nils-herrmann/clean_scirepeval huggingface dataset:
Im2Text: Describing Images Using 1 Million Captioned Photographs

We develop and demonstrate automatic image description methods using a large captioned photo collection. One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. We also develop methods incorporating many state of the art, but fairly noisy, estimates of image content to produce even more pleasing results. Finally we introduce a new objective performance measure for image captioning.
Friendlink: Link prediction in social networks via bounded local path traversal

Online social networks (OSNs) like Facebook, Myspace, and Hi5 have become popular, because they allow users to easily share content or expand their social circle. OSNs recommend new friends to registered users based on local graph features (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a bounded length, based on the “algorithmic small world hypothesis”. As a result, we are able to provide more accurate and faster friend recommendations. We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using two real data sets (Hi5 and Epinions). Our experimental results show that our FriendLink algorithm outperforms other approaches in terms of effectiveness and efficiency in both real data sets.
FaceNet: A unified embedding for face recognition and clustering

Despite significant recent advances in the field of face recognition [10, 14, 15, 17], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings asfeature vectors. Our method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches. To train, we use triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet mining method. The benefit of our approach is much greater representational efficiency: we achieve state-of-the-artface recognition performance using only 128-bytes perface. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%. On YouTube Faces DB it achieves 95.12%. Our system cuts the error rate in comparison to the best published result  by 30% on both datasets.
Long-Term Visual Object Tracking Benchmark

In this paper, we propose a new long video dataset (called Track Long and Prosper TLP) and benchmark for visual object tracking. The dataset consists of 50 videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking. The proposed dataset paves a way to suitably assess long term tracking performance and train better deep learning architectures (avoiding/reducing augmentation, which may not reflect realistic real world behaviour). We benchmark the dataset on 17 state of the art trackers and rank them according to tracking accuracy and run time speeds. We further present thorough qualitative and quantitative evaluation highlighting the importance of long term aspect of tracking. Our most interesting observations are (a) existing short sequence benchmarks fail to bring out the inherent differences in tracking algorithms which widen up while tracking on long sequences and (b) the accuracy of most trackers abruptly drops on challenging long sequences, suggesting the potential need of research efforts in the direction of long term tracking.
Moral dilemmas in cognitive neuroscience of moral decision-making: A principled review

Moral dilemma tasks have been a much appreciated experimental paradigm in empirical studies on moral cognition for decades and have, more recently, also become a preferred paradigm in the field of cognitive neuroscience of moral decision-making. Yet, studies using moral dilemmas suffer from two main shortcomings: they lack methodological homogeneity which impedes reliable comparisons of results across studies, thus making a metaanalysis manifestly impossible; and second, they overlook control of relevant design parameters. In this paper, we review from a principled standpoint the studies that use moral dilemmas to approach the psychology of moral judgment and its neural underpinnings. We present a systematic review of 19 experimental design parameters that can be identified in moral dilemmas. Accordingly, our analysis establishes a methodological basis for the required homogeneity between studies and suggests the consideration of experimental aspects that have not yet received much attention despite their relevance.
Verified Security for Browser Extensions

Popup blocking, form filling, and many other features of modern web browsers were first introduced as third-party extensions. New extensions continue to enrich browsers in unanticipated ways. However, powerful extensions require capabilities, such as cross-domain network access and local storage, which, if used improperly, pose a security risk. Several browsers try to limit extension capabilities, but an empirical survey we conducted shows that many extensions are over-privileged under existing mechanisms. This paper presents \ibex, a new framework for authoring, analyzing, verifying, and deploying secure browser extensions. Our approach is based on using type-safe, high-level languages to program extensions against an API providing access to a variety of browser features. We propose using Data log to specify fine-grained access control and dataflow policies to limit the ways in which an extension can use this API, thus restricting its privilege over security-sensitive web content and browser resources. We formalize the semantics of policies in terms of a safety property on the execution of extensions and develop a verification methodology that allows us to statically check extensions for policy compliance. Additionally, we provide visualization tools to assist with policy analysis, and compilers to translate extension source code to either. NET byte code or JavaScript, facilitating cross-browser deployment of extensions. We evaluate our work by implementing and verifying~\NumExt extensions with a diverse set of features and security policies. We deploy our extensions in Internet Explorer, Chrome, Fire fox, and a new experimental HTML5 platform called C3. In so doing, we demonstrate the versatility and effectiveness of our approach.
A Logic Programming Approach to Aspect Extraction in Opinion Mining

