Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Remote Biopsy Sampling on Long-Finned Pilot Whales (Globicephala melas) in Nova Scotia

A long-term photo-identification study on longfinned pilot whales (Globicephala melas) off Pleasant Bay, Nova Scotia, Canada, expanded to include remote biopsy sampling via crossbow in 2010 to 2012. The present study aims to investigate any negative effects biopsy sampling may have on the animals. During sampling, each shot was videotaped for later analysis. We ranked the reaction of the target individual on a standard scale, where 1 was no response, 2 was a low-level response, 3 was a moderate response, and 4 was a strong response. Additionally, in the 2012 field season, we recorded group behaviour before and after sampling and opportunistically observed wound healing. Shortterm responses to sampling were mostly low level, with no strong responses observed. Sampling did not change group behaviour any more than was normally observed from non-biopsy vessels, and the pilot whales were regularly re-approached by vessels post-sampling without difficulty. Wounds were found to close as early as 4 d post-sampling and showed no evidence to indicate infection or other problems with healing. This study found no indication that remote biopsy sampling has detrimental effects on long-finned pilot whales in Nova Scotia and, thus, is a viable and ethical technique for obtaining samples from this population.
Callophyllis cornu-cervi (Perestenko) comb. nov. (Gigartinales, Rhodophyta): re-evaluation of the endemic monotypic genus Ionia from the Sea of Okhotsk based on morphology and molecular evidence

Abstract The monotypic kallymeniacean genus Ionia established as Iona cornu-cervi is endemic to the Sea of Okhotsk. We observed the type material of the species deposited in the Komarov Botanical Institute of the Russian Academy of Sciences (LE, St. Petersburg, Russia) and compared it with fresh collections near the type locality. Analysis of the rbcL dataset revealed that I. cornu-cervi was not distinct from other genera of the Kallymeniaceae but consistently nested within the genus Callophyllis and formed a sister relationship with Callophyllis variegata. Here, we propose the new combination Callophyllis cornu-cervi based on I. cornu-cervi and treat the genus Ionia as a synonym of Callophyllis. Callophyllis cornu-cervi is distinguished by a thallus with many slightly convoluted segments positioned at an angle to each other, forming an erect and more or less rigid plant, and a medulla composed of intermingled large and small cells.
High frequency of conspecific brood parasitism in a colonial waterbird, the Eared Grebe Podiceps nigricollis

We made daily visits to 47 Eared Grebe Podiceps nigricollis nests in two colonies to determine the frequency of conspecific brood parasitism. Three criteria were used to detect parasitism: the appearance of two or more new eggs in a 24 hour period, new eggs after the host had ceased laying, and the disappearance of a marked egg with the simultaneous appearance of a new unmarked egg. We detected parasitism at 68% of nests in one colony, 14% of nests in the second colony, and 38% of nests in both colonies combined. The spatial dispersion of parasitic eggs among host nests within one colony did not differ from random expectation but, with respect to temporal dispersion, parasites were more likely to lay in host nests in the laying stage than expected by chance. Egg loss was more common at parasitized nests than unparasitized nests, but further studies are needed to confirm whether this is due to egg rejection by hosts. A comparison of clutch sizes in parasitized and unparasitized nests indicated that unparasitized nests never contained more that four eggs - supernormal clutches of five or more eggs thus indicate that parasitism has taken place and, if used as an assay for parasitism, would have identified two thirds of the cases of parasitism in our study. Applying this assay to clutch size information collected 40 years ago at a site close to our study area indicates that parasitism may be a regular feature of Eared Grebe reproduction.
Half-full or half-empty? Framing of UK–EU relations during the Brexit referendum campaign

ABSTRACT This article analyses the political construction and politicisation of UK–EU relations by British parliamentarians in Westminster and in the European Parliament (EP) between May 2015 and May 2017. Using computer-assisted content analysis techniques and qualitative analysis of sampled speeches, we investigate how parties used frames and emotions in order to mobilise voters. Results indicate that the ‘Leave’ campaign succeeded in triggering sentiments of distrust and anger against the establishment and mobilizing voters while, economic arguments used by ‘remainers’ failed to convince citizens from deprived areas of the benefits of EU membership. Intra-party divisions were stronger among Westminster members than in the EP, but the visibility of the ‘Leave’ discourse in the EP was disproportionately higher to the number of pro-Brexit parliamentarians. Polarisation and values-based arguments are observed in the EP early in the campaign, suggesting that the process initiated at the European level and then migrated to the national level.
Isolation of Advantageous Mutants of Pseudomonas chlororaphis B23 for the Enzymatic Production of Acrylamide

