nils-herrmann/clean_scirepeval huggingface dataset:
METTLE: A Metamorphic Testing Approach To Validating Unsupervised Machine Learning Methods.

Unsupervised (machine) learning is the training of an artificial intelligence (AI) algorithm using information that is neither classified nor labeled, with a view to modeling the underlying structure or distribution in a dataset. Since unsupervised learning algorithms are widely used in many real-world applications, validating the implementations of these algorithms is indisputably an important task in software quality assurance. This validation task, however, is fairly challenging due to the absence of a priori knowledge of the data. In view of this challenge, we develop a METamorphic Testing approach to validating unsupervised machine LEarning methods, abbreviated as METTLE. This validation approach takes consideration of the specific expectations and requirements on the learning methods by individual users. To demonstrate the viability and effectiveness of METTLE, we describe an experiment involving six common clustering algorithms. Furthermore, we discuss how METTLE can be used by the users to devise their own and specific quality assessment schemes for selecting the suitable unsupervised learning algorithms to use in different application scenarios.
Cave or Community? An Empirical Examination of 100 Mature Open Source Projects

Starting with Eric Raymond’s groundbreaking work, The Cathedral and the Bazaar, open-source software (OSS) has commonly been regarded as work produced by a community of developers. Yet, given the nature of software programs, one also hears of developers with no lives that work very hard to achieve great product results. In this paper, I sought empirical evidence that would help us understand which is more commonthe cave (i.e., lone producer) or the community. Based on a study of the top 100 mature products on Sourceforge, I find a few surprising things. First, most OSS programs are developed by individuals, rather than communities. The median number of developers in the 100 projects I looked at was 4 and the mode was 1numbers much lower than previous ones reported for highly successful projects! Second, most OSS programs do not generate a lot of discussion. Third, products with more developers tend to be viewed and downloaded more often. Fourth, the number of developers associated with a project is unrelated to the age of the project. Fifth, the larger the project, the smaller the percent of project administrators.
Architectural considerations for playback of quality adaptive video over the Internet

Lack of QoS support in the Internet has not prevented rapid growth of streaming applications. However many of these applications do not perform congestion control effectively. Thus there is significant concern about the effects on co-existing well-behaved traffic and the potential for congestion collapse. In addition, most such applications are unable to perform quality adaptation on-the-fly as available bandwidth changes during a session. This paper aims to provide some architectural insights on the design of video playback applications in the Internet. We present fundamental design principles for Internet applications and identify end-to-end congestion control, quality adaptation and error control as the three major building blocks for Internet video playback applications. We discuss the design space for each of these components, and within that space, present an end-to-end architecture suited for playback of layered-encoded stored video streams. Our architecture reconciles congestion control and quality adaptation which occur on different timescales. It exhibits a TCP-friendly behavior by adopting the RAP protocol for end-to-end congestion control. Additionally, it uses a layered framework for quality adaptation with selective retransmission to maximize the quality of the delivered stream as available bandwidth changes. We argue that the architecture can be generalized by replacing the suggested mechanism for each component by another from the same design space as long as all components remain compatible. This work was carried out while Reza Rejaie was with USC/ISI. This work was supported at USC/ISI by DARPA under contract No. DABT6395-C0095 and DABT63-96-C-0054 as part of SPT and VINT projects.
Generalized Modified Ratio Estimator for Estimation of Finite Population Mean

A generalized modified ratio estimator is proposed for estimating the population mean using the known population parameters. It is shown that the simple random sampling without replacement sample mean, the usual ratio estimator, the linear regression estimator and all the existing modified ratio estimators are the particular cases of the proposed estimator. The bias and the mean squared error of the proposed estimator are derived and are compared with that of existing estimators. The conditions for which the proposed estimator performs better than the existing estimators are also derived. The performance of the proposed estimator is assessed with that of the existing estimators for certain natural populations
Sword: Scalable and Flexible Workload Generator for Distributed Data Processing Systems

Workload generation is commonly employed for performance characterization, testing and benchmarking of computer systems and networks. Workload generation typically aims at simulating or emulating traffic generated by different types of applications, protocols and activities, such as web browsing, email, chat, as well as stream multimedia traffic. We present a Scalable WORkloaD generator (SWORD) that we have developed for the testing and benchmarking of high-volume data processing systems. The tool is not only scalable but is also flexible and extensible allowing the generation of workload of a variety of types of applications and of contents.
Simulation Optimization: Methods and Applications

