nils-herrmann/clean_scirepeval huggingface dataset:
To Plan or not to Plan? Discourse Planning in Slot-Value Informed Sequence to Sequence Models for Language Generation

Natural language generation for task-oriented dialogue systems aims to effectively realize system dialogue actions. All natural language generators (NLGs) must realize grammatical, natural and appropriate output, but in addition, generators for taskoriented dialogue must faithfully perform a specific dialogue act that conveys specific semantic information, as dictated by the dialogue policy of the system dialogue manager. Most previous work on deep learning methods for task-oriented NLG assumes that generation output can be an utterance skeleton. Utterances are delexicalized, with variable names for slots, which are then replaced with actual values as part of post-processing. However, the value of slots do, in fact, influence the lexical selection in the surrounding context as well as the overall sentence plan. To model this effect, we investigate sequence-to-sequence (seq2seq) models in which slot values are included as part of the input sequence and the output surface form. Furthermore, we study whether a separate sentence planning module that decides on grouping of slot value mentions as input to the seq2seq model results in more natural sentences than a seq2seq model that aims to jointly learn the plan and the surface realization.
Harmonisation of knowledge management - comparing 160 KM frameworks around the globe

The purpose of this paper is to look at how knowledge management (KM) has entered into a new phase where consolidation and harmonisation of concepts is required. Some first standards have been published in Europe and Australia in order to foster a common understanding of terms and concepts. The aim of this study was to analyse KM frameworks from research and practice regarding their model elements and try to discover differences and correspondences.  A total of 160 KM frameworks from science, practice, associations and standardization bodies have been collected worldwide. These frameworks have been analysed regarding the use and understanding of the term knowledge, the terms used to describe the knowledge process activities and the factors influencing the success of knowledge management. Quantitative and qualitative content analysis methods have been applied.  The result shows that despite the wide range of terms used in the KM frameworks an underlying consensus was detected regarding the basic categories used to describe the knowledge management activities and the critical success factors of KM. Nevertheless regarding the core term knowledge there is still a need to develop an improved understanding in research and practice.  The first quantitative and qualitative analysis of 160 KM frameworks from different origin worldwide.
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction. To better understand such attacks, a characterization is needed of the properties of regions (the so-called ‘adversarial subspaces’) in which adversarial examples lie. We tackle this challenge by characterizing the dimensional properties of adversarial regions, via the use of Local Intrinsic Dimensionality (LID). LID assesses the space-filling capability of the region surrounding a reference example, based on the distance distribution of the example to its neighbors. We first provide explanations about how adversarial perturbation can affect the LID characteristic of adversarial regions, and then show empirically that LID characteristics can facilitate the distinction of adversarial examples generated using state-of-the-art attacks. As a proof-of-concept, we show that a potential application of LID is to distinguish adversarial examples, and the preliminary results show that it can outperform several state-of-the-art detection measures by large margins for five attack strategies considered in this paper across three benchmark datasets . Our analysis of the LID characteristic for adversarial regions not only motivates new directions of effective adversarial defense, but also opens up more challenges for developing new attacks to better understand the vulnerabilities of DNNs.
Experimental development of a circularly polarized antipodal tapered slot antenna using SIW feed printed on thick substrate

In this paper, we propose a SIW fed circularly polarized (CP) tapered slot antenna which have a good return loss and a wide band axial-ratio. The performance of the antenna has been extensively optimized based on both CST Microwave Studio and Ansoft-HFSS simulation to obtain wide band CP operation. In the following sections, we introduce the theory of operation, the details of the design and the measured results.
Tutorial on recent progress in collaborative filtering

Collaborative filtering is a relatively young algorithmic approach, which already found its way into many commercial applications and established itself as a prime component of recommender systems. The field enjoyed a surge of interest thanks to the Netflix Prize competition that began in October 2006. Impact on research was significant in several ways. For the first time, public research community could access a large-scale industrial strong dataset. In addition, thousands of scientists, students, engineers and enthusiasts were attracted to the field. Importantly, all are judged by the same rigid yardstick, what makes comparisons and experiments much more meaningful. The nature of the competition encourages a rapid development pace, where first generation techniques were quickly replaced by second and third generation innovations.
Hand tracking and gesture recognition system for human-computer interaction using low-cost hardware

