Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-to-Code Transformation from Product-Line Architecture Models to AspectJ

Software Product Line Engineering has significant advantages in family-based software development. The common and variable structure for all products of a family is defined through a Product-Line Architecture (PLA) that consists of a common set of reusable components and connectors which can be configured to build the different products. The design of PLA requires solutions for capturing such configuration (variability). The Flexible-PLA Model is a solution that supports the specification of external variability of the PLA configuration, as well as internal variability of components. However, a complete support for product-line development requires translating architecture specifications into code. This complex task needs automation to avoid human error. Since Model-Driven Development allows automatic code generation from models, this paper presents a solution to automatically generate AspectJ code from Flexible-PLA models - previously configured to derive specific products. This solution is supported by a modeling framework and validated in a software factory.
Born and raised distributively: fully distributed non-interactive adaptively-secure threshold signatures with short shares

Threshold cryptography is a fundamental distributed computational paradigm for enhancing the availability and the security of cryptographic public-key schemes. It does it by dividing private keys into n shares handed out to distinct servers. In threshold signature schemes, a set of at least t+1 ≤ n servers is needed to produce a valid digital signature. Availability is assured by the fact that any subset of t+1 servers can produce a signature when authorized. At the same time, the scheme should remain robust (in the fault tolerance sense) and unforgeable (cryptographically) against up to t corrupted servers; i.e., it adds quorum control to traditional cryptographic services and introduces redundancy. Originally, most practical threshold signatures have a number of demerits: They have been analyzed in a static corruption model (where the set of corrupted servers is fixed at the very beginning of the attack), they require interaction, they assume a trusted dealer in the key generation phase (so that the system is not fully distributed), or they suffer from certain overheads in terms of storage (large share sizes). In this paper, we construct practical fully distributed (the private key is born distributed), non-interactive schemes --- where the servers can compute their partial signatures without communication with other servers--- with adaptive security (i.e., the adversary corrupts servers dynamically based on its full view of the history of the system). Our schemes are very efficient in terms of computation, communication, and scalable storage (with private key shares of size O(1), where certain solutions incur O(n) storage costs at each server). Unlike other adaptively secure schemes, our schemes are erasure-free (reliable erasure is a hard to assure and hard to administer property in actual systems). To the best of our knowledge, such a fully distributed highly constrained scheme has been an open problem in the area. In particular, and of special interest, is the fact that Pedersen's traditional distributed key generation (DKG) protocol can be safely employed in the initial key generation phase when the system is born -- although it is well-known not to ensure uniformly distributed public keys. An advantage of this is that this protocol only takes one round optimistically (in the absence of faulty player).
ATO L P – a tool for domain relevant terms extraction

This paper presents a software tool to extract relevant terms from Portuguese texts. EχATOLP extracts the most frequent noun phrases in an annotated corpus. The annotation is provided by the PALAVRAS parser. The software tool offers different options to improve the quality of extraction that goes from post-treatment of the parser annotation to application of linguistic and statistical criteria. EχATOLP also offers some additional features to compare extracted terms with reference lists, to compute efficiency numerical indexes and to search for terms in the corpus. Term extraction from corpora is usually the basis of many Natural Language Processing (NLP) task such as automatic glossary construction [7], text categorization [4] and even ontology learning [3]. Term extraction, as many other NLP applications, can benefit from both linguistic and statistical approaches, as the combination of these two approaches often offers better results than each of the approaches separately. EχATOLP software tool [6] thus uses both linguistic and statistical approaches to extract and select domain significant terms from a an annotated domain corpus. From a linguistic point of view, the extraction is based on the syntactic annotation performed by the parser PALAVRAS [2]. The candidate terms are terms annotated as noun phrases by the parser according to an extra set of discard and transformation rules. From a statistical point of view, those candidate terms are subject to frequency analysis, i.e., in order to select the more frequent ones. Figure 1(a) graphically presents the software architecture. The basic input is a set of . xml files which are the files with the annotated texts of the corpus. The extraction process consider a set of discard and transformation rules to, respectively, discard some noun phrases that may be unwanted, e.g., noun phrases with numerals, or to adapt some noun phrases to the purpose of extraction, e.g., remove articles. The user can chose by the tool options which rules of these two sets are to be applied. Figure 1(b) upper screenshot presents the interface where the user can choose from all these options. Once the candidate terms are extracted, their frequencies in the corpus and in each text is computed. Then, by user choice, some of the candidate terms can be selected according to different criteria, e.g., keeping only the 10% more frequent terms in all corpus. The lists of extracted terms can be compared with reference
What is the Most EfficientWay to Select Nearest Neighbor Candidates for Fast Approximate Nearest Neighbor Search?

