nils-herrmann/clean_scirepeval huggingface dataset:
Edge-based split-and-merge superpixel segmentation

Superpixels are an oversegmentation of an image and popularly used as a preprocessing in many computer vision applications. Many state-of-the-art superpixel segmentation algorithms rely either on minimizing special energy functions or on clustering pixels in the effective distance space. While in this paper, we introduce a novel algorithm to produce superpixels based on the edge map by utilizing a split-andmerge strategy. Firstly, we obtain the initial superpixels with uniform size and shape. Secondly, in the splitting stage, we find all possible splitting contours for each superpixel by overlapping the boundaries of this superpixel with the edge map, and then choose the best one to split it which ensure the superpixels produced by this splitting are dissimilarity in color and similarity in size. Thirdly, in the merging stage, the Bhattacharyya distance between two color histograms in the RGB space for each pair of adjacent superpixels is computed to evaluate their color similarity for merging superpixels. At last, we iterate the split-and-merge steps until no superpixels have changed. Experimental results on the Berkeley Segmentation Dataset (BSD) show that the proposed algorithm can achieve a good performance compared with the state-of-the-art superpixel segmentation algorithms.
Orion: a power-performance simulator for interconnection networks

With the prevalence of server blades and systems-on-a-chip (SoCs), interconnection networks are becoming an important part of the microprocessor landscape. However, there is limited tool support available for their design. While performance simulators have been built that enable performance estimation while varying network parameters, these cover only one metric of interest in modern designs. System power consumption is increasingly becoming equally, if not more important than performance. It is now critical to get detailed power-performance tradeoff information early in the microarchitectural design cycle. This is especially so as interconnection networks consume a significant fraction of total system power. It is exactly this gap that the work presented in this paper aims to fill.We present Orion, a power-performance interconnection network simulator that is capable of providing detailed power characteristics, in addition to performance characteristics, to enable rapid power-performance trade-offs at the architectural-level. This capability is provided within a general framework that builds a simulator starting from a microarchitectural specification of the interconnection network. A key component of this construction is the architectural-level parameterized power models that we have derived as part of this effort. Using component power models and a synthesized efficient power (and performance) simulator, a microarchitect can rapidly explore the design space. As case studies, we demonstrate the use of Orion in determining optimal system parameters, in examining the effect of diverse traffic conditions, as well as evaluating new network microarchitectures. In each of the above, the ability to simultaneously monitor power and performance is key in determining suitable microarchitectures.
The enduring effects of abuse and related adverse experiences in childhood

Childhood maltreatment has been linked to a variety of changes in brain structure and function and stress–responsive neurobiological systems. Epidemiological studies have documented the impact of childhood maltreatment on health and emotional well–being. After a brief review of the neurobiology of childhood trauma, we use the Adverse Childhood Experiences (ACE) Study as an epidemiological “case example” of the convergence between epidemiologic and neurobiological evidence of the effects of childhood trauma. The ACE Study included 17,337 adult HMO members and assessed 8 adverse childhood experiences (ACEs) including abuse, witnessing domestic violence, and serious household dysfunction. We used the number of ACEs (ACE score) as a measure of cumulative childhood stress and hypothesized a "doseresponse" relationship of the ACE score to 18 selected outcomes and to the total number of these outcomes (comorbidity). Based upon logistic regression analysis, the risk of every outcome in the affective, somatic, substance abuse, memory, sexual,and aggressionrelated domains increased in a graded fashion as the ACE score increased (P <0.001). The mean number of comorbid outcomes tripled across the range of the ACE score. The graded relationship of the ACE score to 18 different outcomes in multiple domains theoretically parallels the cumulative exposure of the developing brain to the stress response with resulting impairment in multiple brain structures and functions.
Examination of current handover practice: evidence to support changing the ritual.

Examining systems of nursing handover provides the opportunity to identify areas in need of improvement and modification. The aim of this study was to describe nursing handover practices for one organization and explore clinical nurses' opinions regarding the quality of the current shift-to-shift handover style. One hundred and fifty-three registered nurses employed in 23 wards in an acute tertiary hospital in Australia completed a survey. The survey collected information about demographic data, current methods and preferences for style of handover and opinions about the quality of nursing handover. This study found that existing handover practice is time-consuming, lacks patient involvement and essential information, and is varied in style. Only one ward (4.3%) conducted handover at the bedside. The majority (82%) expressed reluctance to change their current handover style. Modification of existing nursing handover is needed but might be challenging in view of the reluctance to change this ritual.
Adaptive Fragmentation for Latency Control and Energy Management in Wireless Real-time Environments

