nils-herrmann/clean_scirepeval huggingface dataset:
Combining Generational and Conservative Garbage Collection: Framework and Implementations

Two key ideas in garbage collection are generational collection and conservative pointer-finding. Generational collection and conservative pointer-finding are hard to use together, because generational collection is usually expressed in terms of copying objects, while conservative pointer-finding precludes copying. We present a new framework for defining garbage collectors. When applied to generational collection, it generalizes the notion of younger/older to a partial order. It can describe traditional generational and conservative techniques, and lends itself to combining different techniques in novel ways. We study in particular two new garbage collectors inspired by this framework. Both these collectors use conservative pointer-finding. The first one is based on a rewrite of an existing trace-and-sweep collector to use one level of generation. The second one has a single parameter, which controls how objects are partitioned into generations: the value of this parameter can be changed dynamically with no overhead. We have implemented both collectors and present measurements of their performance in practice.
The Design and Implementation of a Log-Structured File System

This paper presents a new technique for disk storage management called a log-structured file system. A log-structured file system writes all modifications to disk sequentially in a log-like structure, thereby speeding up both file writing and crash recovery. The log is the only structure on disk; it contains indexing information so that files can be read back from the log efficiently. In order to maintain large free areas on disk for fast writing, we divide the log into segments and use a segment cleaner to compress the live information from heavily fragmented segments. We present a series of simulations that demonstrate the efficiency of a simple cleaning policy based on cost and benefit. We have implemented a prototype log-structured file system called Sprite LFS; it outperforms current Unix file systems by an order of magnitude for small-file writes while matching or exceeding Unix performance for reads and large writes. Even when the overhead for cleaning is included, Sprite LFS can use 70% of the disk bandwidth for writing, whereas Unix file systems typically can use only 5--10%.
The importance of incisor positioning in the esthetic smile: the smile arc.

The smile arc is defined as the relationship of the curvature of the incisal edges of the maxillary incisors and canines to the curvature of the lower lip in the posed smile. The ideal smile arc has the maxillary incisal edge curvature parallel to the curvature of the lower lip. Evaluation of anterior smile esthetics must include both static and dynamic evaluations of profile, frontal, and 45 degrees views to optimize both dental and facial appearance in orthodontic planning and treatment. This article presents the concept of the smile arc and how it relates to orthodontics-from the recognition of its importance, to its impact on orthodontic treatment planning, to how procedures and mechanics are adapted to optimize the appearance of the smile. Three cases are used to illustrate how treatment is directed, emphasizing how facial and smile goal setting go hand in hand.
SOUL: An Edge-Cloud System for Mobile Applications in a Sensor-Rich World

With the Internet of Things, sensors are becoming ever more ubiquitous, but interacting with them continues to present numerous challenges, particularly for applications running on resource-constrained devices like smartphones. The SOUL abstractions in this paper address two issues faced by such applications: (1) access to sensors with the levels of convenience needed for their ubiquitous, dynamic use, and only by parties authorized to do so, and (2) scalability in sensor access, given today's multitude of sensors. Toward this end, SOUL, first, introduces a new abstraction for the applications to transparently and uniformly access both on-device and ambient sensors with associated actuators. Second, potentially expensive sensor-related processing needs not just occur on smartphones, but can also leverage edge-and remote-cloud resources. Finally, SOUL provides access control methods that permit users to easily define access permissions for sensors, which leverages users' social ties and captures the context in which access requests are made. SOUL demonstrates that the applications on Android platforms can scale the use of 100s of sensors with performance and energy efficiency.
Prevalence and Risk Factors of Hypertension in Myanmar: A Systematic Review and Meta-Analysis

