nils-herrmann/clean_scirepeval huggingface dataset:
A Survey on Content-Based Retrieval for Multimedia Databases

Conventional database systems are designed for managing textual and numerical data, and retrieving such data is often based on simple comparisons of text/numerical values. However, this simple method of retrieval is no longer adequate for the multimedia data, since the digitized representation of images, video, or data itself does not convey the reality of these media items. In addition, composite data consisting of heterogeneous types of data also associates with the semantic content acquired by a user’s recognition. Therefore, content-based retrieval for multimedia data is realized taking such intrinsic features of multimedia data into account. Implementation of the content-based retrieval facility is not based on a single fundamental, but is closely related to an underlying data model, a priori knowledge of the area of interest, and the scheme for representing queries. This paper surveys recent studies on content-based retrieval for multimedia databases from the point of view of three fundamental issues. Throughout the discussion, we assume databases that manage only nontextual/numerical data, such as image or video, are also in the category of multimedia databases.
IMAGINATION: a robust image-based CAPTCHA generation system

We propose IMAGINATION (IMAge Generation for INternet AuthenticaTION), a system for the generation of attack-resistant, user-friendly, image-based CAPTCHAs. In our system, we produce controlled distortions on randomly chosen images and present them to the user for annotation from a given list of words. The distortions are performed in a way that satisfies the incongruous requirements of low perceptual degradation and high resistance to attack by content-based image retrieval systems. Word choices are carefully generated to avoid ambiguity as well as to avoid attacks based on the choices themselves. Preliminary results demonstrate the attack-resistance and user-friendliness of our system compared to text-based CAPTCHAs.
Automatic Construction of an Emoji Sentiment Lexicon

Emojis have been frequently used to express users' sentiments, emotions, and feelings in text-based communication. To facilitate sentiment analysis of users' posts, an emoji sentiment lexicon with positive, neutral, and negative scores has been recently constructed using manually labeled tweets. However, the number of emojis listed in the lexicon is smaller than that of currently existing emojis, and expanding the lexicon manually requires time and effort to reconstruct the labeled dataset. This paper presents a simple and efficient method for automatically constructing an emoji sentiment lexicon with arbitrary sentiment categories. The proposed method extracts sentiment words from WordNet-Affect and calculates the cooccurrence frequency between the sentiment words and each emoji. Based on the ratio of the number of occurrences of each emoji among the sentiment categories, each emoji is assigned a multidimensional vector whose elements indicate the strength of the corresponding sentiment. In experiments conducted on a collection of tweets, we show a high correlation between the conventional lexicon and our lexicon for three sentiment categories. We also show the results for a new lexicon constructed with additional sentiment categories.
Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT)

For over a decade, researchers have devoted much effort to construct theoretical models, such as the Technology Acceptance Model (TAM) and the Expectation Confirmation Model (ECM) for explaining and predicting user behavior in IS acceptance and continuance. Another model, the Cognitive Model (COG), was proposed for continuance behavior; it combines some of the variables used in both TAM and ECM. This study applied the technique of structured equation modeling with multiple group analysis to compare the TAM, ECM, and COG models. Results indicate that TAM, ECM, and COG have quite different assumptions about the underlying constructs that dictate user behavior and thus have different explanatory powers. The six constructs in the three models were synthesized to propose a new Technology Continuance Theory (TCT). A major contribution of TCT is that it combines two central constructs: attitude and satisfaction into one continuance model, and has applicability for users at different stages of the adoption life cycle, i.e., initial, short-term and long-term users. The TCT represents a substantial improvement over the TAM, ECM and COG models in terms of both breadth of applicability and explanatory power.
Ramp sequence analysis to resolve multi target scenarios for a 77-GHz FMCW radar sensor

Many practical frequency-modulated continuous-wave (FMCW) radars utilize consecutive upchirps and/or downchirps of the same ramp slope to extract the desired range and velocity information of the targets. In this contribution it is demonstrated that consecutive ramp sequences provide only little more information compared to a non-consecutive sequence, but lead to a huge calculation complexity. Additional significant information on the target states for a non-consecutive sequence is gained by using the ramp slope as a design parameter. The ramp sequence distributed over a certain period is temporally aligned to a given point in time. State estimation is done by minimizing a cost function. A significant advantage of a cost function approach is that ghost targets are suppressed directly. For test purposes a 77-GHz FMCW radar prototype is used in an automotive environment.
Dexhand: A Space qualified multi-fingered robotic hand

