Miracle-dz/UniFAR-FTUs huggingface dataset:
BGP-LSChain: An Inter-domain Link State Sharing Framework Based on Blockchain

Nowadays, the Internet has become a huge network composed of multiple autonomous systems (ASs) which are managed by various network operators. Network operators try to apply different network optimization technologies, based on link state information such as link bandwidth, link delay, to improve the network performance. However, inter-domain network optimization is hard to realize due to the lack of inter-domain link state information while one AS can only gather the link state information inside itself. Obviously, network information sharing among different ASs is necessary. Supporting for "trustless disintermediation", blockchain technology provides capabilities for cooperation among different ASs. We propose a new blockchain-based framework named BGP Link State Sharing Chain(BGP-LSChain), to help different AS owners sharing inter-domain link state. In this framework, we integrate a data verification mechanism and an AS credit rating mechanism to prevent fake data, which are realized by various smart contracts to control data sharing, data verification and credit rating. We implement an BGP-LSChain prototype and conduct experiments to evaluate it. The results show that while helping sharing data among different AS owners, the time cost of BGP-LSChain operations is within a reasonable and acceptable range.
Robust Active Learning of Expressive Linkage Rules

The goal of entity resolution, also known as duplicate detection and record linkage, is to identify all records in one or more data sets that refer to the same real-world entity. To achieve this goal, matching rules, encoding the matching patterns in the data, can be learned with the help of manually annotated record pairs. Active learning for entity resolution aims to minimize the human labeling effort by including the human into the learning loop and by selecting the most informative pairs for labeling. While active learning methods are quite successful at reducing the human labeling effort, we show that their performance decreases when applied to data sets having a large number of sparse attributes. We evaluate the ActiveGenLink active learning method using e-commerce data sets with such characteristics and observe that it is prone to suboptimal convergence points, thus producing highly varying results in different runs of the same experiment. In this paper we present our ongoing work on building a robust active learning method which is able to tackle the instability. Our method applies unsupervised matching of the record pairs as a first step. The unsupervised matching results are used afterwards for bootstrapping the active learning process and for preventing it from converging to suboptimal matching rules. The evaluation shows that the proposed method increases the robustness of the active learning process as it reduces the variation of the results of different runs by 10% to 16%.
Modeling and analysis of magnet skew in axial flux permanent-magnet motor via field reconstruction method

An axial flux permanent-magnet (AFPM) motor requires 3-dimensional (3-D) modeling and analysis in order to describe completely its 3-D behavior. Although the 3-D finite element analysis (FEA) provides accurate solution, its computational cost limits its use as a design tool. To address this problem, a 3-D field reconstruction method (FRM) is introduced as an alternative to the 3-D FEA. In this paper, an AFPM motor is designed and analyzed by using the 3-D FRM. The prototype does not have pole shoe on the teeth, thus it has high cogging torque due to high slot harmonics. Therefore, skewed PMs are employed on the rotor for cogging torque mitigation. This paper presents the improved 3-D FRM to take into account the magnet skew effect. To verify a validation of the proposed 3-D FRM, the analysis results are compared with the ones estimated by 3-D FEM.
Research on the Periodical Behavior Discovery of Funds in Anti-money Laundering Investigation

Some money laundering activities had periodic fund transfer behaviors, and discovering these cyclical behaviors was conducive to narrowing the scope of investigation. This paper treated the capital transaction data as a time series and found each periodic subsequence in the time series through the sub-period discovery algorithm, and designed the tolerance index to improve the robustness of the algorithm. In money laundering activities, there maight be linkage between related accounts. Through the relevant sub-period discovery algorithm, the highly correlated periodic behavior between different accounts were found, and then the suspicious accounts were found. A data set based on police investigation experience is constructed, and on this data set, the algorithm is validated to be effective.
Agreement Between Electronic Medical Record-based and Self-administered Pain Numeric Rating Scale: Clinical and Research Implications

