nils-herrmann/clean_scirepeval huggingface dataset:
Affective understanding in film

Affective understanding of film plays an important role in sophisticated movie analysis, ranking and indexing. However, due to the seemingly inscrutable nature of emotions and the broad affective gap from low-level features, this problem is seldom addressed. In this paper, we develop a systematic approach grounded upon psychology and cinematography to address several important issues in affective understanding. An appropriate set of affective categories are identified and steps for their classification developed. A number of effective audiovisual cues are formulated to help bridge the affective gap. In particular, a holistic method of extracting affective information from the multifaceted audio stream has been introduced. Besides classifying every scene in Hollywood domain movies probabilistically into the affective categories, some exciting applications are demonstrated. The experimental results validate the proposed approach and the efficacy of the audiovisual cues.
The MIT - Cornell Collision and Why It Happened

Mid-way through the 2007 DARPA Urban Challenge, MIT’s autonomous Land Rover LR3 ‘Talos’ and Team Cornell’s autonomous Chevrolet Tahoe ‘Skynet’ collided in a low-speed accident, one of the first well-documented collisions between two full-size autonomous vehicles. This collaborative study between MIT and Cornell examines the root causes of the collision, which are identified in both teams’ system designs. Systems-level descriptions of both autonomous vehicles are given, and additional detail is provided on sub-systems and algorithms implicated in the collision. A brief summary of robot–robot interactions during the race is presented, followed by an in-depth analysis of both robots’ behaviors leading up to and during the Skynet–Talos collision. Data logs from the vehicles are used to show the gulf between autonomous and human-driven vehicle behavior at low speeds and close proximities. Contributing factors are shown to be: (1) difficulties in sensor data association leading to phantom obstacles and an inability to detect slow moving vehicles, (2) failure to anticipate vehicle intent, and (3) an over emphasis on lane constraints versus vehicle proximity in motion planning. Eye contact between human road users is a crucial communications channel for slow-moving close encounters between vehicles. Inter-vehicle communication may play a similar role for autonomous vehicles; however, there are availability and denial-of-service issues to be addressed.
Ordinal Common-sense Inference

Humans have the capacity to draw common-sense inferences from natural language: various things that are likely but not certain to hold based on established discourse, and are rarely stated explicitly. We propose an evaluation of automated common-sense inference based on an extension of recognizing textual entailment: predicting ordinal human responses on the subjective likelihood of an inference holding in a given context. We describe a framework for extracting common-sense knowledge from corpora, which is then used to construct a dataset for this ordinal entailment task. We train a neural sequence-to-sequence model on this dataset, which we use to score and generate possible inferences. Further, we annotate subsets of previously established datasets via our ordinal annotation protocol in order to then analyze the distinctions between these and what we have constructed.
Practical Dynamic Searchable Encryption with Small Leakage

Dynamic Searchable Symmetric Encryption (DSSE) enables a client to encrypt his document collection in a way that it is still searchable and efficiently updatable. However, all DSSE constructions that have been presented in the literature so far come with several problems: Either they leak a significant amount of information (e.g., hashes of the keywords contained in the updated document) or are inefficient in terms of space or search/update time (e.g., linear in the number of documents). In this paper we revisit the DSSE problem. We propose the first DSSE scheme that achieves the best of both worlds, i.e., both small leakage and efficiency. In particular, our DSSE scheme leaks significantly less information than any other previous DSSE construction and supports both updates and searches in sublinear time in the worst case, maintaining at the same time a data structure of only linear size. We finally provide an implementation of our construction, showing its practical efficiency.
Hinging Hyperplanes for Time-Series Segmentation

Division of a time series into segments is a common technique for time-series processing, and is known as segmentation. Segmentation is traditionally done by linear interpolation in order to guarantee the continuity of the reconstructed time series. The interpolation-based segmentation methods may perform poorly for data with a level of noise because interpolation is noise sensitive. To handle the problem, this paper establishes an explicit expression for segmentation from a compact representation for piecewise linear functions using hinging hyperplanes. This expression enables the use of regression to obtain a continuous reconstructed signal and, as a consequence, application of advanced techniques in segmentation. In this paper, a least squares support vector machine with lasso using a hinging feature map is given and analyzed, based on which a segmentation algorithm and its online version are established. Numerical experiments conducted on synthetic and real-world datasets demonstrate the advantages of our methods compared to existing segmentation algorithms.
From RankNet to LambdaRank to LambdaMART: An Overview