Approximate nearest neighbor search (ANNS) is a basic and important technique used in many tasks such as object recognition. It involves two processes: selecting nearest neighbor candidates and performing a brute-force search of these candidates. Only the former though has scope for improvement. In most existing methods, it approximates the space by quantization. It then calculates all the distances between the query and all the quantized values (e.g., clusters or bit sequences), and selects a fixed number of candidates close to the query. The performance of the method is evaluated based on accuracy as a function of the number of candidates. This evaluation seems rational but poses a serious problem; it ignores the computational cost of the process of selection. In this paper, we propose a new ANNS method that takes into account costs in the selection process. Whereas existing methods employ computationally expensive techniques such as comparative sort and heap, the proposed method does not. This realizes a significantly more efficient search. We have succeeded in reducing computation times by one-third compared with the state-of-theart on an experiment using 100 million SIFT features.
Integrating Simulation With Robotic Learning From Demonstration

Robots that co-habitat an environment with humans, e.g., in a domestic or an agricultural environment, must be capable of learning task related information from people who are not skilled in robotics. Learning from demonstration (LfD) offers a natural way for such communication. Learning motion primitives based on the demonstrated trajectories facilitate robustness to dynamic changes in the environment and task. Yet since the robot and human operator typically differ, a phase of autonomous learning is needed for optimizing the robotic motion. Autonomous learning using the physical hardware is costly and time consuming. Thus finding ways to minimize this learning time is of importance. In the current paper we investigate the contribution of integrating an intermediate stage of learning using simulation, after LfD and before learning using robotic hardware. We use dynamic motion primitives for motion planning, and optimize their learned parameters using the PI 2 algorithm which is based on reinforcement learning. We implemented the method for reach-tograsp motion for harvesting an artificial apple. Our results show learning using simulation drastically improves the robotic paths and that for reach-to-grasp motion such a stage may eliminate the need for learning using physical hardware. Future research will test the method for motion that requires interaction with the
Identiﬁcation and Characterization of an ATP Binding Cassette L -Carnitine Transporter in Listeria monocytogenes

