nils-herrmann/clean_scirepeval huggingface dataset:
Music making as a tool for promoting brain plasticity across the life span.

Playing a musical instrument is an intense, multisensory, and motor experience that usually commences at an early age and requires the acquisition and maintenance of a range of skills over the course of a musician's lifetime. Thus, musicians offer an excellent human model for studying the brain effects of acquiring specialized sensorimotor skills. For example, musicians learn and repeatedly practice the association of motor actions with specific sound and visual patterns (musical notation) while receiving continuous multisensory feedback. This association learning can strengthen connections between auditory and motor regions (e.g., arcuate fasciculus) while activating multimodal integration regions (e.g., around the intraparietal sulcus). We argue that training of this neural network may produce cross-modal effects on other behavioral or cognitive operations that draw on this network. Plasticity in this network may explain some of the sensorimotor and cognitive enhancements that have been associated with music training. These enhancements suggest the potential for music making as an interactive treatment or intervention for neurological and developmental disorders, as well as those associated with normal aging.
Ballerina: Automatic generation and clustering of efficient random unit tests for multithreaded code

Testing multithreaded code is hard and expensive. Each multithreaded unit test creates two or more threads, each executing one or more methods on shared objects of the class under test. Such unit tests can be generated at random, but basic generation produces tests that are either slow or do not trigger concurrency bugs. Worse, such tests have many false alarms, which require human effort to filter out. We present BALLERINA, a novel technique for automatic generation of efficient multithreaded random tests that effectively trigger concurrency bugs. BALLERINA makes tests efficient by having only two threads, each executing a single, randomly selected method. BALLERINA increases chances that such a simple parallel code finds bugs by appending it to more complex, randomly generated sequential code. We also propose a clustering technique to reduce the manual effort in inspecting failures of automatically generated multithreaded tests. We evaluate BALLERINA on 14 real-world bugs from 6 popular codebases: Groovy, Java JDK, jFreeChart, Log4j, Lucene, and Pool. The experiments show that tests generated by BALLERINA can find bugs on average 2X-10X faster than various configurations of basic random generation, and our clustering technique reduces the number of inspected failures on average 4X-8X. Using BALLERINA, we found three previously unknown bugs in Apache Pool and Log4j, one of which was already confirmed and fixed.
Near Field Communication in Smartphones

Near Field Communication (NFC) is an emerging wireless short-range communication technology that is based on existing standards of the Radio Frequency Identification (RFID) infrastructure. In combination with NFC-capable smartphones it enables intuitive application scenarios for contactless transactions, in particular services for mobile payment and over-theair ticketing. The intention of this paper is to describe basic characteristics and benefits of the underlaying technology, to classify modes of operation and to present various use cases. Both existing NFC applications and possible future scenarios will be analyzed in this context. Furthermore, security concerns, challenges and present conflicts will be discussed eventually.
Linked open data to support content-based recommender systems

The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data. Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets. These datasets are connected with each other to form the so called Linked Open Data cloud. As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power. In this paper we show how these data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded in the Web of Data. We implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users. We extensively evaluated the approach and validated the effectiveness of the algorithms by experimentally measuring their accuracy with precision and recall metrics.
Simultaneous localization and mapping with infinite planes

Simultaneous localization and mapping with infinite planes is attractive because of the reduced complexity with respect to both sparse point-based and dense volumetric methods. We show how to include infinite planes into a least-squares formulation for mapping, using a homogeneous plane parametrization with a corresponding minimal representation for the optimization. Because it is a minimal representation, it is suitable for use with Gauss-Newton, Powell's Dog Leg and incremental solvers such as iSAM. We also introduce a relative plane formulation that improves convergence. We evaluate our proposed approach on simulated data to show its advantages over alternative solutions. We also introduce a simple mapping system and present experimental results, showing real-time mapping of select indoor environments with a hand-held RGB-D sensor.
On-line relevant anomaly detection in the Twitter stream: an efficient bursty keyword detection model

