nils-herrmann/clean_scirepeval huggingface dataset:
Bullet: high bandwidth data dissemination using an overlay mesh

In recent years, overlay networks have become an effective alternative to IP multicast for efficient point to multipoint communication across the Internet. Typically, nodes self-organize with the goal of forming an efficient overlay tree, one that meets performance targets without placing undue burden on the underlying network. In this paper, we target high-bandwidth data distribution from a single source to a large number of receivers. Applications include large-file transfers and real-time multimedia streaming. For these applications, we argue that an overlay mesh, rather than a tree, can deliver fundamentally higher bandwidth and reliability relative to typical tree structures. This paper presents Bullet, a scalable and distributed algorithm that enables nodes spread across the Internet to self-organize into a high bandwidth overlay mesh. We construct Bullet around the insight that data should be distributed in a disjoint manner to strategic points in the network. Individual Bullet receivers are then responsible for locating and retrieving the data from multiple points in parallel.Key contributions of this work include: i) an algorithm that sends data to different points in the overlay such that any data object is equally likely to appear at any node, ii) a scalable and decentralized algorithm that allows nodes to locate and recover missing data items, and iii) a complete implementation and evaluation of Bullet running across the Internet and in a large-scale emulation environment reveals up to a factor two bandwidth improvements under a variety of circumstances. In addition, we find that, relative to tree-based solutions, Bullet reduces the need to perform expensive bandwidth probing. In a tree, it is critical that a node's parent delivers a high rate of application data to each child. In Bullet however, nodes simultaneously receive data from multiple sources in parallel, making it less important to locate any single source capable of sustaining a high transmission rate.
FACTORS AFFECTING STUDENTS ’ QUALITY OF ACADEMIC PERFORMANCE : A CASE OF SECONDARY

This study was conducted to examine different factors influencing the academic performance of secondary school students in a metropolitan city of Pakistan. The respondents for this study were 10th grade students (300 male & 300 female). A survey was conducted by using a questionnaire for information gathering about different factors relating to academic performance of students. The academic performance was gauged by the result of their 9th grade annual examination. Standard t-test and ANOVA were applied to investigate the effect of different factors on students’ achievement. The results of the study revealed that socioeconomic status (SES) and parents’ education have a significant effect on students’ overall academic achievement as well as achievement in the subjects of Mathematics and English. The high and average socio-economic level affects the performance more than the lower level. It is very interesting that parents’ education means more than their occupation in relation to their children’s academic performance at school. It was found that girls perform better than the male students.
Antihistamine: A Useful Medication with Minimal Adverse Drug Reactions to Improve Acne Symptoms and Reduce Sebum Production

Acne vulgaris is a common skin disease that affects not only teenagers but also the general population. Although acne is not physically disabling, its psychological impact can be striking, contributing to low self-esteem, depression, and anxiety. As a result, there is a significant demand for effective acne therapies. Antihistamine is a widely used medication to treat several allergic skin conditions and yet it also has been found to decrease complications of acne and improve acne symptoms. For the severe cystic acne vulgaris, oral retinoids such as isotretinoin is the primary treatment; however, health care providers hesitate to prescribe isotretinoin due to its adverse drug reactions. On the other hand, antihistamine is well known by its safe and minimal side effects. Can an antihistamine intervention in standardized treatment of acne vulgaris significantly impact the improvement of acne symptoms and reduce sebum production?  An exhaustive search was conducted by MEDLINE-OVID, CINAHL, UptoDate, Web of Science, Google scholar, MEDLINE-PubMed, Clinicalkey, and ProQuest by using keywords: acne vulgaris and antihistamine. Relevant articles were assessed for quality using GRADE.  After the exhaustive search, two studies met the inclusion criteria and eligibility criteria. Effect of antihistamine as an adjuvant treatment of isotretinoin in acne:a randomized, controlled comparative study contains the comparison of 20 patients with moderate acne are treated with isotretinoin and another 20 patients with moderate acne are treated with additional antihistamine. Identification of Histamine Receptors and Reduction of Squalene Levels by an Antihistamine in Sebocytes was conducted on human tissue to verify the decrease of sebum production by the antihistamine’s effect.  Both studies demonstrate the usefulness of an histamine antagonist in reducing sebum production and improving acne symptoms. Due to its low cost and safety, a recommendation can be made for antihistamine to treat acne vulgaris as an adjuvant therapy to standardized treatment. Degree Type Capstone Project Degree Name Master of Science in Physician Assistant Studies First Advisor David Keene PA-C
Probabilistic Line Searches for Stochastic Optimization

