nils-herrmann/clean_scirepeval huggingface dataset:
Creativity in Authentic STEAM Education with EarSketch

STEAM education is a method for driving student engagement in STEM topics through personal expression, creativity and aesthetics. EarSketch, a collaborative and authentic learning tool which introduces students to programming through music remixing, has previously been shown to enhance student engagement and intent to persist in computing. The goal of EarSketch is to broaden participation in computing through a thickly authentic learning environment that has personal and real world relevance in both computational and music domains. This mixed methods study extends previous work by 1) using a newly- developed instrument to assess creativity and 2) testing a theory of change model that provides an explanatory framework for increasing student engagement in STEAM. The results suggest that students who used EarSketch express statistically significant gains in computing attitudes and creativity. Furthermore, a series of multiple regression analyses found that a creative learning environment, fueled by a meaningful and personally relevant EarSketch curriculum, drives improvements in students' attitudes and intent to persist in computing. This work makes a significant contribution to computer science education by establishing the effectiveness of an authentic STEAM curriculum and advancing our knowledge of the underlying mechanisms driving students' motivations to persist in STEM disciplines.
Recovering device drivers

This article presents a new mechanism that enables applications to run correctly when device drivers fail. Because device drivers are the principal failing component in most systems, reducing driver-induced failures greatly improves overall reliability. Earlier work has shown that an operating system can survive driver failures [Swift et al. 2005], but the applications that depend on them cannot. Thus, while operating system reliability was greatly improved, application reliability generally was not.To remedy this situation, we introduce a new operating system mechanism called a shadow driver. A shadow driver monitors device drivers and transparently recovers from driver failures. Moreover, it assumes the role of the failed driver during recovery. In this way, applications using the failed driver, as well as the kernel itself, continue to function as expected.We implemented shadow drivers for the Linux operating system and tested them on over a dozen device drivers. Our results show that applications and the OS can indeed survive the failure of a variety of device drivers. Moreover, shadow drivers impose minimal performance overhead. Lastly, they can be introduced with only modest changes to the OS kernel and with no changes at all to existing device drivers.
The effects of varying acidity on Helicobacter pylori growth and the bactericidal efficacy of ampicillin.

Penicillins inhibit cell wall synthesis; therefore, Helicobacter pylori must be dividing for this class of antibiotics to be effective in eradication therapy. Identifying growth responses to varying medium pH may allow design of more effective treatment regimens.



To determine the effects of acidity on bacterial growth and the bactericidal efficacy of ampicillin.



H. pylori were incubated in dialysis chambers suspended in 1.5-L of media at various pHs with 5 mM urea, with or without ampicillin, for 4, 8 or 16 h, thus mimicking unbuffered gastric juice. Changes in gene expression, viability and survival were determined.



At pH 3.0, but not at pH 4.5 or 7.4, there was decreased expression of ~400 genes, including many cell envelope biosynthesis, cell division and penicillin-binding protein genes. Ampicillin was bactericidal at pH 4.5 and 7.4, but not at pH 3.0.



Ampicillin is bactericidal at pH 4.5 and 7.4, but not at pH 3.0, due to decreased expression of cell envelope and division genes with loss of cell division at pH 3.0. Therefore, at pH 3.0, the likely pH at the gastric surface, the bacteria are nondividing and persist with ampicillin treatment. A more effective inhibitor of acid secretion that maintains gastric pH near neutrality for 24 h/day should enhance the efficacy of amoxicillin, improving triple therapy and likely even allowing dual amoxicillin-based therapy for H. pylori eradication.
A compiler for throughput optimization of graph algorithms on GPUs

Writing high-performance GPU implementations of graph algorithms can be challenging. In this paper, we argue that three optimizations called throughput optimizations are key to high-performance for this application class. These optimizations describe a large implementation space making it unrealistic for programmers to implement them by hand. 
 To address this problem, we have implemented these optimizations in a compiler that produces CUDA code from an intermediate-level program representation called IrGL. Compared to state-of-the-art handwritten CUDA implementations of eight graph applications, code generated by the IrGL compiler is up to 5.95x times faster (median 1.4x) for five applications and never more than 30% slower for the others. Throughput optimizations contribute an improvement up to 4.16x (median 1.4x) to the performance of unoptimized IrGL code.
Antibacterial effectiveness of ciprofloxacin 0.3% ophthalmic solution in the treatment of bacterial conjunctivitis.

