nils-herrmann/clean_scirepeval huggingface dataset:
Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms

Many clustering and segmentation algorithms both suffer from the limitation that the number of clusters/segments is specified by a human user. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters/segments to return. We investigate techniques to determine the number of clusters or segments to return from hierarchical clustering and segmentation algorithms. We propose an efficient algorithm, the L method that finds the "knee" in a '# of clusters vs. clustering evaluation metric' graph. Using the knee is well-known, but is not a particularly well-understood method to determine the number of clusters. We explore the feasibility of this method, and attempt to determine in which situations it will and will not work. We also compare the L method to existing methods based on the accuracy of the number of clusters that are determined and efficiency. Our results show favorable performance for these criteria compared to the existing methods that were evaluated.
Genetic algorithms and neural networks: optimizing connections and connectivity

Genetic algorithms are a robust adaptive optimization method based on biological principles. A population of strings representing possible problem solutions is maintained. Search proceeds by recombining strings in the population. The theoretical foundations of genetic algorithms are based on the notion that selective reproduction and recombination of binary strings changes the sampling rate of hyperplanes in the search space so as to reflect the average fitness of strings that reside in any particular hyperplane. Thus, genetic algorithms need not search along the contours of the function being optimized and tend not to become trapped in local minima. This paper is an overview of several different experiments applying genetic algorithms to neural network problems. These problems include (1) optimizing the weighted connections in feed-forward neural networks using both binary and real-valued representations, and (2) using a genetic algorithm to discover novel architectures in the form of connectivity patterns for neural networks that learn using error propagation. Future applications in neural network optimization in which genetic algorithm can perhaps play a significant role are also presented.
Weakly-Supervised Deep Learning for Customer Review Sentiment Classification

Sentiment analysis is one of the key challenges for mining online user generated content. In this work, we focus on customer reviews which are an important form of opinionated content. The goal is to identify each sentence’s semantic orientation (e.g. positive or negative) of a review. Traditional sentiment classification methods often involve substantial human efforts, e.g. lexicon construction, feature engineering. In recent years, deep learning has emerged as an effective means for solving sentiment classification problems. A neural network intrinsically learns a useful representation automatically without human efforts. However, the success of deep learning highly relies on the availability of large-scale training data. In this paper, we propose a novel deep learning framework for review sentiment classification which employs prevalently available ratings as weak supervision signals. The framework consists of two steps: (1) learn a high level representation (embedding space) which captures the general sentiment distribution of sentences through rating information; (2) add a classification layer on top of the embedding layer and use labeled sentences for supervised fine-tuning. Experiments on review data obtained from Amazon show the efficacy of our method and its superiority over baseline methods.
Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction

Recognizing objects in fine-grained domains can be extremely challenging due to the subtle differences between subcategories. Discriminative markings are often highly localized, leading traditional object recognition approaches to struggle with the large pose variation often present in these domains. Pose-normalization seeks to align training exemplars, either piecewise by part or globally for the whole object, effectively factoring out differences in pose and in viewing angle. Prior approaches relied on computationally-expensive filter ensembles for part localization and required extensive supervision. This paper proposes two pose-normalized descriptors based on computationally-efficient deformable part models. The first leverages the semantics inherent in strongly-supervised DPM parts. The second exploits weak semantic annotations to learn cross-component correspondences, computing pose-normalized descriptors from the latent parts of a weakly-supervised DPM. These representations enable pooling across pose and viewpoint, in turn facilitating tasks such as fine-grained recognition and attribute prediction. Experiments conducted on the Caltech-UCSD Birds 200 dataset and Berkeley Human Attribute dataset demonstrate significant improvements of our approach over state-of-art algorithms.
Human–Robot Synchrony: Flexible Assistance Using Adaptive Oscillators

