nils-herrmann/clean_scirepeval huggingface dataset:
A View on Fuzzy Systems for Big Data: Progress and Opportunities

Currently, we are witnessing a growing trend in the study and application of problems in the framework of Big Data. This is mainly due to the great advantages which come from the knowledge extraction from a high volume of information. For this reason, we observe a migration of the standard Data Mining systems towards a new functional paradigm that allows at working with Big Data. By means of the MapReduce model and its different extensions, scalability can be successfully addressed, while maintaining a good fault tolerance during the execution of the algorithms. Among the different approaches used in Data Mining, those models based on fuzzy systems stand out for many applications. Among their advantages, we must stress the use of a representation close to the natural language. Additionally, they use an inference model that allows a good adaptation to different scenarios, especially those with a given degree of uncertainty. Despite the success of this type of systems, their migration to the Big Data environment in the different learning areas is at a preliminary stage yet. In this paper, we will carry out an overview of the main existing proposals on the topic, analyzing the design of these models. Additionally, we will discuss those problems related to the data distribution and parallelization of the current algorithms, and also its relationship with the fuzzy representation of the information. Finally, we will provide our view on the expectations for the future in this framework according to the design of those methods based on fuzzy sets, as well as the open challenges on the topic.
Mobile robot navigation system in outdoor pedestrian environment using vision-based road recognition

We present a mobile robot navigation system guided by a novel vision-based road recognition approach. The system represents the road as a set of lines extrapolated from the detected image contour segments. These lines enable the robot to maintain its heading by centering the vanishing point in its field of view, and to correct the long term drift from its original lateral position. We integrate odometry and our visual road recognition system into a grid-based local map that estimates the robot pose as well as its surroundings to generate a movement path. Our road recognition system is able to estimate the road center on a standard dataset with 25,076 images to within 11.42 cm (with respect to roads at least 3 m wide). It outperforms three other state-of-the-art systems. In addition, we extensively test our navigation system in four busy college campus environments using a wheeled robot. Our tests cover more than 5 km of autonomous driving without failure. This demonstrates robustness of the proposed approach against challenges that include occlusion by pedestrians, non-standard complex road markings and shapes, shadows, and miscellaneous obstacle objects.
An Automatic Multidocument Text Summarization Approach Based on Naïve Bayesian Classifier Using Timestamp Strategy

Nowadays, automatic multidocument text summarization systems can successfully retrieve the summary sentences from the input documents. But, it has many limitations such as inaccurate extraction to essential sentences, low coverage, poor coherence among the sentences, and redundancy. This paper introduces a new concept of timestamp approach with Naïve Bayesian Classification approach for multidocument text summarization. The timestamp provides the summary an ordered look, which achieves the coherent looking summary. It extracts the more relevant information from the multiple documents. Here, scoring strategy is also used to calculate the score for the words to obtain the word frequency. The higher linguistic quality is estimated in terms of readability and comprehensibility. In order to show the efficiency of the proposed method, this paper presents the comparison between the proposed methods with the existing MEAD algorithm. The timestamp procedure is also applied on the MEAD algorithm and the results are examined with the proposed method. The results show that the proposed method results in lesser time than the existing MEAD algorithm to execute the summarization process. Moreover, the proposed method results in better precision, recall, and F-score than the existing clustering with lexical chaining approach.
A Comparative Study of Android and iOS for Accessing Internet Streaming Services

Android and iOS devices are leading the mobile device market. While various user experiences have been reported from the general user community about their differences, such as battery lifetime, display, and touchpad control, few in-depth reports can be found about their comparative performance when receiving the increasingly popular Internet streaming services. Today, video traffic starts to dominate the Internet mobile data traffic. In this work, focusing on Internet streaming accesses, we set to analyze and compare the performance when Android and iOS devices are accessing Internet streaming services. Starting from the analysis of a server-side workload collected from a top mobile streaming service provider, we find Android and iOS use different approaches to request media content, leading to different amounts of received traffic on Android and iOS devices when a same video clip is accessed. Further studies on the client side show that different data requesting approaches (standard HTTP request vs. HTTP range request) and different buffer management methods (static vs. dynamic) are used in Android and iOS mediaplayers, and their interplay has led to our observations. Our empirical results and analysis provide some insights for the current Android and iOS users, streaming service providers, and mobile mediaplayer developers.
Spatial and Frequency Domain Comparison of Interpolation Techniques in Digital Image Processing

