nils-herrmann/clean_scirepeval huggingface dataset:
RGB color decomposition and image feature extraction of flame image in rear of sintering machine

The stability of the sintering end point is an important precondition for the smooth running of the sintering machine and the assurance of the quality and quantity of sintering ore. The sintering flame of the sintering machine tail involves a great deal of associated information about sintering end point. Extracting the image feature of the flame has important significance for judging the operation state of sintering. Firstly, taking flame image of normal sintering as research objects, analyzing the energy information in the flame image; Secondly, reducing the halo effect on the image by processing image with Discrete Fourier transform (DFT); Thirdly, using the color decomposition method to decompose the RGB image into three components of R, G and B, obtaining the principal component image of RGB by removing the background of image; Finally, obtaining ideal binary image through threshold segmentation and mathematical morphology processing on image, and realizing geometric features extraction of the flame image.
Building a Low Latency, Highly Associative DRAM Cache with the Buffered Way Predictor

The emerging die-stacked DRAM technology allows computer architects to design a last-level cache (LLC) with high memory bandwidth and large capacity. There are four key requirements for DRAM cache design: minimizing on-chip tag storage overhead, optimizing access latency, improving hit rate, and reducing off-chip traffic. These requirements seem mutually incompatible. For example, to reduce the tag storage overhead, the recent proposed LH-cache co-locates tags and data in the same DRAM cache row, and the Alloy Cache proposed to alloy data and tags in the same cache line in a direct-mapped design. However, these ideas either require significant tag lookup latency or sacrifice hit rate for hit latency. To optimize all four key requirements, we propose the Buffered Way Predictor (BWP). The BWP predicts the way ID of a DRAM cache request with high accuracy and coverage, allowing data and tag to be fetched back to back. Thus, the read latency for the data can be completely hidden so that DRAM cache hitting requests have low access latency. The BWP technique is designed for highly associative block-based DRAM caches and achieves a low miss rate and low off-chip traffic. Our evaluation with multi-programmed workloads and a 128MB DRAM cache shows that a 128KB BWP achieves a 76.2% hit rate. The BWP improves performance by 8.8% and 12.3% compared to LH-cache and Alloy Cache, respectively.
Genetic Learning Particle Swarm Optimization

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.
VDBSCAN: Varied Density Based Spatial Clustering of Applications with Noise

Clustering analysis is a primary method for data mining. Density clustering has such advantages as: its clusters are easy to understand and it does not limit itself to shapes of clusters. But existing density-based algorithms have trouble in finding out all the meaningful clusters for datasets with varied densities. This paper introduces a new algorithm called VDBSCAN for the purpose of varied-density datasets analysis. The basic idea of VDBSCAN is that, before adopting traditional DBSCAN algorithm, some methods are used to select several values of parameter Eps for different densities according to a k-dist plot. With different values of Eps, it is possible to find out clusters with varied densities simultaneity. For each value of Eps, DBSCAN algorithm is adopted in order to make sure that all the clusters with respect to corresponding density are clustered. And for the next process, the points that have been clustered are ignored, which avoids marking both denser areas and sparser ones as one cluster. Finally, a synthetic database with 2-dimension data is used for demonstration, and experiments show that VDBSCAN is efficient in successfully clustering uneven datasets.
Implementing shortest path routing mechanism using Openflow POX controller

Network management is a challenging problem of wide impact with many enterprises suffering significant financial losses. The Software Defined Networks (SDN) approach is a new paradigm that enables the management of networks with low cost and complexity. The goal of SDN is to make sure that all control-level logical decisions are taken at a central way, as compared to traditional networking, wherein control-level decisions are taken locally and intelligence is distributed in each switch. The aim of this paper is to present a routing solution based on SDN architecture implemented in OpenFlow environment and providing the shortest path routing. The simulations have been carried out in an emulation environment based on Linux and POX controller.
Bayesian Theory of Mind

