nils-herrmann/clean_scirepeval huggingface dataset:
A logic for reasoning about time and reliability

We present a logic for stating properties such as, “after a request for service there is at least a 98% probability that the service will be carried out within 2 seconds”. The logic extends the temporal logic CTL by Emerson, Clarke and Sistla with time and probabilities. Formulas are interpreted over discrete time Markov chains. We give algorithms for checking that a given Markov chain satisfies a formula in the logic. The algorithms require a polynomial number of arithmetic operations, in size of both the formula and the Markov chain. A simple example is included to illustrate the algorithms.
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies

Reinforcement learning is a promising new approach for automatically developing effective policies for real-time self-management. RL can achieve superior performance to traditional methods, while requiring less built-in domain knowledge. Several case studies from real and simulated systems management applications demonstrate RL's promises and challenges. These studies show that standard online RL can learn effective policies in feasible training times. Moreover, a Hybrid RL approach can profit from any knowledge contained in an existing policy by training on the policy's observable behavior, without needing to interface directly to such knowledge
Impact of a population-based HPV vaccination program on cervical abnormalities: a data linkage study

Australia was one of the first countries to introduce a publicly funded national human papillomavirus (HPV) vaccination program that commenced in April 2007, using the quadrivalent HPV vaccine targeting 12- to 13-year-old girls on an ongoing basis. Two-year catch-up programs were offered to 14- to 17- year-old girls in schools and 18- to 26-year-old women in community-based settings. We present data from the school-based program on population-level vaccine effectiveness against cervical abnormalities in Victoria, Australia.



Data for women age-eligible for the HPV vaccination program were linked between the Victorian Cervical Cytology Registry and the National HPV Vaccination Program Register to create a cohort of screening women who were either vaccinated or unvaccinated. Entry into the cohort was 1 April 2007 or at first Pap test for women not already screening. Vaccine effectiveness (VE) and hazard ratios (HR) for cervical abnormalities by vaccination status between 1 April 2007 and 31 December 2011 were calculated using proportional hazards regression.



The study included 14,085 unvaccinated and 24,871 vaccinated women attending screening who were eligible for vaccination at school, 85.0% of whom had received three doses. Detection rates of histologically confirmed high-grade (HG) cervical abnormalities and high-grade cytology (HGC) were significantly lower for vaccinated women (any dose) (HG 4.8 per 1,000 person-years, HGC 11.9 per 1,000 person-years) compared with unvaccinated women (HG 6.4 per 1,000 person-years, HGC 15.3 per 1,000 person-years) HR 0.72 (95% CI 0.58 to 0.91) and HR 0.75 (95% CI 0.65 to 0.87), respectively. The HR for low-grade (LG) cytological abnormalities was 0.76 (95% CI 0.72 to 0.80). VE adjusted a priori for age at first screening, socioeconomic status and remoteness index, for women who were completely vaccinated, was greatest for CIN3+/AIS at 47.5% (95% CI 22.7 to 64.4) and 36.4% (95% CI 9.8 to 55.1) for women who received any dose of vaccine, and was negatively associated with age. For women who received only one or two doses of vaccine, HRs for HG histology were not significantly different from 1.0, although the number of outcomes was small.



A population-based HPV vaccination program in schools significantly reduced cervical abnormalities for vaccinated women within five years of implementation, with the greatest vaccine effectiveness observed for the youngest women.
Updated Three-Column Concept in surgical treatment for tibial plateau fractures - A prospective cohort study of 287 patients.

This study introduces an updated Three-Column Concept for the classification and treatment of complex tibial plateau fractures. A combined preoperative assessment of fracture morphology and injury mechanism is utilized to determine surgical approach, implant placement and fixation sequence. The effectiveness of this updated concept is demonstrated through evaluation of both clinical and radiographic outcome measures.



From 2008 to 2012, 355 tibial plateau fractures were treated using the updated Three-Column Concept. Standard radiographic and computed tomography imaging are used to systematically assess and classify fracture patterns as follows: (1) identify column(s) injured and locate associated articular depression or comminution, (2) determine injury mechanism including varus/valgus and flexion/extension forces, and (3) determine surgical approach(es) as well as the location and function of applied fixation. Quality and maintenance of reduction and alignment, fracture healing, complications, and functional outcomes were assessed.



