nils-herrmann/clean_scirepeval huggingface dataset:
Joint Intra and Multiple Description Coding for Packet Loss Resilient Video Transmission

Multiple description coding (MDC) is a technique for video transmission over error prone networks where the descriptions are routed over multiple paths. Intra coding such as MDC provides error resiliency but coding in this mode must be decided with care since it degrades the compression ratio. In this paper, we present our investigation results for a new intra coding approach in MDC. We have found that, in MDC streams, the best policy is to encode selective frames as I-frame instead of coding some macroblocks of frames in intra mode. In order to find the most suitable I-frame positions within a given video stream, we developed a cost function based on which intra/inter frame type is decided. The MDC scheme with the proposed intra coding criterion, with and without redundancy optimization, is implemented in the H.264/AVC reference software, JM16.0. Based on the experimental performance evaluation, we show that our method achieves higher average PSNR compared to the other optimized MDCs found in the literature.
SPIKING NEURAL NETWORKS SPIKING NEURAL NETWORKS

Infectious complications are the major cause of death in acute pancreatitis. Small bowel bacterial overgrowth and subsequent bacterial translocation are held responsible for the vast majority of these infections. Goal of this study is to determine whether selected probiotics are capable of preventing infectious complications without the disadvantages of antibiotic prophylaxis; antibiotic resistance and fungal overgrowth.  PROPATRIA is a double-blind, placebo-controlled randomised multicenter trial in which 200 patients will be randomly allocated to a multispecies probiotic preparation (Ecologic 641) or placebo. The study is performed in all 8 Dutch University Hospitals and 7 non-University hospitals. The study-product is administered twice daily through a nasojejunal tube for 28 days or until discharge. Patients eligible for randomisation are adult patients with a first onset of predicted severe acute pancreatitis: Imrie criteria 3 or more, CRP 150 mg/L or more, APACHE II score 8 or more. Exclusion criteria are post-ERCP pancreatitis, malignancy, infection/sepsis caused by a second disease, intra-operative diagnosis of pancreatitis and use of probiotics during the study. Administration of the study product is started within 72 hours after onset of abdominal pain. The primary endpoint is the total number of infectious complications. Secondary endpoints are mortality, necrosectomy, antibiotic resistance, hospital stay and adverse events. To demonstrate that probiotic prophylaxis reduces the proportion of patients with infectious complications from 50% to 30%, with alpha 0,05 and power 80%, a total sample size of 200 patients was calculated.  The PROPATRIA study is aimed to show a reduction in infectious complications due to early enteral use of multispecies probiotics in severe acute pancreatitis.  21 July 2004  29 September 2004
Establishing the computer-patient working alliance in automated health behavior change interventions.

Current user interfaces for automated patient and consumer health care systems can be improved by leveraging the results of several decades of research into effective patient-provider communication skills. A research project is presented in which several such "relational" skills - including empathy, social dialogue, nonverbal immediacy behaviors, and other behaviors to build and maintain good working relationships over multiple interactions - are explicitly designed into a computer interface within the context of a longitudinal health behavior change intervention for physical activity adoption. Results of a comparison among 33 subjects interacting near-daily with the relational system and 27 interacting near-daily with an identical system with the relational behaviors ablated, each for 30 days indicate, that the use of relational behaviors by the system significantly increases working alliance and desire to continue working with the system. Comparison of the above groups to another group of 31 subjects interacting with a control system near-daily for 30 days also indicated a significant increase in proactive viewing of health information.
Acceptance and use of electronic library services in ugandan universities

University libraries in Developing Countries (DCs), hampered by developmental problems, find it hard to provide electronic services. Donor communities have come in to bridge this technology gap by providing funds to university libraries for information technology infrastructure, enabling these university libraries to provide electronic library services to patrons. However, for these services to be utilized effectively, library end-users must accept and use them. To investigate this process in Uganda, this study modifies "The Unified Theory of Acceptance and Use of Technology" (UTAUT) by replacing "effort expectancy" and "voluntariness" with "relevancy", "awareness" and "benefits" factors. In so doing, we developed the Service Oriented UTAUT (SOUTAUT) model whose dependent constructs predict 133% of the variances in user acceptance and use of e-library services. The study revealed that relevancy moderated by awareness plays a major factor in acceptance and use of e-library services in DCs.
An Efficient Statistical Method for Image Noise Level Estimation

