nils-herrmann/clean_scirepeval huggingface dataset:
Centralized network utility maximization over aggregate flows

We study a network utility maximization (NUM) decomposition in which the set of flow rates is grouped by source-destination pairs. We develop theorems for both single-path and multipath cases, which relate an arbitrary NUM problem involving all flow rates to a simpler problem involving only the aggregate rates for each source-destination pair. The optimal aggregate flows are then apportioned among the constituent flows of each pair. This apportionment is simple for the case of a-fair utility functions. We also show how the decomposition can be implemented with the alternating direction method of multipliers (ADMM) algorithm.
Pharmacogenetics and adverse drug reactions

Polymorphisms in the genes that code for drug-metabolising enzymes, drug transporters, drug receptors, and ion channels can affect an individual's risk of having an adverse drug reaction, or can alter the efficacy of drug treatment in that individual. Mutant alleles at a single gene locus are the best studied individual risk factors for adverse drug reactions, and include many genes coding for drug-metabolising enzymes. These genetic polymorphisms of drug metabolism produce the phenotypes of "poor metabolisers" or "ultrarapid metabolisers" of numerous drugs. Together, such phenotypes make up a substantial proportion of the population. Pharmacogenomic techniques allow efficient analysis of these risk factors, and genotyping tests have the potential to optimise drug therapy in the future.
The Quest Data Mining System

The goal of the Quest project at the IBM Almaden Research center is to develop technology to enable a new breed of da&intensive decision-support applications. This paper is a capsule summary of the current functionality and architecture of the Quest data mining System. Our overall approach has been to identify basic data mining operations that cut across applications and develop fast, scalable algorithms for their execution (Agrawal, Imielinski, & Swami 1993a). We wanted our algorithms to:
On the Security of a Privacy-Aware Authentication Scheme for Distributed Mobile Cloud Computing Services

Recently, Tsai and Lo proposed a privacy aware authentication scheme for distributed mobile cloud computing services. It is claimed that the scheme achieves mutual authentication and withstands all major security threats. However, we first identify that their scheme fails to achieve mutual authentication, because it is vulnerable to the service provider impersonation attack. Beside this major defect, it also suffers from some minor design flaws, including the problem of biometrics misuse, wrong password, and fingerprint login, no user revocation facility when the smart card is lost/stolen. Some suggestions are provided to avoid these design flaws in the future design of authentication schemes.
Managing the knowledge lifecycle: A integrated knowledge management process model

The main purpose of this study is to propose an integrated conceptual model for exploring knowledge management process. Reviewing literature on knowledge management and tracing its historical background, definitions and dominant paradigms and analysis 32 frameworks of KM, a relatively integrated model of KM was presented in this paper. The study found many similarities of models and its presents a model which combine all the best features of the models analyzed. A 10-stage integrated model was proposed. These stages include some activities such as knowledge goal setting, identification, acquisition, creation, organization, sharing, evaluation, preservation, retention and updating and KM effectiveness evaluation. The findings can be used by managers in all organizational environments to implementation and auditing of KM practices.
Producing radiologist-quality reports for interpretable artificial intelligence

Current approaches to explaining the decisions of deep learning systems for medical tasks have focused on visualising the elements that have contributed to each decision. We argue that such approaches are not enough to “open the black box” of medical decision making systems because they are missing a key component that has been used as a standard communication tool between doctors for centuries: language. We propose a model-agnostic interpretability method that involves training a simple recurrent neural network model to produce descriptive sentences to clarify the decision of deep learning classifiers. We test our method on the task of detecting hip fractures from frontal pelvic x-rays. This process requires minimal additional labelling despite producing text containing elements that the original deep learning classification model was not specifically trained to detect. The experimental results show that: 1) the sentences produced by our method consistently contain the desired information, 2) the generated sentences are preferred by doctors compared to current tools that create saliency maps, and 3) the combination of visualisations and generated text is better than either alone.
Guidelines for haptic interpersonal communication applications: an exploration of foot interaction styles

A new method for researching haptic interaction styles is presented, based on a layered interaction model and a classification of existing devices. The method is illustrated by designing a new foot interaction device. The aim of which is to enhance non-verbal communication over a computer network. A layered protocols interaction model allows to consider all aspects of the haptic communication process: the intention to perform an action, limitations of the human body, and specifications of the communication device and the network. We demonstrate how this model can be used to derive design-guidelines by analyzing and classifying existing communication devices. By designing and evaluating a foot interaction device, we not only demonstrate that feet are suited for personal, concealed communication over a network, but also show the added value of the design-guidelines. Results of user tests provide clues for designing stimuli for foot interaction and indicate applications of foot communication devices.
Crown and post-free adhesive restorations for endodontically treated posterior teeth: from direct composite to endocrowns.

