Reviewerly/exHarmony huggingface dataset:
The ICOS Carbon Portal as example of a&amp;#160; FAIR community data repository supporting scientific workflows

&amp;lt;p&amp;gt;The Integrated Carbon Observation System (ICOS) provides long term, high quality observations that follow (and cooperatively set) the global standards for the best possible quality data on the atmospheric composition for greenhouse gases (GHG), greenhouse gas exchange fluxes measured by eddy covariance and CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; partial pressure at water surfaces. The ICOS observational data feeds into a wide area of science that covers for example plant physiology, agriculture, biology, ecology, energy &amp;amp; fuels, forestry, hydrology, (micro)meteorology, environmental, oceanography, geochemistry, physical geography, remote sensing, earth-, climate-, soil- science and combinations of these in multi-disciplinary projects.&amp;lt;br&amp;gt;As ICOS is committed to provide all data and methods in an open and transparent way as free data, a dedicated system is needed to secure the long term archiving and availability of the data together with the descriptive metadata that belongs to the data and is needed to find, identify, understand and properly use the data, also in the far future, following the FAIR data principles. An added requirement is that the full data lifecycle should be completely reproducible to enable full trust in the observations and the derived data products.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this presentation we will introduce the ICOS operational data repository named ICOS Carbon Portal that is based on the linked open data approach. All metadata is modelled in an ontology coded in OWL and based on a RDF triple store that is available through an open SparQL endpoint. The repository supports versioning, collections and models provenance through a simplified Prov-O ontology. All data objects are ingested under strict control for the identified data types on provision of the correct and sufficient (provenance) metadata, data format and data integrity. All data, including raw data, is stored in the long term trusted repository&amp;amp;#160; B2SAFE with two replicates. On top of the triple store and SparQL endpoint we have built a series of services, APIs and graphical interfaces that allow machines to machine and user interaction with the data and metadata. Examples are a full faceted search with connected data cart and download facility, preview of higher level data products (time series of&amp;amp;#160; point observations and spatial data), and cloud computing services like eddy covariance data processing and on demand atmospheric footprint calculations, all connected to the observational data from ICOS.&amp;amp;#160; Another interesting development is the community support for scientific workflows using Jupyter notebook services that connect to our repository through a dedicated python library for direct metadata and data access.&amp;lt;/p&amp;gt;
On the Correlation of Context-Aware Language Models With the Intelligibility of Polish Target Words to Czech Readers

This contribution seeks to provide a rational probabilistic explanation for the intelligibility of words in a genetically related language that is unknown to the reader, a phenomenon referred to as intercomprehension. In this research domain, linguistic distance, among other factors, was proved to correlate well with the mutual intelligibility of individual words. However, the role of context for the intelligibility of target words in sentences was subject to very few studies. To address this, we analyze data from web-based experiments in which Czech (CS) respondents were asked to translate highly predictable target words at the final position of Polish sentences. We compare correlations of target word intelligibility with data from 3-g language models (LMs) to their correlations with data obtained from context-aware LMs. More specifically, we evaluate two context-aware LM architectures: Long Short-Term Memory (LSTMs) that can, theoretically, take infinitely long-distance dependencies into account and Transformer-based LMs which can access the whole input sequence at the same time. We investigate how their use of context affects surprisal and its correlation with intelligibility.
Crowdsourcing Ecologically-Valid Dialogue Data for German

Despite their increasing success, user interactions with smart speech assistants (SAs) are still very limited compared to human-human dialogue. One way to make SA interactions more natural is to train the underlying natural language processing modules on data which reflects how humans would talk to a SA if it was capable of understanding and producing natural dialogue given a specific task. Such data can be collected applying a Wizard-of-Oz approach (WOz), where user and system side are played by humans. WOz allows researchers to simulate human-machine interaction while benefitting from the fact that all participants are human and thus dialogue-competent. More recent approaches have leveraged simple templates specifying a dialogue scenario for crowdsourcing large-scale datasets. Template-based collection efforts, however, come at the cost of data diversity and naturalness. We present a method to crowdsource dialogue data for the SA domain in the WOz framework, which aims at limiting researcher-induced bias in the data while still allowing for a low-resource, scalable data collection. Our method can also be applied to languages other than English (in our case German), for which fewer crowd-workers may be available. We collected data asynchronously, relying only on existing functionalities of Amazon Mechanical Turk, by formulating the task as a dialogue continuation task. Coherence in dialogues is ensured, as crowd-workers always read the dialogue history, and as a unifying scenario is provided for each dialogue. In order to limit bias in the data, rather than using template-based scenarios, we handcrafted situated scenarios which aimed at not pre- script -ing the task into every single detail and not priming the participants’ lexical choices. Our scenarios cued people’s knowledge of common situations and entities relevant for our task, without directly mentioning them, but relying on vague language and circumlocutions. We compare our data (which we publish as the CROWDSS corpus; n = 113 dialogues) with data from MultiWOZ, showing that our scenario approach led to considerably less scripting and priming and thus more ecologically-valid dialogue data. This suggests that small investments in the collection setup can go a long way in improving data quality, even in a low-resource setup.
Delete-Relaxation Heuristics for Lifted Classical Planning