Despite the progress since the first attempts of mankind to explore space, it appears that sending man in space remains challenging. While robotic systems are not yet ready to replace human presence, they provide an excellent support for astronauts during maintenance and hazardous tasks. This paper presents the development of a space qualified multi-fingered robotic hand and highlights the most interesting challenges. The design concept, the mechanical structure, the electronics architecture and the control system are presented throughout this overview paper.
Incorporating Unstructured Textual Knowledge Sources into Neural Dialogue Systems

We present initial methods for incorporating unstructured external textual information into neural dialogue systems for predicting the next utterance of a user in a two-party chat conversation. The main objective is to leverage additional information about the topic of the conversation to improve the prediction accuracy. We propose a simple method for extracting this knowledge, using a combination of hashing and TF-IDF, and a way to use it for selecting the best next utterance of a conversation, by encoding a vector representation with a recurrent neural network (RNN). This is combined with an RNN encoding of the context and response of the conversation in order to make a prediction. We perform a case study using the recently released Ubuntu Dialogue Corpus, where the additional knowledge considered consists of the Ubuntu manpages. Preliminary results suggest that leveraging external knowledge sources in such a manner could lead to performance improvements for predicting the next utterance.
Evidence from functional magnetic resonance imaging of crossmodal binding in the human heteromodal cortex

Integrating information from the different senses markedly enhances the detection and identification of external stimuli. Compared with unimodal inputs, semantically and/or spatially congruent multisensory cues speed discrimination and improve reaction times. Discordant inputs have the opposite effect, reducing performance and slowing responses. These behavioural features of crossmodal processing appear to have parallels in the response properties of multisensory cells in the superior colliculi and cerebral cortex of non-human mammals. Although spatially concordant multisensory inputs can produce a dramatic, often multiplicative, increase in cellular activity, spatially disparate cues tend to induce a profound response depression.



Using functional magnetic resonance imaging (fMRI), we investigated whether similar indices of crossmodal integration are detectable in human cerebral cortex, and for the synthesis of complex inputs relating to stimulus identity. Ten human subjects were exposed to varying epochs of semantically congruent and incongruent audio-visual speech and to each modality in isolation. Brain activations to matched and mismatched audio-visual inputs were contrasted with the combined response to both unimodal conditions. This strategy identified an area of heteromodal cortex in the left superior temporal sulcus that exhibited significant supra-additive response enhancement to matched audio-visual inputs and a corresponding sub-additive response to mismatched inputs.



The data provide fMRI evidence of crossmodal binding by convergence in the human heteromodal cortex. They further suggest that response enhancement and depression may be a general property of multisensory integration operating at different levels of the neuroaxis and irrespective of the purpose for which sensory inputs are combined.
The Smart Transformer: A solid-state transformer tailored to provide ancillary services to the distribution grid

The solid-state transformer (SST) was conceived as a replacement for the conventional power transformer, with both lower volume and weight. The smart transformer (ST) is an SST that provides ancillary services to the distribution and transmission grids to optimize their performance. Hence, the focus shifts from hardware advantages to functionalities. One of the most desired functionalities is the dc connectivity to enable a hybrid distribution system. For this reason, the ST architecture shall be composed of at least two power stages. The standard design procedure for this kind of system is to design each power stage for the maximum load. However, this design approach might limit additional services, like the reactive power compensation on the medium voltage (MV) side, and it does not consider the load regulation capability of the ST on the low voltage (LV) side. If the SST is tailored to the services that it shall provide, different stages will have different designs, so that the ST is no longer a mere application of the SST but an entirely new subject.
Cosmic rays don't strike twice: understanding the nature of DRAM errors and the implications for system design

Main memory is one of the leading hardware causes for machine crashes in today's datacenters. Designing, evaluating and modeling systems that are resilient against memory errors requires a good understanding of the underlying characteristics of errors in DRAM in the field. While there have recently been a few first studies on DRAM errors in production systems, these have been too limited in either the size of the data set or the granularity of the data to conclusively answer many of the open questions on DRAM errors. Such questions include, for example, the prevalence of soft errors compared to hard errors, or the analysis of typical patterns of hard errors. In this paper, we study data on DRAM errors collected on a diverse range of production systems in total covering nearly 300 terabyte-years of main memory. As a first contribution, we provide a detailed analytical study of DRAM error characteristics, including both hard and soft errors. We find that a large fraction of DRAM errors in the field can be attributed to hard errors and we provide a detailed analytical study of their characteristics. As a second contribution, the paper uses the results from the measurement study to identify a number of promising directions for designing more resilient systems and evaluates the potential of different protection mechanisms in the light of realistic error patterns. One of our findings is that simple page retirement policies might be able to mask a large number of DRAM errors in production systems, while sacrificing only a negligible fraction of the total DRAM in the system.
A Dual Band Unequal Wilkinson Power Divider Without Reactive Components