Background:Pain screening may improve the quality of care by identifying patients in need of further assessment and management. Many health care systems use the numeric rating scale (NRS) for pain screening, and record the score in the patients’ electronic medical record (EMR). Objective:Determine the level of agreement between EMR and patient survey NRS, and whether discrepancies vary by demographic and clinical characteristics. Methods:We linked survey data from a sample of veterans receiving care in 8 Veterans Affairs medical facilities, to EMR data including an NRS collected on the day of the survey to compare responses to the NRS question from these 2 sources. We assessed correlation, agreement on clinical cut-points (eg, severe), and, using the survey as the gold standard, whether patient characteristics were associated with a discrepancy on moderate-severe pain. Results:A total of 1643 participants had a survey and EMR NRS score on the same day. The correlation was 0.56 (95% confidence interval, 0.52–0.59), but the mean EMR score was significantly lower than the survey score (1.72 vs. 2.79; P<0.0001). Agreement was moderate (&kgr;=0.35). Characteristics associated with an increased odds of a discrepancy included: diabetes [adjusted odds ratio (AOR)=1.48], posttraumatic stress disorder (AOR=1.59), major depressive disorder (AOR=1.81), other race versus white (AOR=2.29), and facility in which care was received. Conclusions:The underestimation of pain using EMR data, especially clinically actionable levels of pain, has important clinical and research implications. Improving the quality of pain care may require better screening.
The undermethylated state of a CpG island region in igf2 transgenes is dependent on the H19 enhancers.

CpG islands are GC-rich regions located in the promoter regions of housekeeping genes and many tissue-specific genes. While most CpG islands are normally unmethylated, island methylation can occur and is associated with silencing of the corresponding gene. Experiments with transgenic mice and DNA transfection in pluripotential embryonic cells have led to the conclusion that the information required for protecting the islands from methylation is contained within the CpG islands themselves and have identified Sp1 binding sites as an important element in establishing and/or maintaining the methylation-free state of CpG islands. To examine the generality of these observations, we analyzed the methylation of one of the mouse Igf2 CpG islands and its flanks in transgenic mice. We observed that the undermethylated state of this region is dependent on the presence of a separate cis-regulatory element, the H19 enhancers. These tissue-specific enhancers had a ubiquitous, non-tissue-specific effect on island region methylation. Structural alterations outside of the island and these enhancers also affected this region's methylation. These findings indicate that the methylation of some CpG island-containing regions is more sensitive than previously believed to the activity of distant cis-regulatory elements and to structural alterations in nonisland sequences in cis.
Spectral analysis of heart rate variability in dogs with mild mitral regurgitation.

OBJECTIVE To assess autonomic function in dogs with mild mitral regurgitation (MR) that did not have clinical signs of the condition. ANIMALS 6 healthy adult Beagles. PROCEDURE Mild MR was experimentally induced. A 24-hour ambulatory ECG was recorded before and after induction of MR. Heart rate variability was analyzed in frequency domains by use of the ambulatory ECG. Low-frequency (LF) and high-frequency (HF) power were calculated by integrating over their frequency intervals, and the ratio of LF to HF was also calculated. Measurements of frequency domains were analyzed for 4 time periods (midnight to 6 AM, 6 AM to noon, noon to 6 PM, and 6 PM to midnight). RESULTS Dogs with experimentally induced MR were classified as International Small Animal Cardiac Health Council class Ia. The HF power of dogs with MR was significantly decreased between 6 AM and noon. The ratio of LF to HF in dogs with MR was significantly increased for the periods between midnight and 6 AM, 6 AM and noon, and noon and 6 PM. CONCLUSIONS AND CLINICAL RELEVANCE Compensatory response through autonomic modulation was observed in dogs with mild MR that did not have abnormalities, except for cardiac murmur, during clinical examination. This result suggests that treatment during the early stages of mild MR may be beneficial. Additional studies are necessary to determine whether such treatment will delay the onset of congestive heart failure and prolong survival in dogs affected with mild MR.
Non‐pharmacological approaches for dementia that informal carers might try or access: a systematic review