Simulation optimization can be defined as the process of finding the best input variable values from among all possibilities without explicitly evaluating each possibility. The objective of simulation optimization is to minimize the resources spent while maximizing the information obtained in a simulation experiment. The purpose of this paper is to review the area of simulation optimization. A critical review of the methods employed and applications developed in this relatively new area are presented and notable successes are highlighted. Simulation optimization software tools are discussed. The intended audience is simulation practitioners and theoreticians as well as beginners in the field of simulation.
Walking on foot to explore a virtual environment with uneven terrain

In this work we examine people's ability to maintain spatial awareness of an HMD--based immersive virtual environment when the terrain of the virtual world does not match the real physical world in which they are locomoting. More specifically, we examine what happens when a subject explores a hilly virtual environment while physically walking in a flat room. In our virtual environments, subjects maintain their height (or distance to the ground plane) at all times. In the experiment described in this work, we directly compare spatial orientation in a flat virtual environment to two variations of a hilly virtual environment. Interestingly, we find that subjects' spatial awareness of the environment is improved with the addition of uneven terrain.
Smart Configuration of Smart Environments

One of the central research challenges in the Internet of Things and Ubiquitous Computing domains is how users can be enabled to “program” their personal and industrial smart environments by combining services that are provided by devices around them. We present a service composition system that enables the goal-driven configuration of smart environments for end users by combining semantic metadata and reasoning with a visual modeling tool. In contrast to process-driven approaches where service mashups are statically defined, we make use of embedded semantic API descriptions to dynamically create mashups that fulfill the user's goal. The main advantage of our system is its high degree of flexibility, as service mashups can adapt to dynamic environments and are fault-tolerant with respect to individual services becoming unavailable. To support users in expressing their goals, we integrated a visual programming tool with our system that allows to model the desired state of a smart environment graphically, thereby hiding the technicalities of the underlying semantics. Possible applications of the presented system include the management of smart homes to increase individual well-being, and reconfigurations of smart environments, for instance in the industrial automation or healthcare domains.
A particle filter for monocular vision-aided odometry

We propose a particle filter-based algorithm for monocular vision-aided odometry for mobile robot localization. The algorithm fuses information from odometry with observations of naturally occurring static point features in the environment. A key contribution of this work is a novel approach for computing the particle weights, which does not require including the feature positions in the state vector. As a result, the computational and sample complexities of the algorithm remain low even in feature-dense environments. We validate the effectiveness of the approach extensively with both simulations as well as real-world data, and compare its performance against that of the extended Kalman filter (EKF) and FastSLAM. Results from the simulation tests show that the particle filter approach is better than these competing approaches in terms of the RMS error. Moreover, the experiments demonstrate that the approach is capable of achieving good localization accuracy in complex environments.
Simultaneous object detection and localization using convolutional neural networks

Nowadays deep learning is considered as a trendy technique in the computer vision domain. It becomes a pioneer in its main tasks as object classification, object localization, and object detection. Therefore it gave amazing results and records. In this paper, we propose a new approach to identify and localize objects, simultaneously, in a given scene using Convolutional Neural Networks (CNNs). We propose an end-to-end approach for object detection and pose estimation by formulating bounding boxes containing the targeted object and their pose. Our method is based on two main steps, i) produce Bounding boxes on the training images for generating the pose coordinates of each object in the scene and, ii) detect and localize simultaneously each object present in image during the testing step. The contribution performance is assessed on two datasets, Washington RGB scene dataset and LIMIARF dataset that is constructed in our laboratory. We demonstrate that our proposal is able to obtain high precision and reasonable recall levels.
Speech-to-text development for Slovak, a low-resourced language

Development of an automatic speech recognition (ASR) system for low-resourced languages is an important research topic in ASR. This paper reports on the development of a speech-to-text (STT) system targeting broadcast news and broadcast conversation transcription for the low-resourced Slovak language. Context-dependent acoustic models are trained without any manually transcribed audio data via cross-language transfer and unsupervised training. In addition, a pronunciation dictionary for Slovak language is created using efficient rule-based pronunciation modeling. For language modeling, large N-gram language models were estimated on 63M words of texts downloaded from the Internet. The system uses MLP (multilayer perceptron) features imported from English which are concatenated with cepstral PLP (perceptual linear prediction) and F0 (pitch) features. These techniques were applied to develop a Slovak STT system with performance similar to that obtained by state-of-the-art systems for other languages. Furthermore, we propose to reduce the dimension of the MLP+PLP+F0 features from 81 to 50, using principal component analysis (PCA), in order to reduce the redundancy between the MLP and the PLP+F0 features. This feature reduction makes it possible to reduce the word error rate (WER) and the recognition time while reducing the CMLLR adaptation time by a factor of 3.
Reliability and validity of the Medical Research Council (MRC) scale and a modified scale for testing muscle strength in patients with radial palsy.

