nils-herrmann/clean_scirepeval huggingface dataset:
EFFICIENT CYCLIC LEARNING RATE SCHEDULES AND THEIR EVALUATIONS FOR NEURAL NETWORK ENSEMBLE

Warm restart techniques on training deep neural networks often achieve better recognition accuracies and can be regarded as easy methods to obtain multiple neural networks with no additional training cost from a single training process. Ensembles of intermediate neural networks obtained by warm restart techniques can provide higher accuracy than a single neural network obtained finally by a whole training process. However, existing methods on both of warm restart and its ensemble techniques use fixed cyclic schedules and have little degree of parameter adaption. This paper extends a class of possible schedule strategies of warm restart, and clarifies their effectiveness for recognition performance. Specifically, we propose parameterized functions and various cycle schedules to improve recognition accuracies by the use of deep neural networks with no additional training cost. Experiments on CIFAR-10 and CIFAR-100 show that our methods can achieve more accurate rates than the existing cyclic training and ensemble methods.
Learning Steerable Filters for Rotation Equivariant CNNs

In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input. Convolutional neural networks (CNNs) implement translational equivariance by construction; for other transformations, however, they are compelled to learn the proper mapping. In this work, we develop Steerable Filter CNNs (SFCNNs) which achieve joint equivariance under translations and rotations by design. The proposed architecture employs steerable filters to efficiently compute orientation dependent responses for many orientations without suffering interpolation artifacts from filter rotation. We utilize group convolutions which guarantee an equivariant mapping. In addition, we generalize He's weight initialization scheme to filters which are defined as a linear combination of a system of atomic filters. Numerical experiments show a substantial enhancement of the sample complexity with a growing number of sampled filter orientations and confirm that the network generalizes learned patterns over orientations. The proposed approach achieves state-of-the-art on the rotated MNIST benchmark and on the ISBI 2012 2D EM segmentation challenge.
Behavioral Finance : A Review and Synthesis

I provide a synthesis of the behavioral finance literature over the past two decades. I review the literature in three parts, namely, (i) empirical and theoretical analyses of patterns in the cross-section of average stock returns, (ii) studies on trading activity, and (iii) research in corporate finance. Behavioral finance is an exciting new field because it presents a number of normative implications for both individual investors and CEOs. The papers reviewed here allow us to learn more about these specific implications.
Malware Analysis using Multiple API Sequence Mining Control Flow Graph

Malwares are becoming persistent by creating fulledged variants of the same or different family. Malwares belonging to same family share same characteristics in their functionality of spreading infections into the victim computer. These similar characteristics among malware families can be taken as a measure for creating a solution that can help in the detection of the malware belonging to particular family. In our approach we have taken the advantage of detecting these malware families by creating the database of these characteristics in the form of n-grams of API sequences. We use various similarity score methods and also extract multiple API sequences to analyze malware effectively.
Revisiting the Importance of Encoding Logic Rules in Sentiment Classification

We analyze the performance of different sentiment classification models on syntacticallycomplex inputs like A-but-B sentences. The first contribution of this analysis addresses reproducible research: to meaningfully compare different models, their accuracies must be averaged over far more random seeds than what has traditionally been reported. With proper averaging in place, we notice that the distillation model described in Hu et al. (2016), which incorporates explicit logic rules for sentiment classification, is ineffective. In contrast, using contextualized ELMo embeddings (Peters et al., 2018a) instead of logic rules yields significantly better performance. Additionally, we provide analysis and visualizations that demonstrate ELMo’s ability to implicitly learn logic rules. Finally, a crowdsourced analysis reveals how ELMo outperforms baseline models even on sentences with ambiguous sentiment labels.
Passphone: Outsourcing Phone-based Web Authentication while Protecting User Privacy

This work introduces Passphone, a new smartphone-based authentication scheme that outsources user verification to a trusted third party without sacrificing privacy: neither can the trusted third party learn the relation between users and service providers, nor can service providers learn those of their users to others. When employed as a second factor in conjunction with, for instance, passwords as a first factor, our scheme maximizes the deployability of two-factor authentication for service providers while maintaining user privacy. We conduct a twofold formal analysis of our scheme, the first regarding its general security, and the second regarding anonymity and unlinkability of its users. Moreover, we provide an automatic analysis using AVISPA, a comparative evaluation to existing schemes under Bonneau et al.’s framework, and an evaluation of a prototypical implementation.
Gay marriage, same-sex parenting, and America's children.

