Miracle-dz/UniFAR-FTUs huggingface dataset:
Preserving privacy information flow security in composite service evolution

After a composite service is deployed, user privacy requirements and trust levels of component services are subject to variation. When the changes occur, it is critical to preserve privacy information flow security. We propose an approach to preserve privacy information flow security in composite service evolution. First, a privacy data item dependency analysis method based on a Petri net model is presented. Then the set of privacy data items collected by each component service is derived through a privacy data item dependency graph, and the security scope of each component service is calculated. Finally, the evolution operations that preserve privacy information flow security are defined. By applying these evolution operations, the re-verification process is avoided and the evolution efficiency is improved. To illustrate the effectiveness of our approach, a case study is presented. The experimental results indicate that our approach has high evolution efficiency and can greatly reduce the cost of evolution compared with re-verifying the entire composite service.
Language Generation via DAG Transduction

A DAG automaton is a formal device for manipulating graphs. By augmenting a DAG automaton with transduction rules, a DAG transducer has potential applications in fundamental NLP tasks. In this paper, we propose a novel DAG transducer to perform graph-to-program transformation. The target structure of our transducer is a program licensed by a declarative programming language rather than linguistic structures. By executing such a program, we can easily get a surface string. Our transducer is designed especially for natural language generation (NLG) from type-logical semantic graphs. Taking Elementary Dependency Structures, a format of English Resource Semantics, as input, our NLG system achieves a BLEU-4 score of 68.07. This remarkable result demonstrates the feasibility of applying a DAG transducer to resolve NLG, as well as the effectiveness of our design.
Forest-Based Neural Machine Translation

Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors. For statistical machine translation (SMT), forest-based methods have been proven to be effective for solving this problem, while for NMT this kind of approach has not been attempted. This paper proposes a forest-based NMT method that translates a linearized packed forest under a simple sequence-to-sequence framework (i.e., a forest-to-sequence NMT model). The BLEU score of the proposed method is higher than that of the sequence-to-sequence NMT, tree-based NMT, and forest-based SMT systems.
Improving Vehicle Safety Through a Fog Collaborative Infrastructure

The introduction of Information and Communication Technology in modern cities enhances quality, performance, and interactivity of urban services. The ultimate goal is twofold: the reduction of costs and of resource consumption and the increasing number of services offered to citizens. As drawback, smart cities become more vulnerable from the point of view of safety, security, and preservation of citizen privacy. In this paper, we propose a fog-computing based infrastructure to manage the sharing of information among vehicles and smart traffic lights in a urban network, with the aim of improving the safety of end-users of the network. For this purpose, our infrastructure provides to drivers several services to retrieve information in a private and secure way. The services we consider, are mainly four and are oriented to the traffic prediction, incident prevention, managing of emergency, and driver recognition.
Implementing and delivering personalised budgets for drug and alcohol users: A narrative systematic review.

Personalised budgets have historically been provided to groups of people with varying long-term health and social care needs. Since 2010, there has been increasing interest in providing personal budgets (PBs) to individuals with a history of drug and alcohol use in the UK, reflecting the policy and practice shift towards whole person recovery from substance use. However, information on implementing, delivering, and receiving such initiatives with this group is limited. This systematic review was conducted to identify and collate the existing experiences of providing personalised budgets to drug and alcohol users. Between March and April 2017, we searched six electronic and 11 grey literature databases for English language studies published between 1990 and April 2017 which described the implementation and delivery of personalised budget initiatives with drug and alcohol users. Search results (n = 6,749) were screened against inclusion and exclusion criteria; six records met the inclusion criteria. Across the studies, staff reported specific moral, ethical, and practical issues which affected the implementation and delivery of personalised budgets to drug and alcohol users. Staff working with drug and alcohol users with PBs reported greater job satisfaction due to having greater flexibility and autonomy but they had increased workloads and additional responsibilities beyond their remit and training. Drug and alcohol users' experiences of receiving personalised budgets included varying levels of awareness, knowledge, and control of their budgets, and difficulties in understanding what the budgets could be used for. Nevertheless, personalised budgets had been used to purchase various services and items beyond traditional drug and alcohol treatment. Outcomes for drug and alcohol users included reduced drug use, improved relationships, improved mental and physical well-being, and better daily structure. Although the review suggests that providing personalised budgets to drug and alcohol users presents unique implementation and delivery challenges, these were not insurmountable.
Dysbiosis of the buccal mucosa microbiome in primary Sjögren’s syndrome patients

