nils-herrmann/clean_scirepeval huggingface dataset:
Design of Dynamic Petri Recurrent Fuzzy Neural Network and Its Application to Path-Tracking Control of Nonholonomic Mobile Robot

This paper focuses on the design of a dynamic Petri recurrent fuzzy neural network (DPRFNN), and this network structure is applied to the path-tracking control of a nonholonomic mobile robot for verifying its validity. In the DPRFNN, the concept of a Petri net and the recurrent frame of internal-feedback loops are incorporated into a traditional FNN to alleviate the computation burden of parameter learning and to enhance the dynamic mapping of network ability. Moreover, the supervised gradient-descent method is used to develop the online-training algorithm for the DPRFNN control. In order to guarantee the convergence of path-tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine varied learning rates for DPRFNN. In addition, the effectiveness of the proposed DPRFNN control scheme under different moving paths is verified by experimental results, and its superiority is indicated in comparison with FNN, RFNN, Petri FNN, and PRFNN control systems.
Towards a sustainable fashion retail supply chain in Europe : Organisation and performance

Sustainability issues are particularly sensitive to the fashion supply chain, given current fierce competition, intensive resource use, and the exposure of penurious labour conditions in some regions. In this paper we discuss how the sustainability movement is impacting the fashion retail supply chain organization and its performance. We carried out a study with stakeholders of the fashion industry and we report on their views. We elaborate on the challenges and conflicts of the different dimensions of sustainability, and we discuss how to leverage both the internal and external organisations in the European supply chain.
Hemisphere lens-loaded Vivaldi antenna for time domain microwave imaging of concealed objects

The hemisphere lens-loaded Vivaldi antenna for the microwave imaging applications is designed and tested in this paper. The proposed antenna is designed to work in the wide frequency band of 1–14 GHz, and is fabricated on the FR-4 substrate. The directivity of the proposed Vivaldi antenna is enhanced using a hemispherical shape dielectric lens, which is fixed on the end-fire direction of the antenna. The proposed antenna is well suited for the microwave imaging applications because of the wide frequency range and high directivity. The design of the antenna is carried out using the CST microwave studio, and various parameters such as the return loss, the radiation pattern, the directivity, and input impedance are optimized. The maximum improvement of 4.19 dB in the directivity is observed with the designed hemisphere lens. The antenna design is validated by fabricating and testing it in an anechoic environment. Finally, the designed antenna is utilized to establish a setup for measuring the scattering coefficients of various objects and structures in the frequency band of 1–14 GHz. The two-dimensional (2D) microwave images of these objects are successfully obtained in terms of the measured wide band scattering data using a novel time domain inverse scattering approach, which shows the applicability of the proposed antenna.
Analysing the connectivity and communication of suicidal users on twitter

In this paper we aim to understand the connectivity and communication characteristics of Twitter users who post content subsequently classified by human annotators as containing possible suicidal intent or thinking, commonly referred to as suicidal ideation. We achieve this understanding by analysing the characteristics of their social networks. Starting from a set of human annotated Tweets we retrieved the authors' followers and friends lists, and identified users who retweeted the suicidal content. We subsequently built the social network graphs. Our results show a high degree of reciprocal connectivity between the authors of suicidal content when compared to other studies of Twitter users, suggesting a tightly-coupled virtual community. In addition, an analysis of the retweet graph has identified bridge nodes and hub nodes connecting users posting suicidal ideation with users who were not, thus suggesting a potential for information cascade and risk of a possible contagion effect. This is particularly emphasised by considering the combined graph merging friendship and retweeting links.
Percentage and Relative Error Measures in Forecast Evaluation

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Parallel Stream Processing Against Workload Skewness and Variance