Aspect extraction aims to extract fine-grained opinion targets from opinion texts. Recent work has shown that the syntactical approach performs well. In this paper, we show that Logic Programming, particularly Answer Set Programming (ASP), can be used to elegantly and efficiently implement the key components of syntax based aspect extraction. Specifically, the well known double propagation (DP) method is implemented using 8 ASP rules that naturally model all key ideas in the DP method. Our experiment on a widely used data set also shows that the ASP implementation is much faster than a Java-based implementation. Syntactical approach has its limitation too. To further improve the performance of syntactical approach, we identify a set of general words from Word Net that have little chance to be an aspect and prune them when extracting aspects. The concept of general words and their pruning are concisely captured by 10 new ASP rules, and a natural extension of the 8 rules for the original DP method. Experimental results show a major improvement in precision with almost no drop in recall compared with those reported in the existing work on a typical benchmark data set. Logic Programming provides a convenient and effective tool to encode and thus test knowledge needed to improve the aspect extraction methods so that the researchers can focus on the identification and discovery of new knowledge to improve aspect extraction.
The A2iA Arabic Handwritten Text Recognition System at the Open HaRT2013 Evaluation

This paper describes the Arabic handwriting recognition systems proposed by A2iA to the NIST OpenHaRT2013 evaluation. These systems were based on an optical model using Long Short-Term Memory (LSTM) recurrent neural networks, trained to recognize the different forms of the Arabic characters directly from the image, without explicit feature extraction nor segmentation.Large vocabulary selection techniques and n-gram language modeling were used to provide a full paragraph recognition, without explicit word segmentation. Several recognition systems were also combined with the ROVER combination algorithm. The best system exceeded 80% of recognition rate.
GHT: a geographic hash table for data-centric storage

Making effective use of the vast amounts of data gathered by large-scale sensor networks will require scalable, self-organizing, and energy-efficient data dissemination algorithms. Previous work has identified data-centric routing as one such method. In an asso-ciated position paper , we argue that a companion method, data-centric storage (DCS), is also a useful approach. Under DCS, sensed data are stored at a node determined by the name associated with the sensed data. In this paper, we describe GHT, a Geographic Hash Table system for DCS on sensornets. GHT hashes keys into geographic coordi-nates, and stores a key-value pair at the sensor node geographically nearest the hash of its key. The system replicates stored data lo-cally to ensure persistence when nodes fail. It uses an efficient consistency protocol to ensure that key-value pairs are stored at the appropriate nodes after topological changes. And it distributes load throughout the network using a geographic hierarchy. We evaluate the performance of GHT as a DCS system in simulation against two other dissemination approaches. Our results demonstrate that GHT is the preferable approach for the application workloads predicted in , offers high data availability, and scales to large sensornet deployments, even when nodes fail or are mobile.
Optogenetics: 10 years of microbial opsins in neuroscience

Over the past 10 years, the development and convergence of microbial opsin engineering, modular genetic methods for cell-type targeting and optical strategies for guiding light through tissue have enabled versatile optical control of defined cells in living systems, defining modern optogenetics. Despite widespread recognition of the importance of spatiotemporally precise causal control over cellular signaling, for nearly the first half (2005–2009) of this 10-year period, as optogenetics was being created, there were difficulties in implementation, few publications and limited biological findings. In contrast, the ensuing years have witnessed a substantial acceleration in the application domain, with the publication of thousands of discoveries and insights into the function of nervous systems and beyond. This Historical Commentary reflects on the scientific landscape of this decade-long transition.
The influence of online product recommendations on consumers ’ online choices

This study investigates consumers’ usage of online recommendation sources and their influence on online product choices. A 3 (websites) × 4 (recommendation sources) × 2 (products) online experiment was conducted with 487 subjects. Results indicate that subjects who consulted product recommendations selected recommended products twice as often as subjects who did not consult recommendations. The online recommendation source labeled “recommender system,” typical of the personalization possibilities offered by online retailing, was more influential than more traditional recommendation sources such as “human experts” and “other consumers”. The type of product also had a significant influence on the propensity to follow product recommendations. Theoretical and managerial implications of these findings are provided.
Viewability Prediction for Online Display Ads