Objectives Environmental factors in the aetiology of primary Sjögren's syndrome (pSS) are largely unknown. Host-microbiome interaction at mucosal surfaces is presumed to be involved in the aetiopathogenesis of pSS. Here, we assessed whether the microbiome of the buccal mucosa is specific for pSS compared with symptom-controls. Methods The bacterial composition of buccal swab samples from 37 pSS patients, 86 non-SS sicca patients (with similar dryness symptoms to pSS patients, but not fulfilling the classification criteria) and 24 healthy controls (HCs) was determined with 16S rRNA sequencing. Multivariate Association with Linear Models was used to find associations between individual taxa and pSS, taking into account smoking and dental status. Associations were replicated in a general population cohort (n = 103). Results The buccal mucosa microbiome of pSS and non-SS sicca patients both differed from HCs. A higher Firmicutes/Proteobacteria ratio was characteristic for both pSS and non-SS sicca patients. Disease status (pSS, non-SS sicca, HCs) and salivary secretion rate contributed almost equally to the variation in bacterial composition between individuals (3.8 and 4.3%, respectively). Two taxa were associated with pSS compared with non-SS sicca patients and 19 compared with HCs. When salivary secretion rate was taken into account, no taxon was associated with pSS compared with non-SS sicca. Twelve of the 19 pSS-associated taxa were correlated with salivary secretion. Conclusion Dysbiosis of the buccal mucosa microbiome in pSS patients resembles that of symptom-controls. The buccal mucosa microbiome in pSS patients is determined by a combination of reduced salivary secretion and disease-specific factors.
Locality-Aware Software Throttling for Sparse Matrix Operation on GPUs

This paper tackles the cache thrashing problem caused by the non-deterministic scheduling feature of bulk synchronous parallel (BSP) execution in GPUs. In the BSP model, threads can be executed and interleaved in any order before reaching a barrier synchronization point, which requires the entire working set to be in cache for maximum data reuse over time. However, it is not always possible to ﬁt all the data in cache at once. Thus, we pro-pose a locality-aware software throttling framework that throttles the number of active execution tasks, prevents cache thrashing, and enhances data reuse over time. Our locality-aware software throttling framework focuses on an important class of applications that operate on sparse matrices (graphs). These applications come from the domains of linear algebra, graph processing, machine learning and scientiﬁc simulation. Evaluated on over 200 real sparse matrices and graphs that suffer from cache thrashing in the Florida sparse matrix collection, our technique achieves an average of 2.01X speedup, a maximum of 6.45X speedup, and a maximum performance loss ≤ 5%.
Pattern of DNA Methylation in Daphnia: Evolutionary Perspective

Abstract DNA methylation is an evolutionary ancient epigenetic modification that is phylogenetically widespread. Comparative studies of the methylome across a diverse range of non-conventional and conventional model organisms is expected to help reveal how the landscape of DNA methylation and its functions have evolved. Here, we explore the DNA methylation profile of two species of the crustacean Daphnia using whole genome bisulfite sequencing. We then compare our data with the methylomes of two insects and two mammals to achieve a better understanding of the function of DNA methylation in Daphnia. Using RNA-sequencing data for all six species, we investigate the correlation between DNA methylation and gene expression. DNA methylation in Daphnia is mainly enriched within the coding regions of genes, with the highest methylation levels observed at exons 2–4. In contrast, vertebrate genomes are globally methylated, and increase towards the highest methylation levels observed at exon 2, and maintained across the rest of the gene body. Although DNA methylation patterns differ among all species, their methylation profiles share a bimodal distribution across the genomes. Genes with low levels of CpG methylation and gene expression are mainly enriched for species specific genes. In contrast, genes associated with high methylated CpG sites are highly transcribed and evolutionary conserved across all species. Finally, the positive correlation between internal exons and gene expression potentially points to an evolutionary conserved mechanism, whereas the negative regulation of gene expression via methylation of promoters and exon 1 is potentially a secondary mechanism that has been evolved in vertebrates.
Improved Evaluation Framework for Complex Plagiarism Detection

Plagiarism is a major issue in science and education. Complex plagiarism, such as plagiarism of ideas, is hard to detect, and therefore it is especially important to track improvement of methods correctly. In this paper, we study the performance of plagdet, the main measure for plagiarim detection, on manually paraphrased datasets (such as PAN Summary). We reveal its fallibility under certain conditions and propose an evaluation framework with normalization of inner terms, which is resilient to the dataset imbalance. We conclude with the experimental justification of the proposed measure. The implementation of the new framework is made publicly available as a Github repository.
A Distance Approach for Open Information Extraction Based on Word Vector