To review non‐drug treatments for dementia; to provide a source of evidence for informal carers who want ideas about non‐drug approaches for dementia, that they might try or that they could try to access. The systematic review addresses: what non‐drug treatments work and what do they work for? What non‐drug treatments might work and what for? What non‐drug treatments do not work?
Indraj: digital certificate enrollment for battery-powered wireless devices

A public key infrastructure (PKI) has been widely deployed and well tested on the Internet. However, this standard practice of delivering scalable security has not yet been extended to the rapidly growing Internet of Things (IoT). Thanks to vendor hardware support and standardization of resource-efficient communication protocols, asymmetric cryptography is no longer unfeasible on small devices. To migrate IoT from poorly scalable, pair-wise symmetric encryption to PKI, a major obstacle remains: how do we certify the public keys of billions of small devices without manual checks or complex logistics? The process of certifying a public key in form of a digital certificate is called enrollment. In this paper, we design an enrollment protocol, called Indraj, to automate enrollment of certificate-based digital identities on resource-constrained IoT devices. Reusing the semantics of the Enrollment over Secure Transport (EST) protocol designed for Internet hosts, Indraj optimizes resource usage by leveraging an IoT stack consisting of Constrained Application Protocol (CoAP), Datagram Transport Layer Security (DTLS) and IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). We evaluate our implementation on a low power 32-bit MCU, showing the feasibility of our protocol in terms of latency, power consumption and memory usage. Asymmetric cryptography enabled by automatic certificate enrollment will finally turn IoT devices into well behaved, first-class citizens on the Internet.
Blockchain Anchoring of Public Registries: Options and Challenges

Governments across the world are testing different uses of the blockchain for the delivery of their public services. Blockchain hashing-or the insertion of data in the blockchain (anchoring)-is one of the potential applications of the blockchain in this space. With this method, users can apply special scripts to add their data to blockchain transactions, ensuring both immutability and publicity. Blockchain hashing also secures the integrity of the original data stored on central governmental databases. The objective of this paper is to analyse the use of data hashing (anchoring) on the blockchain for public state-owned registries. This paper starts by analysing possible scenarios of hashing on the blockchain and assesses in which cases it may work and in which it is less likely to add value to a public administration. Second, the paper also compares this method with traditional digital signatures using PKI (Public Key Infrastructure) and discusses standardisation in each domain. Third, it also addresses issues related with concepts such as "distributed ledger technology" and "permissioned blockchains. " Finally, it raises the question of whether blockchain hashing is an effective solution for electronic governance, and concludes that its value is controversial, even if it is improved by PKI and other security measures. In this regard, we claim that governments need to identify pain points in governance in the first place, and then consider the trade-offs of the blockchain as a potential solution versus other alternatives.
Sternomastoid muscle function and fatigue in breathless patients with severe respiratory disease.

In patients with severe respiratory disease, the work of breathing is increased and the respiratory muscles, particularly those of inspiration, may become fatigued. Hitherto, there has been little information on the incidence of respiratory muscle fatigue in acutely breathless patients. We studied 34 patients with severe respiratory disease on admission to hospital when they were most breathless, and then, if possible, 7 to 14 days later after recovery for evidence of sternomastoid muscle fatigue or increased fatigability. Frequency/force curves, numerically expressed as the 20:50 ratio, were carried out in all patients on admission. Three of the 34 patients had evidence of low frequency fatigue (i.e., greater than 15% reduction in 20:50 ratio) in the sternomastoid muscle on admission when first studied (mean +/- SEM 20:50 ratio, 56.3 +/- 1.2%; n = 3). The mean 20:50 ratio in the remaining 31 patients on admission was 75.7 +/- 1.6% (n = 31) compared with 77.8 +/- 1.4% (n = 25) when symptomatically better (p less than 0.05). The mean 20:50 ratio on admission was also significantly lower than the mean 20:50 ratio in a group of age- and sex-matched normal control subjects (i.e., 78.5 +/- 1.4%, n = 25; p less than 0.05). Twenty-five patients were studied completely both on admission and recovery, including a fatigability test that involved the performance of 50 fatiguing head lifts with measurements of the 20:50 ratio 10 and 60 min later. Sternomastoid muscle fatigability was significantly increased on admission when the patients were most breathless, compared with recovery when they were less breathless (p less than 0.001 at both 10 and 60 min). (ABSTRACT TRUNCATED AT 250 WORDS)
Some measures to detect the influencer on social network based on Information Propagation