This article presents a comparison of different interpolation techniques both in the spatial and frequency domains. Various methods are presented, from a very simple linear interpolation to more complex cubic convolution. The Fourier analysis is used to compare the methods in the frequency domain. Methods are compared by doubling the images in both spatial directions and calculating the signal-to-noise ratio between the original and interpolated images. The paper will also include some notes on subjective image quality.
Biological Activities of Essential Oils: From Plant Chemoecology to Traditional Healing Systems

Essential oils are complex mixtures of hydrocarbons and their oxygenated derivatives arising from two different isoprenoid pathways. Essential oils are produced by glandular trichomes and other secretory structures, specialized secretory tissues mainly diffused onto the surface of plant organs, particularly flowers and leaves, thus exerting a pivotal ecological role in plant. In addition, essential oils have been used, since ancient times, in many different traditional healing systems all over the world, because of their biological activities. Many preclinical studies have documented antimicrobial, antioxidant, anti-inflammatory and anticancer activities of essential oils in a number of cell and animal models, also elucidating their mechanism of action and pharmacological targets, though the paucity of in human studies limits the potential of essential oils as effective and safe phytotherapeutic agents. More well-designed clinical trials are needed in order to ascertain the real efficacy and safety of these plant products.
Purdue Ontology for Pharmaceutical Engineering: Part I. Conceptual Framework

In pharmaceutical drug development and manufacturing, the amount and complexity of information of different types, ranging from raw experimental data to lab reports to complex mathematical models that needs to be stored, accessed, validated, manipulated, managed, and used for decision making is staggering. The information is often in different formats, used in different computer tools, making smooth interaction between these tools difficult. A common, explicit, and platform-independent vocabulary that is both machine accessible and human usable is needed to streamline the flow of information and knowledge generation. The Purdue Ontology for Pharmaceutical Engineering (POPE) was developed to address this informatics challenge. POPE models information and knowledge and includes models of phases, material properties, molecular structures, experiments, reactions, and unit operations. In Part 1, we describe the conceptual framework of POPE and in Part 2 its applications.
Challenges for Software Engineering in Automation

This paper gives an introduction to the essential challenges of software engineering and requirements that software has to fulfill in the domain of automation. Besides, the functional characteristics, specific constraints and circumstances are considered for deriving requirements concerning usability, the technical process, the automation functions, used platform and the well-established models, which are described in detail. On the other hand, challenges result from the circumstances at different points in the single phases of the life cycle of the automated system. The requirements for life-cycle-management, tools and the changeability during runtime are described in detail.
Reversible GANs for Memory-efficient Image-to-Image Translation

The Pix2pix [17] and CycleGAN [40] losses have vastly improved the qualitative and quantitative visual quality of results in image-to-image translation tasks. We extend this framework by exploring approximately invertible architectures which are well suited to these losses. These architectures are approximately invertible by design and thus partially satisfy cycle-consistency before training even begins. Furthermore, since invertible architectures have constant memory complexity in depth, these models can be built arbitrarily deep. We are able to demonstrate superior quantitative output on the Cityscapes and Maps datasets at near constant memory budget.
A Dependency Parser for Tweets

We describe a new dependency parser for English tweets, TWEEBOPARSER. The parser builds on several contributions: new syntactic annotations for a corpus of tweets (TWEEBANK), with conventions informed by the domain; adaptations to a statistical parsing algorithm; and a new approach to exploiting out-of-domain Penn Treebank data. Our experiments show that the parser achieves over 80% unlabeled attachment accuracy on our new, high-quality test set and measure the benefit of our contributions. Our dataset and parser can be found at http://www.ark.cs.cmu.edu/TweetNLP.
Analysis of the antecedents of knowledge sharing and its implication for SMEs internationalization