Recent research in classical planning has shown the importance of search techniques that operate directly on the lifted representation of the problem, particularly in domains where the ground representation is prohibitively large. In this paper, we show how to compute the additive and maximum heuristics from the lifted representation of a problem. We do this by adapting well-known reachability analysis techniques based on a Datalog formulation of the delete relaxation of the problem. Our adaptation allows us to obtain not only the desired heuristic value, but also other useful heuristic information such as helpful actions. Our empirical evaluation shows that our lifted version of the additive heuristic is competitive with its ground counterpart on most of the standard international competition benchmarks, and significantly outperforms other state-of-the-art lifted heuristic methods in the literature.
Automatic Instance Generation for Classical Planning

The benchmarks from previous International Planning Competitions (IPCs) are the de-facto standard for evaluating planning algorithms. The IPC set is both a collection of planning domains and a selection of instances from these domains. Most of the domains come with a parameterized generator that generates new instances for a given set of parameter values. Due to the steady progress of planning research some of the instances that were generated for past IPCs are inadequate for evaluating current planners. To alleviate this problem, we introduce Autoscale, an automatic tool that selects instances for a given domain. Autoscale takes into account constraints from the domain designer as well as the performance of current planners to generate an instance set of appropriate difficulty, while avoiding too much bias with respect to the considered planners. We show that the resulting benchmark set is superior to the IPC set and has the potential of improving empirical evaluation of planning research.
Learning to Reweight with Deep Interactions

Recently the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc. Learning to reweight, which is a specific kind of teaching that reweights training data using a teacher model, receives much attention due to its simplicity and effectiveness. In existing learning to reweight works, the teacher model only utilizes shallow/surface information such as training iteration number and loss/accuracy of the student model from training/validation sets, but ignores the internal states of the student model, which limits the potential of learning to reweight. In this work, we propose an improved data reweighting algorithm, in which the student model provides its internal states to the teacher model, and the teacher model returns adaptive weights of training samples to enhance the training of the student model. The teacher model is jointly trained with the student model using meta gradients propagated from a validation set. Experiments on image classification with clean/noisy labels and neural machine translation empirically demonstrate that our algorithm makes significant improvement over previous methods.
Sentiment Classification Algorithm of Danmaku Comment Based on Modified Bayes Model

To research the real time performance of Danmaku comments in sentiment classification, three clustering algorithms were used to divide comments into periods based on their time of occurrence. Research on these periods found some features of Danmaku comments: The outbreak of comments is of a periodical property; In the same period the comments have the same sentimental polarity. Based on above studies, a modified Bayesian algorithm based on period division is proposed. The algorithm uses the consistency of polarity in the same period to correct the Bayes model when processing sentiment classification. Experiments show that the new algorithm is effective and especially suitable for the Danmaku in high consistency of polarity.
Factorisation of Greedoid Polynomials of Rooted Digraphs

Gordon and McMahon defined a two-variable greedoid polynomial f(G; t, z) for any greedoid G. They studied greedoid polynomials for greedoids associated with rooted graphs and rooted digraphs. They proved that greedoid polynomials of rooted digraphs have the multiplicative direct sum property. In addition, these polynomials are divisible by $$1 + z$$ under certain conditions. We compute the greedoid polynomials for all rooted digraphs up to order six. A polynomial is said to factorise if it has a non-constant factor of lower degree. We study the factorability of greedoid polynomials of rooted digraphs, particularly those that are not divisible by $$1 + z$$ . We give some examples and an infinite family of rooted digraphs that are not direct sums but their greedoid polynomials factorise.
Next-generation infrastructure for next-generation people