Hypertension (HPT) is the most common condition seen in primary care that can lead to health consequences and death if not detected early and treated appropriately.This study aimed to synthesize the prevalence, awareness, and control of HPT, and investigate the risk factors for HPT in Myanmar.We performed a meta-analysis of observational studies. Relevant studies were searched in electronic databases. The methodological quality of the included studies was assessed in 3 domains: selection bias, measurement bias, and bias related to data analysis. The overall prevalence and proportions was calculated using random-effect model of DerSimonian-Laird method. To identify the risk factors for HPT in Myanmar, we entered the ratio measures of the (adjusted) effect as a log odds ratio (OR) and the standard error of the log OR using generic inverse-variance weighting method. For stability of results, we performed leave-one-study-out sensitivity analysis by omitting individual studies one at a time from the meta-analysis.Seven studies (n = 20,901) were included in this analysis. Overall prevalence of HPT in Myanmar was 22% (95% confidence interval (CI): 14%-31.7%, I: 99.6%), stratified as 21.5% (95% CI: 14.1%-29.9%, I: 98.7%) in men and 22.7% (95% CI: 10.8%-34.6%, I: 99.5%) in women. Overall, prevalence of HPT increased with an advancing age of the participants. The proportions of awareness and controlled HPT were 55% (95% CI: 43%-67%, I: 97.7%) and 11% (95% CI: 6%-15%, I: 93.8%), respectively. A weak but significant association was observed between HPT and alcohol drinking (summary OR: 1.38, 95% CI: 1.14%-1.65, I: 0%) and smoking (summary OR: 1.32, 95% CI: 1.0%-1.74, I: 50%). In sensitivity analysis, when a study that made confirmation of HPT by the former World Health Organization criteria was dropped, the prevalence increased to 26% (95% CI: 20.8%-32.1%, I: 98.1%).HPT was considerably prevalent in Myanmar, while the levels of awareness and controlled HPT were low. Health promotion strategy tailored to the education on modifiable risk factors and establishment of blood pressure screening in primary health care context would be of immense value. Upcoming well-powered studies, using the standardized research design and covering more regions of the country are recommended.
Named data networking

Named Data Networking (NDN) is one of five projects funded by the U.S. National Science Foundation under its Future Internet Architecture Program. NDN has its roots in an earlier project, Content-Centric Networking (CCN), which Van Jacobson first publicly presented in 2006. The NDN project investigates Jacobson's proposed evolution from today's host-centric network architecture (IP) to a data-centric network architecture (NDN). This conceptually simple shift has far-reaching implications for how we design, develop, deploy, and use networks and applications. We describe the motivation and vision of this new architecture, and its basic components and operations. We also provide a snapshot of its current design, development status, and research challenges. More information about the project, including prototype implementations, publications, and annual reports, is available on named-data.net.
Adapting boosting for information retrieval measures

We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaRank, which has been shown to be empirically optimal for a widely used information retrieval measure. Our algorithm is based on boosted regression trees, although the ideas apply to any weak learners, and it is significantly faster in both train and test phases than the state of the art, for comparable accuracy. We also show how to find the optimal linear combination for any two rankers, and we use this method to solve the line search problem exactly during boosting. In addition, we show that starting with a previously trained model, and boosting using its residuals, furnishes an effective technique for model adaptation, and we give significantly improved results for a particularly pressing problem in web search—training rankers for markets for which only small amounts of labeled data are available, given a ranker trained on much more data from a larger market.
Min-cut based segmentation of point clouds

We present a min-cut based method of segmenting objects in point clouds. Given an object location, our method builds a k-nearest neighbors graph, assumes a background prior, adds hard foreground (and optionally background) constraints, and finds the min-cut to compute a foreground-background segmentation. Our method can be run fully automatically, or interactively with a user interface. We test our system on an outdoor urban scan, quantitatively evaluate our algorithm on a test set of about 1000 objects, and compare to several alternative approaches.
The architecture of virtual machines

A virtual machine can support individual processes or a complete system depending on the abstraction level where virtualization occurs. Some VMs support flexible hardware usage and software isolation, while others translate from one instruction set to another. Virtualizing a system or component -such as a processor, memory, or an I/O device - at a given abstraction level maps its interface and visible resources onto the interface and resources of an underlying, possibly different, real system. Consequently, the real system appears as a different virtual system or even as multiple virtual systems. Interjecting virtualizing software between abstraction layers near the HW/SW interface forms a virtual machine that allows otherwise incompatible subsystems to work together. Further, replication by virtualization enables more flexible and efficient and efficient use of hardware resources.
SPAIN: Security Patch Analysis for Binaries towards Understanding the Pain and Pills

