nils-herrmann/clean_scirepeval huggingface dataset:
Correcting for missing data in information cascades

Transmission of infectious diseases, propagation of information, and spread of ideas and influence through social networks are all examples of diffusion. In such cases we say that a contagion spreads through the network, a process that can be modeled by a cascade graph. Studying cascades and network diffusion is challenging due to missing data. Even a single missing observation in a sequence of propagation events can significantly alter our inferences about the diffusion process.
 We address the problem of missing data in information cascades. Specifically, given only a fraction C' of the complete cascade C, our goal is to estimate the properties of the complete cascade C, such as its size or depth. To estimate the properties of C, we first formulate k-tree model of cascades and analytically study its properties in the face of missing data. We then propose a numerical method that given a cascade model and observed cascade C' can estimate properties of the complete cascade C. We evaluate our methodology using information propagation cascades in the Twitter network (70 million nodes and 2 billion edges), as well as information cascades arising in the blogosphere. Our experiments show that the k-tree model is an effective tool to study the effects of missing data in cascades. Most importantly, we show that our method (and the k-tree model) can accurately estimate properties of the complete cascade C even when 90% of the data is missing.
Visualizing Workplace Learning Data with the SSS Dashboard

This paper reports the design and development of a visual Dashboard, called the SSS Dashboard, which visualizes data from informal workplace learning processes from different viewpoints. The SSS Dashboard retrieves its data from the Social Semantic Server (SSS), an infrastructure that integrates data from several workplace learning applications into a semantically-enriched Artifact-Actor Network. A first evaluation with end users in a course for professional teachers gave promising results. Both a trainer and a learner could understand the learning process from different perspectives using the SSS Dashboard. The results obtained will pave the way for the development of future Learning Analytics applications that exploit the data collected by the SSS.
More Content-Less Control : Access Control in the Web 2 . 0

The popularity of social-networking sites, blogging and other content-sharing sites has exploded, resulting in more personal information and opinions being available with less access control than ever before . Many content-sharing sites provide only the most rudimentary access control: a document can be either completely private or completely public. Other sites offer the slightly more flexible private/friends/public access-control model, but this still fails to support natural distinctions users need, such as separating real-world friends from online friends. The traditional response to these privacy concerns is to post anonymously or pseudonymously, but recent psychological research shows that some Internet users do not establish separate, online personae, but instead consider their online identity as an extension of their real-life self . And although privacy expectations that users desire are easy to state, there is a large gap between the users’ mental models and the policy languages of traditional access-control systems . The consequences of poor access control are welldocumented in the news media. Bloggers have lost their jobs when their employer discovered the employee’s personal blog . Sexual predators use social-networking sites to find victims [7]. Bloggers have been stalked based on the opinions and personal information placed on their blog . Universities have disciplined students using photographs published on social-networking sites . For all these reasons, we advocate that blogs and social networks need a policy mechanism that supports high-level policies that can be expressed succinctly, applied automatically, and updated easily. Current access-control systems fail to meet these goals. Users manually enforce and manage their policies, users, groups, and roles of the system. Furthermore, these systems lack intuitive tools and interfaces for policy generation. We propose to solve all these problems by specifying access-control policies in terms of the content being mediated, e.g. “Blog posts about my home-town are visible to my high school friends.” The system will then automatically infer the posts that are subject to policy rules based on the posts’ contents. Similarly, the system can infer relationships and interests of the users based on the content of objects they create (see Section 3). Such policies will be intuitive and easy to specify, greatly enhancing usability for non-technical users. We first discuss the current state of access control on content-driven sites and analyze approaches proposed in literature for implementing access control for the web. We then describe our proposed method of access control for content-sharing sites.
Scalable inside-out image-based rendering

Our aim is to give users real-time free-viewpoint rendering of real indoor scenes, captured with off-the-shelf equipment such as a high-quality color camera and a commodity depth sensor. Image-based Rendering (IBR) can provide the realistic imagery required at real-time speed. For indoor scenes however, two challenges are especially prominent. First, the reconstructed 3D geometry must be compact, but faithful enough to respect occlusion relationships when viewed up close. Second, man-made materials call for view-dependent texturing, but using too many input photographs reduces performance. We customize a typical RGB-D 3D surface reconstruction pipeline to produce a coarse global 3D surface, and local, per-view geometry for each input image. Our tiled IBR preserves quality by economizing on the expected contributions that entire groups of input pixels make to a final image. The two components are designed to work together, giving real-time performance, while hardly sacrificing quality. Testing on a variety of challenging scenes shows that our inside-out IBR scales favorably with the number of input images.
Music Emotion Recognition: From Content- to Context-Based Models

