nils-herrmann/clean_scirepeval huggingface dataset:
Fast Recognition of Remixed Music Audio

We present an efficient algorithm for automatically detecting remixes of pop songs in large commercial collections. Remixes are closely related as commercial products but they are not closely related in their audio spectral content because of the nature of the remixing process. Therefore spectral modelling approaches to audio similarity fail to recognize them. We propose a new approach - that chops songs into small chunks called audio shingles - to recognize remixed songs. We model the distribution of pair-wise distances between shingles by two independent processes - one corresponding to remix content and the other corresponding to non-remix content in a database. A nearest neighbour algorithm groups songs if they share shingles drawn from the remix process. Our results show 1) log-chromagram shingles separate remixed from non-remixed content with 75%-75% precision-recall performance, cepstral coefficient features do not separate the two distributions adequately 2) increasing the observations from the remix distribution increases the separability. Efficient implementation follows from the separability of the distributions using locality sensitive hashing (LSH) which speeds up automatic grouping of remixes by between one to two orders of magnitude in a 2018-song test set.
Constructive Induction of Cartesian Product Attributes

Constructive induction is the process of changing the representation of examples by creating new attributes from existing attributes. In classi cation, the goal of constructive induction is to nd a representation that facilitates learning a concept description by a particular learning system. Typically, the new attributes are Boolean or arithmetic combinations of existing attributes and the learning algorithms used are decision trees or rule learners. We describe the construction of new attributes that are the Cartesian product of existing attributes. We consider the e ects of this operator on a Bayesian classi er an a nearest neighbor algorithm.
Pixel Perfect : Fingerprinting Canvas in HTML 5

Tying the browser more closely to operating system functionality and system hardware means that websites have more access to these resources, and that browser behavior varies depending on the behavior of these resources. We propose a new system fingerprint, inspired by the observation above: render text and WebGL scenes to a <canvas> element, then examine the pixels produced. The new fingerprint is consistent, high-entropy, orthogonal to other fingerprints, transparent to the user, and readily obtainable.
Bilingualism, aging, and cognitive control: evidence from the Simon task.

Previous work has shown that bilingualism is associated with more effective controlled processing in children; the assumption is that the constant management of 2 competing languages enhances executive functions (E. Bialystok, 2001). The present research attempted to determine whether this bilingual advantage persists for adults and whether bilingualism attenuates the negative effects of aging on cognitive control in older adults. Three studies are reported that compared the performance of monolingual and bilingual middle-aged and older adults on the Simon task. Bilingualism was associated with smaller Simon effect costs for both age groups; bilingual participants also responded more rapidly to conditions that placed greater demands on working memory. In all cases the bilingual advantage was greater for older participants. It appears, therefore, that controlled processing is carried out more effectively by bilinguals and that bilingualism helps to offset age-related losses in certain executive processes.
Automating Mappings between Use Case Diagrams and Feature Models for Software Product Lines

Features have been widely used by the product line community to model variability. They represent the common and variable characteristics of the members of a product line. They are very well suited for the configuration of product line members. Outside the product line community, use cases are also widely used to model the functionality of systems at a similar level of abstraction but from a user perspective. Significant work has been done by several authors regarding the possible relationship between these two perspectives of a system. Nonetheless, this has been done in an informal way. In this paper we explore the relationships between these two perspectives and describe a possible approach to automate the transformation from UML use case to feature models.
Robust TiN HM process to overcome under etch issue for SAV scheme on 14nm node

For advance node such as 14nm technology and beyond, back end of line interconnect has implemented self-aligned via (SAV) schemes for better via-metal short process window . A TiN metal hard mask (MHM) is used for the trench pattern definition, after which via lithography and partial via (PV) etch is performed where the TiN was opened. The via etch condition has very good selectivity so that via is formed in a self-aligned fashion by TiN HM . It is indeed to have significant benefit of via to metal short . However, one of the trade off in SAV scheme is the via under etch that whether or not via can well land on an opened oxide area during PV etch. To define the contact area between via resist hole and opened HM oxide as an effective area for via to open successfully. “Fig. 1” shows the effective area change during process variation due to Via alignment, Via photo resist CD variation (Via ADICD) and post hard mask etch CD variation (AMICD). “Fig. 2” shows the mechanism of this under etch failure mode and typical TEM image from 64nm metal pitch test vehicle. In this work, we try to enlarge the process window by an aggressive AMICD targeting in combining with a higher density TiN material to maintain profile.
Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going?

