nils-herrmann/clean_scirepeval huggingface dataset:
Hierarchical Ray Tracing for Fast Volumetric Next-Best-View Planning

A mobile robot must have the ability of building a representation of its environment and the objects in it. To build a three-dimensional (3D) model of a physical object, several scans must be taken at different locations. Selecting each location is the next-best-view problem. Search based methods, where candidate views are generated and evaluated by a utility function, are a solution. However, such methods are slow for high resolution models given that the evaluation requires visibility computation in 3D. We propose a scene representation by octrees with a hierarchical ray tracing that reduces the visibility computation time. Such method performs a coarse ray tracing, except for the interesting volumes where a finer resolution is applied. The method decreases the computation time at least one order of magnitude. Saving time with this method leads to evaluate more constraints and more candidate views in high resolution models.
dCat: dynamic cache management for efficient, performance-sensitive infrastructure-as-a-service

In the modern multi-tenant cloud, resource sharing increases utilization but causes performance interference between tenants. More generally, performance isolation is also relevant in any multi-workload scenario involving shared resources. Last level cache (LLC) on processors is shared by all CPU cores in x86, thus the cloud tenants inevitably suffer from the cache flush by their noisy neighbors running on the same socket. Intel Cache Allocation Technology (CAT) provides a mechanism to assign cache ways to cores to enable cache isolation, but its static configuration can result in underutilized cache when a workload cannot benefit from its allocated cache capacity, and/or lead to sub-optimal performance for workloads that do not have enough assigned capacity to fit their working set.
 In this work, we propose a new dynamic cache management technology (dCat) to provide strong cache isolation with better performance. For each workload, we target a consistent, minimum performance bound irrespective of others on the socket and dependent only on its rightful share of the LLC capacity. In addition, when there is spare capacity on the socket, or when some workloads are not obtaining beneficial performance from their cache allocation, dCat dynamically reallocates cache space to cache-intensive workloads. We have implemented dCat in Linux on top of CAT to dynamically adjust cache mappings. dCat requires no modifications to applications so that it can be applied to all cloud workloads. Based on our evaluation, we see an average of 25% improvement over shared cache and 15.7% over static CAT for selected, memory intensive, SPEC CPU2006 workloads. For typical cloud workloads, with Redis we see 57.6% improvement (over shared LLC) and 26.6% improvement (over static partition) and with ElasticSearch we see 11.9% improvement over both.
Vaginal Outlet Obstruction - A Review of Cases

The paper focuses on anatomical varieties of developmental anomalies of female vagina’s outlet, emphasizing the role of their early recognition in preventing retrograde menstruation by surgical treatment at the beginning of puberty. Three different types of vaginal outlet obstruction are described: imperforate hymen from the group of hymen misdevelopment, transversal vaginal septum from the group of failed transverse Mullerian fusion, and vaginal atresia from the Mullerian aplasia group of disorders. Diagnoses were made in different age of life, from neonatal to the teenage years. Clinical and ultrasound examinations dominated in establishing the diagnosis. Computed tomography and magnetic resonance imaging were included in search for associated congenital abnormalities. The time period of the analysis was 2003-2012. The total number of vaginal outlet obstructions was 19. Imperforate hymen was diagnosed in 15 patients 4 neonates, 1 toddler, 2 pre-pubertal girls without any oestrogen stimulation and 8 pubertal girls, after a period of recurrent low abdominal pain. The signs of hydrocolpos were in 5 cases, and only two female newborns were diagnosed on antenatal fetal ultrasound. Distal septum of the vagina was diagnosed and operated in 3 patients: one in a newborn, two in pre-pubertal girls. One girl was diagnosed with vaginal atresia at puberty. Prenatal ultrasound and careful genital examination of the newborn females may be helpful in early diagnostics of very distal vaginal obstructions like imperforate hymen and distal vaginal septum. Vaginal atresia is difficult to notice before the puberty and the menstruation cycle. A search for associated genital anomalies is necessary. In pubertal girls who haven’t got a menarche and suffer the recurrent lower abdominal pain, the pelvic ultrasound is recommended to identify the cause(s).
Introduction Advances in Text Comprehension : Model , Process and Development