Web-scale open information extraction (Open IE) plays an important role in NLP tasks like acquiring common-sense knowledge, learning selectional preferences and automatic text understanding. A large number of Open IE approaches have been proposed in the last decade, and the majority of these approaches are based on supervised learning or dependency parsing. In this paper, we present a novel method for web scale open information extraction, which employs cosine distance based on G oogle word vector as the confidence score of the extraction. The proposed method is a purely unsupervised learning algorithm without requiring any hand-labeled training data or dependency parse features. We also present the mathematically rigorous proof for the new method with Bayes Inference and Artificial Neural Network theory. I t turns out that the proposed algorithm is equivalent to Maximum Likelihood Estimation of the joint probability distribution over the elements of the candidate extraction. The proof itself also theoretically suggests a typical usage of word vector for other NLP tasks. Experiments show that the distance-based method leads to further improvements over the newly presented Open IE systems on three benchmark datasets, in terms of effectiveness and efficiency.
Bitstream: A Flexible SDN Protocol for Service Provider Networks

SDNs could be a game changer for next generation provider networks. OpenFlow (OF) - the dominant SDN protocol, is rigid in its South Bound Interface (SBI) - any new protocol field that the hardware must support, must await complete OF standardization. In contrast, OF alternatives such as protocol oblivious forwarding (POF) and ForCES have simpler schemes for insertion of new protocol identifiers. Even with these there is an inherent limitation on network hardware - the tables must support specific table format and configuration at each node as per protocol semantics. We ask the question - can we design an open system - one that is carrier-class, yet able to meet the requirements of any protocol forwarding/action with a minimal set of dataplane function. We propose bitstream, a low-latency, source-routing based scheme that can support new protocols, be compatible with existing protocols and facilitate a minimum semantic set for acting on a packet. A prototype is built to show bitstream working.
Prevalence of Metabolic Syndrome in Mexico: A Systematic Review and Meta-Analysis.

BACKGROUND Metabolic syndrome (MetS) is closely linked with type 2 diabetes mellitus (T2DM) and cardiovascular diseases. The T2DM is one of the major causes of mortality and public health concern in Mexico. Some studies reported MetS prevalence in different regions from Mexico. However, a systematic report or meta-analysis on MetS prevalence is not available. The aim of this study was to estimate the pooled prevalence of MetS among apparently healthy Mexican adults. METHODS A systematic review was done of scientific articles published and available from different sources, including MEDLINE/PubMed, Web of Science, Cochrane Library, LILACS, and SCIELO. The overall prevalence of MetS and prevalence based on different diagnostic criteria [National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III), International Diabetes Federation (IDF), American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI), and World Health Organization (WHO)] were pooled using a random-effects model, and the results were presented in a forest plot. The study was performed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). RESULTS Altogether, 15 studies were included in the systematic meta-analysis. The estimated prevalence of MetS, based on different criteria, was as follows: IDF 54% (95% CI 0.44-0.63), AHA/NHLBI 48% (95% CI 0.34-0.62), ATP III 36% (95% CI 0.30-0.42), and WHO 31% (95% CI 0.04-0.81). According to the Der Simonian-Laird random-effects model, a pooled prevalence of MetS in Mexico was 41% (95% CI 0.34-0.47). CONCLUSIONS This study reported a high prevalence of MetS among healthy Mexican adults, in comparison with reports from other countries, including United States and Latin America. An urgent need to control and prevent MetS and its consequent health complications in Mexican populations is recommended.
Fournier's Gangrene Does Not Spare Young Adults.

INTRODUCTION Fournier's gangrene is a rare but severe form of necrotizing fasciitis, primarily affecting the perianal, genital, and perineal regions. It is a rapidly progressive disease and can be potentially fatal; therefore, it should be treated as an emergency. CASE REPORT Presented herein is the case of a 30-year-old man, without significant medical history, who presented to general surgery with complaints of swelling and pain in the gluteal area. The patient underwent emergency surgery on the day of admission to the emergency department where excessive fasciectomy and necrectomy were performed; broad-spectrum parenteral antibiotic therapy also was administered. CONCLUSIONS Fournier's gangrene is a surgical emergency, and the main cornerstones of treatment are patient resuscitation, broad-spectrum parenteral antibiotic therapy, and, most importantly, surgical debridement. Any delay in treatment can dramatically increase mortality.
Energy and Area Efficient Tunnel FET-based Spiking Neural Networks

Exploiting the Tunnel FET (TFET) properties such as unidirectional conduction and asymmetric drain and source, we propose for the first time a novel TFET-based circuit design mechanism for spike timing dependent plasticity process. In the proposed circuit, we are able to reduce the transistor count by half, making the circuit more area and energy efficient. A neuron receiving input from ten synapses, containing the proposed learning circuit, was simulated and it operated with reduced area and energy consumption compared to the MOSFET-based implementation.
An automatic fusion algorithm for multi-modal medical images