Coronal rehabilitation of endodontically treated posterior teeth is still a controversial issue. Although the classical crown supported by radicular metal posts remains widely spread in dentistry, its invasiveness has been largely criticized. New materials and therapeutic options based entirely on adhesion are nowadays available. They allow performing a more conservative, faster and less expensive dental treatment. All clinical cases presented in this paper are solved by using these modern techniques, from direct composite restorations to indirect endocrowns.
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web

Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach.
CovertBand: Activity Information Leakage using Music

This paper contributes a novel method for low-cost, covert physical sensing and, by doing so, surfaces new privacy threats. We demonstrate how a smartphone and portable speaker playing music with embedded, inaudible signals can track multiple individuals’ locations and activities both within a room and through barriers in 2D space. We achieve this by transforming a smartphone into an active sonar system that emits a combination of a sonar pulse and music and listens to the reflections off of humans in the environment. Our implementation, CovertBand, monitors minute changes to these reflections to track multiple people concurrently and to recognize different types of motion, leaking information about where people are in addition to what they may be doing. We evaluated CovertBand by running experiments in five homes in the Seattle area, showing that we can localize both single and multiple individuals through barriers. These tests show CovertBand can track walking subjects with a mean tracking error of 18 cm and subjects moving at a fixed position with an accuracy of 8 cm at up to 6 m in line-of-sight and 3 m through barriers. We test a variety of rhythmic motions such as pumping arms, jumping, and supine pelvic tilts in through-wall scenarios and show that they produce discernibly different spectrograms from walking in the acoustic reflections. In tests with 33 subjects, we also show that even in ideal scenarios, listeners were unlikely to detect a CovertBand attack.
Topic Extraction from Scientific Literature for Competency Management

We describe an approach towards automatic, dynamic and timecritical support for competency management and expertise search through topic extraction from scientific publications. In the use case we present, we focus on the automatic extraction of scientific topics and technologies from publicly available publications using web sites like Google Scholar. We discuss an experiment for our own organization, DFKI, as example of a knowledge organization. The paper presents evaluation results over a sample of 48 DFKI researchers that responded to our request for a-posteriori evaluation of automatically extracted topics. The results of this evaluation are encouraging and provided us with useful feedback for further improving our methods. The extracted topics can be organized in an association network that can be used further to analyze how competencies are interconnected, thereby enabling also a better exchange of expertise and competence between researchers.
AntMiner: Mining More Bugs by Reducing Noise Interference

Detecting bugs with code mining has proven to be an effective approach. However, the existing methods suffer from reporting serious false positives and false negatives. In this paper, we developed an approach called AntMiner to improve the precision of code mining by carefully preprocessing the source code. Specifically, we employ the program slicing technique to decompose the original source repository into independent sub-repositories, taking critical operations (automatically extracted from source code) as slicing criteria. In this way, the statements irrelevant to a critical operation are excluded from the corresponding sub-repository. Besides, various semantics-equivalent representations are normalized into a canonical form. Eventually, the mining process can be performed on a refined code database, and false positives and false negatives can be significantly pruned. We have implemented AntMiner and applied it to detect bugs in the Linux kernel. It reported 52 violations that have been either confirmed as real bugs by the kernel development community or fixed in new kernel versions. Among them, 41 cannot be detected by a widely used representative analysis tool Coverity. Besides, the result of a comparative analysis shows that our approach can effectively improve the precision of code mining and detect subtle bugs that have previously been missed.
Sphinx-4: a flexible open source framework for speech recognition

Sphinx-4 is a flexible, modular and pluggable framework to he lp foster new innovations in the core research of hidden Mark ov model (HMM) recognition systems. The design of Sphinx-4 is b a ed on patterns that have emerged from the design of past sys tems as well as new requirements based on areas that researchers c urr ntly want to explore. To exercise this framework, and to pr vide researchers with a ”research-ready” system, Sphinx-4 also includes several implementations of both simple and stateofhe-art techniques. The framework and the implementations are all f reely available via open source.
Low-Cost 60-GHz Smart Antenna Receiver Subsystem Based on Substrate Integrated Waveguide Technology