We present a computational framework for Theory of Mind (ToM): the human ability to make joint inferences about the unobservable beliefs and preferences underlying the observed actions of other agents. These mental state attributions can be understood as Bayesian inferences in a probabilistic generative model for rational action, or planning under uncertain and incomplete information, formalized as a Partially Observable Markov Decision Problem (POMDP). That is, we posit that ToM inferences approximately reconstruct the combination of a reward function and belief state trajectory for an agent based on observing that agent’s action sequence in a given environment. We test this POMDP model by showing human subjects the trajectories of agents moving in simple spatial environments and asking for joint inferences about the agents’ utilities and beliefs about unobserved aspects of the environment. Our model performs substantially better than two simpler variants: one in which preferences are inferred without reference to an agents’ beliefs, and another in which beliefs are inferred without reference to the agent’s dynamic observations in the environment. We find that preference inferences are substantially more robust and consistent with our model’s predictions than are belief inferences, in line with classic work showing that the ability to infer goals is more concretely grounded in visual data, develops earlier in infancy, and can be localized to specific neurons in the primate brain.
Study and Handling Methods of Power IGBT Module Failures in Power Electronic Converter Systems

Power electronics plays an important role in a wide range of applications in order to achieve high efficiency and performance. Increasing efforts are being made to improve the reliability of power electronics systems to ensure compliance with more stringent constraints on cost, safety, and availability in different applications. This paper presents an overview of the major failure mechanisms of IGBT modules and their handling methods in power converter systems improving reliability. The major failure mechanisms of IGBT modules are presented first, and methods for predicting lifetime and estimating the junction temperature of IGBT modules are then discussed. Subsequently, different methods for detecting open- and short-circuit faults are presented. Finally, fault-tolerant strategies for improving the reliability of power electronic systems under field operation are explained and compared in terms of performance and cost.
The causal Model of effective factors on Intention to use of information technology among payam noor and Traditional universities students

The objective of this study is presenting the causal modeling of intention to use technology among university student. Correlation is used as the method of research. Instrument of this study is standard questionnaire. The collected data is analyzed with AMOS software. The result indicate that facilitative condition, cognitive absorption, perceived enjoyment, perceived ease of use, and perceived usefulness have significant and direct effect on intention to use technology. Also, facilitative condition, cognitive absorption, perceived enjoyment, perceived ease of use and computer playfulness have significant and direct of effect on perceived usefulness. Facilitative condition, cognitive absorption, perceived enjoyment, and playfulness have significant and direct effect on perceived ease of use.
Learning to Incentivize: Eliciting Effort via Output Agreement

In crowdsourcing when there is a lack of verification for contributed answers, output agreement mechanisms are often used to incentivize participants to provide truthful answers when the correct answer is hold by the majority. In this paper, we focus on using output agreement mechanisms to elicit effort, in addition to eliciting truthful answers, from a population of workers. We consider a setting where workers have heterogeneous cost of effort exertion and examine the data requester’s problem of deciding the reward level in output agreement for optimal elicitation. In particular, when the requester knows the cost distribution, we derive the optimal reward level for output agreement mechanisms. This is achieved by first characterizing Bayesian Nash equilibria of output agreement mechanisms for a given reward level. When the cost distribution is unknown to the requester, we develop sequential mechanisms that combine learning the cost distribution with incentivizing effort exertion to approximately determine the optimal reward level.
Insular and insulo-opercular epilepsy in childhood: An SEEG study

In recent years, there have been series analysing the electro-clinical correlations of insular epilepsy in adult populations. In contrast, the ictal semiology in children with insular epilepsy is poorly described. Considering that early and successful surgery may greatly improve the cognitive outcome and quality of life, it is worthwhile to deepen our knowledge of insular epilepsy in children.



We retrospectively evaluated ten children with drug-resistant focal insular epilepsy who had been consecutively explored with stereoelectroencephalography (SEEG), followed by individually tailored resective surgery that included part of the insula in all cases. A detailed anatomo-electro-clinical analysis of non-invasive EEG and SEEG data was performed. At least one of the electrodes explored the insular cortex. SEEG analysis confirmed that the insular cortex was included in the ictal onset zone.



Epilepsy onset was mostly during the first year of life, characterized by subtle seizures as well as spasms and myoclonic seizures. Later on, neurovegetative signs and asymmetric tonic and hypermotor seizures (HMS) dominated the ictal semiology. The epileptogenic zone was frequently wider than insular with frontal and central predominance. In eight patients, the tailored resection included a lesion. In seven patients, an Engel class 1 outcome as well as neuropsychological and behavioural improvement was obtained.