In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent.
A Generalized Optical Flow Constraint and its Physical Interpretation

This paper addresses the issue of motion estimation on image sequences. The standard motion equation used to compute the apparent motion of image irradiance patterns is an invariance brightness based hypothesis called the optical flow constraint. Other equations can be used, in particular the extended optical flow constraint, which is a variant of the optical flow constraint, inspired by the fluid mechanic mass conservation principle. In this paper, we propose a physical interpretation of this extended optical flow equation and a new model unifying the optical flow and the extended optical flow constraints. We present results obtained for synthetic and meteorological images.
CS168: The Modern Algorithmic Toolbox Lecture #2: Approximate Heavy Hitters and the Count-Min Sketch

Let’s begin with a problem that many of you have seen before. It’s a common question in technical interviews. You’re given as input an array A of length n, with the promise that it has a majority element — a value that is repeated in strictly more than n/2 of the array’s entries. Your task is to find the majority element. In algorithm design, the usual “holy grail” is a linear-time algorithm. For this problem, your post-CS161 toolbox already contains a subroutine that gives a linear-time solution — just compute the median of A. (Note: it must be the majority element.) So let’s be more ambitious: can we compute the majority element with a single left-to-right pass through the array? If you haven’t seen it before, here’s the solution:
FashionBrain Project: A Vision for Understanding Europe's Fashion Data Universe

A core business in the fashion industry is the understanding and prediction of customer needs and trends. Search engines and social networks are at the same time a fundamental bridge and a costly middleman between the customer’s purchase intention and the retailer. To better exploit Europe’s distinctive characteristics e.g., multiple languages, fashion and cultural differences, it is pivotal to reduce retailers’ dependence to search engines. This goal can be achieved by harnessing various data channels (manufacturers and distribution networks, online shops, large retailers, social media, market observers, call centers, press/magazines etc.) that retailers can leverage in order to gain more insight about potential buyers, and on the industry trends as a whole. This can enable the creation of novel on-line shopping experiences, the detection of influencers, and the prediction of upcoming fashion trends. In this paper, we provide an overview of the main research challenges and an analysis of the most promising technological solutions that we are investigating in the FashionBrain project.
The SAT Phase Transition

We describe a detailed experimental investigation of the phase transition for several different classes of randomly generated satisfiability problems. We observe a remarkable consistency of features in the phase transition despite the presence in some of the problem classes of clauses of mixed lengths. For instance, each of the problem classes considered has a sharp transition from satisfiable to unsatisfiable problems at a critical value. In addition, there is a common easy-hard-easy pattern in the median difficulty of the problems, with the hardest problems being associated with the phase transition. However, the difficulty of problems of mixed clause lengths is much more variable than that of fixed clause length. Indeed, whilst the median difficulty of random problems of mixed clause lengths can be orders of magnitude easier than that of equivalently sized problems of fixed clause length, the hardest problems of mixed clause lengths can be orders of magnitude harder than the hardest equivalently sized problems of fixed clause length. Such very hard random problems may be of considerable practical and theoretical use in analysing algorithm performance.
Observability-constrained Vision-aided Inertial Navigation

In this technical report, we study estimator inconsistency in Vision-aided Inertial Navigation Systems (VINS) from a standpoint of system observability. We postulate that a leading cause of inconsistency is the gain of spurious information along unobservable directions, resulting in smaller uncertainties, larger estimation errors, and divergence. We support our claim with an analytical study of the Observability Gramian, along with its right nullspace, which constitutes the basis of the unobservable directions of the system. We develop an Observability-Constrained VINS (OC-VINS), which explicitly enforces the unobservable directions of the system, hence preventing spurious information gain and reducing inconsistency. Our analysis, along with the proposed method for reducing inconsistency, are extensively validated with simulation trials and real-world experimentation.
Hydrolysis of lignocellulosic materials for ethanol production: a review.