In this paper, a low-cost integrated 60-GHz switched-beam smart antenna subsystem is studied and demonstrated experimentally for the first time based on almost all 60-GHz substrate integrated waveguide (SIW) components including a slot antenna, 4×4 Butler matrix network, bandpass filter, sub-harmonically pumped mixer, and local oscillator (LO) source. In this study, an antenna array, a Butler matrix, and a bandpass filter are integrated and fabricated into one single substrate. Instead of using a 60-GHz LO source, a 30-GHz LO source is developed to drive a low-cost 60-GHz sub-harmonically pumped mixer. This 30-GHz LO circuit consists of 10-GHz SIW voltage-controlled oscillator and frequency tripler. Following the frequency down-conversion of four 60-GHz signals coming from the 4×4 Butler matrix and a comparison of the four IF signals executed in the digital processor based on the maximum received power criterion, control signals will be feed-backed to drive the single-pole four-throw switch array and then the beam is tuned in order to point toward the main beam of the transmit antenna. In this way, the arriving 60-GHz RF signal can be tracked effectively. All designed components are verified experimentally. The proposed smart receiver subsystem that integrates all those front-end components is concluded with satisfactory measured results.
Modelling of IoT Traffic and Its Impact on LoRaWAN

The recent growth of IoT use-cases in a wide array of industrial, utility and environmental applications has necessitated the need for connectivity solutions with diverse requirements. Connectivity through BLE, Zigbee and 6LoPAN are examples of short-range IoT deployments. But to provide connectivity to a high density of devices over larger coverage areas, Low-Power Wide-Area Network (LPWAN) technologies in both licensed as well as unlicensed bands have been considered. In this paper, we consider modelling the traffic from IoT devices connected through LPWAN technologies. Due to diverse applications of IoT, it is not trivial to have a single traffic model to represent all of them, but the traffic can be broadly classified as either periodic, event-triggered, or a combination of both. We evaluate the performance of LoRaWAN, one such LPWAN technology, in the presence of a hybrid of both traffic types, where the event propagates spatially over time. In a practical deployment of sensor based IoT devices, the devices are usually densely deployed to ensure sufficient & reliable measurement. Thereby, when an event occurs, they exhibit spatial & temporal correlation in their traffic rate due to the natural phenomena of the metric they measure. We use the CMMPP model to represent such characteristic traffic from independent IoT devices triggered by an event. The characteristics of LoRa, the physical layer of LoRaWAN, is abstracted based on required signal strength and interference thresholds for different modulation parameters. Through system simulations, we demonstrate that there is a significant performance hit in LoRaWAN based networks, during the occurrence of events. In particular, using the packet delivery rate (PDR) as the metric, we found that while the system was able to handle regular updates from the devices with a PDR > 80%, event-driven traffic nearly impaired the network causing the PDR to drop below 10%.
Convergent validity of the international physical activity questionnaire (IPAQ): meta-analysis.

The purpose of the present study was to use a meta-analytic approach to examine the convergent validity of the international physical activity questionnaire (IPAQ).



Systematic review by meta-analysis.



The relevant studies were surveyed from five electronic databases. Primary outcomes of interest were the product-moment correlation coefficients between IPAQ and other instruments. Five separate meta-analyses were performed for each physical activity (PA) category of IPAQ: walking, moderate PA (MPA), total moderate PA (TMPA), vigorous PA (VPA) and total PA (TPA). The corrected mean effect size (ESρ) unaffected by statistical artefacts (i.e., sampling error and reliability) was calculated for each PA category. Selected moderator variables were length of IPAQ (i.e., short and long form), reference period (i.e., last 7 d and usual week), mode of administration (i.e., interviewer and self-reported), language (i.e., English and translated) and instruments (i.e., accelerometer, pedometer and subjective measure).



A total of 152 ESρ across five PA categories were retrieved from twenty-one studies.



The results showed small- to medium-sized ESρ (0.27-0.49). The highest value was observed in VPA while the lowest value was found in MPA. The ESρ were differentiated by some of the moderator variables across PA categories.