Software vulnerability is one of the major threats to software security. Once discovered, vulnerabilities are often fixed by applying security patches. In that sense, security patches carry valuable information about vulnerabilities, which could be used to discover, understand and fix (similar) vulnerabilities. However, most existing patch analysis approaches work at the source code level, while binary-level patch analysis often heavily relies on a lot of human efforts and expertise. Even worse, some vulnerabilities may be secretly patched without applying CVE numbers, or only the patched binary programs are available while the patches are not publicly released. These practices greatly hinder patch analysis and vulnerability analysis. In this paper, we propose a scalable binary-level patch analysis framework, named SPAIN, which can automatically identify security patches and summarize patch patterns and their corresponding vulnerability patterns. Specifically, given the original and patched versions of a binary program, we locate the patched functions and identify the changed traces (i.e., a sequence of basic blocks) that may contain security or non-security patches. Then we identify security patches through a semantic analysis of these traces and summarize the patterns through a taint analysis on the patched functions. The summarized patterns can be used to search similar patches or vulnerabilities in binary programs. Our experimental results on several real-world projects have shown that: i) SPAIN identified security patches with high accuracy and high scalability, ii) SPAIN summarized 5 patch patterns and their corresponding vulnerability patterns for 5 vulnerability types, and iii) SPAIN discovered security patches that were not documented, and discovered 3 zero-day vulnerabilities.
Intelligent automatic generation control: Multi-agent Bayesian networks approach

A new intelligent agent based control scheme, using Bayesian networks (BNs), to design automatic generation control (AGC) system in a multi-area power system is addressed. Model independency and flexibility in specifying the control objectives, make the proposed approach as an attractive solution for AGC design in a real-world power system. The proposed control scheme is tested in simulation on a three areas power system and shows desirable performance. The results are also compared with the multi-agent reinforcement learning based AGC design technique.
Purchase Conversion Using Web Path Analysis

Clickstream data provides information about the sequence of pages viewed by a visitor as they move through a web site. A valuable facet of this data is the navigation or web path the user has chosen to traverse the web site. This path reflects a user’s goals, which we use to predict a user’s potential to purchase. One application of path analysis is to predict which users are likely to make a purchase as they browse through web site. An online retailer could use path analysis to improve the design of their web site and better target customers. In our research we propose a dynamic multinomial probit model to predict the path that a user will take through a web site. Our model is formulated within a hierarchical Bayesian framework to account for consumer’s observed and unobserved heterogeneity. Additionally, our model incorporates a mixture process whose multiple states are governed by a hidden Markov switching model to capture within user heterogeneity. Results show that more promotional messages on a page, removing the presence of price information on a page, and reducing the number of hypertext links can positively affect the purchase conversion rate of users who are surfing but these changes can negatively impact visitors who are purchase oriented. Therefore, online retailers should use different marketing mix tools, web design, and navigation paths to target the right customers at the right time to bolster their purchase conversion rates.
Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading

Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.
The chameleon effect: the perception-behavior link and social interaction.

The chameleon effect refers to nonconscious mimicry of the postures, mannerisms, facial expressions, and other behaviors of one's interaction partners, such that one's behavior passively and unintentionally changes to match that of others in one's current social environment. The authors suggest that the mechanism involved is the perception-behavior link, the recently documented finding (e.g., J. A. Bargh, M. Chen, & L. Burrows, 1996) that the mere perception of another's behavior automatically increases the likelihood of engaging in that behavior oneself. Experiment 1 showed that the motor behavior of participants unintentionally matched that of strangers with whom they worked on a task. Experiment 2 had confederates mimic the posture and movements of participants and showed that mimicry facilitates the smoothness of interactions and increases liking between interaction partners. Experiment 3 showed that dispositionally empathic individuals exhibit the chameleon effect to a greater extent than do other people.
Service-oriented middleware for the Future Internet: state of the art and research directions

Service-oriented computing is now acknowledged as a central paradigm for Internet computing, supported by tremendous research and technology development over the last 10 years. However, the evolution of the Internet, and in particular, the latest Future Internet vision, challenges the paradigm. Indeed, service-oriented computing has to face the ultra large scale and heterogeneity of the Future Internet, which are orders of magnitude higher than those of today’s service-oriented systems. This article aims at contributing to this objective by identifying the key research directions to be followed in light of the latest state of the art. This article more specifically focuses on research challenges for service-oriented middleware design, therefore, investigating service description, discovery, access, and composition in the Future Internet of services.
Fat grafting in facial rejuvenation.