To assess the inter-rater and intra-rater reliability and validity of the original and a modified Medical Research Council scale for testing muscle strength in radial palsy.



Prospective, randomized validation study.



Thirty-one patients with peripheral paresis of radial innervated forearm muscles were included.



Wrist extension, finger extension and grip strength were evaluated by manual muscle testing. Dynamometric measurement of grip strength was performed. Pair-wise weighted kappa coefficients were calculated to determine inter-rater and intra-rater reliability. The 2 scores were compared using the signed-rank test. Spearman's correlation coefficients of the maximal relative force measurements with the median (over-raters) Medical Research Council and modified Medical Research Council scores were calculated to determine validity.



Inter-rater agreement of the Medical Research Council scale (finger extension: 0.77; wrist extension: 0.78; grip strength: 0.78) and the modified Medical Research Council scale (finger extension: 0.81; wrist extension: 0.78; grip strength: 0.81) as well as intra-rater agreement of the Medical Research Council scale (finger extension: 0.86; wrist extension: 0.82; grip strength: 0.84) and the modified Medical Research Council scale (finger extension: 0.84, wrist extension: 0.81; grip strength: 0.88) showed almost perfect agreement. Spearman's correlation coefficients of the maximal relative force measurements with the median Medical Research Council and modified Medical Research Council score were both 0.78.



Medical Research Council and modified Medical Research Council scales are measurements with substantial inter-rater and intra-rater reliability in evaluating forearm muscles.
Applying long short term momory neural networks for predicting stock closing price

The main goal of this paper is to assess the hypothesis that combining RNNs with informative input variables can provide a more effective method for predicting the next-day stock movement. Moreover, we propose using long short term memory (LSTM) aand stock basic trading data to realize the stock prediction model. For training the model, we utilize some optimization strategies, such as adaptive moment estimation (Adam) and glorot uniform initialization. We present a case study based on Standard & Poor's (S&P500) and NASDAQ. Quantities of comparison experiments were performed to evaluate this model. At last we analyze the performance of different models with a series of evaluation criteria. Stock market prediction has garnered significant interest among investment and researchers. However, accurate prediction of stock market is an extremely challenging task. Hopefully, based on the case study, we show that our forecasting system gives slightly higher prediction accuracy for the stock closing price of next day, which outperforms the comparison models.
Denial of Service in Sensor Networks

S ensor networks hold the promise of facilitating large-scale, real-time data processing in complex environments. Their foreseeable applications will help protect and monitor critical military, environmental, safety-critical , or domestic infrastructures and resources. In these and other vital or security-sensitive deployments, keeping the network available for its intended use is essential. The stakes are high: Denial of service attacks against such networks may permit real-world damage to the health and safety of people. Without proper security mechanisms, networks will be confined to limited, controlled environments , negating much of the promise they hold. The limited ability of individual sensor nodes to thwart failure or attack makes ensuring network availability more difficult. To identify denial of service vulnerabilities, we analyze two effective sensor network protocols that did not initially consider security. These examples demonstrate that consideration of security at design time is the best way to ensure successful network deployment. Advances in miniaturization combined with an insatiable appetite for previously unrealizable information gathering have lead to the development of new kinds of networks. In many areas, static infrastruc-tures are giving way to dynamic ad-hoc networks. One manifestation of these trends is the development of highly application-dependent sensor networks. Developers build sensor networks to collect and analyze low-level data from an environment of interest. Accomplishing the network's goal often depends on local cooperation, aggregation, or data processing because individual nodes have limited capabilities. Physically small, nodes have tiny or irreplaceable power reserves, communicate wirelessly, and may not possess unique identifiers. Further, they must form ad hoc relationships in a dense network with little or no preexisting infrastructure. Protocols and algorithms operating in the network must support large-scale distribution, often with only localized interactions among nodes. The network must continue operating even after significant node failure, and it must meet real-time requirements. In addition to the limitations imposed by application-dependent deadlines, because it reflects a changing environment, the data the network gathers may intrinsically be valid for only a short time. Sensor networks may be deployed in a host of different environments, and they often figure into military scenarios. These networks may gather intelligence in battlefield conditions, track enemy troop movements, monitor a secured zone for activity , or measure damage and casualties. An airplane or artillery 1 could deploy these networks to otherwise unreachable regions. Although military applications may be the easiest to imagine, much broader opportunities await. Sensor networks could form …
Accurate, Robust, and Flexible Real-time Hand Tracking