Determining the influencer for a brand plays an important role in the influence marketing. The influencer makes a brand approaching goal customers easily and quickly. There are many current methods to measure the influence of a user. In this paper, a model for representing a social network is presented. It includes two main objects: users and tags. This model can represent relationship between these objects clearly. Besides that, some influence measures are proposed. They represent the ability of the influence on other people by relationships between users and the concern to user's tags, as well as the speed of the user's tag propagation on the social network. Based on them, the problem about determining the influencer of a specific brand on a social network is solved. Our method is tested in practice and gets some positive results.
Signal Regulatory Protein Molecules Are Differentially Expressed by CD8− Dendritic Cells1

A normalized subtracted gene expression library was generated from freshly isolated mouse dendritic cells (DC) of all subtypes, then used to construct cDNA microarrays. The gene expression profiles of the three splenic conventional DC (cDC) subsets were compared by microarray hybridization and two genes encoding signal regulatory protein β (Sirpβ1 and Sirpβ4) molecules were identified as differentially expressed in CD8− cDC. Genomic sequence analysis revealed a third Sirpβ member localized in the same gene cluster. These Sirpβ genes encode cell surface molecules containing extracellular Ig domains and short intracytoplasmic domains that have a charged amino acid in the transmembrane region which can potentially interact with ITAM-bearing molecules to mediate signaling. Indeed, we demonstrated interactions between Sirpβ1 and β2 with the ITAM-bearing signaling molecule Dap12. Real-time PCR analysis showed that all three Sirpβ genes were expressed by CD8− cDC, but not by CD8+ cDC or plasmacytoid pre-DC. The related Sirpα gene showed a similar expression profile on cDC subtypes but was also expressed by plasmacytoid pre-DC. The differential expression of Sirpα and Sirpβ1 molecules on DC was confirmed by staining with mAbs, including a new mAb recognizing Sirpβ1. Cross-linking of Sirpβ1 on DC resulted in a reduction in phagocytosis of Leishmania major parasites, but did not affect phagocytosis of latex beads, perhaps indicating that the regulation of phagocytosis by Sirpβ1 is a ligand-dependent interaction. Thus, we postulate that the differential expression of these molecules may confer the ability to regulate the phagocytosis of particular ligands to CD8− cDC.
Prediction Demand for Classified Ads Using Machine Learning: an Experiment Study

Classified ads prediction is a very interesting activity for organizations in order to increase the purchase quantity of a product and thereafter the possibility of sale. Used goods predicting can be done by calculating the probability of sale for each selected product. In this paper, we conduct an empirical analysis on classified ads prediction of Avito dataset in order to develop prediction models using three individual machine-learning techniques and five ensemble learners. We compare and evaluate the performance of the proposed models using Root Mean Square Error (RMSE) measure. The stacked generalization method was also used to combine the best-performed models to select the best one. The results show that the Extreme Gradient Boosting Machine algorithm (XGBoost) is the most accurate model with an RMSE value of 0.2253.
Distance bounding under different assumptions: opinion