To a very large extent, children learn in and out of school from written text. Information Communications Technologies (ICT) offers many possibilities to facilitate learning by confronting children with multimodal texts. In order to be able to implement learning environments that optimally facilitate children’s learning, insight is needed into the cognitive processes underlying text comprehension. In this light, the aim of this special issue is to report on new advances in text comprehension research in perspective of educational implications. Starting from recent theoretical frameworks on the cognitive processes underlying text comprehension, the online processing of text will be discussed in adults and in school children.
A probabilistic fuzzy logic system for modeling and control

In this paper, a probabilistic fuzzy logic system (PFLS) is proposed for the modeling and control problems. Similar to the ordinary fuzzy logic system (FLS), the PFLS consists of the fuzzification, inference engine and defuzzification operation to process the fuzzy information. Different to the FLS, it uses the probabilistic modeling method to improve the stochastic modeling capability. By using a three-dimensional membership function (MF), the PFLS is able to handle the effect of random noise and stochastic uncertainties existing in the process. A unique defuzzification method is proposed to simplify the complex operation. Finally, the proposed PFLS is applied to a function approximation problem and a robotic system. It shows a better performance than an ordinary FLS in stochastic circumstance.
Modeling and control of a three-port DC-DC converter for PV-battery systems

This paper presents modeling and decoupled control design for an isolated three-port DC-DC converter (TPC), which interfaces a photovoltaic (PV) panel, a rechargeable battery, and an isolated load. A small signal model is derived from the state-space equations of the TPC. Based on the model, two decoupled controllers are designed to control the power flows of the two sources and the load voltage independently. Experimental studies are performed to verify that the two controllers are capable of regulating the power flows of the PV panel and the battery while maintaining a constant output voltage for the TPC.
Offline handwritten signature verification — Literature review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5–10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.
Project Success : A Multidimensional Strategic Concept

This article presents projects as powerful strategic weapons, initiated to create economic value and competitive advantage. It suggests that project managers are the new strategic leaders, who must take on total responsibility for project business results. Defining and assessing project success is therefore a strategic management concept, which should help align project efforts with the shortand long-term goals of the organization. While this concept seems simple and intuitive, there is very little agreement in previous studies as to what really constitutes project success. Traditionally, projects were perceived as successful when they met time, budget, and performance goals. However, many would agree that there is more to project success than meeting time and budget. The object of this study was to develop a multidimensional framework for assessing project success, showing how different dimensions mean different things to different stakeholders at different times and for different projects. Given the complexity of this question, a combination of qualitative and quantitative methods and two data sets were used. The analysis identified four major distinct success dimensions: (1) project efficiency, (2) impact on the customer, (3) direct business and organizational success, and (4) preparing for the future. The importance of the dimensions varies according to time and the level of technological uncertainty involved in the project. The article demonstrates how these dimensions should be addressed during the project’s definition, planning, and execution phases, and provides a set of guidelines for project managers and senior managers, as well as suggestions for further research.
The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger

We describe the ATILF-LLF system built for the MWE 2017 Shared Task on automatic identification of verbal multiword expressions. We participated in the closed track only, for all the 18 available languages. Our system is a robust greedy transition-based system, in which MWE are identified through a MERGE transition. The system was meant to accommodate the variety of linguistic resources provided for each language, in terms of accompanying morphological and syntactic information. Using per-MWE Fscore, the system was ranked first1 for all but two languages (Hungarian and Romanian).
A Review of Agile Software Effort Estimation Methods

Software cost estimation is an essential aspect of software project management and therefore the success or failure of a software project depends on accuracy in estimating effort, time and cost. Software cost estimation is a scientific activity that requires knowledge of a number of relevant attributes that will determine which estimation method to use in a given situation. Over the years various studies were done to evaluate software effort estimation methods however due to introduction of new software development methods, the reviews have not captured new software development methods. Agile software development method is one of the recent popular methods that were not taken into account in previous cost estimation reviews. The main aim of this paper is to review existing software effort estimation methods exhaustively by exploring estimation methods suitable for new software development methods.
OPC-UA Communication Framework for PLC-based Industrial IoT Applications: Poster Abstract

Programmable Logic Controllers (PLCs) are specialized computing systems for the control and monitoring of distributed industrial devices. Aiming for a highly connected industrial Internet-of-Things (IoT) ecosystem, PLCopen OPC Unified Architecture (UA) specification has been released. This paper presents the implementation and evaluation of a PLCopen OPC-UA software component for industrial control systems. A preliminary evaluation of the developed software has been conducted to analyze the performance of OPC-UA client FBs.
Terrain Rendering Using GPU-Based Geometry Clipmaps