We propose a novel method for movement assistance that is based on adaptive oscillators, i.e., mathematical tools that are capable of extracting the high-level features (amplitude, frequency, and offset) of a periodic signal. Such an oscillator acts like a filter on these features, but keeps its output in phase with respect to the input signal. Using a simple inverse model, we predicted the torque produced by human participants during rhythmic flexion extension of the elbow. Feeding back a fraction of this estimated torque to the participant through an elbow exoskeleton, we were able to prove the assistance efficiency through a marked decrease of the biceps and triceps electromyography. Importantly, since the oscillator adapted to the movement imposed by the user, the method flexibly allowed us to change the movement pattern and was still efficient during the nonstationary epochs. This method holds promise for the development of new robot-assisted rehabilitation protocols because it does not require prespecifying a reference trajectory and does not require complex signal sensing or single-user calibration: the only signal that is measured is the position of the augmented joint. In this paper, we further demonstrate that this assistance was very intuitive for the participants who adapted almost instantaneously.
Community Detection in Networks: The Leader-Follower Algorithm

Traditional spectral clustering methods cannot naturally learn the number of communities in a network and often fail to detect smaller community structure in dense networks because they are based upon external community connectivity properties such as graph cuts. We propose an algorithm for detecting community structure in networks called the leader-follower algorithm which is based upon the natural internal structure expected of communities in social networks. The algorithm uses the notion of network centrality in a novel manner to differentiate leaders (nodes which connect different communities) from loyal followers (nodes which only have neighbors within a single community). Using this approach, it is able to naturally learn the communities from the network structure and does not require the number of communities as an input, in contrast to other common methods such as spectral clustering. We prove that it will detect all of the communities exactly for any network possessing communities with the natural internal structure expected in social networks. More importantly, we demonstrate the effectiveness of the leader-follower algorithm in the context of various real networks ranging from social networks such as Facebook to biological networks such as an fMRI based human brain network. We find that the leader-follower algorithm finds the relevant community structure in these networks without knowing the number of communities beforehand. Also, because the leader-follower algorithm detects communities using their internal structure, we find that it can resolve a finer community structure in dense networks than common spectral clustering methods based on external community structure.
Competitive Coevolution through Evolutionary Complexification

Two major goals in machine learning are the discovery of comp lex multidimensional solutions and continual improvement of existing solutions. In this paper, we argue thatcomplexification, i.e. the incremental elaboration of solutions through adding new structure, ach ieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenti ng Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT i s applied to an open-ended coevolutionary robot duel domain where robot controllers compete head to head. Be caus the robot duel domain supports a wide range of sophisticated strategies, and because coevolutio n benefits from an escalating arms race, it serves as a suitable testbed for observing the effect of evolving in creasingly complex controllers. The result is an arms race of increasingly sophisticated strategies. When c ompared to the evolution of networks with fixed structure, complexifying networks discover significantly more sophisticated strategies. The results suggest that in order to realize the full potential of evolution, and search in general, solutions must be allowed to complexify as well as optimize.
ZhuSuan: A Library for Bayesian Deep Learning

In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan is featured for its deep root into Bayesian inference, thus supporting various kinds of probabilistic models, including both the traditional hierarchical Bayesian models and recent deep generative models. We use running examples to illustrate the probabilistic programming on ZhuSuan, including Bayesian logistic regression, variational auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural networks.
Towards generating arcade game rules with VGDL

We describe an attempt to generate complete arcade games using the Video Game Description Language (VGDL) and the General Video Game Playing environment (GVG-AI). Games are generated by an evolutionary algorithm working on genotypes represented as VGDL descriptions. In order to direct evolution towards good games, we need an evaluation function that accurately estimates game quality. The evaluation function used here is based on the differential performance of several game-playing algorithms, or Relative Algorithm Performance Profiles (RAPP): it is assumed that good games allow good players to play better than bad players. For the purpose of such evaluations, we introduce two new game tree search algorithms, DeepSearch and Explorer; these perform very well on benchmark games and constitute a substantial subsidiary contribution of the paper. In the end, the attempt to generate arcade games is only partially successful, as some of the games have interesting design features but are barely playable as generated. An analysis of these shortcomings yields several suggestions to guide future attempts at arcade game generation.
GloudSim: Google trace based cloud simulator with virtual machines