Key-based workload partitioning is a common strategy used in parallel stream processing engines, enabling effective key-value tuple distribution over worker threads in a logical operator. It is likely to generate poor balancing performance when workload variance occurs on the incoming data stream. This paper presents a new key-based workload partitioning framework, with practical algorithms to support dynamic workload assignment for stateful operators. The framework combines hash-based and explicit key-based routing strategies for workload distribution, which specifies the destination worker threads for a handful of keys and assigns the other keys with the hash function. When short-term distribution fluctuations occur to the incoming data stream, the system adaptively updates the routing table containing the chosen keys, in order to rebalance the workload with minimal migration overhead within the stateful operator. We formulate the rebalance operation as an optimization problem, with multiple objectives on minimizing state migration costs, controlling the size of the routing table and breaking workload imbalance among worker threads. Despite of the NP-hardness nature behind the optimization formulation, we carefully investigate and justify the heuristics behind key (re)routing and state migration, to facilitate fast response to workload variance with ignorable cost to the normal processing in the distributed system. Empirical studies on synthetic data and real-world stream applications validate the usefulness of our proposals.
Predicting the types of file fragments

A problem that arises in computer forensics is to determine the type of a file fragment. An extension to the file name indicating the type is stored in the disk directory, but when a file is deleted, the entry for the file in the directory may be overwritten. This problem is easily solved when the fragment includes the initial header, which contains explicit type-identifying information, but it is more difficult to determine the type of a fragment from the middle of a file. We investigate two algorithms for predicting the type of a fragment: one based on Fisher’s linear discriminant and the other based on longest common subsequences of the fragment with various sets of test files. We test the ability of the algorithms to predict a variety of common file types. Algorithms of this kind may be useful in designing the next generation of file-carvers – programs that reconstruct files when directory information is lost or deleted. These methods may also be useful in designing virus scanners, firewalls and search engines to find files that are similar to a given file. a 2008 Digital Forensic Research Workshop.
Neural End-to-End Learning for Computational Argumentation Mining

We investigate neural techniques for endto-end computational argumentation mining (AM). We frame AM both as a tokenbased dependency parsing and as a tokenbased sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch longrange dependencies inherent to the AM problem. Moreover, we find that jointly learning ‘natural’ subtasks, in a multi-task learning setup, improves performance.
Byte Rotation Encryption Algorithm through parallel processing and multi-core utilization

Securing digital data has become tedious task as the technology is increasing. Existing encryption algorithms such as AES,DES and Blowfish ensure information security but consume lot of time as the security level increases. In this paper, Byte Rotation Encryption Algorithm (BREA) has implemented using parallel processing and multi-core utilization. BREA divides data into fixed size blocks. Each block is processed parallely using random number key. So the set of blocks are executed in parallel by utilizing all the available CPU cores. Finally, from the experimental analysis, it is observed that the proposed BREA algorithm reduces execution time when the number of cores has increased.
Recommending Self-Regulated Learning Strategies Does Not Improve Performance in a MOOC

Many committed learners struggle to achieve their goal of completing a Massive Open Online Course (MOOC). This work investigates self-regulated learning (SRL) in MOOCs and tests if encouraging the use of SRL strategies can improve course performance. We asked a group of 17 highly successful learners about their own strategies for how to succeed in a MOOC. Their responses were coded based on a SRL framework and synthesized into seven recommendations. In a randomized experiment, we evaluated the effect of providing those recommendations to learners in the same course (N = 653). Although most learners rated the study tips as very helpful, the intervention did not improve course persistence or achievement. Results suggest that a single SRL prompt at the beginning of the course provides insufficient support. Instead, embedding technological aids that adaptively support SRL throughout the course could better support learners in MOOCs.
General Formal Ontology (GFO) - A Foundational Ontology Integrating Objects and Processes [Version 1.0]

This report is a living document of the Research Group Ontologies in Medicine (Onto-Med) which represents work in progress towards a proposal for an integrated system of foundational ontologies. It will be applied to several fields of medicine, biomedicine, and biology, and a number of applications are carried out in collaboration with the Center for Clinical Trials at the University of Leipzig, with the MaxPlanck-Institute for Evolutionary Anthropology, and with the ICCAS at the University of Leipzig. The General Formal Ontology (GFO) is a component of the Integrated System of Foundational Ontologies (ISFO), and ISFO is a part of the Integrated Framework for the Development and Application of Ontologies (IFDAO). The predecessor of IFDAO was the GOL project which was launched in 1999 as a collaborative research effort of the Institute of Medical Informatics, Statistics and Epidemiology (IMISE) and the Institute of Informatics (IfI) at the University of Leipzig.
Bidirectional Generative Adversarial Networks for Neural Machine Translation

