nils-herrmann/clean_scirepeval huggingface dataset:
Density-Based Clustering over an Evolving Data Stream with Noise

Clustering is an important task in mining evolving data streams. Beside the limited memory and one-pass constraints, the nature of evolving data streams implies the following requirements for stream clustering: no assumption on the number of clusters, discovery of clusters with arbitrary shape and ability to handle outliers. While a lot of clustering algorithms for data streams have been proposed, they offer no solution to the combination of these requirements. In this paper, we present DenStream, a new approach for discovering clusters in an evolving data stream. The “dense” micro-cluster (named core-micro-cluster) is introduced to summarize the clusters with arbitrary shape, while the potential core-micro-cluster and outlier micro-cluster structures are proposed to maintain and distinguish the potential clusters and outliers. A novel pruning strategy is designed based on these concepts, which guarantees the precision of the weights of the micro-clusters with limited memory. Our performance study over a number of real and synthetic data sets demonstrates the effectiveness and efficiency of our method.
Game-based forensics course for first year students

This paper focuses on the design and development of a game-based forensics course. This course uses the game-based learning (GBL) approach that builds the game in a real computing environment that has direct access to actual forensics tools from a forensics machine and the evidence from a suspect machine. Interactive visualizations will be used to help students to understand the intangible and inaccessible abstract concepts such as deleted/hidden/encrypted/over-written digital evidence.
A Tool for Security Measuring and Probe Management

Designing and maintaining correct security countermeasures to support trustworthiness require comprehensive understanding of current security posture, i.e., Situational Awareness (SA). Security SA means that a decision maker is aware of protected assets, existing vulnerabilities, and risk mitigation techniques. Applying security metrics offers a holistic way to gain Situational Awareness. However, it is a challenge to get input data for security measuring. Currently, systems produce the huge amount of security monitoring data, but this raw data is not in a uniform format or easily available for the measuring purposes. In this paper, we propose a Host-based Security Instrumentation System (HSIS) to facilitate the development of security measuring probes for uniform data collection. The proposed solution supports reusability of measuring probes and makes it possible to manage and adapt probes at operation time. Security administrators and auditors can support trustworthiness with the solution by evaluating systems' security state.
Emotion Enhances Learning via Norepinephrine Regulation of AMPA-Receptor Trafficking

Emotion enhances our ability to form vivid memories of even trivial events. Norepinephrine (NE), a neuromodulator released during emotional arousal, plays a central role in the emotional regulation of memory. However, the underlying molecular mechanism remains elusive. Toward this aim, we have examined the role of NE in contextual memory formation and in the synaptic delivery of GluR1-containing alpha-amino-3-hydroxy-5-methyl-4-isoxazoleproprionic acid (AMPA)-type glutamate receptors during long-term potentiation (LTP), a candidate synaptic mechanism for learning. We found that NE, as well as emotional stress, induces phosphorylation of GluR1 at sites critical for its synaptic delivery. Phosphorylation at these sites is necessary and sufficient to lower the threshold for GluR1 synaptic incorporation during LTP. In behavioral experiments, NE can lower the threshold for memory formation in wild-type mice but not in mice carrying mutations in the GluR1 phosphorylation sites. Our results indicate that NE-driven phosphorylation of GluR1 facilitates the synaptic delivery of GluR1-containing AMPARs, lowering the threshold for LTP, thereby providing a molecular mechanism for how emotion enhances learning and memory.
Tendril Perversion in Intrinsically Curved Rods

A straight elastic rod with intrinsic curvature under varying tension can undergo an instability and bifurcate to a filament made out of two helices with opposite handedness. This inversion of handedness, known as perversion, appears in a wide range of biological and physical systems and is investigated here within the framework of thin elastic rods described by the static Kirchhoff equations. In this context, a perversion is represented by a heteroclinic orbit joining asymptotically two fixed points representing helices with opposite torsion. A center manifold reduction and a normal form transformation for a triple zero eigenvalue reduce the dynamics to a third-order reversible dynamical system. The analysis of this reduced system reveals that the heteroclinic connection representing the physical solution results from the collapse of pairs of symmetric homoclinic orbits. Results of the normal form calculation are compared with numerical solutions obtained by continuation methods. The possibility of self-contact and the elastic characteristics of the perverted rod are also studied.
Asking the Right Questions in Crowd Data Sourcing