The growth of detection datasets and the multiple directions of object detection research provide both an unprecedented need and a great opportunity for a thorough evaluation of the current state of the field of categorical object detection. In this paper we strive to answer two key questions. First, where are we currently as a field: what have we done right, what still needs to be improved? Second, where should we be going in designing the next generation of object detectors? Inspired by the recent work of Hoiem et al. on the standard PASCAL VOC detection dataset, we perform a large-scale study on the Image Net Large Scale Visual Recognition Challenge (ILSVRC) data. First, we quantitatively demonstrate that this dataset provides many of the same detection challenges as the PASCAL VOC. Due to its scale of 1000 object categories, ILSVRC also provides an excellent test bed for understanding the performance of detectors as a function of several key properties of the object classes. We conduct a series of analyses looking at how different detection methods perform on a number of image-level and object-class-level properties such as texture, color, deformation, and clutter. We learn important lessons of the current object detection methods and propose a number of insights for designing the next generation object detectors.
Adversarial machine learning

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.
Will 5G See its Blind Side? Evolving 5G for Universal Internet Access

Internet has shown itself to be a catalyst for economic growth and social equity but its potency is thwarted by the fact that the Internet is off limits for the vast majority of human beings. Mobile phones—the fastest growing technology in the world that now reaches around 80% of humanity—can enable universal Internet access if it can resolve coverage problems that have historically plagued previous cellular architectures (2G, 3G, and 4G). These conventional architectures have not been able to sustain universal service provisioning since these architectures depend on having enough users per cell for their economic viability and thus are not well suited to rural areas (which are by definition sparsely populated). The new generation of mobile cellular technology (5G), currently in a formative phase and expected to be finalized around 2020, is aimed at orders of magnitude performance enhancement. 5G offers a clean slate to network designers and can be molded into an architecture also amenable to universal Internet provisioning. Keeping in mind the great social benefits of democratizing Internet and connectivity, we believe that the time is ripe for emphasizing universal Internet provisioning as an important goal on the 5G research agenda. In this paper, we investigate the opportunities and challenges in utilizing 5G for global access to the Internet for all (GAIA). We have also identified the major technical issues involved in a 5G-based GAIA solution and have set up a future research agenda by defining open research problems.
Toward Optimal Pedagogical Action Patterns by Means of Partially Observable Markov Decision Process

Good pedagogical actions are key components in all learning-teaching schemes. Automate that is an important Intelligent Tutoring Systems objective. We propose apply Partially Observable Markov Decision Process (POMDP) in order to obtain automatic and optimal pedagogical recommended action patterns in benefit of human students, in the context of Intelligent Tutoring System. To achieve that goal, we need previously create an efficient POMDP solver framework with the ability to work with real world tutoring cases. At present time, there are several Web available POMDP open tool solvers, but their capacity is limited, as experiments showed in this paper exhibit. In this work, we describe and discuss several design ideas toward obtain an efficient POMDP solver, useful in our problem domain.
Recall of childhood trauma: a prospective study of women's memories of child sexual abuse.