Advantageous mutants of Pseudomons chlororaphis B23 for the enzymatic production of acrylamide were isolated. A mucilage polysaccharide non-producing mutant, Am-3, was precipitated completely by brief centrifugation, in contrast with the parent strain. A mutant, AM-324, derived from Am-3 exhibited about 3.8-fold higher nitrile hydratase activity than that of the parent strain. These mutants are promising for acrylamide production on an industrial scale.
Collaborative Denoising Graph Attention Autoencoders for Social Recommendation

—In recent years, social recommendation has at-tracted more and more attention from researchers, because it can effectively solve the problems of data sparsity and cold start. But social recommendation faces two problems: The ﬁrst is how to deeply integrate social information with user-item preference information to obtain accurate user and item latent vectors. The second problem is how to generate a robust top-N recommendation model from the implicit feedback information. To solve these problems, we propose a novel model: collaborative denoising graph attention autoencoders for social recommendation(CDGAAE). This model uses the currently popular graph neural networks to fuse the interaction preference information graph and social information graph through a multi-head attention message passing mechanism. At the same time, this model delicately merges graph neural networks with denoising autoencoders, which uses the corrupt versions of the original data to make the model more robust and generalized. Finally, we conduct comparative experiments of our model with other baseline algorithms on two real world datasets, and the experimental results prove the superiority of our model.
On the Use of Support Mechanisms to Perform Experimental Variables Selection

The selection of variables in a given experiment is crucial, since it is the theoretical foundation that guides how data should be collected and analyzed. However, selecting variables is an intricate activity, especially considering areas such as Software Engineering and Education, whose studies should also consider human-related variables in the design. In this scenario, we aim to investigate how a support mechanism helps on the variables selection activity of the experiment process. To do so, we conducted a pre-liminary study on the use of an experimental framework composed by a catalog of variables. We explored the domain of the integration of software testing into programming education. Participants were divided into two groups (ad hoc and framework support) and asked to select variables for a given experiment goal. We analyzed the results by identifying threats to validity in their experimental de-sign drafts. Results show a signiﬁcant number of threats of type inadequate explication of constructs for both groups. Nonetheless, the framework helped to increase the clarity of concepts selected as variables. The cause of most raised threats, even with the framework support, was an inaccuracy in selecting the values of such variables (i.e. treatments and ﬁxed values).
Benchmarking the efficiency of RDF-based access for blockchain environments

Blockchain and knowledge graphs are technologies that have become pervasive in several domains where web services have been developed relying on them. The in-mutability of the data offered by the blockchain together with the capabilities of the knowledge graph when consuming data, enables web services to provide richer functionalities. Literature has explored the beneﬁts of combining both qualitatively, and only a few works have exposed quantitatively the feasibility of combining these technologies. In particular, as far as we know no work reports the cost of storing knowledge graphs serialized in RDF into blockchains, or analyses alternatives such as virtualisers that transform on the ﬂy data from different formats into RDF. In this paper we present an empirical analysis of the cost of storing into a blockchain in comparison with storing JSON, and the beneﬁts when solving SPARQL queries by reading directly the RDF or using a virtualiser fed with RDF. For the sake of our experiments, we rely on different sensors that store their data into two blockchains, on top of which we perform our analysis.
Modeling Topic Exhaustion for Programming Languages on StackOverflow

We apply latent Dirichlet allocation on StackOverﬂow questions, spanned across ten years, for Python, JavaScript, Java, C++, and R, in order to discover underlying topics of questions asked for these programming languages. We focus on topics that have exhausted over the years; topics that once peaked in terms of the number of questions being asked about them but are now in a decline. Studying these topics provides insight into a language’s evolution and its cohesion with other programming languages, that may offer similar features. We also measure the average wait times to get answers for questions from exhausted topics, to highlight if the community also plays a role in making these topics exhausted.
A Deep Spatio-temporal Residual Network Model for Commercial Activeness Prediction

—Activeness of regional stores represents the evolvement of their corresponding commercial districts, whose prediction helps practitioners grasp the trend of commercial development and provides support for urban layout. Traditional prediction methods, however, mostly rely on time series analysis and cannot model the complex nonlinear space-time relationship of commercial development as a geographic phenomenon. Inspired by the outstanding performance of deep learning in the field of image and video processing, this paper proposes a deep spatio-temporal residual network model (DSTRN) for commercial activeness prediction using online reviews and check-in records of stores. Specifically, our model includes a spatial dimension that employs local CNN to capture the spatial relation of surrounding commercial districts, and a temporal dimension that applies 3D convolutions and LSTM to deal with the temporal characteristics of commercial activeness. Meanwhile, a residual network is introduced to eliminate gradient vanishing and exploding caused by depth increasing of neural networks. In particular, the recent variations (e.g., sequential changes) and periodic variations (e.g., seasonal changes or holiday effects) of commercial development are both taken into consideration in the model for better prediction. Experiments on public Yelp datasets of Toronto from 2013 to 2018 demonstrate that DSTRN vastly outperforms other approaches and reduces the mean square error by 51.2%, 57.5% and 8.5% compared to Historical average (HA), Autoregressive integrated moving average (ARIMA) and XGBoost, respectively.
Towards Fine-Grained Compiler Identification with Neural Modeling

