nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive photo collection page layout

This paper presents a new photo collection page layout that attempts to maximize page coverage without having photos overlap. Layout is based on a hierarchical page partition, which provides explicit control over the aspect ratios and relative areas of the photos. We present an efficient method for finding a partition that produces a photo arrangement suitable for the shape of the page. Rather than relying on a stochastic search we employ a deterministic procedure that mimics the natural process of adding photos to the layout one by one.
Using word embeddings in Twitter election classification

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models).
Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings

Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types. Distant supervision paradigm is extensively used to generate training data for this task. However, generated training data assigns same set of labels to every mention of an entity without considering its local context. Existing FETC systems have two major drawbacks: assuming training data to be noise free and use of hand crafted features. Our work overcomes both drawbacks. We propose a neural network model that jointly learns entity mentions and their context representation to eliminate use of hand crafted features. Our model treats training data as noisy and uses non-parametric variant of hinge loss function. Experiments show that the proposed model outperforms previous stateof-the-art methods on two publicly available datasets, namely FIGER(GOLD) and BBN with an average relative improvement of 2.69% in micro-F1 score. Knowledge learnt by our model on one dataset can be transferred to other datasets while using same model or other FETC systems. These approaches of transferring knowledge further improve the performance of respective models.
Behavior change techniques in top-ranked mobile apps for physical activity.

Mobile applications (apps) have potential for helping people increase their physical activity, but little is known about the behavior change techniques marketed in these apps.



The aim of this study was to characterize the behavior change techniques represented in online descriptions of top-ranked apps for physical activity.



Top-ranked apps (n=167) were identified on August 28, 2013, and coded using the Coventry, Aberdeen and London-Revised (CALO-RE) taxonomy of behavior change techniques during the following month. Analyses were conducted during 2013.



Most descriptions of apps incorporated fewer than four behavior change techniques. The most common techniques involved providing instruction on how to perform exercises, modeling how to perform exercises, providing feedback on performance, goal-setting for physical activity, and planning social support/change. A latent class analysis revealed the existence of two types of apps, educational and motivational, based on their configurations of behavior change techniques.



Behavior change techniques are not widely marketed in contemporary physical activity apps. Based on the available descriptions and functions of the observed techniques in contemporary health behavior theories, people may need multiple apps to initiate and maintain behavior change. This audit provides a starting point for scientists, developers, clinicians, and consumers to evaluate and enhance apps in this market.
GHTorrent: Github's data from a firehose

A common requirement of many empirical software engineering studies is the acquisition and curation of data from software repositories. During the last few years, GitHub has emerged as a popular project hosting, mirroring and collaboration platform. GitHub provides an extensive rest api, which enables researchers to retrieve both the commits to the projects' repositories and events generated through user actions on project resources. GHTorrent aims to create a scalable off line mirror of GitHub's event streams and persistent data, and offer it to the research community as a service. In this paper, we present the project's design and initial implementation and demonstrate how the provided datasets can be queried and processed.
Decision Tree Induction from Distributed Heterogeneous Autonomous Data Sources

With the growing use of distributed information networks, there is an increasing need for algorithmic and system solutions for data-driven knowledge acquisition using distributed, heterogeneous and autonomous data repositories. In many applications, practical constraints require such systems to provide support for data analysis where the data and the computational resources are available. This presents us with distributed learning problems. We precisely formulate a class of distributed learning problems; present a general strategy for transforming traditional machine learning algorithms into distributed learning algorithms; and demonstrate the application of this strategy to devise algorithms for decision tree induction (using a variety of splitting criteria) from distributed data. The resulting algorithms are provably exact in that the decision tree constructed from distributed data is identical to that obtained by the corresponding algorithm when in the batch setting. The distributed decision tree induction algorithms have been implemented as part of INDUS, an agent-based system for data-driven knowledge acquisition from heterogeneous, distributed, autonomous data sources.
Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events