On-line social networks have become a massive communication and information channel for users world-wide. In particular, the microblogging platform Twitter, is characterized by short-text message exchanges at extremely high rates. In this type of scenario, the detection of emerging topics in text streams becomes an important research area, essential for identifying relevant new conversation topics, such as breaking news and trends. Although emerging topic detection in text is a well established research area, its application to large volumes of streaming text data is quite novel. Making scalability, efficiency and rapidness, the key aspects for any emerging topic detection algorithm in this type of environment.
 Our research addresses the aforementioned problem by focusing on detecting significant and unusual bursts in keyword arrival rates or bursty keywords. We propose a scalable and fast on-line method that uses normalized individual frequency signals per term and a windowing variation technique. This method reports keyword bursts which can be composed of single or multiple terms, ranked according to their importance. The average complexity of our method is O(n log n), where n is the number of messages in the time window. This complexity allows our approach to be scalable for large streaming datasets. If bursts are only detected and not ranked, the algorithm remains with lineal complexity O(n), making it the fastest in comparison to the current state-of-the-art. We validate our approach by comparing our performance to similar systems using the TREC Tweet 2011 Challenge tweets, obtaining 91% of matches with LDA, an off-line gold standard used in similar evaluations. In addition, we study Twitter messages related to the SuperBowl football events in 2011 and 2013.
A survey on platforms for big data analytics

The primary purpose of this paper is to provide an in-depth analysis of different platforms available for performing big data analytics. This paper surveys different hardware platforms available for big data analytics and assesses the advantages and drawbacks of each of these platforms based on various metrics such as scalability, data I/O rate, fault tolerance, real-time processing, data size supported and iterative task support. In addition to the hardware, a detailed description of the software frameworks used within each of these platforms is also discussed along with their strengths and drawbacks. Some of the critical characteristics described here can potentially aid the readers in making an informed decision about the right choice of platforms depending on their computational needs. Using a star ratings table, a rigorous qualitative comparison between different platforms is also discussed for each of the six characteristics that are critical for the algorithms of big data analytics. In order to provide more insights into the effectiveness of each of the platform in the context of big data analytics, specific implementation level details of the widely used k-means clustering algorithm on various platforms are also described in the form pseudocode.
Simulation of a memristor-based spiking neural network immune to device variations

We propose a design methodology to exploit adaptive nanodevices (memristors), virtually immune to their variability. Memristors are used as synapses in a spiking neural network performing unsupervised learning. The memristors learn through an adaptation of spike timing dependent plasticity. Neurons' threshold is adjusted following a homeostasis-type rule. System level simulations on a textbook case show that performance can compare with traditional supervised networks of similar complexity. They also show the system can retain functionality with extreme variations of various memristors' parameters, thanks to the robustness of the scheme, its unsupervised nature, and the power of homeostasis. Additionally the network can adjust to stimuli presented with different coding schemes.
PALM-COEIN FIGO Classification for diagnosis of Abnormal Uterine Bleeding : Practical Utility of same at Tertiary Care Centre in North India

In an effort to create a universally accepted system of nomenclature to describe uterine bleeding abnormalities, an alternative classification system polyp, adenomyosis, leiomyoma, malignancy and hyperplasia, coagulopathy, ovulatory dysfunction, endometrial, iatrogenic, and not yet classified, known by the acronym PALM-COEIN, was published in 2011 by the International Federation of Gynecology and Obstetrics. The PALM-COEIN system classifies uterine bleeding abnormalities by bleeding pattern and etiology.The purpose of this study is to categorize 300 women with AUB presenting to the Gynaecology outpatient department (OPD) of Mahatma Gandhi Medical college and Hospital, Jaipur according to PALM-COEIN classification system. It is a retrospective study carried out on 300 patients presenting with complaint of abnormal uterine bleeding. They were evaluated on the basis of history, physical and local examination, USG pelvis, endometrial biopsy if required and other necessary investigations. Categorization was done according to the PALM-COEIN FIGO Classification and then the required treatment was given to the patient. After classifying the patients according to PALM-COEIN classification, it was found that ovulatory dysfunction was the most common cause of AUB in patients presenting to the Gynecology OPD (n=85, 28.33%). It was followed by leiomyoma (n= 68, 22.67%), endometrial causes (n= 62, 20.66%), adenomyosis (n=28, 9.33%), not classified (n = 25, 8.33%), iatrogenic (n= 13, 4.33%), malignancy, polyp (n = 8, 2.66%) and coagulation abnormalities contributing least to the classification (n= 3, 1%). Standardization of related terminology, a systematic approach to diagnosis and investigation, and a step-wise approach to intervention is necessary.
DemographicVis: Analyzing demographic information based on user generated content