LambdaMART is the boosted tree version of LambdaRank, which is based on RankNet. RankNet, LambdaRank, and LambdaMART have proven to be very successful algorithms for solving real world ranking problems: for example an ensemble of LambdaMART rankers won Track 1 of the 2010 Yahoo! Learning To Rank Challenge. The details of these algorithms are spread across several papers and reports, and so here we give a self-contained, detailed and complete description of them.
Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights

Quantitative evaluation of the ability of soccer players to contribute to team offensive performance is typically based on goals scored, assists made, and shots taken. In this paper, we describe a novel player ranking system based entirely on the value of passes completed. This value is derived based on the relationship of pass locations in a possession and shot opportunities generated. This relationship is learned by applying a supervised machine learning model to pass locations in event data from the 2012-2013 La Liga season. Interestingly, though this metric is based entirely on passes, the derived player rankings are largely consistent with general perceptions of offensive ability, e.g., Messi and Ronaldo are near the top. Additionally, when used to rank midfielders, it separates the more offensively-minded players from others.
A Decision Support Approach for Online Stock Forum Sentiment Analysis

The Internet provides the opportunity for investors to post online opinions that they share with fellow investors. Sentiment analysis of online opinion posts can facilitate both investors' investment decision making and stock companies' risk perception. This paper develops a novel sentiment ontology to conduct context-sensitive sentiment analysis of online opinion posts in stock markets. The methodology integrates popular sentiment analysis into machine learning approaches based on support vector machine and generalized autoregressive conditional heteroskedasticity modeling. A typical financial website called Sina Finance has been selected as an experimental platform where a corpus of financial review data was collected. Empirical results suggest solid correlations between stock price volatility trends and stock forum sentiment. Computational results show that the statistical machine learning approach has a higher classification accuracy than that of the semantic approach. Results also imply that investor sentiment has a particularly strong effect for value stocks relative to growth stocks.
X-RHex: A Highly Mobile Hexapedal Robot for Sensorimotor Tasks

We report on the design and development of X-RHex, a hexapedal robot with a single actuator per leg, intended for real-world mobile applications. X-RHex is an updated version of the RHex platform, designed to offer substantial improvements in power, run-time, payload size, durability, and terrain negotiation, with a smaller physical volume and a comparable footprint and weight. Furthermore, X-RHex is designed to be easier to build and maintain by using a variety of commercial off-the-shelf (COTS) components for a majority of its internals. This document describes the X-RHex architecture and design, with a particular focus on the new ability of this robot to carry modular payloads as a laboratory on legs. X-RHex supports a variety of sensor suites on a small, mobile robotic platform intended for broad, general use in research, defense, and search and rescue applications. Comparisons with previous RHex platforms are presented throughout, with preliminary tests indicating that the locomotive capabilities of X-RHex can meet or exceed the previous platforms. With the additional payload capabilities of X-RHex, we claim it to be the first robot of its size to carry a fully programmable GPU for fast, parallel sensor processing. Disciplines Electrical and Computer Engineering | Engineering | Systems Engineering
A hybrid particle swarm optimization for job shop scheduling problem

A hybrid particle swarm optimization (PSO) for the job shop problem (JSP) is proposed in this paper. In previous research, PSO particles search solutions in a continuous solution space. Since the solution space of the JSP is discrete, we modified the particle position representation, particle movement, and particle velocity to better suit PSO for the JSP. We modified the particle position based on preference list-based representation, particle movement based on swap operator, and particle velocity based on the tabu list concept in our algorithm. Giffler and Thompson’s heuristic is used to decode a particle position into a schedule. Furthermore, we applied tabu search to improve the solution quality. The computational results show that the modified PSO performs better than the original design, and that the hybrid PSO is better than other traditional metaheuristics.
Explicit Knowledge-based Reasoning for Visual Question Answering

We describe a method for visual question answering which is capable of reasoning about contents of an image on the basis of information extracted from a large-scale knowledge base. The method not only answers natural language questions using concepts not contained in the image, but can provide an explanation of the reasoning by which it developed its answer. The method is capable of answering far more complex questions than the predominant long short-term memory-based approach, and outperforms it significantly in the testing. We also provide a dataset and a protocol by which to evaluate such methods, thus addressing one of the key issues in general visual question answering.
Enforcing Forward-Edge Control-Flow Integrity in GCC & LLVM

