Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial-Temporal Self-Attention Network for Flow Prediction

Flow prediction (e.g., crowd flow, traffic flow) with features of spatial-temporal is increasingly investigated in AI research field. It is very challenging due to the complicated spatial dependencies between different locations and dynamic temporal dependencies among different time intervals. Although measurements of both dependencies are employed, existing methods suffer from the following two problems. First, the temporal dependencies are measured either uniformly or bias against long-term dependencies, which overlooks the distinctive impacts of short-term and long-term temporal dependencies. Second, the existing methods capture spatial and temporal dependencies independently, which wrongly assumes that the correlations between these dependencies are weak and ignores the complicated mutual influences between them. To address these issues, we propose a Spatial-Temporal Self-Attention Network (ST-SAN). As the path-length of attending long-term dependency is shorter in the self-attention mechanism, the vanishing of long-term temporal dependencies is prevented. In addition, since our model relies solely on attention mechanisms, the spatial and temporal dependencies can be simultaneously measured. Experimental results on real-world data demonstrate that, in comparison with state-of-the-art methods, our model reduces the root mean square errors by 9% in inflow prediction and 4% in outflow prediction on Taxi-NYC data, which is very significant compared to the previous improvement.
Unsupervised Cross-Lingual Word Embeddings Learning with Adversarial Training

Recent works have managed to learn cross-lingual word embeddings (CLWEs) in an unsupervised manner. As a prominent unsupervised model, generative adversarial networks (GANs) have been heavily studied for unsupervised CLWEs learning by aligning the embedding spaces of different languages. Due to disturbing the embedding distribution, the embeddings of low-frequency words (LFEs) are usually treated as noises in the alignment process. To alleviate the impact of LFEs, existing GANs based models utilized a heuristic rule to aggressively sample the embeddings of high-frequency words (HFEs). However, such sampling rule lacks of theoretical support. In this paper, we propose a novel GANs based model to learn cross-lingual word embeddings without any parallel resource. To address the noise problem caused by the LFEs, some perturbations are injected into the LFEs for offsetting the distribution disturbance. In addition, a modified framework based on Cramér GAN is designed to train the perturbed LFEs and the HFEs jointly. Empirical evaluation on bilingual lexicon induction demonstrates that the proposed model outperforms the state-of-the-art GANs based model in several language pairs.
A case study of community-based socioscientific issue program: focusing on the abandoned animal issue

ABSTRACT The purpose of the study was to investigate the effects of community-based socioscientific issue programmes (SSI-COMM) regarding the issue of abandoned animals on promoting middle school students’ understanding of the issue and their character and values as citizens. An SSI-COMM program, called ‘Save Abandoned Pets! (SAP),’ was implemented in four middle schools located in Seoul; 172 seventh graders voluntarily participated in the programme over eight weeks. Data were collected by observation with detailed field-notes, two questionnaires, and individual interviews with the participating students, instructors and teachers, and local experts. Results indicated that there was a statistically significant increase in their understanding of the abandoned animal issue (QU-SSI) and character and values as citizens (CVGCA) after implementing SSI-COMM. Qualitative data analysis also revealed that the SAP programme prompted the students to consider the healthy relationship between human beings and animals, how badly our behaviours could threaten animals or destroy their well-being, and what kinds of efforts we could make for humans and animals to live together. Ultimately, the students felt sympathy and empathy for the abandoned animals and responsibility to take care of them by participating in the SAP programme.
An Enterprise Credit Evaluation Method for Power Grid Service Providers Based on Block Chain is Presented