Lignocellulosic biomass can be utilized to produce ethanol, a promising alternative energy source for the limited crude oil. There are mainly two processes involved in the conversion: hydrolysis of cellulose in the lignocellulosic biomass to produce reducing sugars, and fermentation of the sugars to ethanol. The cost of ethanol production from lignocellulosic materials is relatively high based on current technologies, and the main challenges are the low yield and high cost of the hydrolysis process. Considerable research efforts have been made to improve the hydrolysis of lignocellulosic materials. Pretreatment of lignocellulosic materials to remove lignin and hemicellulose can significantly enhance the hydrolysis of cellulose. Optimization of the cellulase enzymes and the enzyme loading can also improve the hydrolysis. Simultaneous saccharification and fermentation effectively removes glucose, which is an inhibitor to cellulase activity, thus increasing the yield and rate of cellulose hydrolysis.
Safe Reinforcement Learning via Shielding

Reinforcement learning algorithms discover policies that maximize reward, but do not necessarily guarantee safety during learning or execution phases. We introduce a new approach to learn optimal policies while enforcing properties expressed in temporal logic. To this end, given the temporal logic specification that is to be obeyed by the learning system, we propose to synthesize a reactive system called a shield. The shield monitors the actions from the learner and corrects them only if the chosen action causes a violation of the specification. We discuss which requirements a shield must meet to preserve the convergence guarantees of the learner. Finally, we demonstrate the versatility of our approach on several challenging reinforcement learning scenarios.
A Software Requirement Engineering Framework to Enhance Critical Success Factors for ERP Implementation

Requirement it’s most critical success or failure factor for system. Enterprise Resource Planning (ERP) one of famous enterprise system and many studies focus on define CSF of it to reduce failing cases of ERP implementation and negative factors affecting not only on implementing company but also on the ERP vendors. Many papers have studied the CSF influence in ERP implementation but very little concern about requirement engineering (RE). This research will fill the gap by providing critical review and develop an approach in software system engineering framework by taking account feedback from stakeholders. This original approach is how to deal with ERP failure through a depth relation related to requirement engineering traceability to CSF in a system engineering view (SOS) based on ANSI EIA 632 standard.
Inductive Teaching and Learning Methods : Definitions , Comparisons , and Research Bases

Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories. Alternative teaching approaches are more inductive. Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established. This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problembased learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching. The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods. While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes.
A Dual-Notch +27dBm Tx-Power Electrical-Balance Duplexer

An electrical-balance duplexer uses series connected step-down transformers to enhance linearity and power handling capability by reducing the voltage swing across nonlinear components. Wideband, dual-notch Tx-to-Rx isolation is demonstrated experimentally with a planar inverted-F antenna. The 0.18μm CMOS prototype achieves >50dB isolation for 220MHz aggregated bandwidth or >40dB dual-notch isolation for 160MHz bandwidth, +49dBm Tx-path IIP3 and -48dBc ACLR1 for +27dBm at the antenna.
Black start capability assessment in power system restoration

Black start (BS) capability assessment is important for system planners to prepare the power system restoration (PSR) plan. To achieve a faster restoration process, installing new BS generators can be beneficial in accelerating system restoration. While additional BS capability does not automatically benefit the restoration process, power systems have to update the PSR plan and quantify the benefit based on appropriate criteria. In this paper, a decision support tool, developed in an EPRI project, using Generic Restoration Milestones (GRMs)-based strategy is utilized to provide a quantitative way for assessing the optimal installation location and amount of BS capability. Based on the proposed criteria, the benefit from additional BS capability is quantified in terms of system restoration time. The IEEE Reliability Test System (RTS) 24-bus test system is used for validation of the proposed strategy, using the System Restoration Navigator (SRN) restoration tool. It is shown that power systems can benefit from new BS generators to reduce the restoration time. However, there is a threshold beyond which system restoration time cannot be further reduced from additional BS capability. Economic considerations should be taken into account when assessing additional BS capabilities.
PREDICTING AND DETERMINING THE CONTACT PRESSURE DISTRIBUTION IN JOINTS FORMED BY V-BAND CLAMPS

V-band clamps are utilised in a wide range of industries to connect together a pair of circular flanges, for ducts, pipes, turbocharger housings and even to form a joint between satellites and their delivery vehicle. In this paper, using a previously developed axisymmetric finite element model, the impact of contact pressure on the contact surface of the V-band clamp was studied and surface roughness measurements were used to investigate the distribution of contact pressure around the circumference of the V-band.
BizPro: Extracting and categorizing business intelligence factors from textual news articles