Same-sex marriage, barely on the political radar a decade ago, is a reality in America. How will it affect the well-being of children? Some observers worry that legalizing same-sex marriage would send the message that same-sex parenting and opposite-sex parenting are interchangeable, when in fact they may lead to different outcomes for children. To evaluate that concern, William Meezan and Jonathan Rauch review the growing body of research on how same-sex parenting affects children. After considering the methodological problems inherent in studying small, hard-to-locate populations--problems that have bedeviled this literature-the authors find that the children who have been studied are doing about as well as children normally do. What the research does not yet show is whether the children studied are typical of the general population of children raised by gay and lesbian couples. A second important question is how same-sex marriage might affect children who are already being raised by same-sex couples. Meezan and Rauch observe that marriage confers on children three types of benefits that seem likely to carry over to children in same-sex families. First, marriage may increase children's material well-being through such benefits as family leave from work and spousal health insurance eligibility. It may also help ensure financial continuity, should a spouse die or be disabled. Second, same-sex marriage may benefit children by increasing the durability and stability of their parents' relationship. Finally, marriage may bring increased social acceptance of and support for same-sex families, although those benefits might not materialize in communities that meet same-sex marriage with rejection or hostility. The authors note that the best way to ascertain the costs and benefits of the effects of same-sex marriage on children is to compare it with the alternatives. Massachusetts is marrying same-sex couples, Vermont and Connecticut are offering civil unions, and several states offer partner-benefit programs. Studying the effect of these various forms of unions on children could inform the debate over gay marriage to the benefit of all sides of the argument.
Stability Analysis of Lattice Boltzmann Methods

The lattice Boltzmann equation describes the evolution of the velocity distribution function on a lattice in a manner that macroscopic fluid dynamical behavior is recovered. Although the equation is a derivative of lattice gas automata, it may be interpreted as a Lagrangian finite-difference method for the numerical simulation of the discrete-velocity Boltzmann equation that makes use of a BGK collision operator. As a result, it is not surprising that numerical instability of lattice Boltzmann methods have been frequently encountered by researchers. We present an analysis of the stability of perturbations of the particle populations linearized about equilibrium values corresponding to a constantdensity uniform mean flow. The linear stability depends on the following parameters: the distribution of the mass at a site between the different discrete speeds, the BGK relaxation time, the mean velocity, and the wavenumber of the perturbations. This parameter space is too large to compute the complete stability characteristics. We report some stability results for a subset of the parameter space for a 7-velocity hexagonal lattice, a 9-velocity square lattice and a 15-velocity cubic lattice. Results common to all three lattices are 1) the BGK relaxation time τ must be greater than 1 2 corresponding to positive shear viscosity, 2) there exists a maximum stable mean velocity for fixed values of the other parameters and 3) as τ is increased from 1 2 the maximum stable velocity increases monotonically until some fixed velocity is reached which does not change for larger τ .
A Rigorous ODE Solver and Smale's 14th Problem

We present an algorithm for computing rigorous solutions to a large class of ordinary differential equations. The main algorithm is based on a partitioning process and the use of interval arithmetic with directed rounding. As an application, we prove that the Lorenz equations support a strange attractor, as conjectured by Edward Lorenz in 1963. This conjecture was recently listed by Steven Smale as one of several challenging problems for the twenty-first century. We also prove that the attractor is robust, i.e., it persists under small perturbations of the coefficients in the underlying differential equations. Furthermore, the flow of the equations admits a unique SRB measure, whose support coincides with the attractor. The proof is based on a combination of normal form theory and rigorous computations.
Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models

The problems of recognizing mentions of entities in texts and linking them to unique knowledge base identifiers have received considerable attention in recent years. In this paper we present a probabilistic system based on undirected graphical models that jointly addresses both the entity recognition and the linking task. Our framework considers the span of mentions of entities as well as the corresponding knowledge base identifier as random variables and models the joint assignment using a factorized distribution. We show that our approach can be easily applied to different technical domains by merely exchanging the underlying ontology. On the task of recognizing and linking disease names, we show that our approach outperforms the state-of-the-art systems DNorm and TaggerOne, as well as two strong lexicon-based baselines. On the task of recognizing and linking chemical names, our system achieves comparable performance to the state-of-the-art.
Polymers for drug delivery systems.