We identiﬁed an operon in Listeria monocytogenes EGD with high levels of sequence similarity to the operons encoding the OpuC and OpuB compatible solute transporters from Bacillus subtilis , which are members of the ATP binding cassette (ABC) substrate binding protein-dependent transporter superfamily. The operon, des- ignated opuC , consists of four genes which are predicted to encode an ATP binding protein (OpuCA), an extracellular substrate binding protein (OpuCC), and two membrane-associated proteins presumed to form the permease (OpuCB and OpuCD). The operon is preceded by a potential SigB-dependent promoter. An opuC -defective mutant was generated by the insertional inactivation of the opuCA gene. The mutant was impaired for growth at high osmolarity in brain heart infusion broth and failed to grow in a deﬁned medium. Supplementation of the deﬁned medium with peptone restored the growth of the mutant in this medium. The mutant was found to accumulate the compatible solutes glycine betaine and choline to same extent as the parent strain but was defective in the uptake of L -carnitine. We conclude that the opuC operon in L. monocytogenes encodes an ABC compatible solute transporter which is capable of transporting L -carnitine and which plays an important role in osmoregulation in at 30°C with aeration. Cell growth was monitored spectrophotometrically (Ultrospec 4050; LKB, Biochrom) by measuring the op-tical density at 600 nm (OD 600 ) of 1-ml samples taken at suitable intervals during growth. The opuC ::pAULA mutant was maintained on BHI agar plates with 2 m g of erythromycin ml 2 1 . In liquid medium, erythromycin selection was not used, as it was found to reduce the growth rate. PCR was used to conﬁrm that the opuC ::pAULA integration was stable even in the absence of selection. The E. coli strain DH5 a was used routinely for cloning experiments. It was cultured in Luria broth at 37°C. Strains were maintained long term at 2 80°C in 1-ml aliquots after the addition of 7% (vol/vol) dimethyl sulfoxide (Sigma) to overnight cultures. Mutant construction. The opuCA gene was insertionally inactivated using the temperature-sensitive suicide vector pAULA. This 9.2-kb plasmid carries an erythromycin resistance marker, the pUC19 multicloning site, and a tempera- ture-sensitive replication origin which enables chromosomal integration events to be selected at a nonpermissive temperature (7). Oligonucleotide primers directed against the opuCA gene were used to amplify a 400-bp region internal to the gene by PCR. The PCR primers (P121 [5 9 -TGACGGATCCACATCAT GGCGGAAGATC-3 9 ] and P117 [5 9 -TGACGGATCCGCTTCATCCATATCA TGG-3 9 ]) used included at their 5 9 ends Bam HI recognition sites (underlined) which facilitated the cloning of the PCR product into Bam HI-digested pAULA. The resulting clone, designated pCOB26, isolated from E. coli DH5 a and used to electrotransform L. monocytogenes EGD. Chromosomal integration of pCOB26 was selected by repeated plating at 42°C with selection for erythromycin (2 m g ml 2 1 ) resistance as The
Differently Expressed Genes (DEGs) Relevant to Type 2 Diabetes Mellitus Identification and Pathway Analysis via Integrated Bioinformatics Analysis

Background The aim of this study was to evaluate the differently expressed genes (DEGs) relevant to type 2 diabetes mellitus (T2DM) and pathway by performing integrated bioinformatics analysis. Material/Methods The gene expression datasets GSE7014 and GSE29221 were downloaded in GEO database, and DEGs from type 2 diabetes mellitus and normal skeletal muscle tissues were identified. Biological function analysis of the DEGs was enriched by GO and KEEG pathway. A PPI network for the identified DEGs was built using the STRING database. Results Thirty top DEGs were identified from 2 datasets: GSE7014 and GSE29221. Of the 30 top DEGs, 20 were up-regulated and 10 were down-regulated. The 20 up-regulated genes were enriched in regulation of mRNA, protein biding, and phospholipase D signaling pathway. The 10 down-regulated genes were enriched in telomere maintenance via semi-conservative replication, AGE-RAGE signaling pathway in diabetic complications, and insulin resistance pathway. In the PPI network of 20 up-regulated DEGs, there were 40 nodes and 84 edges, with an average node degree of 4.2. For the 10 down-regulated DEGs, we found a total of 30 nodes and 105 edges, with an average node degree of 7.0 and local clustering coefficient of 0.812. Among the 30 DEGs, 10 hub genes (CNOT6L, CNOT6, CNOT1, CNOT7, RQCD1, RFC2, PRIM1, RFC4, RFC5, and RFC1) were also identified through Cytoscape. Conclusions DEGs of T2DM may play an essential role in disease development and may be potential pathogeneses of T2DM.
Substance use among individuals with mild intellectual disability or borderline intellectual functioning in residential care: Examining the relationship between drinking motives and substance use