As a massive industry, display advertising delivers advertisers' marketing messages to attract customers through graphic banners on webpages. Advertisers are charged by ad serving, where their ads are shown in web pages. However, recent studies show that about half of the ads were actually never seen by users because they do not scroll deep enough to bring the ads in-view. Thus, the ad pricing standards are shifting to a new model: ads are paid if they are in view, not just being served. To the best of our knowledge, this paper is the first to address the important problem of ad viewability prediction which can improve the performance of guaranteed ad delivery, real-time bidding, as well as recommender systems. We analyze a real-life dataset from a large publisher, identify a number of features that impact the scroll depth for a given user and a page, and propose a probabilistic latent class model that predicts the viewability of any given scroll depth for a user-page pair. The experiments demonstrate that our model outperforms comparison systems based on singular value decomposition and logistic regression, in terms of prediction quality and training time.
Mapping Agricultural Ecosystem Services across Scales

Given the cross-scale interactions of agricultural ecosystems, it is important to collect ecosystem service data at the multiple spatial scales they operate at. Mapping of ecosystem services helps to assess their spatial and temporal distribution and is a popular communication tool of their availability and value. For example, maps can be used to quantify distance between areas of available ecosystem services and their beneficiaries and how services fluctuate with changes in land use patterns over time, allowing identification of synergies and trade-offs. However, a lack of local context and too large a resolution can reduce the utility of these maps, whilst masking heterogeneities in access due to equity dynamics. This review identifies and summarizes eight main methods of ESS mapping found in the literature – remote sensing, biophysical modelling, agent based modelling, economic valuation, expert opinion, user preference, participatory mapping, and photo-elicitation. We consider what spatial scales these methods are utilized at and the transferability of data created by each method. The analysis concludes with a methodological framework for mapping ecosystem services, intended to help researchers identify appropriate methods for a multi-scale research design. The framework is exemplified with an overview of a research project in Ethiopia.
Joint Multimodal Learning with Deep Generative Models

We investigate deep generative models that can exchange mul tiple modalities bidirectionally, e.g., generating images from correspondin g texts and vice versa. Recently, some studies handle multiple modalities on deep gen erative models, such as variational autoencoders (VAEs). However, these models typically assume that modalities are forced to have a conditioned relation, i.e., we can only generate modalities in one direction. To achieve our objective, we sh ould extract a joint representation that captures high-level concepts among al l modalities and through which we can exchange them bi-directionally. As described h rein, we propose a joint multimodal variational autoencoder (JMVAE), in whicall modalities are independently conditioned on joint representation. In other words, it models a joint distribution of modalities. Furthermore, to be able to gene rate missing modalities from the remaining modalities properly, we develop an additional method, JMVAE-kl, that is trained by reducing the divergence betwee n JMVAE’s encoder and prepared networks of respective modalities. Our experi ments show that our proposed method can obtain appropriate joint representati o from multiple modalities and that it can generate and reconstruct them more prop erly than conventional VAEs. We further demonstrate that JMVAE can generate multip le modalities bidirectionally.
Applying persuasive design in a diabetes mellitus application

This paper describes persuasive design methods and compares this to an application currently under development for diabetes mellitus patients. Various elements of persuasion and a categorization of persuasion types are mentioned. Also discussed are principles of how successful persuasion should be designed, as well as the practical applications and ethics of persuasive design. This paper is not striving for completeness of theories on the topic, but uses the theories to compare it to an application intended for diabetes mellitus patients. The results of this comparison can be used for improvements of the application.
Founding Cryptography on Oblivious Transfer - Efficiently