As the main content of credit investigation, credit management is an important part of enterprise operation management, and it plays a dominant role in the management of enterprise personnel, funds, contracts, bidding and other management. The management of credit is essentially the management of data. Traditional credit investigation agencies collect user data through various technical means and establish a credit investigation management database. However, the reliability of data obtained in this way is not guaranteed. The emergence of blockchain theoretically solves the problem of data reliability, and provides a new method for credit investigation, but it can not guarantee the authenticity of the data itself, that is, the nodes that get the accounting can upload the fake data content to the blockchain without verifying the data content itself. How to reduce the damage caused by this uncertainty to the operation and management of enterprises is a major scientific problem that urgently needs to be solved. Therefore, this paper will make research on the consensus mechanism supporting the reliability of data content and the data standardization technology of uploading to blockchain. The main research content includes: 1. Propose a dynamic weight voting consensus mechanism based on incentive mechanism; 2. Propose the standardization technology of data uploading to block chain based on smart contract.The proposal of two technologies provides theoretical support for the authenticity and standardization of blockchain data content. Finally, taking the power grid bidding management as an example, based on the consensus mechanism and the standardization technology of data uploading to block chain proposed in this paper, construct a power grid service provider credibility evaluation model.
The role of fish as predators of krill (Euphausia superba) and other pelagic resources in the Southern Ocean

Krill forms an important part of the diet of many Antarctic fish species. An understanding of the role of fish as krill predators in the Southern Ocean is critical to understanding how changes in fish abundance, such as through fishing or environmental change, are likely to impact on the food webs in the region. First attempts to estimate the krill and pelagic food consumption by Antarctic demersal fish in the low Antarctic were made in the late 1970s/early 1980s. Those estimates were constrained by a paucity of biomass estimates and the mostly qualitative nature of food studies. Food consumption estimates were extended to the mesopelagic realm and the high-Antarctic Zone in the late 1980s and early 1990s when these areas were exploited commercially for Electrona carlsbergi. Currently, the best estimates of annual krill consumption by fish are 23 000 000–29 000 000 tonnes of krill and other pelagic prey taken annually by demersal fish in the 1980s in the whole Southern Ocean, and 5 000 000–32 000 000 tonnes taken by mesopelagic fish in the Atlantic sector of the Southern Ocean. It is clear from this review that fish are important predators of krill, in particular the larger myctophids and some channichthyids and nototheniids, and that the importance of krill in fish diets varies substantially both with time and location on various scales, as well as with the availability of alternate prey in the different regions in the Southern Ocean. Ecosystem models therefore need to account for their role. However, several key areas of uncertainty exist, which need to be considered in ecosystem and food-web models for the Southern Ocean. For instance, no robust estimates of food consumption by mesopelagic fish can be provided for the vast areas of the Indian and Pacific Ocean sectors, or for several of the most abundant myctophid species in the Atlantic sector, due to the paucity of relevant studies in these regions. Where biomass estimates do exist, such as in the Atlantic sector, there can be a lack of precision in trawl and hydroacoustic surveys. Studies that adequately capture the seasonal variation in consumption rates and changing importance of krill in the diet of fish are also rare. Furthermore, the effects of large changes in abundance and community structure of fishes brought about by industrial fishing need to be considered when evaluating patterns that have emerged in Southern Ocean ecosystems in the course of the 20th century. As a first step towards a modelling approach to include fish in krill-based food-web models, it is suggested to incorporate data on Champsocephalus gunnari from the western Atlantic sector and Dissostichus mawsoni from the Ross Sea into modelling approaches within the CCAMLR Ecosystem Monitoring Program. Both species are important predators of krill (C. gunnari) and fish (D. mawsoni) in turn form key prey for top predators in each region.
Variation in diet of Semipalmated Sandpipers (Calidris pusilla) during stopover in the upper Bay of Fundy, Canada