In this paper, we address the problem of estimating noise level from a single image contaminated by additive zero-mean Gaussian noise. We first provide rigorous analysis on the statistical relationship between the noise variance and the eigenvalues of the covariance matrix of patches within an image, which shows that many state-of-the-art noise estimation methods underestimate the noise level of an image. To this end, we derive a new nonparametric algorithm for efficient noise level estimation based on the observation that patches decomposed from a clean image often lie around a low-dimensional subspace. The performance of our method has been guaranteed both theoretically and empirically. Specifically, our method outperforms existing state-of-the-art algorithms on estimating noise level with the least executing time in our experiments. We further demonstrate that the denoising algorithm BM3D algorithm achieves optimal performance using noise variance estimated by our algorithm.
Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments

We study the incidence (rate of occurrence), persistence (rate of reoccurrence immediately after occurrence), and impact (effect on behavior) of students’ cognitive-affective states during their use of three different computer-based learning environments. Students’ cognitive-affective states are studied using different populations (Philippines, USA), different methods (quantitative field observation, self-report), and different types of learning environments (dialogue tutor, problemsolving game, and problem-solving based Intelligent Tutoring System). By varying the studies along these multiple factors, we can have greater confidence that findings which generalize across studies are robust. The incidence, persistence, and impact of boredom, frustration, confusion, engaged concentration, delight, and surprise were compared. We found that boredom was very persistent across learning environments and was associated with poorer learning and problem behaviors, such as gaming the system. Despite prior hypothesis to the contrary, frustration was less persistent, less associated with poorer learning, and did not appear to be an antecedent to gaming the system. Confusion and engaged concentration were the most common states within all three learning environments. Experiences of delight and surprise were rare. These findings suggest that significant effort should be put into detecting and responding to boredom and confusion, with a particular emphasis on developing pedagogical interventions to disrupt the “vicious cycles” which occur when a student becomes bored and remains bored for long periods of time.
Collaborative location and activity recommendations with GPS history data

With the increasing popularity of location-based services, such as tour guide and location-based social network, we now have accumulated many location data on the Web. In this paper, we show that, by using the location data based on GPS and users' comments at various locations, we can discover interesting locations and possible activities that can be performed there for recommendations. Our research is highlighted in the following location-related queries in our daily life: 1) if we want to do something such as sightseeing or food-hunting in a large city such as Beijing, where should we go? 2) If we have already visited some places such as the Bird's Nest building in Beijing's Olympic park, what else can we do there? By using our system, for the first question, we can recommend her to visit a list of interesting locations such as Tiananmen Square, Bird's Nest, etc. For the second question, if the user visits Bird's Nest, we can recommend her to not only do sightseeing but also to experience its outdoor exercise facilities or try some nice food nearby. To achieve this goal, we first model the users' location and activity histories that we take as input. We then mine knowledge, such as the location features and activity-activity correlations from the geographical databases and the Web, to gather additional inputs. Finally, we apply a collective matrix factorization method to mine interesting locations and activities, and use them to recommend to the users where they can visit if they want to perform some specific activities and what they can do if they visit some specific places. We empirically evaluated our system using a large GPS dataset collected by 162 users over a period of 2.5 years in the real-world. We extensively evaluated our system and showed that our system can outperform several state-of-the-art baselines.
A Single-Stage Two-Switch PFC Rectifier With Wide Output Voltage Range and Automatic AC Ripple Power Decoupling