One hundred twenty-nine women with previously documented histories of sexual victimization in childhood were interviewed and asked detailed questions about their abuse histories to answer the question "Do people actually forget traumatic events such as child sexual abuse, and if so, how common is such forgetting?" A large proportion of the women (38%) did not recall the abuse that had been reported 17 years earlier. Women who were younger at the time of the abuse and those who were molested by someone they knew were more likely to have no recall of the abuse. The implications for research and practice are discussed. Long periods with no memory of abuse should not be regarded as evidence that the abuse did not occur.
Smooth contact-aware facial blendshapes transfer

In this paper, we propose a method to improve the quality of the facial blendshapes synthesized using deformation transfer by introducing the idea of contact and smoothness awareness. Simply applying deformation transfer for face models easily results in penetrations and separations which do not exist in the original data. As there is no concept of smoothness, crumpling artifacts appear where the mesh is extremely concentrated, e.g. eyelids and mouth corners. We solve these issues by first introducing the concept of virtual triangles and then adding a Laplacian energy term into the linear problem to be solved for the deformation transfer. Experimental results show that our system reduces the amount of artifacts caused by the original deformation transfer, improving the efficiency of the animation pipeline.
Predicting Movie Sales from Blogger Sentiment

The volume of discussion about a product in weblogs has recently been shown to correlate with the product’s financial performance. In this paper, we study whether applying sentiment analysis methods to weblog data results in better correlation than volume only, in the domain of movies. Our main finding is that positive sentiment is indeed a better predictor for movie success when applied to a limited context around references to the movie in weblogs, posted prior to its release. If my film makes one more person miserable, I’ve done my job.
Global Linear Neighborhoods for Efficient Label Propagation

Graph-based semi-supervised learning improves classification by combining labeled and unlabeled data through label propagation. It was shown that the sparse representation of graph by weighted local neighbors provides a better similarity measure between data points for label propagation. However, selecting local neighbors can lead to disjoint components and incorrect neighbors in graph, and thus, fail to capture the underlying global structure. In this paper, we propose to learn a nonnegative low-rank graph to capture global linear neighborhoods, under the assumption that each data point can be linearly reconstructed from weighted combinations of its direct neighbors and reachable indirect neighbors. The global linear neighborhoods utilize information from both direct and indirect neighbors to preserve the global cluster structures, while the low-rank property retains a compressed representation of the graph. An efficient algorithm based on a multiplicative update rule is designed to learn a nonnegative low-rank factorization matrix minimizing the neighborhood reconstruction error. Large scale simulations and experiments on UCI datasets and high-dimensional gene expression datasets showed that label propagation based on global linear neighborhoods captures the global cluster structures better and achieved more accurate classification results.
SparkNet: Training Deep Networks in Spark

Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensive workloads of existing distributed deep learning systems. We introduce SparkNet, a framework for training deep networks in Spark. Our implementation includes a convenient interface for reading data from Spark RDDs, a Scala interface to the Caffe deep learning framework, and a lightweight multidimensional tensor library. Using a simple parallelization scheme for stochastic gradient descent, SparkNet scales well with the cluster size and tolerates very high-latency communication. Furthermore, it is easy to deploy and use with no parameter tuning, and it is compatible with existing Caffe models. We quantify the dependence of the speedup obtained by SparkNet on the number of machines, the communication frequency, and the cluster’s communication overhead, and we benchmark our system’s performance on the ImageNet dataset.
Issues in the Specification and Measurement of Software Portability

Portability is becoming universally recognized as a desirable attribute for most software products, and porting is a recognized technique for extending the value and life of a software unit. However, most porting is still done by ad hoc techniques. Portability is not addressed explicitly either by software engineering curricula or by common software development methodologies. Moreover, there are few reports in the literature of academic research focused on portability issues. Many studies deal with reuse issues, and porting is a special case of reuse. However, these studies have not addressed the specialized problems of reuse across substantially different operating environments. A portability research group has been established at West Virginia University to help meet this need. Our overall goal is to develop improved methodologies for addressing portability in a cost-effective way for a wide range of software. A major obstacle to meeting this goal is the lack of established mechanisms to specify and measure portability. In this presentation we propose some initial approaches to the development of such mechanisms. We first describe briefly the issues that arise in developing portable software, and in reimplementing or porting existing software. This analysis is used to identify specification types and metrics that may aid in making effective portability-related decisions when performing these activities. We argue the need for specifications to describe interfaces and environmental attributes and to characterize the “degree of portability” of a software unit. Some approaches to formalizing these concepts are discussed. Cost metrics for software development and porting activities are also discussed, and we consider their use in comparing development and implementation alternatives. A formal definition for degree of portability is proposed, and some relevant software quality measures are briefly considered. Some simple examples are also presented to illustrate these concepts. This research is supported in part by the National Science Foundation, Grant No. CCR-9204461. 2. Specification & Measurement of Portability 15th International Conference on Software Engineering
A Distributed Edge Computing Architecture to Support Sensing and Detecting Leaks in Waterworks Based on Advanced FDM