Two hundred eighty-eight cases of culture-proven bacterial conjunctivities were evaluated as part of two multicentered, randomized, prospective clinical studies comparing the antibacterial efficacy of topically administered ciprofloxacin 0.3% either with a placebo or with tobramycin 0.3%. In the first study, ciprofloxacin was significantly (P less than .001) more effective than the placebo. It eradicated or reduced the various bacterial pathogens in 93.6% of patients, compared to 59.5% for the placebo. In the second study, ciprofloxacin (94.5%) and tobramycin (91.9%) were equally effective. Topically applied ciprofloxacin eradicated or reduced all isolated bacterial species, attesting to its broad antibacterial spectrum and its potential usefulness in treating external ocular infections.
LIPN-UAM at EmoInt-2017: Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination

This paper presents the combined LIPNUAM participation in the WASSA 2017 Shared Task on Emotion Intensity. In particular, the paper provides some highlights on the system that was presented to the shared task, partly based on the Tweetaneuse system used to participate in a French Sentiment Analysis task (DEFT2017). We combined lexicon-based features with sentence-level vector representations to obtain a random forest model.
CONCERT: a cloud-based architecture for next-generation cellular systems

Cellular networks are one of the cornerstones of our information-driven society. However, existing cellular systems have been seriously challenged by the explosion of mobile data traffic, the emergence of machine-type communications, and the flourishing of mobile Internet services. In this article, we propose CONCERT, a converged edge infrastructure for future cellular communications and mobile computing services. The proposed architecture is constructed based on the concept of control/data (C/D) plane decoupling. The data plane includes heterogeneous physical resources such as radio interface equipment, computational resources, and software-defined switches. The control plane jointly coordinates physical resources to present them as virtual resources, over which software-defined services including communications, computing, and management can be deployed in a flexible manner. Moreover, we introduce new designs for physical resources placement and task scheduling so that CONCERT can overcome the drawbacks of the existing baseband-up centralization approach and better facilitate innovations in next-generation cellular networks. These advantages are demonstrated with application examples on radio access networks with C/D decoupled air interface, delaysensitive machine-type communications, and realtime mobile cloud gaming. We also discuss some fundamental research issues arising with the proposed architecture to illuminate future research directions.
A Theory of Universal Artificial Intelligence based on Algorithmic Complexity

Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff’s theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameterless theory of universal Artificial Intelligence. We give strong arguments that the resulting AIξ model is the most intelligent unbiased agent possible. We outline for a number of problem classes, including sequence prediction, strategic games, function minimization, reinforcement and supervised learning, how the AIξ model can formally solve them. The major drawback of the AIξ model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIξtl, which is still effectively more intelligent than any other time t and space l bounded agent. The computation time of AIξtl is of the order t ·2l. Other discussed topics are formal definitions of intelligence order relations, the horizon problem and relations of the AIξ theory to other AI approaches. Any response to marcus@hutter1.de is welcome.
Persistence of improvements in postural strategies following motor control training in people with recurrent low back pain.

This study investigated long-term effects of training on postural control using the model of deficits in activation of transversus abdominis (TrA) in people with recurrent low back pain (LBP). Nine volunteers with LBP attended four sessions for assessment and/or training (initial, two weeks, four weeks and six months). Training of repeated isolated voluntary TrA contractions were performed at the initial and two-week session with feedback from real-time ultrasound imaging. Home program involved training twice daily for four weeks. Electromyographic activity (EMG) of trunk and deltoid muscles was recorded with surface and fine-wire electrodes. Rapid arm movement and walking were performed at each session, and immediately after training on the first two sessions. Onset of trunk muscle activation relative to prime mover deltoid during arm movements, and the coefficient of variation (CV) of EMG during averaged gait cycle were calculated. Over four weeks of training, onset of TrA EMG was earlier during arm movements and CV of TrA EMG was reduced (consistent with more sustained EMG activity). Changes were retained at six months follow-up (p<0.05). These results show persistence of motor control changes following training and demonstrate that this training approach leads to motor learning of automatic postural control strategies.
New superregenerative architectures for direct-sequence spread-spectrum communications