Knowledge is an essential organizational resource that provides a sustainable competitive advantage in a highly competitive and dynamic economy. SMEs must therefore consider how to promote the sharing of knowledge and expertise between experts who possess it and novices who need to know. Thus, they need to emphaisze and more effectively exploit knowledge-based resources that already exist within the firm. A key issue for the failure of any KM initiative to facilitate knowledge sharing is the lack of consideration of how the organizational and interpersonal context as well as individual characteristics influence knowledge sharing behaviors. Due to the potential benefits that could be realized from knowledge sharing, this study focused on knowledge sharing as one fundamental knowledge-centered activity. Based on the review of previous literature regarding knowledge sharing within and across firms, this study infer that knowledge sharing in a workplace can be influenced by the organizational, individuallevel and technological factors. This study proposes a conceptual model of knowledge sharing within a broad KM framework as an indispensable tool for SMEs internationalization. The model was assessed by using data gathered from employees and managers of twenty-five (25) different SMEs in Norway. The proposed model of knowledge sharing argues that knowledge sharing is influenced by the organizational, individual-level and technological factors. The study also found mediated effect between the organizational factors as well as between the technological factor and knowledge sharing behavior (i.e., being mediated by the individual-level factors). The test results were statistically significant. The organizational factors were acknowledged to have a highly significant role in ensuring that knowledge sharing takes place in the workplace, although the remaining factors play a critical in the knowledge sharing process. For instance, the technological factor may effectively help in creating, storing and distributing explicit knowledge in an accessible and expeditious manner. The implications of the empirical findings are also provided in this study.
A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification

Text summarization and sentiment classification both aim to capture the main ideas of the text but at different levels. Text summarization is to describe the text within a few sentences, while sentiment classification can be regarded as a special type of summarization which “summarizes” the text into a even more abstract fashion, i.e., a sentiment class. Based on this idea, we propose a hierarchical endto-end model for joint learning of text summarization and sentiment classification, where the sentiment classification label is treated as the further “summarization” of the text summarization output. Hence, the sentiment classification layer is put upon the text summarization layer, and a hierarchical structure is derived. Experimental results on Amazon online reviews datasets show that our model achieves better performance than the strong baseline systems on both abstractive summarization and sentiment
Workplace Bullying , Mobbing and General Harassment :

Research into workplace bullying has continued to grow and mature since emerging from Scandinavian investigations into school bullying in the late 1970s. Research communities now exist well beyond Scandinavia, including Europe, the UK, Australia, Asia and the USA. While the terms ‘harassment’ and ‘mobbing’ are often used to describe bullying behaviour, ‘workplace bullying’ tends to be the most consistently used term throughout the research community. In the past two decades especially, researchers have made considerable advances in developing conceptual clarity, frameworks and theoretical explanations that help explain and address this very complex, but often oversimplified and misunderstood, phenomenon. Indeed, as a phenomenon, workplace bullying is now better understood with reasonably consistent research findings in relation to its prevalence; its negative effects on targets, bystanders and organizational effectiveness; and some of its likely antecedents. However, as highlighted in this review, many challenges remain, particularly in relation to its theoretical foundations and efficacy of prevention and management strategies. Drawing on Affective Events Theory, this review advances understanding through the development of a new conceptual model and analysis of its interrelated components, which explain the dynamic and complex nature of workplace bullying and emphasize current and future debates. Gaps in the literature and future research directions are discussed, including the vexing problem of developing an agreed definition of workplace bullying among the research community, the emergence of cyberbullying, the importance of bystanders in addressing the phenomenon and the use of both formal and informal approaches to prevention and intervention.
Empath: Understanding Topic Signals in Large-Scale Text

Human language is colored by a broad range of topics, but existing text analysis tools only focus on a small number of them. We present Empath, a tool that can generate and validate new lexical categories on demand from a small set of seed terms (like "bleed" and "punch" to generate the category violence). Empath draws connotations between words and phrases by deep learning a neural embedding across more than 1.8 billion words of modern fiction. Given a small set of seed words that characterize a category, Empath uses its neural embedding to discover new related terms, then validates the category with a crowd-powered filter. Empath also analyzes text across 200 built-in, pre-validated categories we have generated from common topics in our web dataset, like neglect, government, and social media. We show that Empath's data-driven, human validated categories are highly correlated (r=0.906) with similar categories in LIWC.
Analysis of data in the form of graphs

We discuss the problem of extending data mining approaches to cases in which data points arise in the form of individual graphs. Being able to find the intrinsic low-dimensionality in ensembles of graphs can be useful in a variety of modeling contexts, especially when coarse-graining the detailed graph information is of interest. One of the main challenges in mining graph data is the definition of a suitable pairwise similarity metric in the space of graphs. We explore two practical solutions to solving this problem: one based on finding subgraph densities, and one using spectral information. The approach is illustrated on three test data sets (ensembles of graphs); two of these are obtained from standard graph generating algorithms, while the graphs in the third example are sampled as dynamic snapshots from an evolving network simulation.
Beyond the Doubting of a Shadow A Reply to Commentaries on Shadows of the Mind