This paper presents an unequal Wilkinson power divider operating at arbitrary dual band without reactive components (such as inductors and capacitors). To satisfy the unequal characteristic, a novel structure is proposed with two groups of transmission lines and two parallel stubs. Closed-form equations containing all parameters of this structure are derived based on circuit theory and transmission line theory. For verification, two groups of experimental results including open and short stubs are presented. It can be found that all the analytical features of this unequal power divider can be fulfilled at arbitrary dual band simultaneously.
A Reproducibility Study of Information Retrieval Models

Developing effective information retrieval models has been a long standing challenge in Information Retrieval (IR), and significant progresses have been made over the years. With the increasing number of developed retrieval functions and the release of new data collections, it becomes more difficult, if not impossible, to compare a new retrieval function with all existing retrieval functions over all available data collections. To tackle thisproblem, this paper describes our efforts on constructing a platform that aims to improve the reproducibility of IR researchand facilitate the evaluation and comparison of retrieval functions. With the developed platform, more than 20 state of the art retrieval functions have been implemented and systematically evaluated over 16 standard TREC collections (including the newly released ClueWeb datasets). Our reproducibility study leads to several interesting observations. First, the performance difference between the reproduced results and those reported in the original papers is small for most retrieval functions. Second, the optimal performance of a few representative retrieval functions is still comparable over the new TREC ClueWeb collections. Finally, the developed platform (i.e., RISE) is made publicly available so that any IR researchers would be able to utilize it to evaluate other retrieval functions.
Temporal structure of syntactic parsing: early and late event-related brain potential effects.

Event-related brain potentials (ERPs) were recorded from participants listening to or reading sentences that were correct, contained a violation of the required syntactic category, or contained a syntactic-category ambiguity. When sentences were presented auditorily (Experiment 1), there was an early left anterior negativity for syntactic-category violations, but not for syntactic-category ambiguities. Both anomaly types elicited a late centroparietally distributed positivity. When sentences were presented visually word by word (Experiment 2), again an early left anterior negativity was found only for syntactic-category violations, and both types of anomalies elicited a late positivity. The combined data are taken to be consistent with a 2-stage model of parsing, including a 1st stage, during which an initial phrase structure is built and a 2nd stage, during which thematic role assignment and, if necessary, reanalysis takes place. Disruptions to the 1st stage of syntactic parsing appear to be correlated with an early left anterior negativity, whereas disruptions to the 2nd stage might be correlated with a late posterior distributed positivity.
Snorkel: Fast Training Set Generation for Information Extraction

State-of-the art machine learning methods such as deep learning rely on large sets of hand-labeled training data. Collecting training data is prohibitively slow and expensive, especially when technical domain expertise is required; even the largest technology companies struggle with this challenge. We address this critical bottleneck with Snorkel, a new system for quickly creating, managing, and modeling training sets. Snorkel enables users to generate large volumes of training data by writing labeling functions, which are simple functions that express heuristics and other weak supervision strategies. These user-authored labeling functions may have low accuracies and may overlap and conflict, but Snorkel automatically learns their accuracies and synthesizes their output labels. Experiments and theory show that surprisingly, by modeling the labeling process in this way, we can train high-accuracy machine learning models even using potentially lower-accuracy inputs. Snorkel is currently used in production at top technology and consulting companies, and used by researchers to extract information from electronic health records, after-action combat reports, and the scientific literature. In this demonstration, we focus on the challenging task of information extraction, a common application of Snorkel in practice. Using the task of extracting corporate employment relationships from news articles, we will demonstrate and build intuition for a radically different way of developing machine learning systems which allows us to effectively bypass the bottleneck of hand-labeling training data.
Recognizing Street Lighting Poles From Mobile LiDAR Data