SEEG is feasible and useful in children with drug-resistant insular epilepsy which is often characterized by autonomic symptoms as the initial symptoms and should be suspected in cases with HMS, asymmetric tonic seizures and even asymmetric spasms. Early propagation is mostly frontal and central. Analysis of a larger population is required to refine these findings.
Tableau-Based ABox Abduction for the ALCHO Description Logic

Abduction is a useful decision problem that is related to diagnostics. Given some observation in form of a set of axioms, that is not entailed by a knowledge base, we are looking for explanations, sets of axioms, that can be added to the knowledge base in order to entail the observation. ABox abduction limits both observations and explanations to ABox assertions. In this work we focus on direct tableau-based approach to answer ABox abduction. We develop an ABox abduction algorithm for the ALCHO DL, that is based on Reiter’s minimal hitting set algorithm. We focus on the class of explanations allowing atomic and negated atomic concept assertions, role assertions, and negated role assertions. The algorithm is sound and complete for this class. The algorithm was also implemented, on top of the Pellet reasoner.
A multipath based height estimation of targets for radar systems

This paper presents an approach to classify the heights of targets with radar systems. The algorithm is based on the analysis of the superposition of several reflections at a high target. The result is a superposition of the different received signals. On the contrary to a high target, a low target has only one reflection point and one propagation path. In this paper, a technique is proposed to detect the superposition and consequently classify targets as low or high. Finally the algorithm is evaluated with measurements.
Profiling the Potential of Web Tables for Augmenting Cross-domain Knowledge Bases

Cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph have gained increasing attention over the last years and are starting to be deployed within various use cases. However, the content of such knowledge bases is far from being complete, far from always being correct, and suffers from deprecation (i.e. population numbers become outdated after some time). Hence, there are efforts to leverage various types of Web data to complement, update and extend such knowledge bases. A source of Web data that potentially provides a very wide coverage are millions of relational HTML tables that are found on the Web. The existing work on using data from Web tables to augment cross-domain knowledge bases reports only aggregated performance numbers. The actual content of the Web tables and the topical areas of the knowledge bases that can be complemented using the tables remain unclear. In this paper, we match a large, publicly available Web table corpus to the DBpedia knowledge base. Based on the matching results, we profile the potential of Web tables for augmenting different parts of cross-domain knowledge bases and report detailed statistics about classes, properties, and instances for which missing values can be filled using Web table data as evidence. In order to estimate the potential quality of the new values, we empirically examine the Local Closed World Assumption and use it to determine the maximal number of correct facts that an ideal data fusion strategy could generate. Using this as ground truth, we compare three data fusion strategies and conclude that knowledge-based trust outperforms PageRankand voting-based fusion.
Drag , Drop , and Clone : An Interactive Interface for Surface Composition

We describe a novel interface for composition of polygonal meshes based around two artist-oriented tools: Geometry Drag-and-Drop and Mesh Clone Brush. Our drag-and-drop interface allows a complex surface part to be selected and interactively dragged to a new location. We automatically fill the hole left behind and smoothly deform the part to conform to the target surface. The artist may increase the boundary rigidity of this deformation, in which case a fair transition surface is automatically computed. Our clone brush allows for transfer of surface details with precise spatial control. These tools support an interaction style that has not previously been demonstrated for 3D surfaces, allowing detailed 3D models to be quickly assembled from arbitrary input meshes. We evaluated this interface by distributing a basic tool to computer graphics hobbyists and professionals, and based on their feedback, describe potential workflows which could utilize our techniques.
Temporal Difference Variational Auto-Encoder

To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction. Motivated by the absence of a model satisfying all these requirements, we propose TD-VAE, a generative sequence model that learns representations containing explicit beliefs about states several steps into the future, and that can be rolled out directly without single-step transitions. TD-VAE is trained on pairs of temporally separated time points, using an analogue of temporal difference learning used in reinforcement learning.
A system for the functional evaluation of reconstructive procedures after surgical treatment of tumors of the musculoskeletal system.

