nils-herrmann/clean_scirepeval huggingface dataset:
A Survey on M2M Systems for mHealth: A Wireless Communications Perspective

In the new era of connectivity, marked by the explosive number of wireless electronic devices and the need for smart and pervasive applications, Machine-to-Machine (M2M) communications are an emerging technology that enables the seamless device interconnection without the need of human interaction. The use of M2M technology can bring to life a wide range of mHealth applications, with considerable benefits for both patients and healthcare providers. Many technological challenges have to be met, however, to ensure the widespread adoption of mHealth solutions in the future. In this context, we aim to provide a comprehensive survey on M2M systems for mHealth applications from a wireless communication perspective. An end-to-end holistic approach is adopted, focusing on different communication aspects of the M2M architecture. Hence, we first provide a systematic review ofWireless Body Area Networks (WBANs), which constitute the enabling technology at the patient's side, and then discuss end-to-end solutions that involve the design and implementation of practical mHealth applications. We close the survey by identifying challenges and open research issues, thus paving the way for future research opportunities.
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model
Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet.

There is a need for better standardization of the dermoscopic terminology in assessing pigmented skin lesions.



The virtual Consensus Net Meeting on Dermoscopy was organized to investigate reproducibility and validity of the various features and diagnostic algorithms.



Dermoscopic images of 108 lesions were evaluated via the Internet by 40 experienced dermoscopists using a 2-step diagnostic procedure. The first-step algorithm distinguished melanocytic versus nonmelanocytic lesions. The second step in the diagnostic procedure used 4 algorithms (pattern analysis, ABCD rule, Menzies method, and 7-point checklist) to distinguish melanoma versus benign melanocytic lesions. kappa Values, log odds ratios, sensitivity, specificity, and positive likelihood ratios were estimated for all diagnostic algorithms and dermoscopic features.



Interobserver agreement was fair to good for all diagnostic methods, but it was poor for the majority of dermoscopic criteria. Intraobserver agreement was good to excellent for all algorithms and features considered. Pattern analysis allowed the best diagnostic performance (positive likelihood ratio: 5.1), whereas alternative algorithms revealed comparable sensitivity but less specificity. Interobserver agreement on management decisions made by dermoscopy was fairly good (mean kappa value: 0.53).



The virtual Consensus Net Meeting on Dermoscopy represents a valid tool for better standardization of the dermoscopic terminology and, moreover, opens up a new territory for diagnosing and managing pigmented skin lesions.
Bounding Multiple Gaussians Uncertainty with Application to Object Tracking

This paper proves the uncertainty bound for the multiple Gaussian functions, termed multiple Gaussians Uncertainty (MGU), which significantly generalizes the uncertainty principle for the single Gaussian function. First, as a theoretical contribution, we prove that the momentum (velocity) and position for the sum of multiple Gaussians wave function are theoretically bounded. Second, as for a practical application, we show that the bound can be well exploited for object tracking to detect anomalies of local movement in an online learning framework. By integrating MGU with a given object tracker, we demonstrate that uncertainty principle can provide remarkable robustness in tracking. Extensive experiments are done to show that the proposed MGU can significantly help base trackers overcome the object drifting and reach state-of-the-art results.
Harmony: A Hand Wash Monitoring and Reminder System using Smart Watches

Hand hygiene compliance is extremely important in hospitals, clinics and food businesses. Caregivers’ compliance with hand hygiene is one of the most effective tools in preventing healthcare associated infections (HAIs) in hospitals and clinics. In food businesses, hand hygiene compliance is essential to prevent food contamination, and thus food borne illness. Washing hands properly is the cornerstone of hand hygiene. However, the hand wash compliance rate by the workers (care givers, waiters, chefs, food processors and so on) is not up to the mark. Monitoring hand wash compliance along with a reminder system increases the compliance rate significantly. Quality of a hand wash is also important which can be achieved by washing hands in accordance with standard guidelines. In this paper, we present Harmony, a hand wash monitoring and reminder system that monitors hand wash events and their quality, provides real time feedback, reminds the person of interest when he/she is required to wash hands, and stores related data in a server for further use. Worker worn smart watches are the key components of Harmony that can differentiate hand wash gestures from other gestures with an average accuracy of about 88%. Harmony is robust, scalable, and easy to install, and it overcomes most of the problems of existing related systems.
Organic Computing – Addressing Complexity by Controlled Self-Organization