The geometry clipmap introduced in Losasso and Hoppe 2004 is a new level-of-detail structure for rendering terrains. It caches terrain geometry in a set of nested regular grids, which are incrementally shifted as the viewer moves. The grid structure provides a number of benefits over previous irregular-mesh techniques: simplicity of data structures, smooth visual transitions, steady rendering rate, graceful degradation, efficient compression, and runtime detail synthesis. In this chapter, we describe a GPU-based implementation of geometry clipmaps, enabled by vertex textures. By processing terrain geometry as a set of images, we can perform nearly all computations on the GPU itself, thereby reducing CPU load. The technique is easy to implement, and allows interactive flight over a 20-billion-sample grid of the United States stored in just 355 MB of memory, at around 90 frames per second.
Overcoming resource underutilization in spatial CNN accelerators

Convolutional neural networks (CNNs) are revolutionizing a variety of machine learning tasks, but they present significant computational challenges. Recently, FPGA-based accelerators have been proposed to improve the speed and efficiency of CNNs. Current approaches construct an accelerator optimized to maximize the overall throughput of iteratively computing the CNN layers. However, this approach leads to dynamic resource underutilization because the same accelerator is used to compute CNN layers of radically varying dimensions. We present a new CNN accelerator design that improves the dynamic resource utilization. Using the same FPGA resources, we build multiple accelerators, each specialized for specific CNN layers. Our design achieves 1.3× higher throughput than the state of the art when evaluating the convolutional layers of the popular AlexNet CNN on a Xilinx Virtex-7 FPGA.
Email Overload: Exploring Personal Information Management of Email

Email is one o f t l~ most successful computer applicmiom yet devised. Our empin~:al ct~ta show however, that althongh email was origiraUy designed as a c~nmunica/ons application, it is now used for ~ t i o n a l funaions, that it was not designed for, such as t a b management and persona/ afoOt/v/rig. W e call this ernt~l oveHoad We demonstrate that email overload creates problems for personal information manageaa,cnt: users eden have cluttered inboxes cor~mining hundreds of n~:age~¢, incl~rling outstanding tasks, partially read documents and conversational threads. Furthermore,, user attemt:Xs to rationalise their i nbox~ by ~ i n g are ~Ron unsuccessful, with the consequence that important r r ~ g e s get overlooked, or "lost" in archives. We explain how em~l over/oad/ng arises and propose technical solutions to the problem.
An Ontology-based Summarization System for Arabic Documents (OSSAD)

With the problem of increased web resources and the huge amount of information available, the necessity of having automatic summarization systems appeared. Since summarization is needed the most in the process of searching for information on the web, where the user aims at a certain domain of interest according to his query, domain-based summaries would serve the best. Despite the existence of plenty of research work in the domain-based summarization in English, there is lack of them in Arabic due to the shortage of existing knowledge bases. In this paper an Ontology-based Summarization System for Arabic Documents, OSSAD, is introduced. Domain knowledge is extracted from an Arabic corpus and represented by topic related concepts/keywords and the lexical relations among them. The user’s query is first expanded by using the Arabic WordNet and then by adding the domain-specific knowledge base to the expansion. For summarization, decision tree algorithm (C4.5) is used, which was trained by a set of features extracted from the original documents. For the testing dataset, Essex Arabic Summaries Corpus (EASC) was used. Recall Oriented Understudy for Gisting Evaluation (ROUGE) was used to compare OSSAD summaries with the human summaries along with other automatic summarization systems, showing that the proposed approach demonstrated promising results.
(α, k)-anonymous data publishing

Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first present an optimal global-recoding method for the (α, k)-anonymity problem. Next we propose two scalable local-recoding algorithms which are both more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general cases.
Predictive Data Mining for Diagnosis of Thyroid Disease using Neural Network

This paper presents a systematic approach for earlier diagnosis of Thyroid disease using back propagation algorithm used in neural network. Back propagation algorithm is a widely used algorithm in this field. ANN has been developed based on back propagation of error used for earlier prediction of disease. ANN was subsequently trained with experimental data and testing is carried out using data that was not used during training process. Results show that outcome of ANN is in good agreement with experimental data; this indicates that developed neural network can be used as an alternative for earlier prediction of a disease. Back propagation, decision tree, gradient descent, prediction, Supervised Learning
Naturalistic Observations of Peer Interventions in Bullying