We present a new real-time hand tracking system based on a single depth camera. The system can accurately reconstruct complex hand poses across a variety of subjects. It also allows for robust tracking, rapidly recovering from any temporary failures. Most uniquely, our tracker is highly flexible, dramatically improving upon previous approaches which have focused on front-facing close-range scenarios. This flexibility opens up new possibilities for human-computer interaction with examples including tracking at distances from tens of centimeters through to several meters (for controlling the TV at a distance), supporting tracking using a moving depth camera (for mobile scenarios), and arbitrary camera placements (for VR headsets). These features are achieved through a new pipeline that combines a multi-layered discriminative reinitialization strategy for per-frame pose estimation, followed by a generative model-fitting stage. We provide extensive technical details and a detailed qualitative and quantitative analysis.
Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features.

For a successful analysis of the relation between amino acid sequence and protein structure, an unambiguous and physically meaningful definition of secondary structure is essential. We have developed a set of simple and physically motivated criteria for secondary structure, programmed as a pattern-recognition process of hydrogen-bonded and geometrical features extracted from x-ray coordinates. Cooperative secondary structure is recognized as repeats of the elementary hydrogen-bonding patterns “turn” and “bridge.” Repeating turns are “helices,” repeating bridges are “ladders,” connected ladders are “sheets.” Geometric structure is defined in terms of the concepts torsion and curvature of differential geometry. Local chain “chirality” is the torsional handedness of four consecutive Ca positions and is positive for right-handed helices and negative for ideal twisted @-sheets. Curved pieces are defined as “bends.” Solvent “exposure” is given as the number of water molecules in possible contact with a residue. The end result is a compilation of the primary structure, including SS bonds, secondary structure, and solvent exposure of 62 different globular proteins. The presentation is in linear form: strip graphs for an overall view and strip tables for the details of each of 10,925 residues. The dictionary is also available in computer-readable form for protein structure prediction work.
A dynamically stable single-wheeled mobile robot with inverse mouse-ball drive

Multi-wheel statically-stable mobile robots tall enough to interact meaningfully with people must have low centers of gravity, wide bases of support, and low accelerations to avoid tipping over. These conditions present a number of performance limitations. Accordingly, we are developing an inverse of this type of mobile robot that is the height, width, and weight of a person, having a high center of gravity, that balances dynamically on a single spherical wheel. Unlike balancing 2-wheel platforms which must turn before driving in some direction, the single-wheel robot can move directly in any direction. We present the overall design, actuator mechanism based on an inverse mouse-ball drive, control system, and initial results including dynamic balancing, station keeping, and point-to-point motion
BodyAvatar: creating freeform 3D avatars using first-person body gestures

BodyAvatar is a Kinect-based interactive system that allows users without professional skills to create freeform 3D avatars using body gestures. Unlike existing gesture-based 3D modeling tools, BodyAvatar centers around a first-person "you're the avatar" metaphor, where the user treats their own body as a physical proxy of the virtual avatar. Based on an intuitive body-centric mapping, the user performs gestures to their own body as if wanting to modify it, which in turn results in corresponding modifications to the avatar. BodyAvatar provides an intuitive, immersive, and playful creation experience for the user. We present a formative study that leads to the design of BodyAvatar, the system's interactions and underlying algorithms, and results from initial user trials.
Block Chain Technologies & The Semantic Web : A Framework for Symbiotic Development

The concept of peer-to-peer applications is not new, nor is the concept of distributed hash tables. What emerged in 2008 with the publication of the Bitcoin white paper was an incentive structure that unified these two software paradigms with a set of economic stimuli to motivate the creation of a dedicated computing network orders of magnitude more powerful than the world’s fastest supercomputers. The purpose of which is the maintenance of a massive distributed database known as the Bitcoin “block chain”. Apart from the digital currency it enables, block chain technology is a fascinating new computing paradigm with broad implications for the future development of the World Wide Web, and by extension, the further growth of Linked Data and the Semantic Web. This work is divided into two main sections, we first demonstrate how block chain technologies can contribute toward the realization of a more robust Semantic Web, and subsequently we provide a framework wherein the Semantic Web is utilized to ameliorate block chain technology itself.