The superregenerative receiver has been used for many decades as a low-cost and low-power receiver in short-range narrow-band communications. In this paper, we present two new architectures that make use of the superregeneration principle to achieve noncoherent detection of direct-sequence spread-spectrum signals. The local pseudorandom code generator is clocked by the quench oscillator, making the quench frequency equal to the chip rate. Under this condition, it is possible to take advantage of the characteristic broad reception bandwidth and the pulsating nature of the receiver to filter and despread the signal. The two superregenerative architectures, operating under periodic and pseudorandom quench, respectively, are analyzed and compared. Theoretical predictions are confirmed by experimental results in the ISM band of 2.4 GHz.
A participatory approach to define user requirements of a platform for intangible cultural heritage education

In the last years the protection and safeguarding of cultural heritage has become a key issue of European cultural policy and this applies not only to tangible artefacts (monuments, sites, etc.), but also to intangible cultural expressions (singing, dancing, etc.). The i-Treasures project focuses on some Intangible Cultural Heritages (ICH) and investigates whether and to what extent new technology can play a role in the preservation and dissemination of these expressions. To this aim, the project will develop a system, based on cutting edge technology and sensors, that digitally captures the performances of living human treasures, analyses the digital information to semantically index the performances and their constituting elements, and builds an educational platform on top of the semantically indexed content. The main purpose of this paper is to describe how the user requirements of this system were defined. The requirements definition process was based on a participatory approach, where ICH experts, performers and users were actively involved through surveys and interviews, and extensively collaborated in the complex tasks of identifying specificities of rare traditional know-how, discovering existing teaching and learning practices and finally identifying the most cutting edge technologies able to support innovative teaching and learning approaches to ICH.
Kullback-Leibler Divergence Revisited

Thee KL divergence is the most commonly used measure for comparing query and document language models in the language modeling framework to ad hoc retrieval. Since KL is rank equivalent to a specific weighted geometric mean, we examine alternative weighted means for language-model comparison, as well as alternative divergence measures. The study includes analysis of the inverse document frequency (IDF) effect of the language-model comparison methods. Empirical evaluation, performed with different types of queries (short and verbose) and query-model induction approaches, shows that there are methods that often outperform the KL divergence in some settings.
Body cues, not facial expressions, discriminate between intense positive and negative emotions.

The distinction between positive and negative emotions is fundamental in emotion models. Intriguingly, neurobiological work suggests shared mechanisms across positive and negative emotions. We tested whether similar overlap occurs in real-life facial expressions. During peak intensities of emotion, positive and negative situations were successfully discriminated from isolated bodies but not faces. Nevertheless, viewers perceived illusory positivity or negativity in the nondiagnostic faces when seen with bodies. To reveal the underlying mechanisms, we created compounds of intense negative faces combined with positive bodies, and vice versa. Perceived affect and mimicry of the faces shifted systematically as a function of their contextual body emotion. These findings challenge standard models of emotion expression and highlight the role of the body in expressing and perceiving emotions.
Snake venom causes apoptosis by increasing the reactive oxygen species in colorectal and breast cancer cell lines

Snake venom possesses various kinds of proteins and neurotoxic polypeptides, which can negatively interfere with the neurotransmitter signaling cascade. This phenomenon occurs mainly due to the blocking of ion channels in the body system. Envenomation prevents or severely interrupts nerve impulses from being transmitted, inhibition of adenosine triphosphate synthesis, and proper functioning of the cardiac muscles. However, some beneficial properties of venoms have also been reported. The aim of this study was to examine the snake venom as an anticancer agent due to its inhibitory effects on cancer progression such as cell motility, cell invasion, and colony formation. In this study, the effect of venoms on phenotypic changes and the change on molecular level in colorectal and breast cancer cell lines were examined. A reduction of 60%-90% in cell motility, colony formation, and cell invasion was observed when these cell lines were treated with different concentrations of snake venom. In addition, the increase in oxidative stress that results in an increase in the number of apoptotic cancer cells was significantly higher in the venom-treated cell lines. Further analysis showed that there was a decrease in the expression of pro-inflammatory cytokines and signaling proteins, strongly suggesting a promising role for snake venom against breast and colorectal cancer cell progression. In conclusion, the snake venoms used in this study showed significant anticancer properties against colorectal and breast cancer cell lines.
Self-Driving Cars: A Survey