Wireless environments are typically characterized by unpredictable and unreliable channel conditions. In such environments, fragmentation of network-bound data is a commonly adapted technique to improve the probability of successful data transmissions and reduce the energy overheads incurred due to re-transmissions. The overall latencies involved with fragmentation and consequent re-assembly of fragments are often neglected which bear significant effects on the real-time guarantees of the participating applications. This work studies the latencies introduced as a result of the fragmentation performed at the link layer (MAC layer in IEEE 802.11) of the source device and their effects on end-to-end delay constraints of mobile applications (e.g., media streaming). Based on the observed effects, this work proposes a feedback-based adaptive approach that chooses an optimal fragment size to (a) satisfy end-to-end delay requirements of the distributed application and (b) minimize the energy consumption of the source device by increasing the probability of successful transmissions, thereby reducing re-transmissions and their associated costs.
Measurements of variable chlorophyll fluorescence using fast repetition rate techniques: defining methodology and experimental protocols

We present a methodology, called fast repetition rate (FRR) fluorescence, that measures the functional absorption cross-section (sigmaPS II) of Photosystem II (PS II), energy transfer between PS II units (p), photochemical and nonphotochemical quenching of chlorophyll fluorescence, and the kinetics of electron transfer on the acceptor side of PS II. The FRR fluorescence technique applies a sequence of subsaturating excitation pulses ('flashlets') at microsecond intervals to induce fluorescence transients. This approach is extremely flexible and allows the generation of both single-turnover (ST) and multiple-turnover (MT) flashes. Using a combination of ST and MT flashes, we investigated the effect of excitation protocols on the measured fluorescence parameters. The maximum fluorescence yield induced by an ST flash applied shortly (10 &mgr;s to 5 ms) following an MT flash increased to a level comparable to that of an MT flash, while the functional absorption cross-section decreased by about 40%. We interpret this phenomenon as evidence that an MT flash induces an increase in the fluorescence-rate constant, concomitant with a decrease in the photosynthetic-rate constant in PS II reaction centers. The simultaneous measurements of sigmaPS II, p, and the kinetics of Q-A reoxidation, which can be derived only from a combination of ST and MT flash fluorescence transients, permits robust characterization of the processes of photosynthetic energy-conversion.
Using Data Analytics and Data Mining Methods to Determine a High Net Worth IndividualÃ¢ÂÂs Electronic Banking Behavior

Electronic banking is becoming more popular every day. Financial institutions have accepted the transformation to provide electronic banking facilities to their customers in order to remain relevant and thrive in an environment that is competitive. A contributing factor to the customer retention rate is the frequent use of multiple online functionality however despite all the benefits of electronic banking, some are still hesitant to use it because of security concerns. The perception is that gender, age, education level, salary, culture and profession all have an impact on electronic banking usage. This study reports on how the Knowledge Discovery and Data Mining (KDDM) process was used to determine characteristics and electronic banking behavior of high net worth individuals at a South African bank.  2 indicate that product range and age had the biggest impact on electronic banking behavior. The value of user segmentation is that the financial institution can provide a more accurate service to their users based on their preferences and online banking behavior.
Exploring factors that influence Muslim intention to purchase online

This paper focuses on the factors that influence Muslim intention to purchase online. There are mounting concerns among Muslims regarding online transaction from technological and Shariah perspectives which require researchers to examine. Discussion on the intention to purchase in e-commerce environment can be largely found in the literature; yet, it scarcely looks from the Islamic perspective. Library research is employed to search for information on essential factors underpinning intention to purchase online in e-commerce setting. Review of existing literature sheds light on some conditions related to aspect that influence intention to purchace online such as: Shariah compliant, trust, integrity, competence, benevolence, web quality, third party assurance, trustors propensity, religious commitment, trustworthiness and perceived risk. Investigating these factors will rationalize the concern of Muslim buyers online.
Poster: High Quality Real-Time Image-to-Mesh Conversion for Finite Element Simulations

In this paper, we present a parallel Image-to-Mesh Conversion (I2M) algorithm with quality and fidelity guarantees achieved by dynamic point insertions and removals. Starting directly from an image, it is able to recover the isosurface and mesh the volume with tetrahedra of good shape. Our tightly-coupled shared-memory parallel speculative execution paradigm employs carefully designed contention managers, load balancing, synchronization and optimizations schemes which boost the parallel efficiency with little overhead: our single-threaded performance is faster than CGAL, the state of the art sequential mesh generation software we are aware of. The effectiveness of our method is shown on Blacklight, the Pittsburgh Supercomputing Center's cache-coherent NUMA machine, via a series of case studies justifying our choices. We observe a more than 82% strong scaling efficiency for up to 64 cores, and a more than 95% weak scaling efficiency for up to 144 cores, reaching a rate of 14.7 Million Elements per second. To the best of our knowledge, this is the fastest and most scalable 3D Delaunay refinement algorithm.
A Pinch of Humor for Short-Text Conversation: An Information Retrieval Approach