In the past, the focus of the computer industry has been to improve hardware performance and add more and more features to the software. As a result, more and more appliances surrounding us are equipped with embedded computational power and wireless communication. As such, they become ever more flexible and multifunctional, and almost indispensable in daily life. On the other hand, the resulting systems become increasingly complex and unreliable, posing new challenges to designer and user. Organic Computing (OC) has the vision to address the challenges of complex distributed systems by making them more life-like (organic), i.e. endowing them with abilities such as self- organization, self-configuration, self-repair, or adaptation. The designer's task is simplified, because it is no longer necessary to exactly specify the low-level system behavior in all possible situations that might occur, but instead leaving the system with a certain degree of freedom which allows it to react in an intelligent way to new situations. Also, use of such systems is simplified, as they can be controlled by setting few high-level goals, rather than having to manipulate many low-level parameters with unclear influence. In this paper, we give a general introduction to OC, and propose a generic observer-controller architecture as a framework for designing OC systems. Then, it is shown how to use this architecture at the example of a traffic light controller. The paper concludes with a summary and a discussion of future challenges.
Body contouring by non-invasive transdermal focused ultrasound.

The risks of currently available invasive procedures in body contouring motivate a need for safer, non-invasive technologies for improving the appearance of body silhouette. A new device has been developed that uses focused therapeutic ultrasound to reduce adipose tissue non-invasively. The aim of this study was to assess the efficacy and safety of a novel non-invasive focused ultrasound system (UltraShape Ltd, Tel Aviv, Israel) in reducing localized fat deposits to improve body contours.



A prospective study was conducted on 30 healthy patients. All patients underwent three treatments, at 1-month intervals, and were followed for 1 month after the last treatment. Areas treated were the abdomen, inner and outer thighs, flanks, inner knees, and breasts (males only). No other body contouring procedure was used during the study. Efficacy was determined by change in fat thickness, assessed by ultrasound measurements, and by circumference measurements. Weight change was monitored to assess whether reduction in fat thickness or circumference was dependent on or independent of weight loss. Safety was determined by clinical findings, assays of serum triglycerides, and liver ultrasound evaluation for the presence of steatosis.



All patients showed significant reduction in subcutaneous fat thickness within the treated area. The mean reduction in fat thickness after three treatments was 2.28+/-0.80 cm. Circumference was reduced by a mean of 3.95+/-1.99 cm. Weight was unchanged during the treatment and follow-up period. No adverse effects were observed.



This study shows the efficacy and safety of focused ultrasound, using the UltraShape Contour I, as a non-invasive transdermal method for reducing unwanted fat deposits in the body. Multiple treatments combined with appropriate patient and treatment area selection can produce dramatic improvements in body contour.
A Real-time Inversion Attack on the GMR-2 Cipher Used in the Satellite Phones

The GMR-2 cipher is a kind of stream cipher currently being used in some Inmarsat satellite phones. It has been proven that such cipher can be cracked using only one frame known keystream but with a moderate executing times. In this paper, we present a new thorough security analysis of the GMR-2 cipher. We first study the inverse properties and the relationship of the cipher’s components to reveal a bad one-way character of the cipher. Then by introducing a new concept called “valid key chain” according to the cipher’s key schedule, we for the first time propose a real-time inversion attack using one frame keystream. This attack contains three phases: (1) table generation (2) dynamic table looks-up, filtration and combination (3) verification. Our analysis shows that, using the proposed attack, the exhaustive search space for the 64-bit encryption key can be reduced to about 2 when one frame (15 bytes) keystream is available. Compared with previous known attacks, this inversion attack is much more efficient. Finally, the proposed attack are carried out on a 3.3GHz platform, and the experimental results demonstrate that the 64-bit encryption-key could be recovered in around 0.02s on average.
Beyond the One Step Greedy Approach in Reinforcement Learning