Semipalmated Sandpipers (Calidris pusilla (L., 1766)) use the upper Bay of Fundy, Canada, as a critical stopover site during their annual fall migration to wintering grounds in South America. While in the area, they feed extensively on mudflat invertebrates. Historically the amphipod Corophium volutator (Pallas, 1766) has been thought to make up the ma- jority of their diet. However, we have recently observed flexibility in foraging behaviour and prey selection by sandpipers. The extent of this flexibility and the current diet composition is unknown. To address these knowledge gaps, we assessed Semipalmated Sandpiper diets using stable isotope analyses of blood plasma and available prey items. Data were collected in two arms of the Bay of Fundy during summer 2009 and 2010. Diets fluctuated between years and sites, but in all cases the diet was much more diverse than previously thought. Polychaetes and biofilm made substantial contributions, and C. vol- utator was still present in the diet, but at much reduced levels than previously noted. This previously unrecognized inclusion of biofilm in the diet is consistent with recent observations of other calidrid shorebirds. Based on measures of prey availabil- ity, there is little evidence of preference for C. volutator.
Empty Container Allocation in B2C E-commerce

B2C e-commerce has been increasingly embraced worldwide in recent years. Its global sales has reached 1.82 trillion dollars in 2017, with a yearly growth rate of 17.2% (Ecommerce Foundation 2018). In B2C e-commerce companies, paper boxes are frequently used in the last-mile delivery and the goods transfer among distribution centers (DCs) or fulfillment centers (FCs). During 2016, 35.4 million tonnes of paper and cardboard packaging waste were generated in the EU (Eurostat 2019). The overuse of paper boxes has become a giant burden both economically and environmentally. Therefore efforts should be devoted to reduce the use of paper boxes in logistics. In a B2C e-commerce company, a DC is normally replenished based on its regional demand forecast. However, due to demand variation, it is possible to encounter surplus or shortage of goods, in which case goods transfer to or from another DC is needed. In its recent practice the transferred goods are normally packed in reusable and foldable containers. When there is a container deficiency, paper boxes serve as a replacement. In reality, there exists a structural imbalance in container movements among the DCs. For example, the Pearl River Delta area is known as one of the manufacturing centers for electronic products in China. DC Shenzhen is located in the Pearl River Delta area and close to the main suppliers. DC Beijing and DC Shanghai are both far away from the suppliers, and face highly uncertain demands for electronic goods. The transportation from suppliers to DC Beijing and DC Shanghai and the possible goods transfer between them due to demand uncertainty can be costly. Instead, it can be economically beneficial if the company applies a “wait and see” strategy. That is, it holds the products in DC Shenzhen first, and directly ships from DC Shenzhen to DC Beijing and DC Shanghai when the company has a better knowledge of the demand. This imbalanced goods flow gives rise to the buildup of empty containers in Beijing and Shanghai, and the scarcity of them in Shenzhen. Similarly, when a DC is located near a port, the company tends to store more imported goods, rather than sending these goods directly to other DCs.
RELATIONSHIP BETWEEN HYDROGENASE AND NITROGENASE IN HYDROGEN METABOLISM OF PHOTOSYNTHETIC BACTERIUM RHODOPSEUDOMONAS CAPSULATA

The cells from cultures during photoheterotrophic growth with malate and glutamate ascarbon and nitrogen sources evolve substantial amounts of H_2, the evolution being mediatedby nitrogenase. This process is found to be totally dependent on light and electron donorsand is sensitive to ammonia. The cells from photoheterotrophic cultures possess an activehydrogenase too. The enzyme is particulate and bound to meinbranes, and catalyzes H_2uptake by various physiologically active substances, such as NADP, KNO_3, fumarate, methy-lene blue, benzyl viologen, etc. in the dark as electron acceptors. This process is markedlystimulated by ammonia. The photosynthetic nitrogenase activity, supported by H_2, showsmarkedly lowered sensitivity towards oxygen and is more efficient at low malate concentration.It will no longer be supported by H_2, when malate concentration becomes excessively high.The hydrogenase activity is irreversibly inhibited by acetylene. More hydrogen is evolved bynitrogenase after the inhibition of the hydrogenase activity. The physiological significanceof the interaction between hydrogenase and nitrogenase and its importance in the transitionbetween photoheterotrophic and photoautotrophic modes of growth are discussed.
Molecular Studies on Infectious Bronchitis Virus Isolated from Broiler Chickens in Damietta Governorate, Egypt