The way in which people consider next-generation infrastructure needs to be rooted in the history of the planet and, in particular, its most troublesome inhabitant, Homo sapiens. This history has driven the development of infrastructure through the ages at an accelerating rate, from the incipient early cities of 10 000 years ago to the fast-growing metropolises of the twenty-first century. That history teaches people that human beings are essentially social animals and both require and crave social interactions. The need for this has been increasingly excluded from city design since the beginnings of the Industrial Revolutions and increasingly in the past 100 or so years, where the driver has been the development of infrastructure for its own sake rather than that of the everyday person. This paper proposes a refocus for urban engineering, on the concept of sociality, the propensity to interact freely with unknown others, so that infrastructure is directed to enhancing the ability of people to converse as a basic and initial form of the function of social interaction. The challenge is there, but is the infrastructure sector up to meet it? This paper proposes some initial lines of thought and ways forward to answer the challenge.
Efficient Algorithms for Learning from Coarse Labels

For many learning problems one may not have access to fine grained label information; e.g.,an image can be labeled as husky, dog, or even animal depending on the expertise of the annotator.In this work, we formalize these settings and study the problem of learning from such coarse data. Instead of observing the actual labels from a set $\mathcal{Z}$,we observe coarse labels corresponding to a partition of $\mathcal{Z}$ (or a mixture of partitions).Our main algorithmic result is that essentially any problem learnable from fine grained labelscan also be learned efficiently when the coarse data are sufficiently informative. We obtain our result through a generic reduction for answering Statistical Queries (SQ) over fine grained labels given only coarse labels. The number of coarse labels required depends polynomially on the information distortion due to coarsening and the number of fine labels $|\mathcal{Z}|$.We also investigate the case of (infinitely many) real valued labelsfocusing on a central problem in censored and truncated statistics:Gaussian mean estimation from coarse data.We provide an efficient algorithm when the sets in the partition are convex and establish that the problem is NP-hard even for very simple non-convex sets.
On distance covariance in metric and Hilbert spaces

Distance covariance is a measure of dependence between two random variables that take values in two, in general different, metric spaces, see Sz\'ekely, Rizzo and Bakirov (2007) and Lyons (2013). It is known that the distance covariance, and its generalization $\alpha$-distance covariance, can be defined in several different ways that are equivalent under some moment conditions. The present paper considers four such definitions and find minimal moment conditions for each of them, together with some partial results when these conditions are not satisfied. The paper also studies the special case when the variables are Hilbert space valued, and shows under weak moment conditions that two such variables are independent if and only if their ($\alpha$-)distance covariance is 0; this extends results by Lyons (2013) and Dehling et al. (2018+). The proof uses a new definition of distance covariance in the Hilbert space case, generalizing the definition for Euclidean spaces using characteristic functions by Sz\'ekely, Rizzo and Bakirov (2007).
On the Complexity of Sum-of-Products Problems over Semirings

Many important problems in AI, among them SAT, #SAT, and probabilistic inference, amount to Sum-of-Products Problems, i.e. evaluating a sum of products of values from some semiring R. While efficiently solvable cases are known, a systematic study of the complexity of this problem is missing. We characterize the latter by NP(R), a novel generalization of NP over semiring R, and link it to well-known complexity classes. While NP(R) is unlikely to be contained in FPSPACE(poly) in general, for a wide range of commutative (resp. in addition idempotent) semirings, there are reductions to #P (resp. NP) and solutions are thus only mildly harder to compute. We finally discuss NP(R)-complete reasoning problems in well-known semiring formalisms, among them Semiring-based Constraint Satisfaction Problems, obtaining new insights into their computational properties.
K Constant Shows a Mistake in Wavelength Equation in the Wave-Particle Duality and Presents a New Formula

We use quantum of the mass for investigating the correctness of the equation of the wave-particle duality. We show that the equation of the wavelength of a massive object is not correct unless it moves in the speed of the light. According to the equations of the quantum of the mass, we rewrite the equation of the wave-particle duality.
Cross-Lingual Cross-Domain Nested Named Entity Evaluation on English Web Texts

Named Entity Recognition (NER) is a key Natural Language Processing task.However, most existing work on NER targets flat named entities (NEs) and ignores the recognition of nested structures, where entities can be enclosed within other NEs.Moreover, evaluation of Nested Named Entity Recognition (NNER) across domains remains challenging, mainly due to the limited availability of datasets.To address these gaps, we present EWT-NNER, a dataset covering five web domains annotated for nested named entities on top of the English Web Treebank (EWT).We present the corpus and an empirical evaluation, including transfer results from German and Danish.EWT-NNER is annotated for four major entity types, including suffixes for derivational entity markers and partial named entities, spanning a total of 12 classes.We envision the public release of EWT-NNER to encourage further research on nested NER, particularly on cross-lingual cross-domain evaluation.
Integral analysis of GRANULE and ESF block ciphers based on MILP