In 2011, Google released a one-month production trace with hundreds of thousands of jobs running across over 12,000 heterogeneous hosts. In order to perform in-depth research based on the trace, it is necessary to construct a close-to-practice simulation system. In this paper, we devise a distributed cloud simulator (or toolkit) based on virtual machines, with three important features. (1) The dynamic changing resource amounts (such as CPU rate and memory size) consumed by the reproduced jobs can be emulated as closely as possible to the real values in the trace. (2) Various types of events (e.g., kill/evict event) can be emulated precisely based on the trace. (3) Our simulation toolkit is able to emulate more complex and useful cases beyond the original trace to adapt to various research demands. We evaluate the system on a real cluster environment with 16×8=128 cores and 112 virtual machines (VMs) constructed by XEN hypervisor. To the best of our knowledge, this is the first work to reproduce Google cloud environment with real experimental system setting and real-world large scale production trace. Experiments show that our simulation system could effectively reproduce the real checkpointing/restart events based on Google trace, by leveraging Berkeley Lab Checkpoint/Restart (BLCR) tool. It can simultaneously process up to 1200 emulated Google jobs over the 112 VMs. Such a simulation toolkit has been released as a GNU GPL v3 software for free downloading, and it has been successfully applied to the fundamental research on the optimization of checkpoint intervals for Google tasks.
ICNet for Real-Time Semantic Segmentation on High-Resolution Images

We focus on the challenging task of real-time semantic segmentation in this paper. It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel-wise label inference. We propose an image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address this challenge. We provide in-depth analysis of our framework and introduce the cascade feature fusion unit to quickly achieve highquality segmentation. Our system yields real-time inference on a single GPU card with decent quality results evaluated on challenging datasets like Cityscapes, CamVid and COCO-Stuff.
Who should I cite: learning literature search models from citation behavior

Scientists depend on literature search to find prior work that is relevant to their research ideas. We introduce a retrieval model for literature search that incorporates a wide variety of factors important to researchers, and learns the weights of each of these factors by observing citation patterns. We introduce features like topical similarity and author behavioral patterns, and combine these with features from related work like citation count and recency of publication. We present an iterative process for learning weights for these features that alternates between retrieving articles with the current retrieval model, and updating model weights by training a supervised classifier on these articles. We propose a new task for evaluating the resulting retrieval models, where the retrieval system takes only an abstract as its input and must produce as output the list of references at the end of the abstract's article. We evaluate our model on a collection of journal, conference and workshop articles from the ACL Anthology Reference Corpus. Our model achieves a mean average precision of 28.7, a 12.8 point improvement over a term similarity baseline, and a significant improvement both over models using only features from related work and over models without our iterative learning.
A vision-based lane detection system combining appearance segmentation and tracking of salient points

Reliable lane detection is a key component of autonomous vehicles supporting navigation in urban environments. This paper introduces the GOLDIE(Geometric Overture for Lane Detection by Intersections Entirety) system, a vision-based software architecture that uses an on-board single camera to determine the position of road lanes with respect to the vehicle. We propose an efficient vision-based lane-detection system that combines an appearance-based analysis with salient point tracking. The appearance-based analysis consists of segmenting high contrast areas that fit inside a Region-Of-Interest(ROI) on the frame. The salient point tracker selects interesting points based in a reference line, that guides a dynamic ROI. The tracking ROI look for paint lane marks close to the last lane reference found, where road marks are likely to emerge, in order to maintain the usability of the salient point tracker. The tracking is performed with the Lucas-Kanade algorithm and the lane points candidates are selected according to a predefined triangular model. Once such lanes points are detected, the vehicle position is estimated based on the intersection of linearised lanes determined through a vanishing point approach. Experiments and comparisons with other algorithms illustrate the applicability of the method.
Are there such things as ‘ ‘ Narcissists ’ ’ in social psychology ? A taxometric analysis of the Narcissistic Personality Inventory

Narcissism is typically viewed as a dimensional construct in social psychology. Direct evidence supporting this position is lacking, however, and recent research suggests that clinical measures of narcissism exhibit categorical properties. It is therefore unclear whether social psychological researchers should conceptualize narcissism as a category or continuum. To help remedy this, the latent structure of narcissism—measured by the Narcissistic Personality Inventory (NPI)—was examined using 3895 participants and three taxometric procedures. Results suggest that NPI scores are distributed dimensionally. There is no apparent shift from ‘‘normal’’ to ‘‘narcissist’’ observed across the NPI continuum. This is consistent with the prevailing view of narcissism in social psychology and suggests that narcissism is structured similar to other aspects of general personality. This also suggests a difference in how narcissism is structured in clinical versus social psychology (134 words).
Parallel bargrams for consumer-based information exploration and choice