Crowd-based data sourcing is a new and powerful data procurement paradigm that engages Web users to collectively contribute information. In this work, we target the problem of gathering data from the crowd in an economical and principled fashion. We present Ask It!, a system that allows interactive data sourcing applications to effectively determine which questions should be directed to which users for reducing the uncertainty about the collected data.  It! uses a set of novel algorithms for minimizing the number of probing (questions) required from the different users. We demonstrate the challenge and our solution in the context of a multiple-choice question game played by the ICDE'12 attendees, targeted to gather information on the conference's publications, authors and colleagues.
Stiffness analysis for a 3-PUU parallel kinematic machine

This paper presents the stiffness characteristics of a three-prismatic-universal–universal (3-PUU) translational parallel kinematic machine (PKM). The stiffness matrix is derived intuitively based upon an alternative approach considering actuations and constraints, and the compliances subject to both actuators and legs are involved in the stiffness model. The stiffness performance of the manipulator is evaluated by utilizing the extremum stiffness values, and the influences of design parameters on the stiffness properties are presented, which will be valuable for the architecture design of a 3-PUU PKM. Moreover, the stiffness behavior of the PKM is investigated via the eigenscrew decomposition of the stiffness matrix, which provides a physical interpretation of the PKM stiffness and allows the identification of the stiffness center and compliant axis.
Breast Cancer Diagnosis and Prognosis Via Linear Programming

This ongoing multi-disciplinary research directly addresses problems arising in the diagnosis and treatment of breast cancer. Early detection of breast cancer is enhanced and unnecessary surgery avoided by diagnosing breast masses from Fine Needle Aspirates (FNA’s). By using and extending results from the fields of optimization, machine learning, statistics and image processing, a software system was created that allows highly accurate diagnosis of breast FNA’s even by untrained users. The system is in current use at the University of Wisconsin Hospitals. For malignant cases, treatment decisions are enhanced by accurately predicting the long term behavior of the disease. This paper summarizes our recent work in both areas of diagnosis and prognosis, with emphasis on the more difficult latter problem.
Effects of a 7-Day Meditation Retreat on the Brain Function of Meditators and Non-Meditators During an Attention Task

Meditation as a cognitive enhancement technique is of growing interest in the field of health and research on brain function. The Stroop Word-Color Task (SWCT) has been adapted for neuroimaging studies as an interesting paradigm for the understanding of cognitive control mechanisms. Performance in the SWCT requires both attention and impulse control, which is trained in meditation practices. We presented SWCT inside the MRI equipment to measure the performance of meditators compared with non-meditators before and after a meditation retreat. The aim of this study was to evaluate the effects of a 7-day Zen intensive meditation training (a retreat) on meditators and non-meditators in this task on performance level and neural mechanisms. Nineteen meditators and 14 non-meditators were scanned before and after a 7-day Zen meditation retreat. No significant differences were found between meditators and non-meditators in the number of the correct responses and response time (RT) during SWCT before and after the retreat. Probably, due to meditators training in attention, their brain activity in the contrast incongruent > neutral during the SWCT in the anterior cingulate, ventromedial prefrontal cortex/anterior cingulate, caudate/putamen/pallidum/temporal lobe (center), insula/putamen/temporal lobe (right) and posterior cingulate before the retreat, were reduced compared with non-meditators. After the meditation retreat, non-meditators had reduced activation in these regions, becoming similar to meditators before the retreat. This result could be interpreted as an increase in the brain efficiency of non-meditators (less brain activation in attention-related regions and same behavioral response) promoted by their intensive training in meditation in only 7 days. On the other hand, meditators showed an increase in brain activation in these regions after the same training. Intensive meditation training (retreat) presented distinct effects on the attention-related regions in meditators and non-meditators probably due to differences in expertise, attention processing as well as neuroplasticity.
A Review of the Stereotype Threat Literature and Its Application in a Neurological Population

Stereotype threat is a situational phenomenon, leading to test performance decrements, in which a member of a stigmatized group feels pressured by the possibility of confirming or being judged by a negative stereotype. This review article highlights the progression of research in the stereotype threat field, and its relevance to neurological populations. Early studies focused on demonstrating this effect in African American, women, and elderly populations. Since this time, research has continued to focus on these populations but has moved to elucidating stereotype threat’s mediating psychological factors, studying the impact of individual differences in response to stereotype threat, and attempting to reduce its overall effect. A proposal for further study in neurological populations, under the framework of stereotype threat, comprises the last portion of the paper. It is argued that this social psychological phenomenon may, at least in part, account for poor neuropsychological test performance for neurologically compromised individuals.
An automated testing method for AUTOSAR software components based on SiL simulation