The striking ability of music to elicit emotions assures its prominent status in human culture and every day life. Music is often enjoyed and sought for its ability to induce or convey emotions, which may manifest in anything from a slight variation in mood, to changes in our physical condition and actions. Consequently, research on how we might associate musical pieces with emotions and, more generally, how music brings about an emotional response is attracting ever increasing attention. First, this paper provides a thorough review of studies on the relation of music and emotions from di↵erent disciplines. We then propose new insights to enhance automated music emotion recognition models using recent results from psychology, musicology, a↵ective computing, semantic technologies and music information retrieval.
An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories

A novel variant of the familiar backpropagation-through-time approach to training recurrent networks is described. This algorithm is intended to be used on arbitrary recurrent networks that run continually without ever being reset to an initial state, and it is specifically designed for computationally efficient computer implementation. This algorithm can be viewed as a cross between epochwise backpropagation through time, which is not appropriate for continually running networks, and the widely used on-line gradient approximation technique of truncated backpropagation through time.
Multivalued Dependencies and a New Normal Form for Relational Databases

A new type of dependency, which includes the well-known functional dependencies as a special case, is defined for relational databases. By using this concept, a new (“fourth”) normal form for relation schemata is defined. This fourth normal form is strictly stronger than Codd's “improved third normal form” (or “Boyce-Codd normal form”). It is shown that every relation schema can be decomposed into a family of relation schemata in fourth normal form without loss of information (that is, the original relation can be obtained from the new relations by taking joins).
CRSM: Crowdsourcing Based Road Surface Monitoring

Detecting road potholes and road roughness levels is key to road condition monitoring, which impacts transport safety and driving comfort. We propose a crowd sourcing based road surface monitoring system, simply called CRSM. CRSM can effectively detect road potholes and evaluate road roughness levels using our hardware modules mounted on distributed vehicles. These modules use low-end accelerometers and GPS devices to obtain vibration pattern, location, and vehicle velocity. Considering the high cost of onboard storage and wireless transmission, a novel light-weight data mining algorithm is proposed to detect road surface events and transmit potential pothole information to a central server. The server gathers reports from the multiple vehicles, and makes a comprehensive evaluation on road surface quality. We have implemented a product-quality system and deployed it on 100 taxies in the Shenzhen urban area. The results show that CRSM can detect road potholes with up to 90% accuracy, and nearly zero false alarms. CRSM can also evaluate road roughness levels correctly, even with some interferences from small bumps or potholes.
Learning Regularized LDA by Clustering

As a supervised dimensionality reduction technique, linear discriminant analysis has a serious overfitting problem when the number of training samples per class is small. The main reason is that the between- and within-class scatter matrices computed from the limited number of training samples deviate greatly from the underlying ones. To overcome the problem without increasing the number of training samples, we propose making use of the structure of the given training data to regularize the between- and within-class scatter matrices by between- and within-cluster scatter matrices, respectively, and simultaneously. The within- and between-cluster matrices are computed from unsupervised clustered data. The within-cluster scatter matrix contributes to encoding the possible variations in intraclasses and the between-cluster scatter matrix is useful for separating extra classes. The contributions are inversely proportional to the number of training samples per class. The advantages of the proposed method become more remarkable as the number of training samples per class decreases. Experimental results on the AR and Feret face databases demonstrate the effectiveness of the proposed method.
Summarization Through Submodularity and Dispersion

We propose a new optimization framework for summarization by generalizing the submodular framework of (Lin and Bilmes, 2011). In our framework the summarization desideratum is expressed as a sum of a submodular function and a nonsubmodular function, which we call dispersion; the latter uses inter-sentence dissimilarities in different ways in order to ensure non-redundancy of the summary. We consider three natural dispersion functions and show that a greedy algorithm can obtain an approximately optimal summary in all three cases. We conduct experiments on two corpora—DUC 2004 and user comments on news articles—and show that the performance of our algorithm outperforms those that rely only on submodularity.
LSTM with working memory