The wide-spread of social media provides unprecedented sources of written language that can be used to model and infer online demographics. In this paper, we introduce a novel visual text analytics system, DemographicVis, to aid interactive analysis of such demographic information based on user-generated content. Our approach connects categorical data (demographic information) with textual data, allowing users to understand the characteristics of different demographic groups in a transparent and exploratory manner. The modeling and visualization are based on ground truth demographic information collected via a survey conducted on Reddit.com. Detailed user information is taken into our modeling process that connects the demographic groups with features that best describe the distinguishing characteristics of each group. Features including topical and linguistic are generated from the user-generated contents. Such features are then analyzed and ranked based on their ability to predict the users' demographic information. To enable interactive demographic analysis, we introduce a web-based visual interface that presents the relationship of the demographic groups, their topic interests, as well as the predictive power of various features. We present multiple case studies to showcase the utility of our visual analytics approach in exploring and understanding the interests of different demographic groups. We also report results from a comparative evaluation, showing that the DemographicVis is quantitatively superior or competitive and subjectively preferred when compared to a commercial text analysis tool.
Randomized Kinodynamic Motion Planning with Moving Obstacles

This paper presents a novel randomized motion planner for robots that must achieve a specified goal under kinematic and/or dynamic motion constraints while avoiding collision with moving obstacles with known trajectories. The planner encodes the motion constraints on the robot with a control system and samples the robot’s state time space by picking control inputs at random and integrating its equations of motion. The result is a probabilistic roadmap of sampled state time points, called milestones, connected by short admissible trajectories. The planner does not precompute the roadmap; instead, for each planning query, it generates a new roadmap to connect an initial and a goal state time point. The paper presents a detailed analysis of the planner’s convergence rate. It shows that, if the state time space satisfies a geometric property called expansiveness, then a slightly idealized version of our implemented planner is guaranteed to find a trajectory when one exists, with probability quickly converging to 1, as the number of of milestones increases. Our planner was tested extensively not only in simulated environments, but also on a real robot. In the latter case, a vision module estimates obstacle motions just before planning starts. The planner is then allocated a small, fixed amount of time to compute a trajectory. If a change in the expected motion of the obstacles is detected while the robot executes the planned trajectory, the planner recomputes a trajectory on the fly. Experiments on the real robot led to several extensions of the planner in order to deal with time delays and uncertainties that are inherent to an integrated robotic system interacting with the physical world.
An efficient randomized algorithm for rumor blocking in online social networks

Social networks allow rapid spread of ideas and innovations while the negative information can also propagate widely. When the cascades with different opinions reaching the same user, the cascade arriving first is the most likely to be taken by the user. Therefore, once misinformation or rumor is detected, a natural containment method is to introduce a positive cascade competing against the rumor. Given a budget k, the rumor blocking problem asks for k seed users to trigger the spread of the positive cascade such that the number of the users who are not influenced by rumor can be maximized. The prior works have shown that the rumor blocking problem can be approximated within a factor of (1 − 1/e− δ) by a classic greedy algorithm combined with Monte Carlo simulation with the running time of O(k3 mn ln n/δ2), where n and m are the number of users and edges, respectively. Unfortunately, the Monte-Carlo-simulation-based methods are extremely time consuming and the existing algorithms either trade performance guarantees for practical efficiency or vice versa. In this paper, we present a randomized algorithm which runs in O(km ln n/δ2) expected time and provides a (1 − 1/e − δ)-approximation with a high probability. The experimentally results on both the real-world and synthetic social networks have shown that the proposed randomized rumor blocking algorithm is much more efficient than the state-of-the-art method and it is able to find the seed nodes which are effective in limiting the spread of rumor.
AMADA: web data repositories in the amazon cloud

We present AMADA, a platform for storing Web data (in particular, XML documents and RDF graphs) based on the Amazon Web Services (AWS) cloud infrastructure. AMADA operates in a Software as a Service (SaaS) approach, allowing users to upload, index, store, and query large volumes of Web data. The demonstration shows (i) the step-by-step procedure for building and exploiting the warehouse (storing, indexing, querying) and (ii) the monitoring tools enabling one to control the expenses (monetary costs) charged by AWS for the operations involved while running AMADA.
n-Valued Refined Neutrosophic Logic and Its Applications to Physics

In this paper we present a short history of logics: from parti cular cases of 2-symbol or numerical valued logic to the general case of n-symbol or num erical valued logic. We show generalizations of 2-valued Boolean logic to fuzzy log ic, also from the Kleene’s and Lukasiewicz’ 3-symbol valued logics or Belnap’s 4ymbol valued logic to the most generaln-symbol or numerical valued refined neutrosophic logic . Two classes of neutrosophic norm ( n-norm) and neutrosophic conorm ( n-conorm) are defined. Examples of applications of neutrosophic logic to physics are listed in the last section. Similar generalizations can be done for n-Valued Refined Neutrosophic Set , and respectively n-Valued Refined Neutrosopjhic Probability .
An Analysis of Smart Grid Attacks and Countermeasures