Testing automotive software using Software-in-the-Loop (SiL) simulation doesn't require the hardware such as test vehicles and ECUs. Existing testing techniques based on SiL rely on manual processes because automation techniques of testing AUTOSAR components using SiL simulation have not yet been established. In this paper, we present an automated testing technique and its implementation based on SiL simulation. We have evaluated the effectiveness and efficiency of the presented technique through a case study.
Kernel Projects for Linux

Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the author was aware of the claim, the designations have been printed in initial caps or in all caps. Creative Commons Attribution-Noncommercial-No Derivative Work 3.0 License You are free to share (to copy, distribute and transmit the work), under the conditions that (1) you must attribute the work to Gary Nutt (but not in any way that suggests that Gary Nutt endorses your use of the work), (2) you may not use this work for commercial purposes, and (3) you may not alter, transform, or build upon this work. To the Student Experience has shown that the best way to learn the details of how an operating system (OS) works is to experiment with it – to read, modify, and enhance the code. However OS software, by its nature, must be carefully constructed. This is because it directly controls the hardware used by all the processes and threads that execute on it. As a result, experimenting with OS code can be difficult, since an experimental version of the OS may disable the test machine. This laboratory manual provides you with a learning path for studying the Linux kernel with as little risk as possible. By learning about the Linux kernel this way, you will develop a technique by which you can learn and experiment with other kernels as well. Consider this learning path to be a graduated set of exercises. First you will learn to inspect various aspects of the OS internal state without changing any code. Second, you will extend the OS by writing new code to read (but not write) kernel data structures. Third, you will reimplement existing data structures. Finally, you will design and add your own functions and data structures to the Linux kernel. The Linux kernel is written in the C programming language. Therefore you need to relatively proficient in using C before you can study the kernel. If you know C++, you will not have difficulty understanding the source code, though when you add or change parts of the kernel code you will not be able to use objects. This manual is designed as a companion to a general OS textbook. It consists of two parts. Part 1 offers an overview of the Linux design. If you are using …
Combining Textual Entailment and Argumentation Theory for Supporting Online Debates Interactions

Blogs and forums are widely adopted by online communities to debate about various issues. However, a user that wants to cut in on a debate may experience some difficulties in extracting the current accepted positions, and can be discouraged from interacting through these applications. In our paper, we combine textual entailment with argumentation theory to automatically extract the arguments from debates and to evaluate their acceptability.
Closed-Loop Neuromorphic Benchmarks

Evaluating the effectiveness and performance of neuromorphic hardware is difficult. It is even more difficult when the task of interest is a closed-loop task; that is, a task where the output from the neuromorphic hardware affects some environment, which then in turn affects the hardware's future input. However, closed-loop situations are one of the primary potential uses of neuromorphic hardware. To address this, we present a methodology for generating closed-loop benchmarks that makes use of a hybrid of real physical embodiment and a type of "minimal" simulation. Minimal simulation has been shown to lead to robust real-world performance, while still maintaining the practical advantages of simulation, such as making it easy for the same benchmark to be used by many researchers. This method is flexible enough to allow researchers to explicitly modify the benchmarks to identify specific task domains where particular hardware excels. To demonstrate the method, we present a set of novel benchmarks that focus on motor control for an arbitrary system with unknown external forces. Using these benchmarks, we show that an error-driven learning rule can consistently improve motor control performance across a randomly generated family of closed-loop simulations, even when there are up to 15 interacting joints to be controlled.
Imitation, empathy, and mirror neurons.

There is a convergence between cognitive models of imitation, constructs derived from social psychology studies on mimicry and empathy, and recent empirical findings from the neurosciences. The ideomotor framework of human actions assumes a common representational format for action and perception that facilitates imitation. Furthermore, the associative sequence learning model of imitation proposes that experience-based Hebbian learning forms links between sensory processing of the actions of others and motor plans. Social psychology studies have demonstrated that imitation and mimicry are pervasive, automatic, and facilitate empathy. Neuroscience investigations have demonstrated physiological mechanisms of mirroring at single-cell and neural-system levels that support the cognitive and social psychology constructs. Why were these neural mechanisms selected, and what is their adaptive advantage? Neural mirroring solves the "problem of other minds" (how we can access and understand the minds of others) and makes intersubjectivity possible, thus facilitating social behavior.
Address Resolution Protocol (ARP): Spoofing Attack and Proposed Defense