Patients with significant facial atrophy and age-related loss of facial fat generally achieve suboptimal improvement from both surface treatments of facial skin and surgical lifts. Restoring lost facial volume by fat grafting is a powerful technique that is now acknowledged by most plastic surgeons and other physicians engaged in treating the aging face as one of the most important advances in aesthetic surgery. Properly performed, the addition of fat to areas of the face that have atrophied because of age or disease can produce a significant and sustained improvement in appearance that is unobtainable by other means.
IoT smart parking system for reducing green house gas emission

Rapid climate change results natural calamity and severe economic impact and threats to the life. Burning fossil fuels by the medium of transportation contributes 1/3 of portion in increasing greenhouse emission and leading to raise surface temperate. Commuters in and around the developed cities faces difficulties in finding parking lot due to lack of notification process and autonomous parking systems. This causes commuters to take multiple rounds trips to get the parking slot which causes burning additional fuel and ultimately producing excessive CO2 emission. This paper describes solution to smart parking system using Internet of Things (IoT) to override parking hazards and explains how does it helps to minimize emitting greenhouse gases. IoT enables smart parking system using the system of interconnected Raspberry Pi, Distance Sensor, Pi Camera devices together. This hardware reacts to one another collects data and transmits to cloud storage.
On the Robustness of a Neural Network

With the development of neural networks based machine learning and their usage in mission critical applications, voices are rising against the black box aspect of neural networks as it becomes crucial to understand their limits and capabilities. With the rise of neuromorphic hardware, it is even more critical to understand how a neural network, as a distributed system, tolerates the failures of its computing nodes, neurons, and its communication channels, synapses. Experimentally assessing the robustness of neural networks involves the quixotic venture of testing all the possible failures, on all the possible inputs, which ultimately hits a combinatorial explosion for the first, and the impossibility to gather all the possible inputs for the second.In this paper, we prove an upper bound on the expected error of the output when a subset of neurons crashes. This bound involves dependencies on the network parameters that can be seen as being too pessimistic in the average case. It involves a polynomial dependency on the Lipschitz coefficient of the neurons' activation function, and an exponential dependency on the depth of the layer where a failure occurs. We back up our theoretical results with experiments illustrating the extent to which our prediction matches the dependencies between the network parameters and robustness. Our results show that the robustness of neural networks to the average crash can be estimated without the need to neither test the network on all failure configurations, nor access the training set used to train the network, both of which are practically impossible requirements.
Atrial fibrillation detection by heart rate variability in Poincare plot

Atrial fibrillation (AFib) is one of the prominent causes of stroke, and its risk increases with age. We need to detect AFib correctly as early as possible to avoid medical disaster because it is likely to proceed into a more serious form in short time. If we can make a portable AFib monitoring system, it will be helpful to many old people because we cannot predict when a patient will have a spasm of AFib.



We analyzed heart beat variability from inter-beat intervals obtained by a wavelet-based detector. We made a Poincare plot using the inter-beat intervals. By analyzing the plot, we extracted three feature measures characterizing AFib and non-AFib: the number of clusters, mean stepping increment of inter-beat intervals, and dispersion of the points around a diagonal line in the plot. We divided distribution of the number of clusters into two and calculated mean value of the lower part by k-means clustering method. We classified data whose number of clusters is more than one and less than this mean value as non-AFib data. In the other case, we tried to discriminate AFib from non-AFib using support vector machine with the other feature measures: the mean stepping increment and dispersion of the points in the Poincare plot.



We found that Poincare plot from non-AFib data showed some pattern, while the plot from AFib data showed irregularly irregular shape. In case of non-AFib data, the definite pattern in the plot manifested itself with some limited number of clusters or closely packed one cluster. In case of AFib data, the number of clusters in the plot was one or too many. We evaluated the accuracy using leave-one-out cross-validation. Mean sensitivity and mean specificity were 91.4% and 92.9% respectively.



Because pulse beats of ventricles are less likely to be influenced by baseline wandering and noise, we used the inter-beat intervals to diagnose AFib. We visually displayed regularity of the inter-beat intervals by way of Poincare plot. We tried to design an automated algorithm which did not require any human intervention and any specific threshold, and could be installed in a portable AFib monitoring system.