Constraining dynamic control transfers is a common technique for mitigating software vulnerabilities. This defense has been widely and successfully used to protect return addresses and stack data; hence, current attacks instead typically corrupt vtable and function pointers to subvert a forward edge (an indirect jump or call) in the control-flow graph. Forward edges can be protected using Control-Flow Integrity (CFI) but, to date, CFI implementations have been research prototypes, based on impractical assumptions or ad hoc, heuristic techniques. To be widely adoptable, CFI mechanisms must be integrated into production compilers and be compatible with software-engineering aspects such as incremental compilation and dynamic libraries. This paper presents implementations of fine-grained, forward-edge CFI enforcement and analysis for GCC and LLVM that meet the above requirements. An analysis and evaluation of the security, performance, and resource consumption of these mechanisms applied to the SPEC CPU2006 benchmarks and common benchmarks for the Chromium web browser show the practicality of our approach: these fine-grained CFI mechanisms have significantly lower overhead than recent academic CFI prototypes. Implementing CFI in industrial compiler frameworks has also led to insights into design tradeoffs and practical challenges, such as dynamic loading.
A study of the knowledge base requirements for passing an elementary science test

Our long-term interest is in machines that contain large amounts of general and scientific knowledge, stored in a "computable" form that supports reasoning and explanation. As a medium-term focus for this, our goal is to have the computer pass a fourth-grade science test, anticipating that much of the required knowledge will need to be acquired semi-automatically. This paper presents the first step towards this goal, namely a blueprint of the knowledge requirements for an early science exam, and a brief description of the resources, methods, and challenges involved in the semi-automatic acquisition of that knowledge. The result of our analysis suggests that as well as fact extraction from text and statistically driven rule extraction, three other styles of automatic knowledge base construction (AKBC) would be useful: acquiring definitional knowledge, direct 'reading' of rules from texts that state them, and, given a particular representational framework (e.g., qualitative reasoning), acquisition of specific instances of those models from text (e..g, specific qualitative models).
From captions to visual concepts and back

This paper presents a novel approach for automatically generating image descriptions: visual detectors, language models, and multimodal similarity models learnt directly from a dataset of image captions. We use multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives. The word detector outputs serve as conditional inputs to a maximum-entropy language model. The language model learns from a set of over 400,000 image descriptions to capture the statistics of word usage. We capture global semantics by re-ranking caption candidates using sentence-level features and a deep multimodal similarity model. Our system is state-of-the-art on the official Microsoft COCO benchmark, producing a BLEU-4 score of 29.1%. When human judges compare the system captions to ones written by other people on our held-out test set, the system captions have equal or better quality 34% of the time.
Personalized models of search satisfaction

Search engines need to model user satisfaction to improve their services. Since it is not practical to request feedback on searchers' perceptions and search outcomes directly from users, search engines must estimate satisfaction from behavioral signals such as query refinement, result clicks, and dwell times. This analysis of behavior in the aggregate leads to the development of global metrics such as satisfied result clickthrough (typically operationalized as result-page clicks with dwell time exceeding a particular threshold) that are then applied to all searchers' behavior to estimate satisfac-tion levels. However, satisfaction is a personal belief and how users behave when they are satisfied can also differ. In this paper we verify that searcher behavior when satisfied and dissatisfied is indeed different among individual searchers along a number of dimensions. As a result, we introduce and evaluate learned models of satisfaction for individual searchers and searcher cohorts. Through experimentation via logs from a large commercial Web search engine, we show that our proposed models can predict search satisfaction more accurately than a global baseline that applies the same satisfaction model across all users. Our findings have implications for the study and application of user satisfaction in search systems.
Humans' attachment to their mobile phones and its relationship with interpersonal attachment style

Humans have a biological predisposition to form attachment to social partners, and they seem to form attachment even toward non-human and inanimate targets. Attachment styles influence not only interpersonal relationships, but interspecies and object attachment as well. We hypothesized that young people form attachment toward their mobile phone, and that people with higher attachment anxiety use the mobile phone more likely as a compensatory attachment target. We constructed a scale to observe people's attachment to their mobile and we assessed their interpersonal attachment style. In this exploratory study we found that young people readily develop attachment toward their phone: they seek the proximity of it and experience distress on separation. People's higher attachment anxiety predicted higher tendency to show attachment-like features regarding their mobile. Specifically, while the proximity of the phone proved to be equally important for people with different attachment styles, the constant contact with others through the phone was more important for anxiously attached people. We conclude that attachment to recently emerged artificial objects, like the mobile may be the result of cultural co-option of the attachment system. People with anxious attachment style may face challenges as the constant contact and validation the computer-mediated communication offers may deepen their dependence on others.
A Gesture Based Interface for Human-Robot Interaction