Distance-bounding protocols were introduced in 1993 as a countermeasure to relay attacks, in which an adversary fraudulently forwards the communication between a verifier and a distant prover. In the more than 40 different protocols that followed, assumptions were taken on the structure of distance-bounding protocols and their threat models. In this paper, we survey works disrupting these assumptions, and discuss the remaining challenges.
Reduced Insulin Sensitivity in Nondiabetic, HLA-Identical Siblings of Insulin-dependent Diabetic Subjects

Nondiabetic, HLA-identical siblings of insulin-dependent diabetic subjects have an increased risk of developing diabetes. Since insulin resistance is present in newly diagnosed diabetic subjects, we studied 12 HLA-identical siblings to determine whether they have impaired insulin sensitivity. Each sibling was carefully matched for age, sex, and body weight to a control from a nondiabetic family. The insulin sensitivity index (S1,) was determined after an intravenous glucose tolerance test (IVGTT) using the minimal model of insulin kinetics. The insulin sensitivity index was significantly lower in the HLA-identical siblings compared with their matched controls.
Deletion of FgHOG1 Is Suppressive to the mgv1 Mutant by Stimulating Gpmk1 Activation and Avoiding Intracellular Turgor Elevation in Fusarium graminearum

Fusarium head blight caused by Fusarium graminearum is an important disease of wheat and barley. Previous studies have showed that all three MAP kinase genes, MGV1, FgHOG1, and GPMK1, are involved in regulating hyphal growth, sexual reproduction, plant infection, and stress responses in this pathogen. To determine the relationship between the Mgv1 and FgHog1 pathways, in this study, we generated and characterized the mgv1 Fghog1 double mutant. Deletion of FgHOG1 partially rescued the defects of the mgv1 mutant in vegetative growth and cell wall integrity but had no effects on its defects in plant infection and DON production. The mgv1 Fghog1 mutant grew faster and was more tolerant to cell wall stressors than the mgv1 mutant. Swollen compartments and cell burst were observed frequently in the mgv1 mutant but rarely in the mgv1 Fghog1 mutant when treated with fungicide fludioxonil or cell wall stressor Congo red. Conversely, the deletion of MGV1 also alleviated the hyperosmotic sensitivity of the Fghog1 mutant in vegetative growth. TGY assays indicated increased phosphorylation of FgHog1 in the mgv1 mutant, and TEY assays further revealed elevated activation of Gpmk1 in the mgv1 Fghog1 double mutant, particularly under cell wall stress conditions. Overall, our data showed that deletion of FgHOG1 partially suppressed the defects of the mgv1 mutant, possibly by affecting genes related to cell wall integrity and osmoregulation via the over-activation of Gpmk1 MAP kinase and avoiding intracellular turgor elevation.
Polaris: A System-Level Roadmapping Toolchain for On-Chip Interconnection Networks

Technology trends are driving parallel on-chip architectures in the form of multiprocessor systems-on-a-chip (MPSoCs) and chip multiprocessors (CMPs). In these systems, the increasing on-chip communication demand among the computation elements necessitates the use of scalable, high-bandwidth network-on-chip (NoC) fabrics instead of dedicated interconnects and shared buses. As transistor feature sizes are further miniaturized, more complicated NoC architectures become feasible that can support more demanding applications. Given the myriad emerging software-hardware combinations, for cost-effectiveness, a system designer critically needs to prune this widening NoC design-space to predict the interconnect fabric(s) that best balance(s) cost/performance, before the actual design process begins. This prompted us to develop Polaris, a system-level roadmapping toolchain for on-chip interconnection networks that helps designers predict the most suitable interconnection network design(s) tailored to their performance needs and power/silicon area constraints with respect to a range of applications that the system will run. Polaris explores the plethora of NoC designs based on projections of network traffic, architectures, and process characteristics. While Polaris's toolchain is extensible so new traffic, network designs, and technology processes can be added, the current version already incorporates 7872 NoC design points. Polaris is rapid, efficiently iterating over thousands of NoC design points, while maintaining high relative and absolute accuracies when validated against detailed NoC synthesis results.