Previous RNN architectures have largely been superseded by LSTM, or “Long Short-Term Memory”. Since its introduction, there have been many variations on this simple design. However, it is still widely used and we are not aware of a gated-RNN architecture that outperforms LSTM in a broad sense while still being as simple and efficient. In this paper we propose a modified LSTM-like architecture. Our architecture is still simple and achieves better performance on the tasks that we tested on. We also introduce a new RNN performance benchmark that uses the handwritten digits and stresses several important network capabilities.
Object Co-segmentation via Graph Optimized-Flexible Manifold Ranking

Aiming at automatically discovering the common objects contained in a set of relevant images and segmenting them as foreground simultaneously, object co-segmentation has become an active research topic in recent years. Although a number of approaches have been proposed to address this problem, many of them are designed with the misleading assumption, unscalable prior, or low flexibility and thus still suffer from certain limitations, which reduces their capability in the real-world scenarios. To alleviate these limitations, we propose a novel two-stage co-segmentation framework, which introduces the weak background prior to establish a globally close-loop graph to represent the common object and union background separately. Then a novel graph optimized-flexible manifold ranking algorithm is proposed to flexibly optimize the graph connection and node labels to co-segment the common objects. Experiments on three image datasets demonstrate that our method outperforms other state-of-the-art methods.
Attention-Based Models for Speech Recognition

Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks including machine translation, handwriting synthesis [1, 2] and image caption generation . We extend the attention-mechanism with features needed for speech recognition. We show that while an adaptation of the model used for machine translation in  reaches a competitive 18.7% phoneme error rate (PER) on the TIMIT phoneme recognition task, it can only be applied to utterances which are roughly as long as the ones it was trained on. We offer a qualitative explanation of this failure and propose a novel and generic method of adding location-awareness to the attention mechanism to alleviate this issue. The new method yields a model that is robust to long inputs and achieves 18% PER in single utterances and 20% in 10-times longer (repeated) utterances. Finally, we propose a change to the attention mechanism that prevents it from concentrating too much on single frames, which further reduces PER to 17.6% level.
Smart grid cooperative communication with smart relay

Many studies have investigated the smart grid architecture and communication models in the past few years. However, the communication model and architecture for a smart grid still remain unclear. Today's electric power distribution is very complex and maladapted because of the lack of efficient and cost-effective energy generation, distribution, and consumption management systems. A wireless smart grid communication system can playan important role in achieving these goals. In thispaper, we describe a smart grid communication architecture in which we merge customers and distributors into a single domain. In the proposed architecture, all the home area networks, neighborhood area networks, and local electrical equipment form a local wireless mesh network (LWMN). Each device or meter can act as a source, router, or relay. The data generated in any node (device/meter) reaches the data collector via other nodes. The data collector transmits this data via the access point of a wide area network (WAN). Finally, data is transferred to the service provider or to the control center of the smart grid. We propose a wireless cooperative communication model for the LWMN. We deploy a limited number of smart relays to improve the performance of the network. A novel relay selection mechanism is also proposed to reduce the relay selection overhead. Simulation results show that our cooperative smart grid (coopSG) communication model improves the end-to-end packet delivery latency, throughput, and energy efficiency over both the Wang et al. and Niyato et al. models.
Learning Context-Sensitive Word Embeddings with Neural Tensor Skip-Gram Model

Distributed word representations have a rising interest in NLP community. Most of existing models assume only one vector for each individual word, which ignores polysemy and thus degrades their effectiveness for downstream tasks. To address this problem, some recent work adopts multiprototype models to learn multiple embeddings per word type. In this paper, we distinguish the different senses of each word by their latent topics. We present a general architecture to learn the word and topic embeddings efficiently, which is an extension to the Skip-Gram model and can model the interaction between words and topics simultaneously. The experiments on the word similarity and text classification tasks show our model outperforms state-of-the-art methods.
Coordinate descent with arbitrary sampling I: algorithms and complexity

We study the problem of minimizing the sum of a smooth convex function and a convex blockseparable regularizer and propose a new randomized coordinate descent method, which we call ALPHA. Our method at every iteration updates a random subset of coordinates, following an arbitrary distribution. No coordinate descent methods capable to handle an arbitrary sampling have been studied in the literature before for this problem. ALPHA is a remarkably flexible algorithm: in special cases, it reduces to deterministic and randomized methods such as gradient descent, coordinate descent, parallel coordinate descent and distributed coordinate descent – both in nonaccelerated and accelerated variants. The variants with arbitrary (or importance) sampling are new. We provide a complexity analysis of ALPHA, from which we deduce as a direct corollary complexity bounds for its many variants, all matching or improving best known bounds.
Personalizing automated image annotation using cross-entropy