The paper describes a work in progress on humorous response generation for short-text conversation using information retrieval approach. We gathered a large collection of funny tweets and implemented three baseline retrieval models: BM25, the query term reweighting model based on syntactic parsing and named entity recognition, and the doc2vec similarity model. We evaluated these models in two ways: in situ on a popular community question answering platform and in laboratory settings. The approach proved to be promising: even simple search techniques demonstrated satisfactory performance. The collection, test questions, evaluation protocol, and assessors’ judgments create a ground for future research towards more sophisticated models.
Checkpointing algorithms and fault prediction

This paper deals with the impact of fault prediction techniques on checkpointing strategies. We extend the classical first-order analysis of Young and Daly in the presence of a fault prediction system, characterized by its recall and its precision. In this framework, we provide an optimal algorithm to decide when to take predictions into account, and we derive the optimal value of the checkpointing period. These results allow to analytically assess the key parameters that impact the performance of fault predictors at very large scale.  Fault-tolerance, checkpointing, prediction, algorithms, model, exascale ∗ LIP, École Normale Supérieure de Lyon, France † University of Tennessee Knoxville, USA ‡ Institut Universitaire de France § INRIA ha l-0 07 88 31 3, v er si on 1 14 F eb 2 01 3 Étude de l’impact de la prédiction de fautes sur les stratégies de protocoles de checkpoint Résumé : Ce travail considère l’impact des techniques de prédiction de fautes sur les stratégies de protocoles de sauvegarde de points de reprise (checkpoints) et de redémarrage. Nous étendons l’analyse classique de Young en présence d’un système de prédiction de fautes, qui est caractérisé par son rappel (taux de pannes prévues sur nombre total de pannes) et par sa précision (taux de vraies pannes parmi le nombre total de pannes annoncées). Dans ce travail, nous avons pu obtenir la valeur optimale de la période de checkpoint (minimisant ainsi le gaspillage de l’utilisation des ressources dû au coût de prise de ces points de sauvegarde) dans différents scénarios. Ce papier pose les fondations théoriques pour de futures expériences et une validation du modèle. Mots-clés : Tolérance aux pannes, checkpoint, prédiction, algorithmes, modèle, exascale ha l-0 07 88 31 3, v er si on 1 14 F eb 2 01 3 Checkpointing algorithms and fault prediction 3
Advances in wire drawing process by theoretical and numerical analysis

Ductile metal deformation processes aimed at production of wire of some sort are in use since over five thousand years, early ones dating back to the origins of copper and gold produc­ tion. Thin sheets, initially produced by hammering, were cut into thin strips suitable for ornamental pur­ poses. The introduction of die plate paved the way to round wire manufac­ turing, rather severe limitations on gau­ ge being imposed by the crude equip­ ment available. Industrial revolution brought about steam power, and re­ quirement of large amounts of wire and wire based products, driven by exponential development of applica­ tions of electricity. Continuous wire production processes, based upon re­ peated drawing sequences with inter­ mediate heat treatment as required, are in regular use since over a century and a half, progress being made main­ ly in terms of hardness and wear resis­ tance of dies, lubrication, and process control, as well as of material proper­ ties. No major improvement is ex­ pected on die hardness, since the in­ troduction of diamond dies; on the other hand, substantial effort is de­ Overview on the plastic deformation process
Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images

Automated detection of blood vessel structures is becoming of crucial interest for better management of vascular disease. In this paper, we propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularizer, provided by using L2 Lebesgue measure of the γ-neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a feature's boundaries (i.e., H1 Hausdorff measure). Moreover, for better general segmentation performance, the proposed model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity information will guarantee a correct feature's segmentation. We evaluate the performance of the proposed model by applying it to three public retinal image datasets (two datasets of color fundus photography and one fluorescein angiography dataset). The proposed model outperforms its competitors when compared with other widely used unsupervised and supervised methods. For example, the sensitivity (0.742), specificity (0.982) and accuracy (0.954) achieved on the DRIVE dataset are very close to those of the second observer's annotations.
Bangla Parts-of-Speech tagging using Bangla stemmer and rule based analyzer