Conventional single-phase power-factor-correction (PFC) rectifiers with active power decoupling capability typically require more than three active switches in their circuits. By exploring the concept of power-buffer cell, a new single-stage PFC rectifier with two active switches, one inductor and one small power-buffering capacitor is reported in this paper. The proposed converter can achieve high-power factor, wide output voltage range, and power decoupling function without using electrolytic capacitor. Additionally, an automatic power decoupling control scheme that is simple and easy to implement is proposed in this paper. The operating principle, control method, and design considerations of the proposed rectifier are also provided. A 100-W prototype with ac input voltage of 110 Vrms and a regulated dc output voltage ranging from 30 to 100 V has been successfully designed and practically tested. The experimental results show that with only a 15 μF power-buffering film capacitor, the proposed converter can achieve an input power factor of over 0.98, peak efficiency of 93.9%, and output voltage ripple of less than 3%, at 100-W output power.
BAG: Managing GPU as Buffer Cache in Operating Systems

This paper presents the design, implementation and evaluation of BAG, a system that manages GPU as the buffer cache in operating systems. Unlike previous uses of GPUs, which have focused on the computational capabilities of GPUs, BAG is designed to explore a new dimension in managing GPUs in heterogeneous systems where the GPU memory is an exploitable but always ignored resource. With the carefully designed data structures and algorithms, such as concurrent hashtable, log-structured data store for the management of GPU memory, and highly-parallel GPU kernels for garbage collection, BAG achieves good performance under various workloads. In addition, leveraging the existing abstraction of the operating system not only makes the implementation of BAG non-intrusive, but also facilitates the system deployment.
0 User Modeling on Demographic Attributes in Big Mobile Social Networks

Users with demographic profiles in social networks offer the potential to understand the social principles that underpin our highly connected world, from individuals, to groups, to societies. In this paper, we harness the power of network and data sciences to model the interplay between user demographics and social behavior, and further study to what extent users’ demographic profiles can be inferred from their mobile communication patterns. By modeling over 7 million users and 1 billion mobile communication records, we find that during the active dating period (i.e., 18 – 35 years old), users are active in broadening social connections with males and females alike, while after reaching 35 years of age people tend to keep small, closed, and same-gender social circles. Further, we formalize the demographic prediction problem of inferring users’ gender and age simultaneously. We propose a factor graph-based WhoAmI method to address the problem by leveraging not only the correlations between network features and users’ gender/age, but also the interrelations between gender and age. In addition, we identify a new problem—coupled network demographic prediction across multiple mobile operators—and present a coupled variant of the WhoAmI method to address its unique challenges. Our extensive experiments demonstrate both the effectiveness, scalability, and applicability of the WhoAmI methods. Finally, our study finds a greater than 80% potential predictability for inferring users’ gender from phone call behavior and 73% for users’ age from text messaging interactions. CCS Concepts: rInformation systems → Data mining; rHuman-centered computing → Collaborative and social computing; Social networks; Mobile computing; rSocial and professional topics → User characteristics; Gender; Age; rApplied computing→ Sociology;
A BLAS-3 Version of the QR Factorization with Column Pivoting

The QR factorization with column pivoting (QRP), originally suggested by Golub and Businger in 1965, is a popular approach to computing rank-revealing factorizations. Using BLAS Level 1, it was implemented in LINPACK, and, using BLAS Level 2, in LAPACK. While the BLAS Level 2 version delivers, in general, superior performance, it may result in worse performance for large matrix sizes due to cache e ects. We introduce a modi cation of the QRP algorithm which allows the use of BLAS Level 3 kernels while maintaining the numerical behavior of the LINPACK and LAPACK implementations. Experimental comparisons of this approach with the LINPACK and LAPACK implementations on IBM RS/6000, SGI R8000, and DEC Alpha platforms show considerable performance improvements.
Control of distributed generation systems - Part II: Load sharing control

This work is concerned with the control strategy for the parallel operation of distributed generation systems (DGS) in a standalone ac power supply. The proposed control method uses only low-bandwidth data communication signals between each generation system in addition to the locally measurable feedback signals. This is achieved by combining two control methods: droop control method and average power control method. The average power method with slow update rate is used in order to overcome the sensitivity about voltage and current measurement errors. In addition, a harmonic droop scheme for sharing harmonic content of the load currents is proposed based on the voltages and currents control algorithm. Experimental and simulation studies using two parallel three-phase pulsewidth modulation (PWM) inverters are presented to show the effectiveness of the proposed control.
Database updating through user feedback in fingerprint-based Wi-Fi location systems