—Different compilers and optimization levels can be used to compile the source code. Revealed in reverse from the produced binaries, these compiler details facilitate essential binary analysis tasks, such as malware forensics and binary code similarity analysis. Most existing approaches adopt a signature matching based or machine learning based strategy to identify the compiler details, showing limits in either the detection accuracy or granularity. In this work, we propose NeuralCI (Neural modeling-based Compiler Identification) to perform the identification of compiler family and optimization level on individual functions. The basic idea is to formulate sophisticated neural networks to process abstracted instruction sequences generated using a lightweight function abstraction strategy. To evaluate the performance of NeuralCI, a large dataset consisting of 413,119 unique functions collected from real-world projects is constructed. The experiments show that NeuralCI achieves over 99% and 90% accuracy in identifying the compiler family and optimization level respectively, which outperforms most state-of-the-art function level compiler identification methods. Also, we explore for the first time the possibility of conducting compiler identification on binary code snippets rather than complete functions, where NeuralCI still achieves 96% accuracy, indicating its ability of capturing subtle yet significant features.
Revisiting Dependence Cluster Metrics based Defect Prediction

—A dependence cluster is a set of program elements that all depend upon each other. Prior empirical studies have found that the dependence cluster based metrics are useful in effort-aware defect prediction. However, it is still unknown whether they are useful in non-effort-aware defect prediction. In this paper, we perform empirical studies to investigate this issue. We use the product, process, and network metrics to build the “B” model (baseline model), and then use the product, process, network and dependence cluster metrics to build the “B+C” model. Our experimental results, based on ﬁve well-known open-source systems, show that the dependence clusters are useful for non-effort-aware defect prediction. These ﬁndings help us better understand how dependence clusters inﬂuence non-effort-aware defect prediction
Cells Stochastic Monoallelic Expression of IL-10 in T

IL-10 is a potent anti-inﬂammatory and immunomodulatory cytokine, exerting major effects in the degree and quality of the immune response. Using a newly generated IL-10 reporter mouse model, which easily allows the study of IL-10 expression from each allele in a single cell, we report here for the ﬁrst time that IL-10 is predominantly monoallelic expressed in CD4 (cid:1) T cells. Furthermore, we have compelling evidence that this expression pattern is not due to parental imprinting, allelic exclusion, or strong allelic bias. Instead, our results support a stochastic regulation mechanism, in which the probability to initiate allelic transcription depends on the strength of TCR signaling and subsequent capacity to overcome restrictions imposed by chromatin hypoacetylation. In vivo Ag-experienced T cells show a higher basal probability to transcribe IL-10 when compared with naive cells, yet still show mostly monoallelic IL-10 expression. Finally, statistical analysis on allelic expression data shows transcriptional independence between both alleles. We conclude that CD4 (cid:1) T cells have a low probability for IL-10 allelic activation resulting in a predominantly monoallelic expression pattern, and that IL-10 expression appears to be stochastically regulated by controlling the frequency of expressing cells, rather than absolute protein levels per cell. The Journal of Immunology, 2006, 177: 5358–5364.
On the Reuse of Knowledge to Develop Intelligent Software Engineering Solutions

—Intelligent Software Engineering (ISE) is currently a hot topic in research. Besides being a promising ﬁeld, it brings many challenges. Therefore, there is a need for guidelines to help researchers to build an ISE solution. The goal of this study is to identify patterns in developing ISE solutions. For this purpose, we analyzed 42 studies, using a thematic analysis approach, to understand how they reused knowledge and applied it to solve a SE task. As a result, we developed a thematic network composed of the main concepts related to knowledge reuse for ISE. Further, we identiﬁed that researchers use external and internal knowledge sources, and mostly rely on structured data to develop ISE solutions. Despite this, there are alternatives such as eliciting data from humans and literature to identify metrics or build knowledge-based systems. Overall, we concluded that there many research opportunities to guide the construction of ISE solutions.
Large-scale distribution of viruses in deep waters of the Pacific and Southern Oceans