287 treated fractures were followed up for a mean period of 44.5 months (range: 22-96). The mean time to radiographic bony union and full weight-bearing was 13.5 weeks (range: 10-28) and 14.8 weeks (range: 10-26) respectively. The average functional Knee Society Score was 93.0 (range: 80-95). The average range of motion of the affected knees was 1.5-121.5°. No significant difference was found in knee alignment between immediate and 18-month post-operative measurements. Additionally, no significant difference was found in functional scores and range of motion between one, two and three-column fracture groups. Twelve patients suffered superficial infection, one had limited skin necrosis and two had wound dehiscence, that healed with nonoperative management. Intraoperative vascular injury occurred in two patients. Fixation of failure was not observed in any of the fractures treated.



An updated Three-Column Concept assessing fracture morphology and injury mechanism in tandem can be used to guide surgical treatment of tibial plateau fractures. Limited results demonstrate successful application of biologically friendly fixation constructs while avoiding fixation failure and associated complications of both simple and complex tibial plateau fractures.
Aligning temporal data by sentinel events: discovering patterns in electronic health records

Electronic Health Records (EHRs) and other temporal databases contain hidden patterns that reveal important cause-and-effect phenomena. Finding these patterns is a challenge when using traditional query languages and tabular displays. We present an interactive visual tool that complements query formulation by providing operations to align, rank and filter the results, and to visualize estimates of the intervals of validity of the data. Display of patient histories aligned on sentinel events (such as a first heart attack) enables users to spot precursor, co-occurring, and aftereffect events. A controlled study demonstrates the benefits of providing alignment (with a 61% speed improvement for complex tasks). A qualitative study and interviews with medical professionals demonstrates that the interface can be learned quickly and seems to address their needs.
Qualitative Process Theory

Objects move, collide, flow, bend, heat up, cool down, stretch, compress, and boil. These and other things that cause changes in objects over time are intuitively characterized as processes. To understand commonsense physical reasoning and make programs that interact with the physical world as well as people do we must understand qualitative reasoning about processes, when they will occur, their effects, and when they will stop. Qualitative process theory defines a simple notion of physical process that appears useful as a language in which to write dynamical theories. Reasoning about processes also motivates a new qualitative representation ]:or quantity in terms of inequalities, called the quantity space. This paper describes the basic concepts of qualitative process theory, several different kinds of reasoning that can be performed with them, and discusses its implications for causal reasoning. Several extended examples illustrate the utility of the theory, including figuring out that a boiler can blow up, that an oscillator with friction will eventually stop, and how to say that you can pull with a string, but not push with it. I . I n t r o d u c t i o n Many kinds of changes occur in physical situations. Objects move, collide, flow, bend, heat up, cool down, stretch, compress, and boil. These and the other things that cause changes in objects over time are intuitively characterized by processes. Much of formal physics consists of characterizations of processes by differential equations that describe how the parameters of objects change over time. But the notion of process is richer and more structured than this. We often reach conclusions about physical processes based on very little information. For example, we know that if we heat water in a sealed container the water can eventually boil, and if we continue to do so the container can explode. To understand commonsense physical reasoning we must understand how to reason qualitatively about processes, when they will occur, their effects, and when they will stop. This paper describes qual i ta t ive process theory, which I have been developing for this purpose. In addition to providing a major part of the representational framework for * Present address: Department of Computer Science, 1304 West Springfield Avenue, Urbana, IL 61801, U.S.A. Artificial Intelligence 24 (1984) 85-168 0004-
Optimizing CNN-Based Object Detection Algorithms on Embedded FPGA Platforms

Algorithms based on Convolutional Neural Network (CNN) have recently been applied to object detection applications, greatly improving their performance. However, many devices intended for these algorithms have limited computation resources and strict power consumption constraints, and are not suitable for algorithms designed for GPU workstations. This paper presents a novel method to optimise CNN-based object detection algorithms targeting embedded FPGA platforms. Given parameterised CNN hardware modules, an optimisation flow takes network architectures and resource constraints as input, and tunes hardware parameters with algorithm-specific information to explore the design space and achieve high performance. The evaluation shows that our design model accuracy is above 85% and, with optimised configuration, our design can achieve 49.6 times speed-up compared with software implementation.
Energy-efficient Machine Learning in Silicon: A Communications-inspired Approach