The threat of malicious attacks against the security of the Smart Grid infrastructure cannot be overlooked. The ever-expanding nature of smart grid user base implies that a larger set of vulnerabilities are exploitable by the adversary class to launch malicious attacks. Extensive research has been conducted to identify various threat types against the smart grid, and to propose counter-measures against these. Work has also been done to measure the significance of threats and how attacks can be perpetrated in a smart grid environment. Through this paper, we categorize these smart grid threats, and how they can transpire into attacks. In particular, we provide five different categories of attack types, and also perform an analysis of the various countermeasures thereof proposed in the literature.
Greening the internet with nano data centers

Motivated by increased concern over energy consumption in modern data centers, we propose a new, distributed computing platform called Nano Data Centers (NaDa). NaDa uses ISP-controlled home gateways to provide computing and storage services and adopts a managed peer-to-peer model to form a distributed data center infrastructure. To evaluate the potential for energy savings in NaDa platform we pick Video-on-Demand (VoD) services. We develop an energy consumption model for VoD in traditional and in NaDa data centers and evaluate this model using a large set of empirical VoD access data. We find that even under the most pessimistic scenarios, NaDa saves at least 20% to 30% of the energy compared to traditional data centers. These savings stem from energy-preserving properties inherent to NaDa such as the reuse of already committed baseline power on underutilized gateways, the avoidance of cooling costs, and the reduction of network energy consumption as a result of demand and service co-localization in NaDa.
A Secure Communication Protocol for Drones and Smart Objects

In many envisioned drone-based applications, drones will communicate with many different smart objects, such as sensors and embedded devices. Securing such communications requires an effective and efficient encryption key establishment protocol. However, the design of such a protocol must take into account constrained resources of smart objects and the mobility of drones. In this paper, a secure communication protocol between drones and smart objects is presented. To support the required security functions, such as authenticated key agreement, non-repudiation, and user revocation, we propose an efficient Certificateless Signcryption Tag Key Encapsulation Mechanism (eCLSC-TKEM). eCLSC-TKEM reduces the time required to establish a shared key between a drone and a smart object by minimizing the computational overhead at the smart object. Also, our protocol improves drone's efficiency by utilizing dual channels which allows many smart objects to concurrently execute eCLSC-TKEM. We evaluate our protocol on commercially available devices, namely AR.Drone2.0 and TelosB, by using a parking management testbed. Our experimental results show that our protocol is much more efficient than other protocols.
A Full GPU Virtualization Solution with Mediated Pass-Through

Graphics Processing Unit (GPU) virtualization is an enabling technology in emerging virtualization scenarios. Unfortunately, existing GPU virtualization approaches are still suboptimal in performance and full feature support. This paper introduces gVirt, a product level GPU virtualization implementation with: 1) full GPU virtualization running native graphics driver in guest, and 2) mediated pass-through that achieves both good performance and scalability, and also secure isolation among guests. gVirt presents a virtual full-fledged GPU to each VM. VMs can directly access performance-critical resources, without intervention from the hypervisor in most cases, while privileged operations from guest are trap-and-emulated at minimal cost. Experiments demonstrate that gVirt can achieve up to 95% native performance for GPU intensive workloads, and scale well up to 7 VMs.
PSO-BASED DBSCAN WITH OBSTACLE CONSTRAINTS

This paper presents a new PSO-based optimization DBSCAN space clustering algorithm with obstacle constraints. The algorithm introduces obstacle model and simplifies two-dimensional coordinates of the cluster object coding to one-dimensional, then uses the PSO algorithm to obtain the shortest path and minimum obstacle distance. At the last stage, this paper fulfills spatial clustering based on obstacle distance. Theoretical analysis and experimental results show that the algorithm can get high-quality clustering result of space constraints with more reasonable and accurate quality.
Gaze+RST: Integrating Gaze and Multitouch for Remote Rotate-Scale-Translate Tasks

Our work investigates the use of gaze and multitouch to fluidly perform rotate-scale-translate (RST) tasks on large displays. The work specifically aims to understand if gaze can provide benefit in such a task, how task complexity affects performance, and how gaze and multitouch can be combined to create an integral input structure suited to the task of RST. We present four techniques that individually strike a different balance between gaze-based and touch-based translation while maintaining concurrent rotation and scaling operations. A 16 participant empirical evaluation revealed that three of our four techniques present viable options for this scenario, and that larger distances and rotation/scaling operations can significantly affect a gaze-based translation configuration. Furthermore we uncover new insights regarding multimodal integrality, finding that gaze and touch can be combined into configurations that pertain to integral or separable input structures.