The need for a standardized system of end result reporting of various surgical alternatives after limb salvaging and ablative procedures for musculoskeletal tumors was clearly recognized during the first International Symposium on Limb Salvage (ISOLS) in 1981. During the ensuing four biannual symposia, there has been an ongoing developmental experience with a system extensively field tested in 1989 by the Musculoskeletal Tumor Society (MSTS). This system of functional evaluation has been adopted by the MSTS and ISOLS for their joint studies and program presentation. In brief, the system assigns numerical values (0-5) for each of six categories: pain, and function and emotional acceptance in upper and lower extremities; supports, and walking and gait in the lower extremity; and hand positioning, and dexterity and lifting ability in the upper extremity. Demographic information and a patient satisfaction component is included. A numerical score and percent rating is calculated to allow for comparison of results. The system has been field tested in 220 patients with low (+/-) interobserver variability. It was well accepted by the participants, and its usage is recommended by the MSTS to facilitate valid comparative end result studies of musculoskeletal tumor reconstructions.
Deep Models for Arabic Dialect Identification on Benchmarked Data

The Arabic Online Commentary (AOC) (Zaidan and Callison-Burch, 2011) is a large-scale repository of Arabic dialects with manual labels for 4 varieties of the language. Existing dialect identification models exploiting the dataset pre-date the recent boost deep learning brought to NLP and hence the data are not benchmarked for use with deep learning, nor is it clear how much neural networks can help tease the categories in the data apart. We treat these two limitations: We (1) benchmark the data, and (2) empirically test 6 different deep learning methods on the task, comparing peformance to several classical machine learning models under different conditions (i.e., both binary and multi-way classification). Our experimental results show that variants of (attention-based) bidirectional recurrent neural networks achieve best accuracy (acc) on the task, significantly outperforming all competitive baselines. On blind test data, our models reach 87.65% acc on the binary task (MSA vs. dialects), 87.4% acc on the 3-way dialect task (Egyptian vs. Gulf vs. Levantine), and 82.45% acc on the 4-way variants task (MSA vs. Egyptian vs. Gulf vs. Levantine). We release our benchmark for future work on the dataset.
Botnet and botnet detection techniques in cyber realm

Due to an increasing growth of Internet usage, cybercrimes has been increasing at an Alarming rate and has become most profitable criminal activity. Botnet is an emerging threat to the cyber security and existence of Command and Control Server(C&C Server) makes it very dangerous attack as compare to all other malware attacks. Botnet is a network of compromised machines which are remotely controlled by bot master to do various malicious activities with the help of command and control server and n-number of slave machines called bots. The main motive behind botnet is Identity theft, Denial of Service attack, Click fraud, Phishing and many other malware activities. Botnets rely on different protocols such as IRC, HTTP and P2P for transmission. Different botnet detection techniques have been proposed in recent years. This paper discusses Botnet, Botnet history, and life cycle of Botnet apart from classifying various Botnet detection techniques. Paper highlights the recent research work under botnets in cyber realm and proposes directions for future research in this area.
Electronic and Computer-Assisted Refreshable Braille Display Developed for Visually Impaired Individuals

Braille alphabet is an important tool that enables visually impaired individuals to have a comfortable life like those who have normal vision. For this reason, new applications related to the Braille alphabet are being developed. In this study, a new Refreshable Braille Display was developed to help visually impaired individuals learn the Braille alphabet easier. By means of this system, any text downloaded on a computer can be read by the visually impaired individual at that moment by feeling it by his/her hands. Through this electronic device, it was aimed to make learning the Braille alphabet easier for visually impaired individuals with whom the necessary tests were conducted.
Pathophysiological Significance of Blood Rheology

Tissue perfusion is determined by both blood vessel geometry and the rheological properties of blood. Blood is a nonNewtonian fluid, its viscosity being dependent on flow conditions. Blood and plasma viscosities, as well as the rheological properties of blood cells (e.g., deformability and aggregation of red blood cells), are influenced by disease processes and extreme physiological conditions. These rheological parameters may in turn affect the blood flow in vessels, and hence tissue perfusion. Unfortunately it is not always possible to determine if a change in rheological parameters is the cause or the result of a disease process. The hemorheology-tissue perfusion relationship is further complicated by the distinct in vivo behavior of blood. Besides the special hemodynamic mechanisms affecting the composition of blood in various regions of the vascular system, autoregulation based on vascular control mechanisms further complicates this relationship. Hemorheological parameters may be especially important for adequate tissue perfusion if the vascular system is geometrically challenged.