This position paper advocates a communicationsinspired approach to the design of machine learning systems on energy-constrained embedded ‘always-on’ platforms. The communicationsinspired approach has two versions 1) a deterministic version where existing low-power communication IC design methods are repurposed, and 2) a stochastic version referred to as Shannon-inspired statistical information processing employing information-based metrics, statistical error compensation (SEC), and retraining-based methods to implement ML systems on stochastic circuit/device fabrics operating at the limits of energy-efficiency. The communications-inspired approach has the potential to fully leverage the opportunities afforded by ML algorithms and applications in order to address the challenges inherent in their deployment on energy-constrained platforms.
Detailed diagnosis in enterprise networks

By studying trouble tickets from small enterprise networks, we conclude that their operators need detailed fault diagnosis. That is, the diagnostic system should be able to diagnose not only generic faults (e.g., performance-related) but also application specific faults (e.g., error codes). It should also identify culprits at a fine granularity such as a process or firewall configuration. We build a system, called NetMedic, that enables detailed diagnosis by harnessing the rich information exposed by modern operating systems and applications. It formulates detailed diagnosis as an inference problem that more faithfully captures the behaviors and interactions of fine-grained network components such as processes. The primary challenge in solving this problem is inferring when a component might be impacting another. Our solution is based on an intuitive technique that uses the joint behavior of two components in the past to estimate the likelihood of them impacting one another in the present. We find that our deployed prototype is effective at diagnosing faults that we inject in a live environment. The faulty component is correctly identified as the most likely culprit in 80% of the cases and is almost always in the list of top five culprits.
Human neural systems for face recognition and social communication

Face perception is mediated by a distributed neural system in humans that consists of multiple, bilateral regions. The functional organization of this system embodies a distinction between the representation of invariant aspects of faces, which is the basis for recognizing individuals, and the representation of changeable aspects, such as eye gaze, expression, and lip movement, which underlies the perception of information that facilitates social communication. The system also has a hierarchical organization. A core system, consisting of occipitotemporal regions in extrastriate visual cortex, mediates the visual analysis of faces. An extended system consists of regions from neural systems for other cognitive functions that can act in concert with the core system to extract meaning from faces. Of regions in the extended system for face perception, the amygdala plays a central role in processing the social relevance of information gleaned from faces, particularly when that information may signal a potential threat.
Comparing the performance of web server architectures

In this paper, we extensively tune and then compare the performance of web servers based on three different server architectures. The μserver utilizes an event-driven architecture, Knot uses the highly-efficient Capriccio thread library to implement a thread-per-connection model, and WatPipe uses a hybrid of events and threads to implement a pipeline-based server that is similar in spirit to a staged event-driven architecture (SEDA) server like Haboob.
 We describe modifications made to the Capriccio thread library to use Linux's zero-copy sendfile interface. We then introduce the SY mmetric Multi-Processor Event Driven (SYMPED) architecture in which relatively minor modifications are made to a single process event-driven (SPED) server (the μserver) to allow it to continue processing requests in the presence of blocking due to disk accesses. Finally, we describe our C++ implementation of WatPipe, which although utilizing a pipeline-based architecture, excludes the dynamic controls over event queues and thread pools used in SEDA. When comparing the performance of these three server architectures on the workload used in our study, we arrive at different conclusions than previous studies. In spite of recent improvements to threading libraries and our further improvements to Capriccio and Knot, both the event-based μserver and pipeline-based Wat-Pipe server provide better throughput (by about 18%). We also observe that when using blocking sockets to send data to clients, the performance obtained with some architectures is quite good and in one case is noticeably better than when using non-blocking sockets.
Automatic test generation for mutation testing on database applications

To assure high quality of database applications, testing database applications remains the most popularly used approach. In testing database applications, tests consist of both program inputs and database states. Assessing the adequacy of tests allows targeted generation of new tests for improving their adequacy (e.g., fault-detection capabilities). Comparing to code coverage criteria, mutation testing has been a stronger criterion for assessing the adequacy of tests. Mutation testing would produce a set of mutants (each being the software under test systematically seeded with a small fault) and then measure how high percentage of these mutants are killed (i.e., detected) by the tests under assessment. However, existing test-generation approaches for database applications do not provide sufficient support for killing mutants in database applications (in either program code or its embedded or resulted SQL queries). To address such issues, in this paper, we propose an approach called MutaGen that conducts test generation for mutation testing on database applications. In our approach, we first apply an existing approach that correlates various constraints within a database application through constructing synthesized database interactions and transforming the constraints from SQL queries into normal program code. Based on the transformed code, we generate program-code mutants and SQL-query mutants, and then derive and incorporate query-mutant-killing constraints into the transformed code. Then, we generate tests to satisfy query-mutant-killing constraints. Evaluation results show that MutaGen can effectively kill mutants in database applications, and MutaGen outperforms existing test-generation approaches for database applications in terms of strong mutant killing.
Semi-Supervised Nonlinear Hashing Using Bootstrap Sequential Projection Learning

