocompatibilities and demonstrated similar responses in the in vitro cytotoxicity and in vivo implantation assay.
Review of the evidence base for treatment of childhood psychopathology: externalizing disorders.

This article reviews controlled research on treatments for childhood externalizing behavior disorders. The review is organized around 2 subsets of such disorders: disruptive behavior disorders (i.e., conduct disorder, oppositional defiant disorder) and attention-deficit/hyperactivity disorder (ADHD). The review was based on a literature review of nonresidential treatments for youths ages 6-12. The pool of studies for this age group was limited, but results suggest positive outcomes for a variety of interventions (particularly parent training and community-based interventions for disruptive behavior disorders and medication for ADHD). The review also highlights the need for additional research examining effectiveness of treatments for this age range and strategies to enhance the implementation of effective practices.
ATOL: A Framework for Automated Analysis and Categorization of the Darkweb Ecosystem

We present a framework for automated analysis and categorization of .onion websites in the darkweb to facilitate analyst situational awareness of new content that emerges from this dynamic landscape. Over the last two years, our team has developed a large-scale darkweb crawling infrastructure called OnionCrawler that acquires new onion domains on a daily basis, and crawls and indexes millions of pages from these new and previously known .onion sites. It stores this data into a research repository designed to help better understand Tor’s hidden service ecosystem. The analysis component of our framework is called Automated Tool for Onion Labeling (ATOL), which introduces a two-stage thematic labeling strategy: (1) it learns descriptive and discriminative keywords for different categories, and (2) uses these terms to map onion site content to a set of thematic labels. We also present empirical results of ATOL and our ongoing experimentation with it, as we have gained experience applying it to the entirety of our darkweb repository, now over 70 million indexed pages. We find that ATOL can perform site-level thematic label assignment more accurately than keywordbased schemes developed by domain experts — we expand the analyst-provided keywords using an automatic keyword discovery algorithm, and get 12% gain in accuracy by using a machine learning classification model. We also show how ATOL can discover categories on previously unlabeled onions and discuss applications of ATOL in supporting various analyses and investigations of the darkweb.
Learning classifier systems with memory condition to solve non-Markov problems

College of Computer and Information Technology, China Three Gorges University, Yichang Hubei, 443000, China wannjy@163.com Abstract In the family of Learning Classifier Systems, the classifier system XCS has been successfully used for many applications. However, the standard XCS has no memory mechanism and can only learn optimal policy in Markov environments, where the optimal action is determined solely by the state of current sensory input. In practice, most environments are partially observable environments on agent’s sensation, which are also known as non-Markov environments. Within these environments, XCS either fails, or only develops a suboptimal policy, since it has no memory. In this work, we develop a new classifier system based on XCS to tackle this problem. It adds an internal message list to XCS as the memory list to record input sensation history, and extends a small number of classifiers with memory conditions. The classifier’s memory condition, as a foothold to disambiguate non-Markov states, is used to sense a specified element in the memory list. Besides, a detection method is employed to recognize non-Markov states in environments, to avoid these states controlling over classifiers’ memory conditions. Furthermore, four sets of different complex maze environments have been tested by the proposed method. Experimental results show that our system is one of the best techniques to solve partially observable environments, compared with some well-known classifier systems proposed for these environments.
Diversity, activity, and evolution of CRISPR loci in Streptococcus thermophilus.

Clustered regularly interspaced short palindromic repeats (CRISPR) are hypervariable loci widely distributed in prokaryotes that provide acquired immunity against foreign genetic elements. Here, we characterize a novel Streptococcus thermophilus locus, CRISPR3, and experimentally demonstrate its ability to integrate novel spacers in response to bacteriophage. Also, we analyze CRISPR diversity and activity across three distinct CRISPR loci in several S. thermophilus strains. We show that both CRISPR repeats and cas genes are locus specific and functionally coupled. A total of 124 strains were studied, and 109 unique spacer arrangements were observed across the three CRISPR loci. Overall, 3,626 spacers were analyzed, including 2,829 for CRISPR1 (782 unique), 173 for CRISPR2 (16 unique), and 624 for CRISPR3 (154 unique). Sequence analysis of the spacers revealed homology and identity to phage sequences (77%), plasmid sequences (16%), and S. thermophilus chromosomal sequences (7%). Polymorphisms were observed for the CRISPR repeats, CRISPR spacers, cas genes, CRISPR motif, locus architecture, and specific sequence content. Interestingly, CRISPR loci evolved both via polarized addition of novel spacers after exposure to foreign genetic elements and via internal deletion of spacers. We hypothesize that the level of diversity is correlated with relative CRISPR activity and propose that the activity is highest for CRISPR1, followed by CRISPR3, while CRISPR2 may be degenerate. Globally, the dynamic nature of CRISPR loci might prove valuable for typing and comparative analyses of strains and microbial populations. Also, CRISPRs provide critical insights into the relationships between prokaryotes and their environments, notably the coevolution of host and viral genomes.
Channel-stacked NAND flash memory with layer selection by multi-level operation (LSM)

In this paper, the channel stacked array (CSTAR) NAND flash memory with layer selection by multi-level operation (LSM) of string select transistor (SST) is proposed and investigated to solve problems of conventional channel stacked array. In case of LSM architecture, the stacked layers can be distinguished by combinations of multi-level states of SST and string select line (SSL) bias. Due to the layer selection performed by the bias of SSL, the placement of bit lines and word lines is similar to the conventional planar structure, and proposed CSTAR with LSM has no island-type SSLs. As a result of the advantages of the proposed architecture, various issues of conventional channel stacked NAND flash memory array can be solved.
Learning a Deep Motion Planning Model for Autonomous Driving