Fifty-two samples of tracheal swabs and organs were collected from broiler chickens at different districts from Damietta Governorate during December 2012 to August 2014. The samples were collected from chicken farms suffered from respiratory signs, diarrhea and high mortality. Thirty-six samples tested positive for IBV infection when screened by real time quantitative polymerase chain reaction (RT-qPCR). Seven of them were subjected for further isolation and characterization of the virus. The seven selected IBV isolates were propagated in embryonated chicken eggs (ECE) and were confirmed using PCR amplification of the partial sequence of the S1 spike gene. The amplified products were sequenced and the phylogenetic analysis was performed. The seven IBV viruses were closely related to each other with 94-100% nucleotide identity and clustered within the same group with IBV/Eg/CLEVB-2/IBV/012 and Eg/12120s/2012 strains (variant 2-like strain), while their identity with the vaccinal strains used in Egypt was ranged from 67-91%, particularly D274 (89%-91%). These results indicated the complexity of IBV control in relation to the current vaccination programs in Damietta.
Ontology-Based Holonic Event-Driven Architecture for Autonomous Networked Manufacturing Systems

The current trends in personalized products drive the paradigms of production systems toward autonomous networked manufacturing systems. This article proposes an ontology-based holonic event-driven architecture for implementing loosely coupled, holonic, autonomous distributed systems. The event-driven architecture (EDA) enables the services provided by different organizations and their suborganizations to be autonomously configured and integrated, while ensuring that the predefined organizational event access rules are compulsorily followed. This is realized by organizing the event services in a holonic manager and modeling the knowledge of services via the ontology developed in this study. The ontology model and the corresponding autonomous configuration mechanism are introduced. We developed a demonstration case to elaborate our approach and implemented a testbed using Java-based technology for testing the effectiveness, efficiency, scalability, and reliability of the proposed approach. Note to Practitioners—The ever-changing customer demands, the volatile market, and the trend of mass personalization require manufacturing systems to be smart in the event of uncertainties. To this end, information sharing in the supply chain is a key factor. However, there are several barriers in implementing a cross-organizational information system, including security, access privileges, and system interoperability. To lift these barriers, this article proposes an ontology model and a hierarchical EDA. Compared with conventional architectures, our approach facilitates the management of access privileges, integration of interorganizational information systems, and their interoperation. Moreover, our approach is competitive compared with the conventional service-oriented architecture in terms of both scalability and reliability of the system. Before the proposed approach can be practically applied, the ontology model needs to be further extended, and the event registration and message exchange protocol need to be standardized.
Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption

Data from smart grids are challenging to analyze due to their very large size, high dimensionality, skewness, sparsity, and number of seasonal fluctuations, including daily and weekly effects. With the data arriving in a sequential form the underlying distribution is subject to changes over the time intervals. Time series data streams have their own specifics in terms of the data processing and data analysis because, usually, it is not possible to process the whole data in memory as the large data volumes are generated fast so the processing and the analysis should be done incrementally using sliding windows. Despite the proposal of many clustering techniques applicable for grouping the observations of a single data stream, only a few of them are focused on splitting the whole data streams into the clusters. In this article we aim to explore individual characteristics of electricity usage and recommend the most suitable tariff to the customer so they can benefit from lower prices. This work investigates various algorithms (and their improvements) what allows us to formulate the clusters, in real time, based on smart meter data.
Substantial intraspecific variation in energy budgets: biology or artefact?