We introduce a novel iterative approach for event coreference resolution that gradually builds event clusters by exploiting inter-dependencies among event mentions within the same chain as well as across event chains. Among event mentions in the same chain, we distinguish withinand cross-document event coreference links by using two distinct pairwise classifiers, trained separately to capture differences in feature distributions of withinand crossdocument event clusters. Our event coreference approach alternates between WD and CD clustering and combines arguments from both event clusters after every merge, continuing till no more merge can be made. And then it performs further merging between event chains that are both closely related to a set of other chains of events. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods in joint task of WD and CD event coreference resolution.
The ganglia distributed monitoring system: design, implementation, and experience

Ganglia is a scalable distributed monitoring system for high performance computing systems such as clusters and Grids. It is based on a hierarchical design targeted at federations of clusters. It relies on a multicast-based listen/announce protocol to monitor state within clusters and uses a tree of point-to-point connections amongst representative cluster nodes to federate clusters and aggregate their state. It leverages widely used technologies such as XML for data representation, XDR for compact, portable data transport, and RRDtool for data storage and visualization. It uses carefully engineered data structures and algorithms to achieve very low per-node overheads and high concurrency. The implementation is robust, has been ported to an extensive set of operating systems and processor architectures, and is currently in use on over 500 clusters around the world. This paper presents the design, implementation, and evaluation of Ganglia along with experience gained through real world deployments on systems of widely varying scale, configurations, and target application domains over the last two and a half years.
A Review: Text Classification on Social Media Data

In today’s world most of us depend on Social Media to communicate, express our feelings and share information with our friends. Social Media is the medium where now a day’s people feel free to express their emotions. Social Media collects the data in structured and unstructured, formal and informal data as users do not care about the spellings and accurate grammatical construction of a sentence while communicating with each other using different social networking websites ( Facebook, Twitter, LinkedIn and YouTube). Gathered data contains sentiments and opinion of users which will be processed using data mining techniques and analyzed for achieving the meaningful information from it. Using Social media data we can classify the type of users by analysis of their posted data on the social web sites. Machine learning algorithms are used for text classification which will extract meaningful data from these websites. Here, in this paper we will discuss the different types of classifiers and their advantages and disadvantages.
Handover management in SDN-based mobile networks

In this paper we discuss the evolution of mobility management mechanisms in mobile networks. We emphasize problems with current mobility management approaches in case of very high dense and heterogeneous networks. The main contribution of the paper is a discussion on how the Software-Defined Networking (SDN) technology can be applied in mobile networks in order to efficiently handle mobility in the context of future mobile networks (5G) or evolved LTE. The discussion addresses the most important problems related to mobility management like preservation of session continuity and scalability of handovers in very dense mobile networks. Three variants of SDN usage in order to handle mobility are described and compared in this paper. The most advanced of these variants shows how mobility management mechanisms can be easily integrated with autonomie management mechanisms, providing much more advanced functionality than is provided now by the SON approach. Such mechanisms increase robustness of the handover and optimize the usage of wireless and wired mobile network resources.
A review of object detection based on convolutional neural network

With the development of intelligent device and social media, the data bulk on Internet has grown with high speed. As an important aspect of image processing, object detection has become one of the international popular research fields. In recent years, the powerful ability with feature learning and transfer learning of Convolutional Neural Network (CNN) has received growing interest within the computer vision community, thus making a series of important breakthroughs in object detection. So it is a significant survey that how to apply CNN to object detection for better performance. First the paper introduced the basic concept and architecture of CNN. Secondly the methods that how to solve the existing problems of conventional object detection are surveyed, mainly analyzing the detection algorithm based on region proposal and based on regression. Thirdly it mentioned some means which improve the performance of object detection. Then the paper introduced some public datasets of object detection and the concept of evaluation criterion. Finally, it combed the current research achievements and thoughts of object detection, summarizing the important progress and discussing the future directions.
Smart traffic light for congestion monitoring using LoRaWAN