To deal with the issue of computational complexity and robustness of traditional motion planning methods for autonomous driving, an end-to-end motion planning model based on a deep cascaded neural network is proposed in this paper. The model can directly predict the driving parameters from the input sequence images. We combine two classical deep learning models including the convolution neural network (CNN) and the long short-term memory (LSTM) which are used to extract spatial and temporary features of the input images, respectively. The proposed model can fit the nonlinear relationship between the input sequence images and the output motion parameters for making the end-to-end planning. The experiments are conducted using the data collected from a driving simulator. Experimental results show that the proposed method can efficiently learn humans' driving behaviors, adapt to different roads, and has a better robustness performance than some existing methods.
A Generative Model for Recognizing Mixed Group Activities in Still Images

Recognizing multiple mixed group activities from one still image is not a hard problem for humans but remains highly challenging for computer recognition systems. When modelling interactions among multiple units (i.e., more than two groups or persons), the existing approaches tend to divide them into interactions between pairwise units. However, no mathematical evidence supports this transformation. Therefore, these approaches’ performance is limited on images containing multiple activities. In this paper, we propose a generative model to provide a more reasonable interpretation for the mixed group activities contained in one image. We design a four level structure and convert the original intra-level interactions into inter-level interactions, in order to implement both interactions among multiple groups and interactions among multiple persons within a group. The proposed four-level structure makes our model more robust against the occlusion and overlap of the visible poses in images. Experimental results demonstrate that our model makes good interpretations for mixed group activities and outperforms the state-of-the-art methods on the Collective Activity Classification dataset.
Microsurgical fenestration of perineural cysts to the thecal sac at the level of the distal dural sleeve

Surgery for symptomatic sacral perineural cysts remains an issue of discussion. Assuming micro-communications between the cyst and thecal sac resulting in a valve mechanism and trapping of CSF as a pathomechanism, microsurgical fenestration from the cyst to the thecal sac was performed to achieve free CSF communication. In 13 consecutive patients (10 female, 3 male), MRI revealed sacral perineural cysts and excluded other pathologies. Micro-communication between the thecal sac and the cysts was shown by delayed contrast filling of the cysts on postmyelographic CT. Surgical fenestration achieved free CSF communication between the thecal sac and cysts in all patients. The patient histories, follow-up examinations and self-assessment scales were analyzed. Symptoms at initial presentation included lumbosacral pain, pseudoradicular symptoms, genital pain and urinary dysfunction. Mean follow-up was 10.7 ± 6.6 months. Besides one CSF fistula, no surgical complications were observed. Five patients did not improve after surgery; in four of these cases multiple cysts were found, but small and promptly filling cysts remained untreated. Seven patients reported lasting benefit following surgery; three of these had single cysts, and all had cysts >1 cm. One patient initially benefited from cyst fenestration but experienced recurrent pain within 2 months postoperatively. Re-myelography revealed delayed contrast filling of the recurrent cyst; however, surgical revision did not lead to an improvement despite successful fenestration and collapse of the cyst revealed by postoperative imaging. Microsurgical fenestration of sacral perineural cysts to the thecal sac is a surgical approach that has shown success in the treatment of lumbosacral pain, pseudoradicular symptoms, genital pain and urinary dysfunction associated with sacral perineural cysts. Our analysis, however, shows that mainly patients with singular large cysts benefit from this treatment.
Data-reusing-based adaptive algorithm for active noise control of impulsive noise sources

In this paper we consider active noise control (ANC) of impulsive noise having peaky distribution with heavy tail. Such impulsive noise can be modeled using non-Gaussian stable process for which second order moments do not exist. The most famous filtered-x least mean square (FxLMS) algorithm for ANC systems is based on second order moment of error signal, and hence, becomes unstable for the impulsive noise. Recently we have proposed variants of the FxLMS algorithm where improved performance has been realized either by thresholding the input data or efficiently normalizing the step-size for adaptation. In the practical ANC systems, these thresholding parameters need to be estimated offline and cannot be updated during online operation of ANC systems. Furthermore, normalizing the steps-size for an impulsive noise source would essentially freeze the adaptation for very large impulses. In order to solve these problems, in this paper we propose a novel approach for ANC of impulsive noise sources. The proposed approach is based on data-reusing (DR) type adaptive algorithm. The main idea is to improve the stability by normalizing the step-size, and improve the convergence speed by reusing the data. The computer simulations are carried out to verify the effectiveness of the proposed algorithm.
Radical changes in multiple sclerosis pathogenesis.

Reactive oxygen species (ROS) contain one or more unpaired electrons and are formed as intermediates in a variety of normal biochemical reactions. However, when generated in excess amounts or not appropriately controlled, ROS initiate extensive cellular damage and tissue injury. ROS have been implicated in the progression of cancer, cardiovascular disease and neurodegenerative and neuroinflammatory disorders, such as multiple sclerosis (MS). In the last decade there has been a major interest in the involvement of ROS in MS pathogenesis and evidence is emerging that free radicals play a key role in various processes underlying MS pathology. To counteract ROS-mediated damage, the central nervous system is equipped with an intrinsic defense mechanism consisting of endogenous antioxidant enzymes. Here, we provide a comprehensive overview on the (sub)cellular origin of ROS during neuroinflammation as well as the detrimental effects of ROS in processing underlying MS lesion development and persistence. In addition, we will discuss clinical and experimental studies highlighting the therapeutic potential of antioxidant protection in the pathogenesis of MS.
The Ethics of Algorithms: Mapping the Debate

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.