Human-Computer Interaction (HCI) exists ubiquitously in our daily lives. It is usually achieved by using a physical controller such as a mouse, keyboard or touch screen. It hinders Natural User Interface (NUI) as there is a strong barrier between the user and computer. There are various hand tracking systems available on the market, but they are complex and expensive. In this paper, we present the design and development of a robust marker-less hand/finger tracking and gesture recognition system using low-cost hardware. We propose a simple but efficient method that allows robust and fast hand tracking despite complex background and motion blur. Our system is able to translate the detected hands or gestures into different functional inputs and interfaces with other applications via several methods. It enables intuitive HCI and interactive motion gaming. We also developed sample applications that can utilize the inputs from the hand tracking system. Our results show that an intuitive HCI and motion gaming system can be achieved with minimum hardware requirements.
Coordination Mechanisms in Human-Robot Collaboration

Robots are envisioned to collaborate with people in tasks that require physical manipulation such as a robot instructing a human in assembling household furniture, a human teaching a robot how to repair machinery, or a robot and a human collaboratively completing construction work. These scenarios characterize joint actions in which the robot and the human must effectively communicate and coordinate their actions with each other in order to successfully achieve task goals. Drawing on recent research in cognitive sciences on joint action, this paper discusses key mechanisms for effective coordination—joint attention, action observation, task-sharing, action coordination, and perception of agency—toward informing the design of communication and coordination mechanisms for robots. It presents two illustrative studies that explore how robot behavior might be designed to employ these mechanisms, particularly joint attention and action observation, to improve measures of task performance and perceptions of the robot in human-robot collaboration.
Elastic extreme learning machine for big data classification

Extreme Learning Machine (ELM) and its variants have been widely used for many applications due to its fast convergence and good generalization performance. Though the distributed ELM based on MapReduce framework can handle very large scale training dataset in big data applications, how to cope with its rapidly updating is still a challenging task. Therefore, in this paper, a novel Elastic Extreme Learning Machine based on MapReduce framework, named Elastic ELM (ELM), is proposed to cover the shortage of ELM whose learning ability is weak to the updated large-scale training dataset. Firstly, after analyzing the property of ELM adequately, it can be found out that its most computation-expensive part, matrix multiplication, can be incrementally, decrementally and correctionally calculated. Next, the Elastic ELM based on MapReduce framework is developed, which first calculates the intermediate matrix multiplications of the updated training data subset, and then update the matrix multiplications by modifying the old matrix multiplications with the intermediate ones. Then, the corresponding new output weight vector can be obtained with centralized computing using the update the matrix multiplications. Therefore, the efficient learning of rapidly updated massive training dataset can be realized effectively. Finally, we conduct extensive experiments on synthetic data to verify the effectiveness and efficiency of our proposed ELM in learning massive rapidly updated training dataset with various experimental settings. & 2014 Elsevier B.V. All rights reserved.
Planar Dual-Band Wide-Scan Phased Array in X-Band

The design of a planar dual-band wide-scan phased array is presented. The array uses novel dual-band comb-slot-loaded patch elements supporting two separate bands with a frequency ratio of 1.4:1. The antenna maintains consistent radiation patterns and incorporates a feeding configuration providing good bandwidths in both bands. The design has been experimentally validated with an X-band planar 9 × 9 array. The array supports wide-angle scanning up to a maximum of 60 ° and 50 ° at the low and high frequency bands respectively.
A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex

This study provides a detailed quantitative estimate for local synaptic connectivity between neocortical pyramidal neurons. A new way of obtaining such an estimate is presented. In acute slices of the rat visual cortex, four layer 2 and four layer 3 pyramidal neurons were intracellularly injected with biocytin. Axonal and dendritic arborizations were three-dimensionally reconstructed with the aid of a computer-based camera lucida system. In a computer experiment, pairs of pre- and postsynaptic neurons were formed and potential synaptic contacts were calculated. For each pair, the calculations were carried out for a whole range of distances (0 to 500 μm) between the presynaptic and the postsynaptic neuron, in order to estimate cortical connectivity as a function of the spatial separation of neurons. It was also differentiated whether neurons were situated in the same or in different cortical layers. The data thus obtained was used to compute connection probabilities, the average number of contacts between neurons, the frequency of specific numbers of contacts and the total number of contacts a dendritic tree receives from the surrounding cortical volume. Connection probabilities ranged from 50% to 80% for directly adjacent neurons and from 0% to 15% for neurons 500 μm apart. In many cases, connections were mediated by one contact only. However, close neighbors made on average up to 3 contacts with each other. The question as to whether the method employed in this study yields a realistic estimate of synaptic connectivity is discussed. It is argued that the results can be used as a detailed blueprint for building artificial neural networks with a cortex-like architecture.
Towards Improving Abstractive Summarization via Entailment Generation