Traffic light congestion normally occurs in urban areas where the number of vehicles is too many on the road. This problem drives the need for innovation and provide efficient solutions regardless this problem. Smart system that will monitor the congestion level at the traffic light will be a new option to replace the old system which is not practical anymore. Implementing internet of thinking (IoT) technology will provide the full advantage for monitoring and creating a congestion model based on sensor readings. Multiple sensor placements for each lane will give a huge advantage in detecting vehicle and increasing the accuracy in collecting data. To gather data from each sensor, the LoRaWAN technology is utilized where it features low power wide area network, low cost of implementation and the communication is secure bi-directional for the internet of thinking. The radio frequency used between end nodes to gateways range is estimated around 15-kilometer radius. A series of test is carried out to estimate the range of signal and it gives a positive result. The level of congestion for each lane will be displayed on Grafana dashboard and the algorithm can be calculated. This provides huge advantages to the implementation of this project, especially the scope of the project will be focus in urban areas where the level of congestion is bad.
Equilibrium Theory Revisited: Mutual Gaze and Personal Space in Virtual Environments

During the last half of the twentieth century, psychologists and anthropologists have studied proxemics, or spacing behavior, among people in many contexts. As we enter the twenty-first century, immersive virtual environment technology promises new experimental venues in which researchers can study proxemics. Immersive virtual environments provide realistic and compelling experimental settings without sacrificing experimental control. The experiment reported here tested Argyle and Dean's (1965) equilibrium theory's specification of an inverse relationship between mutual gaze, a nonverbal cue signaling intimacy, and interpersonal distance. Participants were immersed in a three-dimensional virtual room in which a virtual human representation (that is, an embodied agent) stood. Under the guise of a memory task, participants walked towards and around the agent. Distance between the participant and agent was tracked automatically via our immersive virtual environment system. All participants maintained more space around agents than they did around similarly sized and shaped but nonhuman-like objects. Female participants maintained more interpersonal distance between themselves and agents who engaged them in eye contact (that is, mutual gaze behavior) than between themselves and agents who did not engage them in eye contact, whereas male participants did not. Implications are discussed for the study of proxemics via immersive virtual environment technology, as well as the design of virtual environments and virtual humans.
An analysis of Bitcoin OP_RETURN metadata

The Bitcoin protocol allows to save arbitrary data on the blockchain through a special instruction of the scripting language, called OP RETURN. A growing number of protocols exploit this feature to extend the range of applications of the Bitcoin blockchain beyond transfer of currency. A point of debate in the Bitcoin community is whether loading data through OP RETURN can negatively affect the performance of the Bitcoin network with respect to its primary goal. This paper is an empirical study of the usage of OP RETURN over the years. We identify several protocols based on OP RETURN, which we classify by their application domain. We measure the evolution in time of the usage of each protocol, the distribution of OP RETURN transactions by application domain, and their space consumption.
IoT architecture proposal for disabled people

We are living in a new communication age, which will radically transform the way we live in our society. A world where anything will be connected to Internet is being created, generating an entirely new dynamic network - The Internet of Things (IoT) - enabling new means of communication between people, things and environment. A suitable architecture for Internet of Things (IoT) demands the implementation of several and distinct technologies that range from computing (e.g. Cloud Computing), communications (e.g. 6LowPAN, 3/4G) to semantic (e.g. data mining). Thus, it is necessary to understand very well all these technologies in order to know which of them is most suitable for a given scenario. Therefore, this paper proposes an IoT architecture for disabled people and intends to identify and describe the most relevant IoT technologies and international standards for the stack of the proposed architecture. In particular, the paper discusses the enabling IoT technologies and its feasibility for people with disabilities. At the end, it presents two use cases that are currently being deployed for this population.
LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations

Image super-resolution (SR) aims to recover high-resolution images from their low-resolution counterparts for improving image analysis and visualization. Interpolation methods, widely used for this purpose, often result in images with blurred edges and blocking effects. More advanced methods such as total variation (TV) retain edge sharpness during image recovery. However, these methods only utilize information from local neighborhoods, neglecting useful information from remote voxels. In this paper, we propose a novel image SR method that integrates both local and global information for effective image recovery. This is achieved by, in addition to TV, low-rank regularization that enables utilization of information throughout the image. The optimization problem can be solved effectively via alternating direction method of multipliers (ADMM). Experiments on MR images of both adult and pediatric subjects demonstrate that the proposed method enhances the details in the recovered high-resolution images, and outperforms methods such as the nearest-neighbor interpolation, cubic interpolation, iterative back projection (IBP), non-local means (NLM), and TV-based up-sampling.
A General Theory of Computational Scalability Based on Rational Functions

The universal scalability law of computational capacity is a rational function Cp = P (p)/Q(p) with P (p) a linear polynomial and Q(p) a second-degree polynomial in the number of physical processors p, that has been long used for statistical modeling and prediction of computer system performance. We prove that Cp is equivalent to the synchronous throughput bound for a machinerepairman with state-dependent service rate. Simpler rational functions, such as Amdahl’s law and Gustafson speedup, are corollaries of this queue-theoretic bound. Cp is further shown to be both necessary and sufficient for modeling all practical characteristics of computational scalability.
Towards Automated Classification of Firmware Images and Identification of Embedded Devices

Embedded systems, as opposed to traditional computers, bring an incredible diversity. The number of devices manufactured is constantly increasing and each has a dedicated software, commonly known as firmware. Full firmware images are often delivered as multiple releases, correcting bugs and vulnerabilities, or adding new features. Unfortunately, there is no centralized or standardized firmware distribution mechanism. It is therefore difficult to track which vendor or device a firmware package belongs to, or to identify which firmware version is used in deployed embedded devices. At the same time, discovering devices that run vulnerable firmware packages on public and private networks is crucial to the security of those networks. In this paper, we address these problems with two different, yet complementary approaches: firmware classification and embedded web interface fingerprinting. We use supervised Machine Learning on a database subset of real world firmware files. For this, we first tell apart firmware images from other kind of files and then we classify firmware images per vendor or device type. Next, we fingerprint embedded web interfaces of both physical and emulated devices. This allows recognition of web-enabled devices connected to the network. In some cases, this complementary approach allows to logically link web-enabled online devices with the corresponding firmware package that is running on the devices. Finally, we test the firmware classification approach on 215 images with an accuracy of 93.5%, and the device fingerprinting approach on 31 web interfaces with 89.4% accuracy.
A model for anisotropic reflection

A reflection and refraction model for anisotropic surfaces is introduced. The anisotropy is simulated by small cylinders (added or subtracted) distributed on the anisotropic surface. Different levels of anisotropy are achieved by varying the distance between each cylinder and/or rising the cylinders more or less from the surface. Multidirectional anisotropy is modelled by orienting groups of cylinders in different direction. The intensity of the reflected light is computed by determining the visible and illuminated portion of the cylinders, taking self-blocking into account. We present two techniques to compute this in practice. In one the intensity is computed by sampling the surface of the cylinders. The other is an analytic solution. In the case of the diffuse component, the solution is exact. In the case of the specular component, an approximation is developed using a Chebyshev polynomial approximation of the specular term, and integrating the polynomial.This model can be implemented easily within most rendering system, given a suitable mechanism to define and alter surface tangents. The effectiveness of the model and the visual importance of anisotropy are illustrated with some pictures.
Learning to Generate Reviews and Discovering Sentiment

We explore the properties of byte-level recurrent language models. When given sufficient amounts of capacity, training data, and compute time, the representations learned by these models include disentangled features corresponding to high-level concepts. Specifically, we find a single unit which performs sentiment analysis. These representations, learned in an unsupervised manner, achieve state of the art on the binary subset of the Stanford Sentiment Treebank. They are also very data efficient. When using only a handful of labeled examples, our approach matches the performance of strong baselines trained on full datasets. We also demonstrate the sentiment unit has a direct influence on the generative process of the model. Simply fixing its value to be positive or negative generates samples with the corresponding positive or negative sentiment.