In this paper, a novel segmentation and recognition approach to automatically extract street lighting poles from mobile LiDAR data is proposed. First, points on or around the ground are extracted and removed through a piecewise elevation histogram segmentation method. Then, a new graph-cut-based segmentation method is introduced to extract the street lighting poles from each cluster obtained through a Euclidean distance clustering algorithm. In addition to the spatial information, the street lighting pole's shape and the point's intensity information are also considered to formulate the energy function. Finally, a Gaussian-mixture-model-based method is introduced to recognize the street lighting poles from the candidate clusters. The proposed approach is tested on several point clouds collected by different mobile LiDAR systems. Experimental results show that the proposed method is robust to noises and achieves an overall performance of 90% in terms of true positive rate.
Live intrinsic video

Intrinsic video decomposition refers to the fundamentally ambiguous task of separating a video stream into its constituent layers, in particular reflectance and shading layers. Such a decomposition is the basis for a variety of video manipulation applications, such as realistic recoloring or retexturing of objects. We present a novel variational approach to tackle this underconstrained inverse problem at real-time frame rates, which enables on-line processing of live video footage. The problem of finding the intrinsic decomposition is formulated as a mixed variational ℓ2-ℓp-optimization problem based on an objective function that is specifically tailored for fast optimization. To this end, we propose a novel combination of sophisticated local spatial and global spatio-temporal priors resulting in temporally coherent decompositions at real-time frame rates without the need for explicit correspondence search. We tackle the resulting high-dimensional, non-convex optimization problem via a novel data-parallel iteratively reweighted least squares solver that runs on commodity graphics hardware. Real-time performance is obtained by combining a local-global solution strategy with hierarchical coarse-to-fine optimization. Compelling real-time augmented reality applications, such as recoloring, material editing and retexturing, are demonstrated in a live setup. Our qualitative and quantitative evaluation shows that we obtain high-quality real-time decompositions even for challenging sequences. Our method is able to outperform state-of-the-art approaches in terms of runtime and result quality -- even without user guidance such as scribbles.
'Free lunch' enhancement for collaborative filtering with factorization machines

The advantage of Factorization Machines over other factorization models is their ability to easily integrate and efficiently exploit auxiliary information to improve Collaborative Filtering. Until now, this auxiliary information has been drawn from external knowledge sources beyond the user-item matrix. In this paper, we demonstrate that Factorization Machines can exploit additional representations of information inherent in the user-item matrix to improve recommendation performance. We refer to our approach as 'Free Lunch' enhancement since it leverages clusters that are based on information that is present in the user-item matrix, but not otherwise directly exploited during matrix factorization. Borrowing clustering concepts from codebook sharing, our approach can also make use of 'Free Lunch' information inherent in a user-item matrix from a auxiliary domain that is different from the target domain of the recommender. Our approach improves performance both in the joint case, in which the auxiliary and target domains share users, and in the disjoint case, in which they do not. Although 'Free Lunch' enhancement does not apply equally well to any given domain or domain combination, our overall conclusion is that Factorization Machines present an opportunity to exploit information that is ubiquitously present, but commonly under-appreciated by Collaborative Filtering algorithms.
Real-time obstacle avoidance for fast mobile robots in cluttered environments

A new real-time obstacle avoidance method for mobile robots has been developed and implemented. This method, named the Vector Field Hisfog" (VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. A VFH-controlled mobile robot maneuvers quickly and without stopping among densely cluttered obstacles. The VFH method uses a two-dimensional Cartesian Histognun Gfid as a world model. This world model is updated continuously and in real-time with range data sampled by the onboard ultrasonic range sensors. Based on the accumulated environmental data, the VFH method then computes a one-dimensional Polar Histogram that is constructed around the robot's momentary location. Each sector in the Polar Histogram holds thepolar obstacle density in that direction. Finally, the algorithm selects the most suitable sector from among aU Polar Hisfogmi sectors with low obstacle density, and the steering of the robot is aligned with that direction. Experimental results from a mobile robot traversing a densely cluttered obstacle course at an average speed of 0.7 m/sec demonstrate the power of the VFH method.
Inverse kinematics with floating base and constraints for full body humanoid robot control

This paper explores inverse kinematics for full body, floating base, task space control on a real humanoid robot. We discuss how constraints can be used to address the issue of under-actuation due to floating base, and list the sufficient conditions for maintaining task space control of arbitrary robot tasks. We suggest a controller based on a task priority framework and demonstrate the feasibility of the approach on the SARCOS/ATR CBi humanoid robot. We implement examples of position control via constrained floating base inverse kinematics as first approach to full body model-based inverse dynamics control. The examples demonstrate center of gravity position tracking as well as hand figure-8 tracking while simultaneously balancing.