Polymers have played an integral role in the advancement of drug delivery technology by providing controlled release of therapeutic agents in constant doses over long periods, cyclic dosage, and tunable release of both hydrophilic and hydrophobic drugs. From early beginnings using off-the-shelf materials, the field has grown tremendously, driven in part by the innovations of chemical engineers. Modern advances in drug delivery are now predicated upon the rational design of polymers tailored for specific cargo and engineered to exert distinct biological functions. In this review, we highlight the fundamental drug delivery systems and their mathematical foundations and discuss the physiological barriers to drug delivery. We review the origins and applications of stimuli-responsive polymer systems and polymer therapeutics such as polymer-protein and polymer-drug conjugates. The latest developments in polymers capable of molecular recognition or directing intracellular delivery are surveyed to illustrate areas of research advancing the frontiers of drug delivery.
Hierarchical Imitation and Reinforcement Learning

We study how to effectively leverage expert feedback to learn sequential decision-making policies. We focus on problems with sparse rewards and long time horizons, which typically pose significant challenges in reinforcement learning. We propose an algorithmic framework, called hierarchical guidance, that leverages the hierarchical structure of the underlying problem to integrate different modes of expert interaction. Our framework can incorporate different combinations of imitation learning (IL) and reinforcement learning (RL) at different levels, leading to dramatic reductions in both expert effort and cost of exploration. Using long-horizon benchmarks, including Montezuma’s Revenge, we demonstrate that our approach can learn significantly faster than hierarchical RL, and be significantly more label-efficient than standard IL. We also theoretically analyze labeling cost for certain instantiations of our framework.
A Digital Forensics Triage methodology based on feature manipulation techniques

The evolution of modern digital devices is outpacing the scalability and effectiveness of Digital Forensics techniques. Digital Forensics Triage is one solution to this problem as it can extract evidence quickly at the crime scene and provide vital intelligence in time critical investigations. Similarly, such methodologies can be used in a laboratory to prioritize deeper analysis of digital devices and alleviate examination backlog. Developments in Digital Forensics Triage methodologies have moved towards automating the device classification process and those which incorporate Machine Learning principles have proven to be successful. Such an approach depends on crime-related features which provide a relevant basis upon which device classification can take place. In addition, to be an accepted and viable methodology it should be also as accurate as possible. Previous work has concentrated on the issues of feature extraction and classification, where less attention has been paid to improving classification accuracy through feature manipulation. In this regard, among the several techniques available for the purpose, we concentrate on feature weighting, a process which places more importance on specific features. A twofold approach is followed: on one hand, automated feature weights are quantified using Kullback-Leibler measure and applied to the training set whereas, on the other hand, manual weights are determined with the contribution of surveyed digital forensic experts. Experimental results of manual and automatic feature weighting are described which conclude that both the techniques are effective in improving device classification accuracy in crime investigations.
Real-time part-based visual tracking via adaptive correlation filters

Robust object tracking is a challenging task in computer vision. To better solve the partial occlusion issue, part-based methods are widely used in visual object trackers. However, due to the complicated online training and updating process, most of these part-based trackers cannot run in real-time. Correlation filters have been used in tracking tasks recently because of the high efficiency. However, the conventional correlation filter based trackers cannot deal with occlusion. Furthermore, most correlation filter based trackers fix the scale and rotation of the target which makes the trackers unreliable in long-term tracking tasks. In this paper, we propose a novel tracking method which track objects based on parts with multiple correlation filters. Our method can run in real-time. Additionally, the Bayesian inference framework and a structural constraint mask are adopted to enable our tracker to be robust to various appearance changes. Extensive experiments have been done to prove the effectiveness of our method.
e-government: ITIL-based service management case study

E-government services require certain service levels to be achieved as they replace traditional channels. E-government also increases the dependence of government agencies on information technology based services. High quality services entail high performance, availability and scalability among other service characteristics. Strict measures are required to help e-governments evaluate the service level and assess the quality of the service. In this paper we introduce the IT Infrastructure Library (ITIL) framework - a set of best practices to achieve quality service and overcome difficulties associated with the growth of IT systems [17][21]. We conducted an in depth assessment and gap analysis for both of the service support and service delivery processes [16], in a government institution, which allowed us to assess its maturity level within the context of ITIL. We then proposed and modeled these processes in accordance to ITIL best practices and based upon agency aspirations and environment constraints.
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features

Notation Vectors are denoted by lower-case bold letters, e.g., x, matrices are denoted by upper-case bold letters, e.g., X and sets are denoted by calligraphic upper-case letters, e.g., X. All vectors are assumed to be column vectors. The (i, j) entry of X is xij . Let {x+i }i=1 be a set of pedestrian training examples and {xj }j=1 be a set of non-pedestrian training examples. The tuple of all training samples is written as S = (S+,S−) where S+ = (x + 1 , · · · ,xm) ∈ X and S− = (x − 1 , · · · ,xn ) ∈ X. In this paper, we are interested in the partial AUC (area under the ROC curve) within a specific false positive range [α, β]. Given n negative training samples, we let jα = dnαe and jβ = bnβc. Let Zβ = ( S− jβ ) denote the
Knowledge Graph Search

We introduce a highly scalable approach for open-domain question answering with no dependence on any data set for surface form to logical form mapping or any linguistic analytic tool such as POS tagger or named entity recognizer. We define our approach under the Constrained Conditional Models framework which lets us scale up to a full knowledge graph with no limitation on the size. On a standard benchmark, we obtained near 4 percent improvement over the state-of-the-art in open-domain question answering task.
What is compassion and how can we measure it? A review of definitions and measures.