We examined the full-depth distribu- tions of viruses at 38 stations along a cruise track of ~17 000 km in the Southern Ocean and the central Pacific. In the layer between 200 and 2000 m, viral abundance decreased sharply with depth, and prokaryote abundance accounted for a large frac- tion (60%) of the variability in viral abundance. In contrast, in the deeper layer (> 2000 m), the depth- dependent decrease in viral abundance was less evident, and the relationship between viral and prokaryote abundances was weak. Viral abun- dances were high in the Antarctic bottom water and tended to decrease along the paths of the Cir- cumpolar Deep Water and the Pacific Deep Water. Outstanding peaks of virus-to-prokaryote abun- dance ratios (mean ± SE: 46 ± 1.2) were found in the bottom waters (depth > 3500 m) of the subtropi- cal regions, where sinking particle fluxes are known to be generally low. These results are inter- preted as an indication that large-scale viral distri- bution patterns are generally, if not solely, influ- enced by transport mediated by the meridional overturning circulation, underscoring the necessity of future studies to clarify prevailing mode and mechanisms for viral production and decay in deep oceans, which appear to be largely distinct from those in upper oceans.
Identifying Security Concerns Based on a Use Case Ontology Framework

— Identifying security concerns in an application can be difficult, especially if the analysts lack security knowledge. We propose a use case ontology that can help to identify security concerns based on the use case specifications. We demonstrate the feasibility of the ontology by systematically applying the ontology to use case specifications expressed in Web Ontology Language (OWL). The proposed approach can help model the interrelationship of concepts in the use case and possibly use queries to group use cases that may have similar security concerns. This approach could allow analysts to identify parts of the use cases with similar security concerns and could potentially reduce reoccurrences of known vulnerabilities in software applications. Lastly, we discuss future work about creating an automated tool for recommending attack patterns for the security requirements process.
Correct Software by Design for Software-Defined Networking: A preliminary Study

We report our experience of applying rigorous software speciﬁcation and design methodologies to the development of applications for the emerging software-deﬁned networking (SDN) paradigm. While much of the prior work in the SDN space focused on creating novel algorithms and protocols, in this paper we take the position that the implementation of those algorithms and protocols on the SDN platform is a hard problem on its own that deserves a systematic treatment from the software engineering perspective. Through a concrete case study of implementing an essential switching algorithm as an SDN app, we expose the challenges stemmed from the unique three-tier architecture of SDN, and propose a rigorous approach that ﬂows from functional requirements through stepwise reﬁnement to de-sign and implementation. Our case study shows promises of the proposed approach in supporting correctness arguments for the software developed for the SDN platform.
Controller Synthesis for ROS-based Multi-Robot Collaboration

—Given a multi-robot system and the high-level tasks for the robots. How to ensure the correct behavior of robots to complete their tasks is critical. In this paper, we design a framework that can automatically generate correct-by-construction controllers for multi-robot system. In this framework, we propose a multi-robot speciﬁcation based on the Temporal Logic Synthesis Format (TLSF) to guarantee the robots’ behavior. And two execution algorithms were proposed to abstract the synthesized automata into high-level controllers. These controllers were integrated into Robot Operating System (ROS) to control actions and movements of robots in Gazebo, which is an open-source 3D robotics simulator. Based on this framework, we developed a toolkit, which allows users to achieve system-level controller synthesis and simulation capable to be used for research such as mission planning, motion planning
A Novel Self-Attention Based Automatic Code Completion Neural Network

—Code completion is one branch of source code modeling tasks. Using a deep learning method to implement it has explored the possibilities of modeling source code with a statistic language model. Recurrent Neural Network (RNN) is a universal feature extractor of Natural Language Processing (NLP), which is used in the code completion ﬁeld commonly. However, RNN based models are lack of long-range context dependency and have a poor performance in training speed. Besides, some previous models have not handled the issue of out of vocabulary (OOV) well, which hinders further improvements in prediction accuracy. This paper presents a novel automatic code completion neural network, which is based on a self-attention mechanism with open vocabulary to address issues of OOV, slow training speed, and lacking long context-dependency. Experiments in this paper show that our model has a better performance of predicting tokens compared with the traditional N-gram model and RNN based model. In the meantime, we reduced training time signiﬁcantly. More broadly, the combination of self-attention and open vocabulary has a potential application in the source code modeling ﬁeld.
Patent Technical Function-effect Representation and Mining Method

—With increasing global competition of intellectual property, a large number of unstructured patent texts are generated for technology protection. The ocean of patent texts include many long sentences about technologies, technical functions, technical effects and complexity relations between them, which make it difﬁcult to textual representation and mining. To solve the above issues, we represent a patent by its technical function-effects, which are mined from the patent. The model represents functions/effects by valence utility-technologies and represents text by association relations between functions and effects. We evaluate our model by comparing with the state-of-the-art models on the patent data set. The results show that our model outperforms other models in evaluation measurement. Such representation can be applied to patent information retrieval and patent text analysis.
Meeting Funder Compliance: A Case Study of Challenges, Time Spent, and Dollars Invested

Funders financially support nonprofit organizations to further mutual goals of implementing programs and providing services; as such, nonprofits must meet certain compliance requirements. This case study, the first of its kind, examines the management processes of one nonprofit as it strives to meet funder compliance requirements, and presents recommendations and survey instruments to assess and improve the quality and efficiency of these processes.