Abstractive summarization, the task of rewriting and compressing a document into a short summary, has achieved considerable success with neural sequence-tosequence models. However, these models can still benefit from stronger natural language inference skills, since a correct summary is logically entailed by the input document, i.e., it should not contain any contradictory or unrelated information. We incorporate such knowledge into an abstractive summarization model via multi-task learning, where we share its decoder parameters with those of an entailment generation model. We achieve promising initial improvements based on multiple metrics and datasets (including a test-only setting). The domain mismatch between the entailment (captions) and summarization (news) datasets suggests that the model is learning some domain-agnostic inference skills.ive summarization, the task of rewriting and compressing a document into a short summary, has achieved considerable success with neural sequence-tosequence models. However, these models can still benefit from stronger natural language inference skills, since a correct summary is logically entailed by the input document, i.e., it should not contain any contradictory or unrelated information. We incorporate such knowledge into an abstractive summarization model via multi-task learning, where we share its decoder parameters with those of an entailment generation model. We achieve promising initial improvements based on multiple metrics and datasets (including a test-only setting). The domain mismatch between the entailment (captions) and summarization (news) datasets suggests that the model is learning some domain-agnostic inference skills.
Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery

The application of remote sensing technology and precision agriculture in the oil palm industry is in development. This study investigated the potential of high resolution QuickBird satellite imagery, which has a synoptic overview, for detecting oil palms infected by basal stem rot disease and for mapping the disease. Basal stem rot disease poses a major threat to the oil palm industry, especially in Indonesia. It is caused by Ganoderma boninense and the symptoms can be seen on the leaf and basal stem. At present there is no effective control for this disease and early detection of the infection is essential. A detailed, accurate and rapid method of monitoring the disease is needed urgently. This study used QuickBird imagery to detect the disease and its spatial pattern. Initially, oil palm and non oil palm object segmentation based on the red band was used to map the spatial pattern of the disease. Secondly, six vegetation indices derived from visible and near infrared bands (NIR) were used for to identify palms infected by the disease. Finally, ground truth from field sampling in four fields with different ages of plant and degrees of infection was used to assess the accuracy of the remote sensing approach. The results show that image segmentation effectively delineated areas infected by the disease with a mapping accuracy of 84%. The resulting maps showed two patterns of the disease; a sporadic pattern in fields with older palms and a dendritic pattern in younger palms with medium to low infection. Ground truth data showed that oil palms infected by basal stem rot had a higher reflectance in the visible bands and a lower reflectance in the near infrared band. Different vegetation indices performed differently in each field. The atmospheric resistant vegetation index and green blue normalized difference vegetation index identified the disease with an accuracy of 67% in a field with 21 year old palms and high infection rates. In the field of 10 year old palms with medium rates of infection, the simple ratio (NIR/red) was effective with an accuracy of 62% for identifying the disease. The green blue normalized difference vegetation index was effective in the field of 10 years old palms with low infection rates with an accuracy of 59%. In the field of 15 and 18 years old palms with low infection rates, all the indices showed low levels of accuracy for identifying the disease. This study suggests that high resolution QuickBird imagery offers a quick, detailed and accurate way of estimating the location and extent of basal stem rot disease infections in oil palm plantations.
Fast Domain Adaptation for Neural Machine Translation