Wi-Fi fingerprinting is a technique which can provide location in GPS-denied environments, relying exclusively on Wi-Fi signals. It first requires the construction of a database of “fingerprints”, i.e. signal strengths from different access points (APs) at different reference points in the desired coverage area. The location of the device is then obtained by measuring the signal strengths at its location, and comparing it with the different reference fingerprints in the database. The main disadvantage of this technique is the labour required to build and maintain the fingerprints database, which has to be rebuilt every time a significant change in the wireless environment occurs, such as installation or removal of new APs, changes in the layout of a building, etc. This paper investigates a new method to utilise user feedback as a way of monitoring changes in the wireless environment. It is based on a system of “points” given to each AP in the database. When an AP is switched off, the number of points associated with that AP will gradually reduce as the users give feedback, until it is eventually deleted from the database. If a new AP is installed, the system will detect it and update the database with new fingerprints. Our proposed system has two main advantages. First it can be used as a tool to monitor the wireless environment in a given place, detecting faulty APs or unauthorised installation of new ones. Second, it regulates the size of the database, unlike other systems where feedback is only used to insert new fingerprints in the database.
Clustal W and Clustal X version 2.0

The Clustal W and Clustal X multiple sequence alignment programs have been completely rewritten in C++. This will facilitate the further development of the alignment algorithms in the future and has allowed proper porting of the programs to the latest versions of Linux, Macintosh and Windows operating systems.


AVAILABILITY
The programs can be run on-line from the EBI web server: http://www.ebi.ac.uk/tools/clustalw2. The source code and executables for Windows, Linux and Macintosh computers are available from the EBI ftp site
Articulatory feature-based pronunciation modeling

Spoken language, especially conversational speech, is characterized by great variability in word pronunciation, including many variants that differ grossly from dictionary prototypes. This is one factor in the poor performance of automatic speech recognizers on conversational speech, and it has been very difficult to mitigate in traditional phonebased approaches to speech recognition. An alternative approach, which has been studied by ourselves and others, is one based on sub-phonetic features rather than phones. In such an approach, a word’s pronunciation is represented as multiple streams of phonological features rather than a single stream of phones. Features may correspond to the positions of the speech articulators, such as the lips and tongue, or may be more abstract categories such as manner and place. This article reviews our work on a particular type of articulatory feature-based pronunciation model. The model allows for asynchrony between features, as well as per-feature substitutions, making it more natural to account for many pronunciation changes that are difficult to handle with phone-based models. Such models can be efficiently represented as dynamic Bayesian networks. The feature-based models improve significantly over phone-based counterparts in terms of frame perplexity and lexical access accuracy. The remainder of the article discusses related work and future directions.
Experiences in a 3G network: interplay between the wireless channel and applications

We present an experimental characterization of the physical and MAC layers in CDMA 1xEV-DO and their impact on transport layer performance. The 1xEV-DO network is currently the fastest mobile broadband cellular network, offering data rates of up to 3.1 Mbps for both stationary and mobile users. These rates are achieved by using novel capacity enhancement techniques at the lower layers. Specifically, 1xEV-DO incorporates rapid channel rate adaptation in response to signal conditions, and opportunistic scheduling to exploit channel fluctuations. Although shown to perform well in isolation, there is no comprehensive literature that examines the impact of these features on transport layer and application performance in real networks.
 We take the first step in addressing this issue through a large set of experiments conducted on a commercial 1xEV-DO network. Our evaluation includes both stationary and mobile scenarios wherein we transferred data using four popular transport protocols: TCPReno, TCP-Vegas, TCP-Westwood, and TCP-Cubic, and logged detailed measurements about wireless channel level characteristics as well as transport layer performance. We analyzed data from several days of experiments and inferred the properties of the physical, MAC and transport layers, as well as potential interactions between them. We find that the wireless channel data rate shows significant variability over long time scales on the order of hours, but retains high memory and predictability over small time scales on the order of milliseconds. We also find that loss-based TCP variants are largely unaffected by channel variations due to the presence of large buffers, and hence able to achieve in excess of 80% of the system capacity.