In this paper we introduce multidimensional visualization and interaction techniques that are an extension to related work in parallel histograms and dynamic querying. Bargrams are, in effect, histograms whose bars have been tipped over and lined up end-to-end. We discuss affordances of parallel bargrams in the context of systems that support consumer-based information exploration and choice based on the attributes of the items in the choice set. Our tool called EZChooser has enabled a number of prototypes in such domains as Internet shopping, investment decisions, college choice, and so on, and a limited version has been deployed for car shopping. Evaluations of the techniques include an experiment indicating that trained users prefer EZChooser over static tables for choice tasks among sets of 50 items with 7-9 attributes.
Personalized defect prediction

Many defect prediction techniques have been proposed. While they often take the author of the code into consideration, none of these techniques build a separate prediction model for each developer. Different developers have different coding styles, commit frequencies, and experience levels, causing different defect patterns. When the defects of different developers are combined, such differences are obscured, hurting prediction performance. This paper proposes personalized defect prediction-building a separate prediction model for each developer to predict software defects. As a proof of concept, we apply our personalized defect prediction to classify defects at the file change level. We evaluate our personalized change classification technique on six large software projects written in C and Java-the Linux kernel, PostgreSQL, Xorg, Eclipse, Lucene and Jackrabbit. Our personalized approach can discover up to 155 more bugs than the traditional change classification (210 versus 55) if developers inspect the top 20% lines of code that are predicted buggy. In addition, our approach improves the F1-score by 0.01-0.06 compared to the traditional change classification.
AA-Sort: A New Parallel Sorting Algorithm for Multi-Core SIMD Processors

Many sorting algorithms have been studied in the past, but there are only a few algorithms that can effectively exploit both SIMD instructions and thread-level parallelism. In this paper, we propose a new parallel sorting algorithm, called aligned-access sort (AA-sort), for shared-memory multi processors. The AA-sort algorithm takes advantage of SIMD instructions. The key to high performance is eliminating unaligned memory accesses that would reduce the effectiveness of SIMD instructions. We implemented and evaluated the AA-sort on PowerPCreg 970MP and Cell Broadband Enginetrade. In summary, a sequential version of the AA-sort using SIMD instructions outperformed IBM's optimized sequential sorting library by 1.8 times and GPUTeraSort using SIMD instructions by 3.3 times on PowerPC 970MP when sorting 32 M of random 32-bit integers. Furthermore, a parallel version of AA-sort demonstrated better scalability with increasing numbers of cores than a parallel version of GPUTeraSort on both platforms.
Propminer: A Workflow for Interactive Information Extraction and Exploration using Dependency Trees

The use of deep syntactic information such as typed dependencies has been shown to be very effective in Information Extraction. Despite this potential, the process of manually creating rule-based information extractors that operate on dependency trees is not intuitive for persons without an extensive NLP background. In this system demonstration, we present a tool and a workflow designed to enable initiate users to interactively explore the effect and expressivity of creating Information Extraction rules over dependency trees. We introduce the proposed five step workflow for creating information extractors, the graph query based rule language, as well as the core features of the PROPMINER tool.
Human fingernail segmentation

In this paper we present a method for segmentation of fingernail patterns and differentiate them as distinct nail parts; fingernail plate with lunula and distal free edge of nail plate. In the research work, focus is on fixed area of the fingernail plate plus lunula, as it remains unchanged in structure, where as the distal nail edge extends and changes in structure over a period of time. In order to segment fingernail parts, we have devised an algorithm that automatically separates unchanging region of fingernail plate from free distal edge of nail structure. The fingernail plate that includes lunula within (may or may not be prominently present in fingernails), is used as biometric in our advance study. Theory suggests, every fingernail within finger formation comprises of the brightest regions amongst the captured finger data set (in our system). Proposed method is of two stages. In first stage, color image is converted to gray scale and contrast enhancement is applied using adaptive histogram equalization. In second stage, we perform segmentation using watershed method that exercises maxima and minima properties of marker controlled watershed principles. In order to verify the results of the algorithm, we have constructed a confusion matrix where evaluation has been done with ground truth. Additionally, the segmented object's from both the methods is considered for quality metrics assessment. Similarity accuracy between the ground truth and watershed result is 84.0% correctness for fingernail plate. Initial fingernail segmentation results are promising, supporting its use for biometric application.