We present a simple and efficient compiler for transforming secure multi-party computation (MPC) protocols that enjoy security only with an honest majority into MPC protocols that guarantee security with no honest majority, in the oblivious-transfer (OT) hybrid model. Our technique works by combining a secure protocol in the honest majority setting with a protocol achieving only security against semi-honest parties in the setting of no honest majority. Applying our compiler to variants of protocols from the literature, we get several applications for secure two-party computation and for MPC with no honest majority. These include: • Constant-rate two-party computation in the OT-hybrid model. We obtain a statistically UC-secure two-party protocol in the OT-hybrid model that can evaluate a general circuit C of size s and depth d with a total communication complexity of O(s)+ poly(k, d, log s) and O(d) rounds. The above result generalizes to a constant number of parties. • Extending OTs in the malicious model. We obtain a computationally efficient protocol for generating many string OTs from few string OTs with only a constant amortized communication overhead compared to the total length of the string OTs. • Black-box constructions for constant-round MPC with no honest majority. We obtain general computationally UC-secure MPC protocols in the OT-hybrid model that use only a constant number of rounds, and only make a black-box access to a pseudorandom generator. This gives the first constant-round protocols for three or more parties that only make a black-box use of cryptographic primitives (and avoid expensive zero-knowledge proofs).
Spin observation and trajectory prediction of a ping-pong ball

For ping-pong playing robots, observing a ball and predicting a ball's trajectory accurately in real-time is essential. However, most existing vision systems can only provide ball's position observation, and do not take into consideration the spin of the ball, which is very important in competitions. This paper proposes a way to observe and estimate ball's spin in real-time, and achieve an accurate prediction. Based on the fact that a spinning ball's motion can be separated into global movement and spinning respect to its center, we construct an integrated vision system to observe the two motions separately. With a pan-tilt vision system, the spinning motion is observed through recognizing the position of the brand on the ball and restoring the 3D pose of the ball. Then the spin state is estimated with the method of plane fitting on current and historical observations. With both position and spin information, accurate state estimation and trajectory prediction are realized via Extended Kalman Filter(EKF). Experimental results show the effectiveness and accuracy of the proposed method.
Learning under Feature Drifts in Textual Streams

Huge amounts of textual streams are generated nowadays, especially in social networks like Twitter and Facebook. As the discussion topics and user opinions on those topics change drastically with time, those streams undergo changes in data distribution, leading to changes in the concept to be learned, a phenomenon called concept drift. One particular type of drift, that has not yet attracted a lot of attention is feature drift, i.e., changes in the features that are relevant for the learning task at hand. In this work, we propose an approach for handling feature drifts in textual streams. Our approach integrates i) an ensemble-based mechanism to accurately predict the feature/word values for the next time-point by taking into account the different features might be subject to different temporal trends and ii) a sketch-based feature space maintenance mechanism that allows for a memory-bounded maintenance of the feature space over the stream. Experiments with textual streams from the sentiment analysis, email preference and spam detection demonstrate that our approach achieves significantly better or competitive performance compared to baselines.
Boosting Variational Inference: an Optimization Perspective

Variational inference is a popular technique to approximate a possibly intractable Bayesian posterior with a more tractable one. Recently, boosting variational inference [20, 4] has been proposed as a new paradigm to approximate the posterior by a mixture of densities by greedily adding components to the mixture. However, as is the case with many other variational inference algorithms, its theoretical properties have not been studied. In the present work, we study the convergence properties of this approach from a modern optimization viewpoint by establishing connections to the classic Frank-Wolfe algorithm. Our analyses yields novel theoretical insights regarding the sufficient conditions for convergence, explicit rates, and algorithmic simplifications. Since a lot of focus in previous works for variational inference has been on tractability, our work is especially important as a much needed attempt to bridge the gap between probabilistic models and their corresponding theoretical properties.
Road Sign Detection and Recognition Using Hidden Markov Model †

This paper proposes a novel road sign detection and recognition method. The position of a road sign in an image is detected using the projection technique. The hidden Markov model is then used to match the detected road sign with those in the database so that the goal of road sign recognition can be achieved. More specifically, the color images in terms of the RGB color system are first converted to the HSV color system and then quantized into specific colors. The horizontal and vertical projections of whole images in the specific colors existing in road signs are then used to detect the positions of road signs. In the recognition stage, only local features around the detected positions are used and a two-step strategy is adopted. The horizontal and vertical projections of background in the local area are used to prune irrelevant road signs. The candidate road signs are then sorted using the hidden Markov model. The one with the first rank is regarded as the recognition result. The effectiveness of the proposed method has been demonstrated by various experiments.