Parts-of-Speech (POS) tagging plays vital roles in the field of Natural Language Processing (NLP), such as - machine translation, spell checker, information retrieval, speech processing, emotion analysis and so on. Bangla is a very inflectional language that induces many variants from a single word. Although there is a few POS Tagger in Bangla language, very small of them address the essence of suffices to identify tag of the words. In this regard, we propose an automated POS Tagging system for Bangla language based on word-suffixes. In our system, we use our own stemming technique to retrieve a possible minimum root words and apply rules according to different forms of suffixes. Moreover, we incorporate a Bangla vocabulary that contains more than 45,000 words with their default tag and a patterned based verb-data-set. These facilitate to improve tagging efficiency of Bangla POS Tagger. We experiment our proposed system on a Bangla text corpus. The result shows that our proposed Bangla POS Tagger has outperformed the known related tagging systems.
FPM: Fine Pose Parts-Based Model with 3D CAD Models

We introduce a novel approach to the problem of localizing objects in an image and estimating their fine-pose. Given exact CAD models, and a few real training images with aligned models, we propose to leverage the geometric information from CAD models and appearance information from real images to learn a model that can accurately estimate fine pose in real images. Specifically, we propose FPM, a fine pose parts-based model, that combines geometric information in the form of shared 3D parts in deformable part based models, and appearance information in the form of objectness to achieve both fast and accurate fine pose estimation. Our method significantly outperforms current state-ofthe-art algorithms in both accuracy and speed.
Derivative-free optimization: a review of algorithms and comparison of software implementations

This paper addresses the solution of bound-constrained optimization problems using algorithms that require only the availability of objective function values but no derivative information. We refer to these algorithms as derivative-free algorithms. Fueled by a growing number of applications in science and engineering, the development of derivative-free optimization algorithms has long been studied, and it has found renewed interest in recent time. Along with many derivative-free algorithms, many software implementations have also appeared. The paper presents a review of derivative-free algorithms, followed by a systematic comparison of 22 related implementations using a test set of 502 problems. The test bed includes convex and nonconvex problems, smooth as well as nonsmooth problems. The algorithms were tested under the same conditions and ranked under several criteria, including their ability to find near-global solutions for nonconvex problems, improve a given starting point, and refine a near-optimal solution. A total of 112,448 problem instances were solved. We find that the ability of all these solvers to obtain good solutions diminishes with increasing problem size. For the problems used in this study, TOMLAB/MULTIMIN, TOMLAB/GLCCLUSTER, MCS and TOMLAB/LGO are better, on average, than other derivative-free solvers in terms of solution quality within 2500 function evaluations. These global solvers outperform local solvers even for convex problems. Finally, TOMLAB/OQNLP, NEWUOA, and TOMLAB/MULTIMIN show superior performance in terms of refining a near-optimal solution.
Using context-window overlapping in synonym discovery and ontology extension

This paper describes a new, unsupervised procedure called Context-window overlapping for calculating the semantic distance between two terms. It is based on the distributional semantics hypothesis, and, in particular, in the fact that synonym words should be interchangeable in every context, and hyponyms can be substituted by their hyperonyms in most contexts. The procedure has been applied to synonym identification, and to ontology extension. In the first task, it has been evaluated with 80 synonym test questions from the TOEFL which already constitute a standard test set in this problem, and attains results similar to most other non-ensemble procedures. Interestingly, it clearly outperforms Latent Semantic Analysis, other procedure grounded on the Distributional Semantic hypothesis. Concerning ontology enrichment, the results obtained are promising, although they can still be much improved. Conclusions are drawn from this result, and we outline several possibilities for future work.
Selecting the right objective measure for association analysis

such as support, confidence, interest factor, correlation, and entropy are often used to evaluate the interestingness of association patterns. However, in many situations, these measures may provide conflicting information about the interestingness of a pattern. Data mining practitioners also tend to apply an objective measure without realizing that there may be better alternatives available for their application. In this paper, we describe several key properties one should examine in order to select the right measure for a given application. A comparative study of these properties is made using twenty-one measures that were originally developed in diverse fields such as statistics, social science, machine learning, and data mining. We show that depending on its properties, each measure is useful for some application, but not for others. We also demonstrate two scenarios in which many existing measures become consistent with each other, namely, when support-based pruning and a technique known as table standardization are applied. Finally, we present an algorithm for selecting a small set of patterns such that domain experts can find a measure that best fits their requirements by ranking this small set of patterns.