This study examined peer intervention in bullying using naturalistic observations on school playgrounds. The sample comprised 58 children (37 boys and 21 girls) in Grades 1 to 6 who were observed to intervene in bullying. Peers were present during 88% of bullying episodes and intervened in 19%. In 47% of the episodes, peers intervened aggressively. Interventions directed toward the bully were more likely to be aggressive, whereas interventions directed toward the victim or the bully-victim dyad were more likely to be nonaggressive. The majority (57%) of interventions were effective in stopping bullying. Boys were more likely to intervene when the bully and victim were male and girls when the bully and victim were female. The implications for antibullying interventions are discussed.
Cancer phenotype as the outcome of an evolutionary game between normal and malignant cells

There is variability in the cancer phenotype across individuals: two patients with the same tumour may experience different disease life histories, resulting from genetic variation within the tumour and from the interaction between tumour and host. Until now, phenotypic variability has precluded a clear-cut identification of the fundamental characteristics of a given tumour type.Using multiple myeloma as an example, we apply the principles of evolutionary game theory to determine the fundamental characteristics that define the phenotypic variability of a tumour.Tumour dynamics is determined by the frequency-dependent fitness of different cell populations, resulting from the benefits and costs accrued by each cell type in the presence of others. Our study shows how the phenotypic variability in multiple myeloma bone disease can be understood through the theoretical approach of a game that allows the identification of key genotypic features in a tumour and provides a natural explanation for phenotypic variability. This analysis also illustrates how complex biochemical signals can be translated into cell fitness that determines disease dynamics.The present paradigm is general and extends well beyond multiple myeloma, and even to non-neoplastic disorders. Furthermore, it provides a new perspective in dealing with cancer eradication. Instead of trying to kill all cancer cells, therapies should aim at reducing the fitness of malignant cells compared with normal cells, allowing natural selection to eradicate the tumour.
Client attachment, attachment to the therapist and client-therapist attachment match: how do they relate to change in psychodynamic psychotherapy?

We examined the associations between client attachment, client attachment to the therapist, and symptom change, as well as the effects of client-therapist attachment match on outcome. Clients (n = 67) and their therapists (n = 27) completed the ECR to assess attachment.



Clients completed also the Client Attachment to Therapist scale three times (early, middle, and late sessions) and the OQ-45 at intake and four times over the course of a year of psychodynamic psychotherapy.



Clients characterized by avoidant attachment and by avoidant attachment to their therapist showed the least improvement. A low-avoidant client-therapist attachment match led to a greater decrease in symptom distress than when a low-avoidant therapist treated a high-avoidant client.



These findings suggest the importance of considering client-therapist attachment matching and the need to pay attention to the special challenges involved in treating avoidant clients in order to facilitate progress in psychotherapy.
Search engine coverage bias: evidence and possible causes

Commercial search engines are now playing an increasingly important role in Web information dissemination and access. Of particular interest to business and national governments is whether the big engines have coverage biased towards the U.S. or other countries. In our study we tested for national biases in three major search engines and found significant differences in their coverage of commercial Web sites. The U.S. sites were much better covered than the others in the study: sites from China, Taiwan and Singapore. We then examined the possible technical causes of the differences and found that the language of a site does not affect its coverage by search engines. However, the visibility of a site, measured by the number of links to it, affects its chance to be covered by search engines. We conclude that the coverage bias does exist but this is due not to deliberate choices of the search engines but occurs as a natural result of cumulative advantage effects of U.S. sites on the Web. Nevertheless, the bias remains a cause for international concern.
Sub-event detection from tweets

Social media plays an important role in communication between people in recent times. This includes information about news and events that are currently happening. Most of the research on event detection concentrates on identifying events from social media information. These models assume an event to be a single entity and treat it as such during the detection process. This assumption ignores that the composition of an event changes as new information is made available on social media. To capture the change in information over time, we extend an already existing Event Detection at Onset algorithm to study the evolution of an event over time. We introduce the concept of an event life cycle model that tracks various key events in the evolution of an event. The proposed unsupervised sub-event detection method uses a threshold-based approach to identify relationships between sub-events over time. These related events are mapped to an event life cycle to identify sub-events. We evaluate the proposed sub-event detection approach on a large-scale Twitter corpus.