The study shows the overall convergent validity of IPAQ within each PA category. Some differences in degree of convergent validity across PA categories and moderator variables imply that different research conditions should be taken into account prior to deciding on use of the appropriate type of IPAQ.
Arabic Opinion Mining Using Parallel Decision Trees

Opinion mining is an interested area of research, which epitomize the customer reviews of a product or service and express whether the opinions are positive or negative. Various methods have been proposed as classifiers for opinion mining such as Naïve Bayesian, and Support vector machine, these methods classify opinion without giving us the reasons about why the instance opinion is classified to certain class. Therefore, in our work, we investigate opinion mining of Arabic text at the document level, by applying decision trees classification classifier to have clear, understandable rule, also we apply parallel decision trees classifiers to have efficient results. We applied parallel decision trees on two Arabic corpus of text documents by using parallel implementation of RapidMiner tools. In case of applying parallel decision tree family on OCA we get the best results of accuracy (93.83%), f-measure (93.22) and consumed time 42 Sec at thread 4, one of the resulted rule is Urdu language lines. In case of applying parallel decision tree family on BHA we get the best results of accuracy (90.63%), f-measure (82.29) and consumed time 219 Sec at thread 4, one of the resulted rule is Urdu language lines.
Adopting the Cognitive Complexity Measure for Business Process Models

Business process models, often modelled using graphical languages like UML, serve as a base for communication between the stakeholders in the software development process. To fulfil this purpose, they should be easy to understand and easy to maintain. For this reason, it is useful to have measures that can give us some information about understandability, analyzability and maintainability of a business process model. Shao and Wang (2003) have proposed a cognitive complexity measure. It can be used to estimate the comprehension effort for understanding software. This paper discusses how these research results can be extended in order to analyze the cognitive complexity of graphical business process models
Monitoring critical temperatures in permanent magnet synchronous motors using low-order thermal models

Monitoring critical temperatures in electric motors is crucial for preventing shortened motor life spans due to excessive thermal stress. With regard to interior permanent magnet synchronous motors (IPMSM), critical temperatures typically occur in the magnets and in the stator end winding. As directly measuring temperatures, especially on the rotating part, is costly, sensitive and thus not applicable with respect to automotive applications, model-based approaches are preferred. In this paper, two low-order thermal models for accurate temperature estimations in the permanent magnets, the winding and the end winding are introduced and compared. The model parameters are estimated solely based on experimental data via a multistep identification approach for linear parameter-varying systems. The model performances are validated by extensive experimental results based on a high-speed PMSM typically used as traction motor in subcompact electric cars.
What to believe: Bayesian methods for data analysis

Although Bayesian models of mind have attracted great interest from cognitive scientists, Bayesian methods for data analysis have not. This article reviews several advantages of Bayesian data analysis over traditional null-hypothesis significance testing. Bayesian methods provide tremendous flexibility for data analytic models and yield rich information about parameters that can be used cumulatively across progressive experiments. Because Bayesian statistical methods can be applied to any data, regardless of the type of cognitive model (Bayesian or otherwise) that motivated the data collection, Bayesian methods for data analysis will continue to be appropriate even if Bayesian models of mind lose their appeal.
Physiological Effects of Deep Touch Pressure on Anxiety Alleviation : The Weighted Blanket Approach

The application of deep touch pressure (DTP) has been suggested to provide positive effects on anxiety modulation. However, empirical and theoretical evidence linked to the clinical effects of DTP are relatively rare. This study conducts a quantitative analysis of behavioral assessments and performs physiological measurements, including those of electrodermal activity and heart rate variability, to understand the modulation of the autonomic nervous system (ANS), and the orchestration of sympathetic (SNS) and parasympathetic nervous systems (PsNS). The results suggest that the activation of PsNS plays a critical role in ANS modulation. This study provides physiological evidence to support the positive clinical effects of DTP for reducing anxiety in dental environments.
Standard Cell Layout Regularity and Pin Access Optimization Considering Middle-of-Line

As minimum feature size and pitch spacing further decrease in advanced technology nodes, many new design constraints and challenges are introduced, such as regularity, middle of line (MOL) structures, and pin-access challenges. In this work, we propose a comprehensive study on standard cell layout regularity and pin access optimization. Given irregular cell layout from old technology nodes, our cell optimization tool can search unidirectional migrated result where the self-aligned double patterning (SADP) and MOL based design constraints are satisfied, and the pin-accessibility is optimized. This problem is formulated as a general integer linear programming (ILP), which may suffer from long runtime for some large standard cell cases. Therefore, we also develop a set of hybrid techniques to quickly search for high-quality solutions. The experimental results demonstrate the effectiveness of our approaches.