High costs of urban services, namely, waterworks, transportation, waste collection, wastewater collection and treatment, energy, and public lighting, require their optimization in management. This optimization can be mostly achieved using dedicated technology and strategy by “building” smart cities and smart grids. This paper illustrates findings related to the application of a designed distributed edge computing system for supervising a network of sensors, located on a special configuration of a pipeline, to detect leaks. The plant to be supervised is a zigzag waterworks with leaks to be simulated by opening and closing taps. The pressure variation is detected by magnetic sensors, which convert pressure variation into electric signal to be processed on-line thanks to an advanced and robust algorithm called a filter diagonalization method that performs a spectral analysis. In this paper, we have also developed a 2-D representation of the leak within the pipeline or waterworks, which is a robust way to see the dimensions or the expansion of the leak in a specific space.
Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach

In this paper, we study the task of 3D human pose estimation in the wild. This task is challenging due to lack of training data, as existing datasets are either in the wild images with 2D pose or in the lab images with 3D pose.,, We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure. Our network augments a state-of-the-art 2D pose estimation sub-network with a 3D depth regression sub-network. Unlike previous two stage approaches that train the two sub-networks sequentially and separately, our training is end-to-end and fully exploits the correlation between the 2D pose and depth estimation sub-tasks. The deep features are better learnt through shared representations. In doing so, the 3D pose labels in controlled lab environments are transferred to in the wild images. In addition, we introduce a 3D geometric constraint to regularize the 3D pose prediction, which is effective in the absence of ground truth depth labels. Our method achieves competitive results on both 2D and 3D benchmarks.
Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization

Feature selection is an important component of many machine learning applications. Especially in many bioinformatics tasks, efficient and robust feature selection methods are desired to extract meaningful features and eliminate noisy ones. In this paper, we propose a new robust feature selection method with emphasizing joint `2,1-norm minimization on both loss function and regularization. The `2,1-norm based loss function is robust to outliers in data points and the `2,1norm regularization selects features across all data points with joint sparsity. An efficient algorithm is introduced with proved convergence. Our regression based objective makes the feature selection process more efficient. Our method has been applied into both genomic and proteomic biomarkers discovery. Extensive empirical studies are performed on six data sets to demonstrate the performance of our feature selection method.
Truth in Advertising: The Hidden Cost of Mobile Ads for Software Developers

The "free app" distribution model has been extremely popular with end users and developers. Developers use mobile ads to generate revenue and cover the cost of developing these free apps. Although the apps are ostensibly free, they in fact do come with hidden costs. Our study of 21 real world Android apps shows that the use of ads leads to mobile apps that consume significantly more network data, have increased energy consumption, and require repeated changes to ad related code. We also found that complaints about these hidden costs are significant and can impact the ratings given to an app. Our results provide actionable information and guidance to software developers in weighing the tradeoffs of incorporating ads into their mobile apps.
Using link structure to infer opinions in social networks

The emergence of online social networks in the past few years has generated an enormous amount of information about potentially any subject. Valuable data containing users' opinions and thoughts are available on those repositories and several sentiment analysis techniques have been proposed that address the problem of understanding the opinion orientation of the users' postings. In this paper, we take a different perspective to the problem through a user centric approach, which uses a graph to model users (and their postings) and applies link mining techniques to infer opinions of users. Preliminary experiments on a Twitter corpus have shown promising results.
A deep learning framework for unsupervised affine and deformable image registration

Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.