In this paper, we study the effective semi-supervised hashing method under the framework of regularized learning-based hashing. A nonlinear hash function is introduced to capture the underlying relationship among data points. Thus, the dimensionality of the matrix for computation is not only independent from the dimensionality of the original data space but also much smaller than the one using linear hash function. To effectively deal with the error accumulated during converting the real-value embeddings into the binary code after relaxation, we propose a semi-supervised nonlinear hashing algorithm using bootstrap sequential projection learning which effectively corrects the errors by taking into account of all the previous learned bits holistically without incurring the extra computational overhead. Experimental results on the six benchmark data sets demonstrate that the presented method outperforms the state-of-the-art hashing algorithms at a large margin.
A state space model for online polyphonic audio-score alignment

We present a novel online audio-score alignment approach for multi-instrument polyphonic music. This approach uses a 2-dimensional state vector to model the underlying score position and tempo of each time frame of the audio performance. The process model is defined by dynamic equations to transition between states. Two representations of the observed audio frame are proposed, resulting in two observation models: a multi-pitch-based and a chroma-based. Particle filtering is used to infer the hidden states from observations. Experiments on 150 music pieces with polyphony from one to four show the proposed approach outperforms an existing offline global string alignment-based score alignment approach. Results also show that the multi-pitch-based observation model works better than the chroma-based one.
Convexified Convolutional Neural Networks

We describe the class of convexified convolutional neural networks (CCNNs), which capture the parameter sharing of convolutional neural networks in a convex manner. By representing the nonlinear convolutional filters as vectors in a reproducing kernel Hilbert space, the CNN parameters can be represented in terms of a lowrank matrix, and the rank constraint can be relaxed so as to obtain a convex optimization problem. For learning two-layer convolutional neural networks, we prove that the generalization error obtained by a convexified CNN converges to that of the best possible CNN. For learning deeper networks, we train CCNNs in a layerwise manner. Empirically, we find that CCNNs achieve competitive or better performance than CNNs trained by backpropagation, SVMs, fully-connected neural networks, stacked denoising auto-encoders, and other baseline methods.
Ad-Blocking and Counter Blocking: A Slice of the Arms Race

Adblocking tools like Adblock Plus continue to rise in popularity, potentially threatening the dynamics of advertising revenue streams. In response, a number of publishers have ramped up efforts to develop and deploy mechanisms for detecting and/or counter-blocking adblockers (which we refer to as anti-adblockers), effectively escalating the online advertising arms race. In this paper, we develop a scalable approach for identifying third-party services shared across multiple websites and use it to provide a first characterization of antiadblocking across the Alexa Top-5K websites. We map websites that perform anti-adblocking as well as the entities that provide anti-adblocking scripts. We study the modus operandi of these scripts and their impact on popular adblockers. We find that at least 6.7% of websites in the Alexa Top-5K use anti-adblocking scripts, acquired from 12 distinct entities – some of which have a direct interest in nourishing the online advertising industry.
Probabilistic text analytics framework for information technology service desk tickets

Ticket annotation and search has become an essential research subject for the successful delivery of IT operational analytics. Millions of tickets are created yearly to address business users' IT related problems. In IT service desk management, it is critical to first capture the pain points for a group of tickets to determine root cause; secondly, to obtain the respective distributions in order to layout the priority of addressing these pain points. An advanced ticket analytics system utilizes a combination of topic modeling, clustering and Information Retrieval (IR) technologies to address the above issues and the corresponding architecture which integrates of these features will allow for a wider distribution of this technology and progress to a significant financial benefit for the system owner. Topic modeling has been used to extract topics from given documents; in general, each topic is represented by a unigram language model. However, it is not clear how to interpret the results in an easily readable/understandable way until now. Due to the inefficiency to render top concepts using existing techniques, in this paper, we propose a probabilistic framework, which consists of language modeling (especially the topic models), Part-Of-Speech (POS) tags, query expansion, retrieval modeling and so on for the practical challenge. The rigorously empirical experiments demonstrate the consistent and utility performance of the proposed method on real datasets.