The lightweight block ciphers, which have the characteristics of lightweight structure and low power consumption, are especially suitable for resource-constrained environments. Bit-based division property with Mixed Integer Linear Programming (MILP) model is constructed according to the structural characteristics of the ciphers, which gives an automatic search method for the longest 10-round integral distinguisher of GRAN-ULE and the 9-round integral distinguisher of ESF, respectively. Based on the 8-round integral distinguisher, 12-round integral attack of GRANULE is obtained by extending backward 4-round. Employing the key equivalence relation to reduce the complexity, the time complexity is about 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">58.17</sup> and the data complexity is about 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">62.17</sup> chosen plaintexts of GRANULE. Compared with other attack methods of current GRANULE and ESF, this is the optimal analysis result of the two ciphers on the length of distinguisher and the number of attack rounds.
Cash for the Register? Capturing Rationales of Early COVID-19 Domain Registrations at Internet-scale

The COVID-19 pandemic introduced novel incentives for adversaries to exploit the state of turmoil.As we have witnessed with the increase in for instance phishing attacks and domain name registrations piggybacking the COVID-19 brand name.In this paper, we perform an analysis at Internetscale of COVID-19 domain name registrations during the early stages of the virus' spread, and investigate the rationales behind them.We leverage the DomainTools COVID-19 Threat List and additional measurements to analyze over 150,000 domains registered between January 1 st 2020 and May 1 st 2020.We identify two key rationales for covid-related domain registrations.Online marketing, by either redirecting traffic or hosting a commercial service on the domain, and domain parking, by registering domains containing popular COVID-19 keywords, presumably anticipating a profit when reselling the domain later on.We also highlight three public policy take-aways that can counteract this domain registration behavior.
A Study on the Integration of Quality Management and Product Safety Management System based on International Standards

In the recent business environment, risks related to product safety problems are increasing. These are arising from various factors such as increasing product and production complexity, supply chain diversification, enhanced PL (product liability) law and strengthening regulations of the government. Accordingly, ISO (international organization for standardization) published standards of PSMS (product safety management system) for suppliers such as ISO 10377 and ISO 10393. Meanwhile, the ISO 9001 QMS (quality management system) was revised in 2015, and it has established itself as an effective tool that can consistently meet the various requirements of stakeholders and promote customer satisfaction. This study aims to suggest an integration framework of QMS and PSMS based on the recent ISO international standards. To this end, firstly, the relationship between QMS and PSMS is studied based on the quality and product safety definitions, PDCA (plan-do-check-act) cycle and risk-based thinking. Secondly, the requirements of ISO 10377 and ISO 9001 are compared and classified as the common and ISO 10377 specific requirements. Finally, integration steps of two systems are suggested and guidelines that can systemize the integrated requirements are presented in the aspect of processes and documentation. This study is expected to be used as a guideline that helps companies those have already acquired QMS certification to build an international-level product safety management system early.
Deep-learning Based Auction Resource Allocation in Coded Computation Offloading for Internet-of-Things

Edge computing has been a promising approach that allows resource-constrained Internet-of-Things (IoT) devices to offload their computation-intensive tasks to the edge of the networks. Instead of offloading the entire computation task to a single edge device, the IoT devices can leverage on the resources of multiple edge devices. In order to improve the performance of the distributed computation tasks, coded distributed computing is proposed to mitigate the straggler effects. However, the edge devices may not have sufficient bandwidth to transmit the computed results to the IoT devices reliably. Hence, we present a deep-learning based auction for the edge devices to buy and utilize bandwidth from the service providers. In particular, the edge devices compete for the bandwidth of the service providers and the service providers allocate bandwidth to the edge devices that value it most while maximizing their revenue. The simulation results show that the deep-learning based auction is optimal as it maximizes the revenue of the service providers while satisfying both properties of individual rationality and incentive compatibility.
Text Summary Augmentation for Intelligent Reading Assistant

This paper presents a technique to assist a reader. We aim to reduce manual efforts of the reader by leveraging the state-of-the-art document summarization techniques and providing summaries about unclear descriptions for each reader. Our system acts as a plug-and-play model that can be modified to support additional methodologies. As a backend of the system, we investigated several text summarization techniques and evaluated three techniques of them: TextRank, LexRank, and Luhn’s algorithm.