The famous Policy Iteration algorithm alternates between policy improvement and policy evaluation. Implementations of this algorithm with several variants of the latter evaluation stage, e.g, n-step and trace-based returns, have been analyzed in previous works. However, the case of multiple-step lookahead policy improvement, despite the recent increase in empirical evidence of its strength, has to our knowledge not been carefully analyzed yet. In this work, we introduce the first such analysis. Namely, we formulate variants of multiple-step policy improvement, derive new algorithms using these definitions and prove their convergence. Moreover, we show that recent prominent Reinforcement Learning algorithms fit well into our unified framework. We thus shed light on their empirical success and give a recipe for deriving new algorithms for future study.
Graphcut Texture Synthesis for Single-Image Superresolution

Texture synthesis has proven successful at imitating a wide variety of textures. Adding additional constraints (in the form of a low-resolution version of the texture to be synthesized) makes it possible to use texture synthesis methods for texture superresolution. The Single-image Superresolution Problem The problem we are trying to solve is the following: Given: a low-resolution image and high-resolution versions of each texture within the image
A Novel Iterative CT Reconstruction Approach Based on FBP Algorithm

The Filtered Back-Projection (FBP) algorithm and its modified versions are the most important techniques for CT (Computerized tomography) reconstruction, however, it may produce aliasing degradation in the reconstructed images due to projection discretization. The general iterative reconstruction (IR) algorithms suffer from their heavy calculation burden and other drawbacks. In this paper, an iterative FBP approach is proposed to reduce the aliasing degradation. In the approach, the image reconstructed by FBP algorithm is treated as the intermediate image and projected along the original projection directions to produce the reprojection data. The difference between the original and reprojection data is filtered by a special digital filter, and then is reconstructed by FBP to produce a correction term. The correction term is added to the intermediate image to update it. This procedure can be performed iteratively to improve the reconstruction performance gradually until certain stopping criterion is satisfied. Some simulations and tests on real data show the proposed approach is better than FBP algorithm or some IR algorithms in term of some general image criteria. The calculation burden is several times that of FBP, which is much less than that of general IR algorithms and acceptable in the most situations. Therefore, the proposed algorithm has the potential applications in practical CT systems.
Millibot Trains for Enhanced Mobility

The objective of this work is to enhance the mobility of small mobile robots by enabling them to link into a train configuration capable of crossing relatively large obstacles. In particular, we are building on Millibots, semiautonomous, tracked mobile sensing/communication platforms at the 5-cm scale previously developed at Carnegie Mellon University. TheMillibot Train concept provides couplers that allow the Millibot modules to engage/disengage under computer control and joint actuators that allow lifting of one module by another and control of the whole train shape in two dimensions. A manually configurable train prototype demonstrated the ability to climb standard stairs and vertical steps nearly half the train length. A fully functional module with powered joints has been developed and several have been built and tested. Construction of a set of six modules is well underway and will allow testing of the complete train in the near future. This paper focuses on the development, design, and construction of the electromechanical hardware for the Millibot Train.
Power-efficient beam sweeping for initial synchronization in mm-Wave wireless networks

This paper addresses the problem of initial synchronization of users in an indoor mm-Wave scenario. With the use of a massive number of antenna elements at access nodes, the resulting beams have narrow beam width. However, the transmission of individual narrow beams may cause poor coverage in some areas as the energy is concentrated over the direction of their main lobes. To cope with that, a beam sweep procedure using phased arrays is adopted. Access nodes simultaneously transmit individual beams until a certain area of interest is thoroughly scanned. Then, distant points are covered as each beam can radiate energy using an individual transmit power. The goal is to find the minimum power setting by adjusting the individual power levels so that users over the scanned area can observe a minimum received power level. Due to the combinatorial nature and high dimensionality, this paper describes a non-iterative two-stage algorithm to perform the beam sweeping with moderate complexity, which is compared with a baseline iterative selfish algorithm. Preliminary simulation results indicate that the two-stage algorithm may perform at least equally well as the baseline approach in terms of total transmit power, consuming about 7% less power in the used simulation setup, and without suffering from any convergence issue.
No one (cluster) size fits all: automatic cluster sizing for data-intensive analytics