Abstract Multi-modal medical image fusion provides an informative and qualitative image, which enhances the accuracy of clinical diagnosis and surgical planning. The most common and effective approaches for medical image fusion involve principal component analysis (PCA), the discrete wavelet transform and the dual-tree complex wavelet transform (DTCWT). These existing algorithms perform fusion in either the spatial or transform domains. In this paper, an advanced fusion algorithm, which combines the DTCWT with PCA, is proposed to perform fusion on several medical imaging modalities. The input images are decomposed by the DTCWT and different fusion rules are applied to combine the coefficients. While PCA is used to fuse the low frequency coefficients, the high frequency coefficients are fused using the maximum fusion rule. The main advantages of the proposed method are the use of both DTCWT and PCA methods together. The DTCWT extracts the salient information about the input images, and then the PCA method is applied to calculate the principal component based on the information on the input images instead of taking only the average value of the low frequency components. The performance of the proposed method was compared with several existing methods. The experimental results obtained reveal that our proposed fusion algorithm performs better than existing schemes both quantitatively and in terms of visual perception.
A Framework for Utilizing Lab Test Results for Clinical Prediction of ICU Patients

Clinical decision support has gained signiﬁcant attention in recent years, especially with the advancement of data analytics techniques. One active research area in this domain is survival prediction or deterioration prediction of critical care patients, such as intensive care unit (ICU) patients. Usually, ICUs are equipped with continuous monitoring devices, which monitor vital signs such as heart rate, blood pressure, Oxygen saturation and so on. In addition to this, ICU patients also undergo different pathological (i.e., lab) tests. Recent studies claim that vital signs can be used to predict the near future status of a patient, with the help of predictive analytics. However, in this work, we investigate the usefulness of lab test results in patient survival prediction, which have been rarely used for this purpose. We propose a framework for utilizing the lab test data for this clinical prediction task. We encounter several challenges associated with this task, including variable-length feature vector, longitudinal features, missing data, class imbalance and high dimensionality. The proposed work addresses most of these challenges under this single framework. In this framework we propose a novel orthogonal clustering technique to reduce data dimensions as well as missing data. We also propose a systematic approach to inject informative background knowledge into the data and increase the prediction performance. The proposed technique has been evaluated on a real ICU patients database, achieving notable success in reducing 66% of the data dimensions without discarding any feature, while improving the weighted average F1-score 5% on average and achiving about 3 times speedup. We believe that the proposed technique will provide a powerful framework in the ﬁeld of clinical and healthcare data analytics and healthcare decision support.
A Low-Complexity Reconstruction Algorithm for Compressed Sensing Using Reed-Muller Sequences

Reed-Muller (RM) sequences have been widely used in compressed sensing (CS) to construct deterministic measurement matrices and extract attributes of sparse signals. However, if the signal is not sparse enough, the existing reconstruction algorithms encounter serious performance degradation. In this paper, invoking the elegant nested structure of second-order RM sequences, a soft-decision reconstruction algorithm is proposed. With the soft- information passing through the nested structure, the proposed reconstruction algorithm outperforms the existing ones. Notably, the performance can be further improved based on shuffling operations. Numerical results verify the good performance of the proposed algorithm and show that it preserves quite low computational complexity.
An In-Switch Rule Caching and Replacement Algorithm in Software Defined Networks

In the software defined networks (SDN), the flow table of an OpenFlow switch is usually implemented by the ternary content addressable memory (TCAM). The TCAM has limited capacity which may cause the flow table overflow problem. To address the problem, related works perform a rule replacement scheme according to either the estimated next- packet arrival time of each flow or the hit count of each rule; however, it may remove a rule whose packets will soon arrive at a switch, especially when the network is unstable. This paper proposes a novel TCAM management scheme named in-switch rule caching and replacement (IRCR) to better deal with the flow table overflow problem. The IRCR replaces a rule according to the expected number of incoming matched flows (EIMF), which can be derived from the inter-arrival time distribution models of the corresponding flows. The uniqueness of the IRCR is that it is designed as an in-switch application to avoid link delays between controllers and switches. Using either captured real network traffic or uniform distribution traffic, the IRCR outperforms related works for both exactly matched rules and wildcard rules in terms of hit ratios. In addition, the IRCR has little overhead to the switch.
Syntactic and Lexical Approaches to Reading Comprehension

Among the challenges of teaching reading comprehension in K – 12 are identifying the portions of a text that are difficult for a student, comprehending major critical ideas, and understanding context-dependent polysemous words. We present a simple, unsupervised but robust and accurate syntactic method for achieving the first objective and a modified hierarchical lexical method for the second objective. Focusing on pinpointing troublesome sentences instead of the overall readability and on concepts central to a reading, we believe these methods will greatly facilitate efforts to help students improve reading skills