Abstract Background This study examined the relationship between substance use motives (i.e., social, conformity, coping and enhancement) and substance use in individuals with mild intellectual disability or borderline intellectual functioning (MID‐BIF). Method Data were collected among 163 clients with MID‐BIF using interactive questionnaires with visual cues on a tablet with a web application. Results Results show that social motives were positively related to frequency of alcohol use, while conformity, coping and enhancement motives were positively related to severity of alcohol use. Results for drug use show that social motives were positively related to frequency of cannabis and hard drug use and that conformity motives were negatively related to frequency of cannabis use. Coping motives were positively related to severity of drug use. Conclusions Insight in substance use motives should be used when adapting interventions, as it could contribute to the prevention and reduction of substance use disorders in individuals with MID‐BIF.
Malaria Diagnosis: Current Approaches andFuture Prospects

Scaled up efforts by a consortia of organisations in the diagnosis, treatment and prevention of malaria have led to a significant reduction in the overall malaria mortality and morbidity in the past few years. Malaria has, nonetheless, remained one of the world’s most burdensome diseases with the over 214 million cases and 438,000 deaths recorded in 2015 (2.68% of global DALYs). This burden is unevenly domiciled in sub-Saharan Africa where 89% of all cases and 91% of all deaths occurred. These figures however, only represent a fraction of the actual global burden of Malaria as surveillance fails to cover most cases in sub-Saharan Africa where the majority of malaria endemic regions lack facilities for diagnosis, case management and active surveillance. The emergence of drug resistant strains of the Plasmodium species prompted WHO to recommend a confirmatory diagnosis of each case of Malaria before treatment. The workability of this recommendation however, begs to be questioned as the majority of all malaria diagnosis is done via Clinical diagnosis; which lacks precision, is still the major form of diagnosis in many malaria endemic regions, and contributes to the over-diagnosis of malaria and subsequent under-diagnosis of other febrile illnesses. Of higher import is the risk of the emergence of drug resistant species due to the unregulated antimalarial use caused by inaccurate clinical diagnosis. Microscopy, which is the gold standard of malaria diagnosis, and the Rapid Diagnostic Tests (RDT) for malaria antigens have proven to be very useful in the diagnosis of malaria giving high levels of specificity and sensitivity. They however have the downside of having relatively high limits of detection, invasiveness, being labour intensive and expensive in the light of the low income countries where malaria is endemic. More sophisticated tools such as those that employ nucleic acid techniques (Polymerase Chain Reaction and Gene probes) are not field deployable and are mostly applied for research purposes. This necessitates the need for new diagnostic approaches that are suited to the conditions found in malaria endemic regions. A range of novel diagnostic tools with a do-ityourself approach, leveraging on previously untapped diagnostic material such as urine are currently being assessed. These novel tools promise great results if successful. This review presents an overview of current diagnostic methods, the prospects in malaria diagnostics and finally makes an effort to recommend what an ideal malaria diagnostic tool should be made up of, all the while focusing on sub-Saharan Africa
Hypoperfusion in Cardiac Surgical Patie Central Venous-Arterial pCO 2 Difference Identi fi es Microcirculatory nts With Normal Central Venous Oxygen Saturation : A Retrospective Analysis

Objective: Parameters such as central venous oxygen saturation (ScvO2) are used increasingly to monitor adequate hemodynamic treatment. However, it still remains challenging to identify patients with assumed adequate circulatory status quantified by ScvO2 who suffer from macroand microcirculatory hypoperfusion. The authors hypothesized that central venous-arterial pCO2 difference (dCO2) could serve as an additional parameter to evaluate the adequacy of perfusion in cardiac surgery patients. Design: Retrospective data analysis of a prospective interventional study. Setting: University medical center. Participants: Patients undergoing surgery with cardiopulmonary bypass. Interventions: The dCO2 was measured postoperatively. The patients with an ScvO2 Z 70% were divided into 2 groups, the high-dCO2 group (Z8 mmHg) and the lowdCO2 group (o8 mmHg). Measurements and Main Results: Sixty patients were included in this analysis. Twenty-five patients had ScvO2 Z 70%, 4 patients were assigned to the high-dCO2 group. Patients of the high-dCO2 group had significantly longer intensive care unit (ICU) stays (4 d; 1-29 v 1 d; 1-1; p 1⁄4 0.02),
Graph Neural Network-Based Wind Farm Cluster Speed Prediction