Networks have become an integral part of today’s world. The ease of deployment, low-cost and high data rates have contributed significantly to their popularity. There are many protocols that are tailored to ease the process of establishing these networks. Nevertheless, security-wise precautions were not taken in some of them. In this paper, we expose some of the vulnerability that exists in a commonly and widely used network protocol, the Address Resolution Protocol (ARP) protocol. Effectively, we will implement a user friendly and an easy-to-use tool that exploits the weaknesses of this protocol to deceive a victim’s machine and a router through creating a sort of Man-in-the-Middle (MITM) attack. In MITM, all of the data going out or to the victim machine will pass first through the attacker’s machine. This enables the attacker to inspect victim’s data packets, extract valuable data (like passwords) that belong to the victim and manipulate these data packets. We suggest and implement a defense mechanism and tool that counters this attack, warns the user, and exposes some information about the attacker to isolate him. GNU/Linux is chosen as an operating system to implement both the attack and the defense tools. The results show the success of the defense mechanism in detecting the ARP related attacks in a very simple and efficient way.
Palm-top jumping and crawling robot using snap-through buckling of arched elastica supported by Ω-shaped frame

In this paper, we propose a compact jumping robot using snap-through buckling of an arched closed elastica supported by an Ω-shaped frame in an effort to quick stair climbing. The proposed jumping robot is a modified version of an existing robot using spatially deformable arch-type closed elastica, but has much higher jumping capability due to the following two effects of the Ω-shaped frame. One is appropriate elasticity for supporting the elastica, which drastically enhances the ability to generate impulsive forces for jumping. The other one is easy adjustment of the mass balance of the robot so that the tip of the leg attached to the closed elastica, with which the robot applies the impulse force to the ground, pushes near the ground point below its center of gravity. Experimental results using a palm-top 54.5[g] prototype show that the compact and lightweight robot can jump over 200[mm] corresponding to the height of a normal stair step in spite of having a 40[g] payload. We also show that the robot can turn by back-and-forth twist motions of the closed elastica which generates motions of the blade rubbing the ground.
Information-Theoretic Planning with Trajectory Optimization for Dense 3D Mapping

We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on CauchySchwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map. We then contribute a two stage approach for active mapping. First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives. From this set, we choose a trajectory that maximizes the information-theoretic objective. Second, we employ a gradientbased trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot’s motion constraints. We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments. Real-world experiments suggest our approach reduces the time to explore an environment by 70% compared to a closest frontier exploration strategy and 57% compared to an information-based strategy that uses global planning, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state.
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in a single model. We introduce a joint many-task model together with a strategy for successively growing its depth to solve increasingly complex tasks. Higher layers include shortcut connections to lower-level task predictions to reflect linguistic hierarchies. We use a simple regularization term to allow for optimizing all model weights to improve one task’s loss without exhibiting catastrophic interference of the other tasks. Our single end-to-end model obtains state-of-the-art or competitive results on five different tasks from tagging, parsing, relatedness, and entailment tasks.
Memory Prefetching Using Adaptive Stream Detection

We present Adaptive Stream Detection, a simple technique for modulating the aggressiveness of a stream prefetcher to match a workload's observed spatial locality. We use this concept to design a prefetcher that resides on an on-chip memory controller. The result is a prefetcher with small hardware costs that can exploit workloads with low amounts of spatial locality. Using highly accurate simulators for the IBM Power5+, we show that this prefetcher improves performance of the SPEC2006fp benchmarks by an average of 32.7% when compared against a Power5+ that performs no prefetching. On a set of 5 commercial benchmarks that have low spatial locality, this prefetcher improves performance by an average of 15.1%. When compared against a typical Power5+ that does perform processor-side prefetching, the average performance improvement of these benchmark suites is 10.2% and 8.4%. We also evaluate the power and energy impact of our technique. For the same benchmark suites, DRAM power consumption increases by less than 3%, while energy usage decreases by 9.8% and 8.2%, respectively. Moreover, the power consumption of the prefetcher itself is low; it is estimated to increase the power consumption of the Power5+ chip by 0.06%.
Analysis and development of Weinberg converter with fault tolerant feature

Space applications demand power converters of high reliability, fault tolerance, high power to volume and weight ratios. Weinberg converter is one such topology having these features built in. This topology can also be called as, coupled inductor fed push-pull converter. It provides continuous current at its output , which reduces the requirement of filter bank capacitors at the output stage. In this research article, isolated Weinberg converter working principle is explained with large signal and state space analysis. Also building of working breadboard model and its test results are dealt. The paper brings out the inherent fault tolerant feature of the Weinberg converter.