Generative Adversarial Network (GAN) has been proposed to tackle the exposure bias problem of Neural Machine Translation (NMT). However, the discriminator typically results in the instability of the GAN training due to the inadequate training problem: the search space is so huge that sampled translations are not sufficient for discriminator training. To address this issue and stabilize the GAN training, in this paper, we propose a novel Bidirectional Generative Adversarial Network for Neural Machine Translation (BGAN-NMT), which aims to introduce a generator model to act as the discriminator, whereby the discriminator naturally considers the entire translation space so that the inadequate training problem can be alleviated. To satisfy this property, generator and discriminator are both designed to model the joint probability of sentence pairs, with the difference that, the generator decomposes the joint probability with a source language model and a source-to-target translation model, while the discriminator is formulated as a target language model and a target-to-source translation model. To further leverage the symmetry of them, an auxiliary GAN is introduced and adopts generator and discriminator models of original one as its own discriminator and generator respectively. Two GANs are alternately trained to update the parameters. Experiment results on German-English and ChineseEnglish translation tasks demonstrate that our method not only stabilizes GAN training but also achieves significant improvements over baseline systems.
Concentri Cloud: Word Cloud Visualization for Multiple Text Documents

Word clouds provide a simple and effective means to visually communicate the most frequent words of text documents. However, only few word cloud visualizations support the contrastive analysis of multiple texts. This paper introduces Concentri Cloud, a layered word cloud layout that merges the words from several text documents into a single visualization. The weighted words are arranged in a concentric layout, with those representing the individual documents on the outer circle and the merged ones on inner circles. Interaction techniques allow to analyze the word cloud composition and to provide details on demand. The approach has been implemented and tested on several examples. A qualitative evaluation indicates the general value of Concentri Cloud and reveals benefits and limitations.
Advances in Digital Forensics XIII

In digital forensics, examinations are carried out to explain events and demonstrate the root cause from a number of plausible causes. Yin’s approach to case study research offers a systematic process for investigating occurrences in their real-world contexts. The approach is well suited to examining isolated events and also addresses questions about causality and the reliability of findings. The techniques that make Yin’s approach suitable for research also apply to digital forensic examinations. The merits of case study research are highlighted in previous work that established the suitability of the case study research method for conducting digital forensic examinations. This research extends the previous work by demonstrating the practicality of Yin’s case study method in examining digital events. The research examines the relationship between digital evidence – the effect – and its plausible causes, and how patterns can be identified and applied to explain the events. Establishing these patterns supports the findings of a forensic examination. Analytic strategies and techniques inherent in Yin’s case study method are applied to identify and analyze patterns in order to establish the findings of a digital forensic examination.
Optimization of perturb and observe maximum power point tracking method

Maximum power point tracking (MPPT) techniques are used in photovoltaic (PV) systems to maximize the PV array output power by tracking continuously the maximum power point (MPP) which depends on panels temperature and on irradiance conditions. The issue of MPPT has been addressed in different ways in the literature but, especially for low-cost implementations, the perturb and observe (P&O) maximum power point tracking algorithm is the most commonly used method due to its ease of implementation. A drawback of P&O is that, at steady state, the operating point oscillates around the MPP giving rise to the waste of some amount of available energy; moreover, it is well known that the P&O algorithm can be confused during those time intervals characterized by rapidly changing atmospheric conditions. In this paper it is shown that, in order to limit the negative effects associated to the above drawbacks, the P&O MPPT parameters must be customized to the dynamic behavior of the specific converter adopted. A theoretical analysis allowing the optimal choice of such parameters is also carried out. Results of experimental measurements are in agreement with the predictions of theoretical analysis.
Towards application development for the internet of things

The Internet of Things (IoT) integrates the physical world with the existing Internet, and is rapidly gaining popularity, thanks to the increased adoption of smart phones and sensing devices. One of the important challenges in this domain is to enable domain experts to easily specify applications for the IoT. As a first step towards developing a suitable programming abstraction, in this paper we present a domain model for applications in the Internet of Things, based on a survey of recently proposed IoT applications from the real world that represent a wide class of behaviors found in IoT use cases.
Foundations of human computing: facial expression and emotion