The randomness and volatility of wind speed will weaken the power system's stability after the wind power is connected to the grid. It is prone to deviations from voltage and frequency. Therefore, it is imperative to predict wind speed. However, many factors affect wind speed (such as temperature, air pressure, terrain, etc.), and the mechanism of action is complicated, so it is challenging to model wind speed directly. Existing research directly uses all training sets to train the model and does not consider the different wind patterns of wind speed time series. In different wind patterns, the wind speed will show different characteristics. Therefore, it is not appropriate to use all historical data to train a single model for predicting wind speed. To achieve better forecasting performance, our forecasting method should be based on wind patterns. Current research focuses on the wind speed prediction of a single wind farm, ignoring the temporal and spatial correlation with nearby wind farms. There is no wind speed prediction method of wind farm cluster. The measurement data of wind farm cluster is not fully utilized. Combined with the graph neural network of deep learning in recent years, we propose a pattern-based wind speed prediction method of wind farm cluster. Using the wind speed and wind direction measurement data of many wind farms in Northwest China, the graph structure data were constructed. The graph neural network model is established for training. Compared with other methods, better wind speed prediction results are obtained.
Causal Foundations of Evolutionary Genetics

The causal nature of evolution is one of the central topics in the philosophy of biology. The issue concerns whether equations used in evolutionary genetics point to some causal processes or purely phenomenological patterns. To address this question the present article builds well-defined causal models that underlie standard equations in evolutionary genetics. These models are based on minimal and biologically plausible hypotheses about selection and reproduction, and generate statistics to predict evolutionary changes. The causal reconstruction of the evolutionary principles shows adaptive evolution as a genuine causal process, where fitness and selection are both causes of evolution. 1 Introduction 2 The Philosophical Puzzle 3 Causal Models 4 Causal Foundations of Evolutionary Genetics 4.1 Univariate quantitative genetics model 4.1. 1 The causal graph 4.1. 2 Structural equations 4.1. 3 Deriving evolutionary transition functions 4.2 One-locus population genetics model 5 Evolution as a Causal Process 5.1 Selection as a causal process 5.2 Causes of evolutionary change 6 Conclusion 1 Introduction 2 The Philosophical Puzzle 3 Causal Models 4 Causal Foundations of Evolutionary Genetics 4.1 Univariate quantitative genetics model 4.1. 1 The causal graph 4.1. 2 Structural equations 4.1. 3 Deriving evolutionary transition functions 4.2 One-locus population genetics model 4.1 Univariate quantitative genetics model 4.1. 1 The causal graph 4.1. 2 Structural equations 4.1. 3 Deriving evolutionary transition functions 4.1. 1 The causal graph 4.1. 2 Structural equations 4.1. 3 Deriving evolutionary transition functions 4.2 One-locus population genetics model 5 Evolution as a Causal Process 5.1 Selection as a causal process 5.2 Causes of evolutionary change 5.1 Selection as a causal process 5.2 Causes of evolutionary change 6 Conclusion
Association Between Personality Disorders Traits and Problematic Cannabis Use in Adolescents

Background: There are few studies on the contribution of personality disorder traits to cannabis use disorders in adolescents. Objectives: The aim of the study was to evaluate the association of personality disorder traits to problematic cannabis use. Methods: Participants were 111 high school students who completed self-report questionnaires, mainly the Cannabis Use Disorders Identification Test, assessing problematic cannabis use, and the Personality Diagnostic Questionnaire using the scales evaluating personality disorders most often linked to adolescent cannabis use. Results: A multiple regression analysis showed that personality disorder traits explained a high part of the variance in problematic cannabis use symptoms. Schizotypal and borderline personality traits were positively associated to problematic cannabis use symptoms after adjustment for anxious and depressive symptoms. Conclusions: This study suggests the importance of evaluating personality disorder traits in studies of risk factors or consequences of problematic cannabis use.