1. Bernard J. Baars: Can physics provide a theory of consciousness? 2. David J. Chalmers: Minds, machines, and mathematics 3.  Penrose's Gödelian argument 4. Stanley A. Klein: Is quantum mechanics relevant to understanding consciousness? 5.  Between the motion and the act.... 6.  Awareness and understanding in computer programs 7. Daryl McCullough: Can humans escape Gödel? 8. Drew McDermott: [STAR] Penrose is wrong 9.
Contextual Intent Tracking for Personal Assistants

A new paradigm of recommendation is emerging in intelligent personal assistants such as Apple's Siri, Google Now, and Microsoft Cortana, which recommends "the right information at the right time" and proactively helps you "get things done". This type of recommendation requires precisely tracking users' contemporaneous intent, i.e., what type of information (e.g., weather, stock prices) users currently intend to know, and what tasks (e.g., playing music, getting taxis) they intend to do. Users' intent is closely related to context, which includes both external environments such as time and location, and users' internal activities that can be sensed by personal assistants. The relationship between context and intent exhibits complicated co-occurring and sequential correlation, and contextual signals are also heterogeneous and sparse, which makes modeling the context intent relationship a challenging task. To solve the intent tracking problem, we propose the Kalman filter regularized PARAFAC2 (KP2) nowcasting model, which compactly represents the structure and co-movement of context and intent. The KP2 model utilizes collaborative capabilities among users, and learns for each user a personalized dynamic system that enables efficient nowcasting of users' intent. Extensive experiments using real-world data sets from a commercial personal assistant show that the KP2 model significantly outperforms various methods, and provides inspiring implications for deploying large-scale proactive recommendation systems in personal assistants.
Efficient Client-Side Deduplication of Encrypted Data With Public Auditing in Cloud Storage

At present, there is a considerable increase in the amount of data stored in storage services, along with dramatic evolution of networking techniques. In storage services with huge data, the storage servers may want to reduce the volume of stored data, and the clients may want to monitor the integrity of their data with a low cost, since the cost of the functions related to data storage increase in proportion to the size of the data. To achieve these goals, secure deduplication and integrity auditing delegation techniques have been studied, which can reduce the volume of data stored in storage by eliminating duplicated copies and permit clients to efficiently verify the integrity of stored files by delegating costly operations to a trusted party, respectively. So far many studies have been conducted on each topic, separately, whereas relatively few combined schemes, which support the two functions simultaneously, have been researched. In this paper, we design a combined technique, which performs both secure deduplication of encrypted data and public integrity auditing of data. To support the two functions, the proposed scheme performs challenge-response protocols using the BLS signature-based homomorphic linear authenticator. We utilize a third party auditor for performing public audit, in order to help low-powered clients. The proposed scheme satisfies all the fundamental security requirements. We also propose two variances that provide higher security and better performance.
The Advanced Data Acquisition Model (ADAM): A Process Model for Digital Forensic Practice

As with other types of evidence, the courts make no presumption that digital evidence is reliable without some evidence of empirical testing in relation to the theories and techniques associated with its production. The issue of reliability means that courts pay close attention to the manner in which electronic evidence has been obtained and in particular the process in which the data is captured and stored. Previous process models have tended to focus on one particular area of digital forensic practice, such as law enforcement, and have not incorporated a formal description. We contend that this approach has prevented the establishment of generally-accepted standards and processes that are urgently needed in the domain of digital forensics. This paper presents a generic process model as a step towards developing such a generally-accepted standard for a fundamental digital forensic activity–the acquisition of digital evidence.
Federated Learning of Deep Networks using Model Averaging

Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data-center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks that proves robust to the unbalanced and non-IID data distributions that naturally arise. This method allows high-quality models to be trained in relatively few rounds of communication, the principal constraint for federated learning. The key insight is that despite the non-convex loss functions we optimize, parameter averaging over updates from multiple clients produces surprisingly good results, for example decreasing the communication needed to train an LSTM language model by two orders of magnitude.