Company movements and market changes often are headlines of the news, providing managers with important business intelligence (BI). While existing corporate analyses are often based on numerical financial figures, relatively little work has been done to reveal from textual news articles factors that represent BI. In this research, we developed BizPro, an intelligent system for extracting and categorizing BI factors from news articles. BizPro consists of novel text mining procedures and BI factor modeling and categorization. Expert guidance and human knowledge (with high inter-rater reliability) were used to inform system development and profiling of BI factors. We conducted a case study of using the system to profile BI factors of four major IT companies based on 6859 sentences extracted from 231 news articles published in major news sources. The results show that the chosen techniques used in BizPro – Naïve Bayes (NB) and Logistic Regression (LR) – significantly outperformed a benchmark technique. NB was found to outperform LR in terms of precision, recall, F-measure, and area under ROC curve. This research contributes to developing a new system for profiling company BI factors from news articles, to providing new empirical findings to enhance understanding in BI factor extraction and categorization, and to addressing an important yet under-explored concern of BI analysis.
Structure-aware shape processing

Shape structure is about the arrangement and relations between shape parts. Structure-aware shape processing goes beyond local geometry and low level processing, and analyzes and processes shapes at a high level. It focuses more on the global inter and intra semantic relations among the parts of shape rather than on their local geometry.
 With recent developments in easy shape acquisition, access to vast repositories of 3D models, and simple-to-use desktop fabrication possibilities, the study of structure in shapes has become a central research topic in shape analysis, editing, and modeling. A whole new line of structure-aware shape processing algorithms has emerged that base their operation on an attempt to understand such structure in shapes. The algorithms broadly consist of two key phases: an analysis phase, which extracts structural information from input data; and a (smart) processing phase, which utilizes the extracted information for exploration, editing, and synthesis of novel shapes.
 In this course, we will organize, summarize, and present the key concepts and methodological approaches towards efficient structure-aware shape processing. We discuss common models of structure, their implementation in terms of mathematical formalism and algorithms, and explain the key principles in the context of a number of state-of-the-art approaches. Further, we attempt to list the key open problems and challenges, both at the technical and at the conceptual level, to make it easier for new researchers to better explore and contribute to this topic.
 Our goal is to both give the practitioner an overview of available structure-aware shape processing techniques, as well as identify future research questions in this important, emerging, and fascinating research area.
Anisotropic Noise Samples

We present a practical approach to generate stochastic anisotropic samples with Poisson-disk characteristic over a two-dimensional domain. In contrast to isotropic samples, we understand anisotropic samples as nonoverlapping ellipses whose size and density match a given anisotropic metric. Anisotropic noise samples are useful for many visualization and graphics applications. The spot samples can be used as input for texture generation, for example, line integral convolution (LIC), but can also be used directly for visualization. The definition of the spot samples using a metric tensor makes them especially suitable for the visualization of tensor fields that can be translated into a metric. Our work combines ideas from sampling theory and mesh generation to approximate generalized blue noise properties. To generate these samples with the desired properties, we first construct a set of nonoverlapping ellipses whose distribution closely matches the underlying metric. This set of samples is used as input for a generalized anisotropic Lloyd relaxation to distribute noise samples more evenly. Instead of computing the Voronoi tessellation explicitly, we introduce a discrete approach that combines the Voronoi cell and centroid computation in one step. Our method supports automatic packing of the elliptical samples, resulting in textures similar to those generated by anisotropic reaction-diffusion methods. We use Fourier analysis tools for quality measurement of uniformly distributed samples. The resulting samples have nice sampling properties, for example, they satisfy a blue noise property where low frequencies in the power spectrum are reduced to a minimum..
An Intrusion Detection Model Based on Deep Belief Networks

This paper focuses on an important research problem of Big Data classification in intrusion detection system. Deep Belief Networks is introduced to the field of intrusion detection, and an intrusion detection model based on Deep Belief Networks is proposed to apply in intrusion recognition domain. The deep hierarchical model is a deep neural network classifier of a combination of multilayer unsupervised learning networks, which is called as Restricted Boltzmann Machine, and a supervised learning network, which is called as Back-propagation network. The experimental results on KDD CUP 1999 dataset demonstrate that the performance of Deep Belief Networks model is better than that of SVM and ANN.