Service robotics is currently a pivotal research area in robotics, with enormous societal potential. Since service robots directly interact with people, nding \natural" and easy-to-use user interfaces is of fundamental importance. While past work has predominately focussed on issues such as navigation and manipulation, relatively few robotic systems are equipped with exible user interfaces that permit controlling the robot by \natural" means. This paper describes a gesture interface for the control of a mobile robot equipped with a manipulator. The interface uses a camera to track a person and recognize gestures involving arm motion. A fast, adaptive tracking algorithm enables the robot to track and follow a person reliably through o ce environments with changing lighting conditions. Two alternative methods for gesture recognition are compared: a template based approach and a neural network approach. Both are combined with the Viterbi algorithm for the recognition of gestures de ned through arm motion (in addition to static arm poses). Results are reported in the context of an interactive clean-up task, where a person guides the robot to speci c locations that need to be cleaned and instructs the robot to pick up trash.
An Application of the Goldwasser-Micali Cryptosystem to Biometric Authentication

Issuer Delisting;  Granting the Application of General Motors Corporation to Withdraw its Common Stock, $1 2/3 par value, from Listing and Registration on the Chicago Stock Exchange, Inc. File No. 1-00043 April 4, 2006 On March 2, 2006, General Motors Corporation, a Delaware corporation ("Issuer"), filed an application with the Securities and Exchange Commission ("Commission"), pursuant to Section 12(d) of the Securities Exchange Act of 1934 ("Act") and Rule 12d2-2(d) thereunder, to withdraw its common stock, $1 2/3 par value ("Security"), from listing and registration on the Chicago Stock Exchange, Inc. ("CHX"). Notice of such application requesting comments was published in the Federal Register on March 10, 2006. No comments were received. As discussed below, the Commission is granting the application. The Administrative Committee of the Issuer's Board of Directors ("Board") approved a resolution on September 9, 2005, to delist the Security from listing and registration on CHX. The Issuer stated that the purposes for seeking to delist the Security from CHX are to avoid dual regulatory oversight and dual listing fees. The Security is traded, and will continue to trade, on the New York Stock Exchange ("NYSE"). In addition, the Issuer stated that CHX advised the Issuer that the Security will continue to trade on CHX under unlisted trading privileges. The Issuer stated in its application that it has complied with applicable rules of CHX by providing CHX with the required documents governing the withdrawal of securities from listing and registration on CHX. The Issuer's application relates solely to the
Simple And Efficient Architecture Search for Convolutional Neural Networks

Neural networks have recently had a lot of success for many tasks. However, neural network architectures that perform well are still typically designed manually by experts in a cumbersome trial-and-error process. We propose a new method to automatically search for well-performing CNN architectures based on a simple hill climbing procedure whose operators apply network morphisms, followed by short optimization runs by cosine annealing. Surprisingly, this simple method yields competitive results, despite only requiring resources in the same order of magnitude as training a single network. E.g., on CIFAR-10, our method designs and trains networks with an error rate below 6% in only 12 hours on a single GPU; training for one day reduces this error further, to almost 5%.
A Case Study in Text Mining: Interpreting Twitter Data From World Cup Tweets

Cluster analysis is a field of data analysis that extracts underlying patterns in data. One application of cluster analysis is in text-mining, the analysis of large collections of text to find similarities between documents. We used a collection of about 30,000 tweets extracted from Twitter just before the World Cup started. A common problem with real world text data is the presence of linguistic noise. In our case it would be extraneous tweets that are unrelated to dominant themes. To combat this problem, we created an algorithm that combined the DBSCAN algorithm and a consensus matrix. This way we are left with the tweets that are related to those dominant themes. We then used cluster analysis to find those topics that the tweets describe. We clustered the tweets using k-means, a commonly used clustering algorithm, and Non-Negative Matrix Factorization (NMF) and compared the results. The two algorithms gave similar results, but NMF proved to be faster and provided more easily interpreted results. We explored our results using two visualization tools, Gephi and Wordle.
Learning Semantic Segmentation with Weakly-Annotated Videos

Fully convolutional neural networks (FCNNs) trained on a large number of images with strong pixel-level annotations have become the new state of the art for the semantic segmentation task. While there have been recent attempts to learn FCNNs from image-level weak annotations, they need additional constraints, such as the size of an object, to obtain reasonable performance. To address this issue, we present motion-CNN (M-CNN), a novel FCNN framework which incorporates motion cues and is learned from video-level weak annotations. Our learning scheme to train the network uses motion segments as soft constraints, thereby handling noisy motion information. When trained on weakly-annotated videos, our method outperforms the state-of-the-art approach  on the PASCAL VOC 2012 image segmentation benchmark. We also demonstrate that the performance of M-CNN learned with 150 weak video annotations is on par with state-of-the-art weakly-supervised methods trained with thousands of images. Finally, M-CNN substantially outperforms recent approaches in a related task of video co-localization on the YouTube-Objects dataset.