We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the UFES's car, IARA. Finally, we list prominent autonomous research cars developed by technology companies and reported in the media.
Multi-task and multi-view learning of user state

Several computational approaches have been proposed for inferring the affective state of the user, motivated for example by the goal of building improved interfaces that can adapt to the user’s needs and internal state. While fairly good results have been obtained for inferring the user state under highly controlled conditions, a considerable amount of work remains to be done for learning high-quality estimates of subjective evaluations of the state in more natural conditions. In this work, we discuss how two recent machine learning concepts, multi-view learning and multi-task learning, can be adapted for user state recognition, and demonstrate them on two data collections of varying quality. Multi-view learning enables combining multiple measurement sensors in a justified way while automatically learning the importance of each sensor. Multi-task learning, in turn, tells how multiple learning tasks can be learned together to improve the accuracy. We demonstrate the use of two types of multi-task learning: learning both multiple state indicators and models for multiple users together. We also illustrate how the benefits of multi-task learning and multi-view learning can be effectively combined in a unified model by introducing a novel algorithm.
Sliding-Mode Control of PWM Dual Inverter-Based Grid-Connected PV System: Modeling and Performance Analysis

This paper proposes a novel robust and adaptive sliding-mode (SM) control for a cascaded two-level inverter (CTLI)-based grid-connected photovoltaic (PV) system. The modeling and design of the control scheme for the CTLI-based grid-connected PV system is developed to supply active power and reactive power with variable solar irradiance. A vector controller is developed, keeping the maximum power delivery of the PV in consideration. Two different switching schemes have been considered to design SM controllers and studied under similar operating situations. Instead of the referred space vector pulsewidth modulation (PWM) technique, a simple PWM modulation technique is used for the operation of the proposed SM controller. The performance of the SM controller is improved by using an adaptive hysteresis band calculation. The controller performance is found to be satisfactory for both the schemes at considered load and solar irradiance level variations in simulation environment. The laboratory prototype, operated with the proposed controller, is found to be capable of implementing the control algorithm successfully in the considered situation.
Multi-level Access in Searchable Symmetric Encryption

Remote storage delivers a cost effective solution for data storage. If data is of a sensitive nature, it should be encrypted prior to outsourcing to ensure confidentiality; however, searching then becomes challenging. Searchable encryption is a well-studied solution to this problem. Many schemes only consider the scenario where users can search over the entirety of the encrypted data. In practice, sensitive data is likely to be classified according to an access control policy and different users should have different access rights. It is unlikely that all users have unrestricted access to the entire data set. Current schemes that consider multi-level access to searchable encryption are predominantly based on asymmetric primitives. We investigate symmetric solutions to multi-level access in searchable encryption where users have different access privileges to portions of the encrypted data and are not permitted to search over, or learn information about, data for which they are not authorised.
RAPPORT A Cluster Algorithm for Graphs

A cluster algorithm for graphs called the Markov Cluster algorithm (MCL algorithm) is introduced. The algorithm provides basically an interface to an algebraic process de ned on stochastic matrices, called the MCL process. The graphs may be both weighted (with nonnegative weight) and directed. Let G be such a graph. The MCL algorithm simulates ow in G by rst identifying G in a canonical way with a Markov graph G1. Flow is then alternatingly expanded and contracted, leading to a row of Markov Graphs G(i). Flow expansion corresponds with taking the k power of a stochastic matrix, where k 2 IN . Flow contraction corresponds with a parametrized operator r, r 0, which maps the set of (column) stochastic matrices onto itself. The image rM is obtained by raising each entry in M to the r th power and rescaling each column to have sum 1 again. The heuristic underlying this approach is the expectation that ow between dense regions which are sparsely connected will evaporate. The invariant limits of the process are easily derived and in practice the process converges very fast to such a limit, the structure of which has a generic interpretation as an overlapping clustering of the graph G. Overlap is limited to cases where the input graph has a symmetric structure inducing it. The contraction and expansion parameters of the MCL process in uence the granularity of the output. The algorithm is space and time e cient and lends itself to drastic scaling. This report describes the MCL algorithm and process, convergence towards equilibrium states, interpretation of the states as clusterings, and implementation and scalability. The algorithm is introduced by rst considering several related proposals towards graph clustering, of both combinatorial and probabilistic nature.