The importance of compassion is widely recognized and it is receiving increasing research attention. Yet, there is lack of consensus on definition and a paucity of psychometrically robust measures of this construct. Without an agreed definition and adequate measures, we cannot study compassion, measure compassion or evaluate whether interventions designed to enhance compassion are effective. In response, this paper proposes a definition of compassion and offers a systematic review of self- and observer-rated measures. Following consolidation of existing definitions, we propose that compassion consists of five elements: recognizing suffering, understanding the universality of human suffering, feeling for the person suffering, tolerating uncomfortable feelings, and motivation to act/acting to alleviate suffering. Three databases were searched (Web of Science, PsycInfo, and Medline) and nine measures included and rated for quality. Quality ratings ranged from 2 to 7 out of 14 with low ratings due to poor internal consistency for subscales, insufficient evidence for factor structure and/or failure to examine floor/ceiling effects, test-retest reliability, and discriminant validity. We call our five-element definition, and if supported, the development of a measure of compassion based on this operational definition, and which demonstrates adequate psychometric properties.
Evolving plastic neural networks with novelty search

Biological brains can adapt and learn from past experience. Yet neuroevolution, i.e. automatically creating artificial neural networks (ANNs) through evolutionary algorithms, has sometimes focused on static ANNs that cannot change their weights during their lifetime. A profound problem with evolving adaptive systems is that learning to learn is highly deceptive. Because it is easier at first to improve fitness without evolving the ability to learn, evolution is likely to exploit domain-dependent static (i.e. non-adaptive) heuristics. This paper analyzes this inherent deceptiveness in a variety of different dynamic, reward-based learning tasks, and proposes a way to escape the deceptive trap of static policies based on the novelty search algorithm. The main idea in novelty search is to abandon objective-based fitness and instead simply search only for novel behavior, which avoids deception entirely. A series of experiments and an in-depth analysis show how behaviors that could potentially serve as a stepping stone to finding adaptive solutions are discovered by novelty search yet are missed by fitness-based search. The conclusion is that novelty search has the potential to foster the emergence of adaptive behavior in reward-based learning tasks, thereby opening a new direction for research in evolving plastic ANNs.
Roles of Moringa oleifera Leaf Extract in Improving the Impact of High Dietary Intake of Monosodium Glutamate-Induced Liver Toxicity, Oxidative Stress, Genotoxicity, DNA Damage, and PCNA Alterations in Male Rats

It is common for food to be made more palatable through the use of the flavour enhancer monosodium glutamate, also known as vetsin powder. The purpose of the study described in this paper was to explore how vetsin-induced hepatic toxicity, DNA fragmentation, damage, and oxidative stress modifications could be mitigated with moringa leaf extract (MLE). To that end, 40 male rats were separated into four groups: normal control, positive control or MLE, vetsin, and vetsin combined with MLE. Results indicated that, compared to the control group, the levels of serum alanine aminotransferase (ALT), aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), liver malondialdehyde (MDA), DNA damage, injury, PCNA, and P53 expressions were significantly enhanced by the administration of vetsin (P < 0.05). However, the vetsin group had significantly reduced levels of albumin, globulin, total protein, liver glutathione (GSH), superoxide dismutase enzyme (SOD), catalase, and glutathione S-transferase (GST) enzyme activities (P < 0.05) by comparison to control. Meanwhile, modifications in liver functions, oxidative stress, DNA damage, liver injury, and PCNA expression were alleviated when vetsin was administered alongside MLE. The authors conclude that vetsin may have many side effects and that MLE can ameliorate biochemical changes, oxidative stress, hepatic injury, PCNA, and P53 alterations induced by vetsin administration.
A Case Study on Quality Function Deployment ( QFD )

Quality Function Deployment (QFD) was conceived in Japan in the late 1960's, and introduced to America and Europe in 1983. This paper will provide a general overview of the QFD methodology and approach to product development. Once familiarity with the tool is established, a real-life application of the technique will be provided in a case study. The case study will illustrate how QFD was used to develop a new tape product and provide counsel to those that may want to implement the QFD process. Quality function deployment (QFD) is a “method to transform user demands into design quality, to deploy the functions forming quality, and to deploy methods for achieving the design quality into subsystems and component parts, and ultimately to specific elements of the manufacturing process.”