Neural Machine Translation (NMT) is a new approach for autom atic translation of text from one human language into another. The basic concept in NMT is t o train a large Neural Network that maximizes the translation performance on a given p arallel corpus. NMT is gaining popularity in the research community because it outperform ed traditional SMT approaches in several translation tasks at WMT and other evaluation tas ks/benchmarks at least for some language pairs. However, many of the enhancements in SMT ove r the years have not been incorporated into the NMT framework. In this paper, we focus on one such enhancement namely domain adaptation. We propose an approach for adapting a NMT system to a new domain. The main idea behind domain adaptation is that the availability of large out-of-domain training data and a small in-domain training data. We report significant ga ins with our proposed method in both automatic metrics and a human subjective evaluation me tric on two language pairs. With our adaptation method, we show large improvement on the new d omain while the performance of our general domain only degrades slightly. In addition, o ur approach is fast enough to adapt an already trained system to a new domain within few hours wit hout the need to retrain the NMT model on the combined data which usually takes several da ys/weeks depending on the volume of the data.
Multisensor data fusion: A review of the state-of-the-art

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Learning style Identifier: Improving the precision of learning style identification through computational intelligence algorithms

Identifying students’ learning styles has several benefits such as making students aware of their strengths and weaknesses when it comes to learning and the possibility to personalize their learning environment to their learning styles. While there exist learning style questionnaires for identifying a student’s learning style, such questionnaires have several disadvantages and therefore, research has been conducted on automatically identifying learning styles from students’ behavior in a learning environment. Current approaches to automatically identify learning styles have an average precision between 66% and 77%, which shows the need for improvements in order to use such automatic approaches reliably in learning environments. In this paper, four computational intelligence algorithms (artificial neural network, genetic algorithm, ant colony system and particle swarm optimization) have been investigated with respect to their potential to improve the precision of automatic learning style identification. Each algorithm was evaluated with data from 75 students. The artificial neural network shows the most promising results with an average precision of 80.7%, followed by particle swarm optimization with an average precision of 79.1%. Improving the precision of automatic learning style identification allows more students to benefit from more accurate information about their learning styles as well as more accurate personalization towards accommodating their learning styles in a learning environment. Furthermore, teachers can have a better understanding of their students and be able to provide more appropriate interventions.
The Quest Draft: An Automated Course Allocation Algorithm

Course allocation is one of the most complex issues facing any university, due to the sensitive nature of deciding which subset of students should be granted seats in highly-popular (market-scarce) courses. In recent years, researchers have proposed numerous solutions, using techniques in integer programming, combinatorial auction design, and matching theory. In this paper, we present a four-part AI-based course allocation algorithm that was conceived by an undergraduate student, and recently implemented at a small Canadian liberal arts university. This new allocation process, which builds upon the Harvard Business School Draft, has received overwhelming support from students and faculty for its transparency, impartiality, and effectiveness.
THE CROSS-NATIONAL DIVERSITY OF CORPORATE GOVERNANCE : DIMENSIONS AND DETERMINANTS

We develop a theoretical model to describe and explain variation in corporate governance among advanced capitalist economies, identifying the social relations and institutional arrangements that shape who controls corporations, what interests corporations serve, and the allocation of rights and responsibilities among corporate stakeholders. Our “actor-centered” institutional approach explains firm-level corporate governance practices in terms of institutional factors that shape how actors’ interests are defined (“socially constructed”) and represented. Our model has strong implications for studying issues of international convergence.
Nanosecond pulse Generator using fast recovery diodes for cell electromanipulation

Design and operation of a fast recovery diode based pulse generator is presented. The generator produces 3.5-ns-wide, 1200 V amplitude unipolar pulses or +/-600-V bipolar pulses into 50-/spl Omega/ load at the maximum repetition rate of 100 kHz. Pulses shorter than 10 ns are essential for the studies of biological cell response to high electric fields while avoiding ordinary electroporation effects dominant at long pulses. Bipolar pulses are used for the studies of biological cell response to high electric fields when the net transfer of charge is undesirable. The bipolar pulse is produced from a unipolar pulse with the help of a shorted transmission line. This transmission line delays and inverts the initial pulse, so the output is the sum of the initial and the inverted and delayed pulses. The use of mass-produced fast recovery surface-mount rectifier diodes in this circuit substantially simplifies the generator and results in low cost and very small footprint. Similar diode switched pulse generators have been described in the literature using mostly custom fabricated snap-recovery diodes. Here we give an example of an ordinary low-cost diode performing similarly to the custom fabricated counterpart. The diode switched circuit relaxes the requirement on the speed of the main closing switch; in our case, a low-cost power metal-oxide semiconductor field-effect transistor (MOSFET)-saturable core transformer combination.