Many people believe that emotions and subjective feelings are one and the same and that a goal of human-centered computing is emotion recognition. The first belief is outdated; the second mistaken. For human-centered computing to succeed, a different way of thinking is needed.Emotions are species-typical patterns that evolved because of their value in addressing fundamental life tasks[19]. Emotions consist of multiple components that may include intentions, action tendencies, appraisals, other cognitions, central and peripheral changes in physiology, and subjective feelings. Emotions are not directly observable, but are inferred from expressive behavior, self-report, physiological indicators, and context. I focus on expressive behavior because of its coherence with other indicators and the depth of research on the facial expression of emotion in behavioral and computer science. In this paper, among the topics I include are approaches to measurement, timing or dynamics, individual differences, dyadic interaction, and inference. I propose that design and implementation of perceptual user interfaces may be better informed by considering the complexity of emotion, its various indicators, measurement, individual differences, dyadic interaction, and problems of inference.
Syntactic Stylometry: Using Sentence Structure for Authorship Attribution

Most approaches to statistical stylometry have concentrated on lexical features, such as relative word frequencies or type-token ratios. Syntactic features have been largely ignored. This work attempts to fill that void by introducing a technique for authorship attribution based on dependency grammar. Syntactic features are extracted from texts using a common dependency parser, and those features are used to train a classifier to identify texts by author. While the method described does not outperform existing methods on most tasks, it does demonstrate that purely syntactic features carry information which could be useful for stylometric analysis. Index words: stylometry, authorship attribution, dependency grammar, machine learning Syntactic Stylometry: Using Sentence Structure for Authorship Attribution
KNN Model-Based Approach in Classification

The k-Nearest-Neighbours (kNN) is a simple but effective method for classification. The major drawbacks with respect to kNN are (1) its low efficiency being a lazy learning method prohibits it in many applications such as dynamic web mining for a large repository, and (2) its dependency on the selection of a “good value” for k. In this paper, we propose a novel kNN type method for classification that is aimed at overcoming these shortcomings. Our method constructs a kNN model for the data, which replaces the data to serve as the basis of classification. The value of k is automatically determined, is varied for different data, and is optimal in terms of classification accuracy. The construction of the model reduces the dependency on k and makes classification faster. Experiments were carried out on some public datasets collected from the UCI machine learning repository in order to test our method. The experimental results show that the kNN based model compares well with C5.0 and kNN in terms of classification accuracy, but is more efficient than the standard kNN.
Liveboard: A Large Interactive Display Supporting Group Meetings, Presentations, and Remote Collaboration

This paper describes the Liveboard, a large interactive display system. With nearly one million pixels and an accurate, multi-state, cordless pen, the Liveboard provides a basis for research on user interfaces for group meetings, presentations and remote collaboration. We describe the underlying hardware and software of the Liveboard, along with several software applications that have been developed. In describing the system, we point out the design rationale that was used to make various choices. We present the results of an informal survey of Liveboard users, and describe some of the improvements that have been made in response to user feedback. We conclude with several general observations about the use of large public interactive displays.
IoT based Smart Home design using power and security management

The paper presents the design and implementation of an Ethernet-based Smart Home intelligent system for monitoring the electrical energy consumption based upon the real time tracking of the devices at home an INTEL GALILEO 2ND generation development board, which can be used in homes and societies. The proposed system works on real time monitoring and voice control, so that the electrical devices and switches can be remotely controlled and monitored with or without an android based app. It uses various sensors to not only monitor the real time device tracking but also maintaining the security of your house. It is monitored and controlled remotely from an android app using the Internet or the Intranet connectivity. The proposed outcome of the project aims as multiple benefits of saving on electricity bills of the home as well as keep the users updated about their home security with an option of controlling the switching of the devices by using their voice or simple toggle touch on their smartphone, and last but most importantly, monitor the usage in order to conserve the precious natural resources by reducing electrical energy consumption.