Infrastructure-as-a-Service (IaaS) cloud platforms have brought two unprecedented changes to cluster provisioning practices. First, any (nonexpert) user can provision a cluster of any size on the cloud within minutes to run her data-processing jobs. The user can terminate the cluster once her jobs complete, and she needs to pay only for the resources used and duration of use. Second, cloud platforms enable users to bypass the traditional middleman---the system administrator---in the cluster-provisioning process. These changes give tremendous power to the user, but place a major burden on her shoulders. The user is now faced regularly with complex cluster sizing problems that involve finding the cluster size, the type of resources to use in the cluster from the large number of choices offered by current IaaS cloud platforms, and the job configurations that best meet the performance needs of her workload.
 In this paper, we introduce the Elastisizer, a system to which users can express cluster sizing problems as queries in a declarative fashion. The Elastisizer provides reliable answers to these queries using an automated technique that uses a mix of job profiling, estimation using black-box and white-box models, and simulation. We have prototyped the Elastisizer for the Hadoop MapReduce framework, and present a comprehensive evaluation that shows the benefits of the Elastisizer in common scenarios where cluster sizing problems arise.
'Some tactical problems in digital simulation' for the next 10 years

In his influential 1963 paper “Some Tactical Problems in Digital Simulation,” Conway identified important issues that became the pillars of research in simulation analysis methodology. Naturally these “problems” were a product of the applications of interest at the time, as well as the state of simulation and computing, much of which has changed dramatically. In light of those changes, we attempt to identify the tactical problems that might occupy simulation researchers for the next ten years.
Evaluation Methodology and Control Strategies for Improving Reliability of HEV Power Electronic System

The reliability prediction of hybrid electric vehicles (HEVs) is of paramount importance for planning, design, control, and operation management of vehicles, since it can provide an objective criterion for comparative evaluation of various configurations and topologies and can be used as an effective tool to improve the design and control of the overall system. This paper presents a mission-profile-dependent simulation model based on MATLAB for quantitatively assessing the reliability of the electric drivetrain of HEVs. This model takes into consideration the variable driving scenarios, dormant mode, electrical stresses, and thermal stresses. Therefore, more reliable and accurate prediction of system reliability has been achieved. The methodology is explained in detail, and the results of reliability assessment based on a series HEV are presented. Based on reliability analysis, two control strategies are proposed to increase the mean time to failure of HEV powertrains: 1) variable dc-link voltage control and 2) hybrid discontinuous pulsewidth modulation scheme. These novel control schemes reduce the power losses and thermal stresses of power converters, and consequently, enhance system reliability. Numerical simulation results verify the benefits of two proposed control strategies in terms of power losses and reliability.
The software model checker BLAST Applications to software engineering

Blast is an automatic verification tool for checking temporal safety properties of C programs. Given a C program and a temporal safety property, Blast either statically proves that the program satisfies the safety property, or provides an execution path that exhibits a violation of the property (or, since the problem is undecidable, does not terminate). Blast constructs, explores, and refines abstractions of the program state space based on lazy predicate abstraction and interpolation-based predicate discovery. This paper gives an introduction to Blast and demonstrates, through two case studies, how it can be applied to program verification and test-case generation. In the first case study, we use Blast to statically prove memory safety for C programs. We use CCured, a type-based memory-safety analyzer, to annotate a program with run-time assertions that check for safe memory operations. Then, we use Blast to remove as many of the run-time checks as possible (by proving that these checks never fail), and to generate execution scenarios that violate the assertions for the remaining run-time checks. In our second case study, we use Blast to automatically generate test suites that guarantee full coverage with respect to a given predicate. Given a C program and a target predicate p, Blast determines the program locations q for which there exists a program execution that reaches q with p true, and automatically generates a set of test vectors that D. Beyer (B) Simon Fraser University, Surrey, BC, Canada T. A. Henzinger EPFL, Lausanne, Switzerland R. Jhala University of California, San Diego, USA R. Majumdar University of California, Los Angeles, USA cause such executions. Our experiments show that Blast can provide automated, precise, and scalable analysis for C programs.
Neural Responding Machine for Short-Text Conversation

We propose Neural Responding Machine (NRM), a neural networ k-based response generator for Short-Text Conversation. NRM takes the general encoder-de coder framework: it formalizes the generation of response as a decoding process based on the lat ent representation of the input text, while both encoding and decoding are realized with recurren t n ural networks (RNN). The NRM is trained with a large amount of one-round conversation dat a collected from a microblogging service. Empirical study shows that NRM can generate gramma tically correct and content-wise appropriate responses to over 75% of the input text, outperf orming state-of-the-arts in the same setting, including retrieval-based and SMT-based models.