Annotating the increasing amounts of user-contributed images in a personalized manner is in great demand. However, this demand is largely ignored by the mainstream of automated image annotation research. In this paper we aim for personalizing automated image annotation by jointly exploiting personalized tag statistics and content-based image annotation. We propose a cross-entropy based learning algorithm which personalizes a generic annotation model by learning from a user's multimedia tagging history. Using cross-entropy-minimization based Monte Carlo sampling, the proposed algorithm optimizes the personalization process in terms of a performance measurement which can be flexibly chosen. Automatic image annotation experiments with 5,315 realistic users in the social web show that the proposed method compares favorably to a generic image annotation method and a method using personalized tag statistics only. For 4,442 users the performance improves, where for 1,088 users the absolute performance gain is at least 0.05 in terms of average precision. The results show the value of the proposed method.
Harnessing ISA diversity: Design of a heterogeneous-ISA chip multiprocessor

Heterogeneous multicore architectures have the potential for high performance and energy efficiency. These architectures may be composed of small power-efficient cores, large high-performance cores, and/or specialized cores that accelerate the performance of a particular class of computation. Architects have explored multiple dimensions of heterogeneity, both in terms of micro-architecture and specialization. While early work constrained the cores to share a single ISA, this work shows that allowing heterogeneous ISAs further extends the effectiveness of such architectures
 This work exploits the diversity offered by three modern ISAs: Thumb, x86-64, and Alpha. This architecture has the potential to outperform the best single-ISA heterogeneous architecture by as much as 21%, with 23% energy savings and a reduction of 32% in Energy Delay Product.
Body and mind: a study of avatar personalization in three virtual worlds

An increasingly large number of users connect to virtual worlds on a regular basis to conduct activities ranging from gaming to business meetings. In all these worlds, users project themselves into the environment via an avatar: a 3D body which they control and whose appearance is often customizable. However, considering the prevalence of this form of embodiment, there is a surprising lack of data about how and why users customize their avatar, as well as how easy and satisfying the existing avatar creation tools are. In this paper, we report on a study investigating these issues through a questionnaire administered to more than a hundred users of three virtual worlds offering widely different avatar creation and customization systems (Maple Story, World of Warcraft, and Second Life). We illustrate the often-surprising choices users make when creating their digital representation and discuss the impact of our findings for the design of future avatar creation systems.
The Use of Ideas of Information Theory for Studying "Language" and Intelligence in Ants

In this review we integrate results of long term experimental study on ant ”language” and intelligence which were fully based on fundamental ideas of Information Theory, such as the Shannon entropy, the Kolmogorov complexity, and the Shannon’s equation connecting the length of a message (l) and its frequency (p), i.e. l = − log p for rational communication systems. This approach, new for studying biological communication systems, enabled us to obtain the following important results on ants’ communication and intelligence: i) to reveal ”distant homing” in ants, that is, their ability to transfer information about remote events; ii) to estimate the rate of information transmission; iii) to reveal that ants are able to grasp regularities and to use them for ”compression” of information; iv) to reveal that ants are able to transfer to each other the information about the number of objects; v) to discover that ants can add and subtract small numbers. The obtained results show that Information Theory is not only wonderful mathematical theory, but many its results may be considered as Nature laws.
Unwrapping and Visualizing Cuneiform Tablets

Thousands of historically revealing cuneiform clay tablets, which were inscribed in Mesopotamia millenia ago, still exist today. Visualizing cuneiform writing is important when deciphering what is written on the tablets. It is also important when reproducing the tablets in papers and books. Unfortunately, scholars have found photographs to be an inadequate visualization tool, for two reasons. First, the text wraps around the sides of some tablets, so a single viewpoint is insufficient. Second, a raking light will illuminate some textual features, but will leave others shadowed or invisible because they are either obscured by features on the tablet or are nearly aligned with the lighting direction. We present solutions to these problems by first creating a high-resolution 3D computer model from laser range data, then unwrapping and flattening the inscriptions on the model to a plane, allowing us to represent them as a scalar displacement map, and finally, rendering this map non-photorealistically using accessibility and curvature coloring. The output of this semiautomatic process enables all of a tablet’s text to be perceived in a single concise image. Our technique can also be applied to other types of inscribed surfaces, including bas-reliefs.