Dynamic energy budget (DEB) models provide a mechanistic description of life-histories in terms of fluxes of energy through biological processes. In these models, life-histories are a function of environmental conditions and of fundamental traits of the organism relating to the acquisition, allocation, and use of energy. These traits are described by the parameters of the DEB model, which have been estimated for over 2500 species. Recent work has aimed to compare species on the basis of differences in DEB parameters. We show that caution is required in such analyses, because (i) parameter estimates vary considerably as an artefact of the types of data used to fit the models, and (ii) there is substantial intraspecific variation in parameter values, reflecting biological differences among populations. We show that similar patterns of growth and reproduction can be reproduced with very different parameter sets. Our results imply that direct comparison of DEB parameters across populations or species may be invalid. However, valid comparisons are possible if differences in the types of data used to fit the models are taken into account. We estimated DEB parameters for 16 populations of Trinidadian guppy, identifying differences in resource allocation and metabolic rate consistent with evolved life-history differences among these populations. Variation in parameter values was substantial: if intraspecific variation in DEB parameters is greater than currently measured levels of interspecific variation, the detection of broad-scale patterns in energy budgets across species will be challenging.
Spectral alignment of correlated Gaussian matrices

Abstract In this paper we analyze a simple spectral method (EIG1) for the problem of matrix alignment, consisting in aligning their leading eigenvectors: given two matrices A and B, we compute two corresponding leading eigenvectors $v_1$ and $v'_{\! \! 1}$ . The algorithm returns the permutation $\hat{\pi}$ such that the rank of coordinate $\hat{\pi}(i)$ in $v_1$ and that of coordinate i in $v'_{\! \! 1}$ (up to the sign of $v'_{\! \! 1}$ ) are the same. We consider a model of weighted graphs where the adjacency matrix A belongs to the Gaussian orthogonal ensemble of size $N \times N$ , and B is a noisy version of A where all nodes have been relabeled according to some planted permutation $\pi$ ; that is, $B= \Pi^T (A+\sigma H) \Pi $ , where $\Pi$ is the permutation matrix associated with $\pi$ and H is an independent copy of A. We show the following zero–one law: with high probability, under the condition $\sigma N^{7/6+\epsilon} \to 0$ for some $\epsilon>0$ , EIG1 recovers all but a vanishing part of the underlying permutation $\pi$ , whereas if $\sigma N^{7/6-\epsilon} \to \infty$ , this method cannot recover more than o(N) correct matches. This result gives an understanding of the simplest and fastest spectral method for matrix alignment (or complete weighted graph alignment), and involves proof methods and techniques which could be of independent interest.
Utility-Preserving Privacy Protection of Textual Documents via Word Embeddings

Text is the most usual way to share information in society. Yet, if textual documents contain personal sensitive information, they cannot be shared with third parties or released publicly without adequate protection. Privacy-preserving mechanisms provide ways to sanitize data so that identities and/or confidential attributes are not disclosed. In the last twenty years, a great variety of mechanisms have been proposed to protect structured databases with numerical and categorical attributes; however, little attention has been devoted to unstructured textual data. In general, textual data protection requires first detecting pieces of text that may lead to disclosure of sensitive information and then masking those pieces via suppression or generalization. Current solutions rely on pre-trained classifiers that can recognize a fixed set of (allegedly disclosive) named entities, such as names or locations. Yet, such approaches fall short of providing adequate protection because in reality disclosive information is not limited to a predefined set of entity types, and not all the appearances of certain entity type result in disclosure. Besides, named entity recognition requires considerable manual effort to tag the training data needed to build classifiers. In this work we propose a more general and flexible solution for textual data protection. By means of word embeddings we build vectors that numerically capture the semantic relationships of the textual terms appearing in a collection of documents. Then we evaluate the disclosure caused by the textual terms on the entity to be protected (e.g., an individual’s identity or a confidential attribute) according to the similarity between their vector representations. Our method also limits the semantic loss (and, therefore, the utility loss) of the document by replacing (rather than just suppressing) disclosive terms with privacy-preserving generalizations. Empirical results show that our approach offers much more robust protection and greater utility preservation than methods based on named entity recognition, with the additional important advantage of avoiding the burden of manual data tagging.