Miracle-dz/UniFAR-FTUs huggingface dataset:
Intent-Driven Security Policy Management for Software-Defined Systems

Different network controllers are utilized in a multi-domain software-defined systems (SDx) to manage the networking resources. However, these controllers operate using a different high-level language (intent). Thus, the admin needs to perform cross-layer translation from the user requirements to the underlying network controller format, increasing human-in-the-loop overhead. There are two primary security and management challenges involved in managing multi-domain controllers. The first challenge is how to design an SDN controller language that can effectively convert human-specified networking policies at the control plane into the network flow rules level at the data plane. The second challenge is how to reduce the complexity of network flow rules conflict checking at the data plane. To address these challenges, we present a new intent-based security policy enforcement solution called INTPOL. First, INTPOL provides a unified intent rules that abstracts the network admin from the underlying network controller’s format. Second, INTPOL develops a networking service solution to use a bounded formal model for network service compliance checking that significantly reduces the complexity of flow rules conflicts checking at the data plane level. Finally, INTPOL is expendable from a single SDN domain to multiple SDN domains and hybrid networks by applying network service function chaining (SFC) for inter-domain policy management.
Reweighted Low-Rank Factorization With Deep Prior for Image Restoration

The low-rank recovery is a powerful tool to restore images from incomplete and corrupted observations. Conventional low-rank recovery techniques employ the reweighted nuclear norm minimization, which requires performing the full singular value decomposition and thus is computationally expensive. Using the scheme of bilinear factorization, we propose the Reweighted Low-rank Matrix Factorization (RLMF) method for single channel image restoration. The RLMF method can not only inherit the computational efficiency of bilinear factorization, but also incorporate the empirical distribution of the singular values in natural images. Then, considering the correlation between image channels, we generalize the reweighted nuclear norm from matrices to tensors, and develop the Reweighted Low-rank Tensor Factorization (RLTF) method for multichannel image restoration. Moreover, we enhance the RLMF and RLTF methods by introducing the deep image prior information, which is capable of capturing the implicit image structure through the neural network architecture to improve restoration accuracy. Experimental results show the computational efficiency of the proposed low-rank factorization scheme, and the superior restoration accuracy of the proposed methods compared with the state-of-the-art methods.
A triple‐mode reconfigurable wearable repeater antenna for WBAN applications

A reconfigurable wearable repeater antenna (RWRA) for wireless body area network (WBAN) applications is proposed. The RWRA can work at triple‐mode by controlling the state of PIN diodes, which are repeater, on‐body and off‐body modes. The antenna is fed by a single port at side instead of bottom for the sake of reducing the profile and improving the coupling strength between the RWRA and an implantable antenna. The total size of the antenna is π × 282 × 4.8 mm3. To validate the performance, the RWRA is fabricated and measured on minced pork. Measured bandwidths at repeater and on‐/off‐body modes are 5.8, 84, and 54 MHz, respectively. Radiation patterns are omnidirectional with vertical polarization at on‐body mode and broadside at off‐body mode, which measured peak gains are 1.2 and 5.4 dBi, respectively. Specific absorption rate (SAR) values at all three modes are analyzed in this article as well. Coupling strength between the RWRA and an implantable antenna is also measured. Besides, the effect of distance and misalignment between them are analyzed.
Hypolipidemic Effect of Hemp Seed Oil from the Northern Morocco Endemic Beldiya Ecotype in a Mice Model: Comparison with Fenofibrate Hypolipidemic Drugs

Introduction. Cannabis sativa is a source of oil seeds for pharmaceutical, cosmetic, and food uses. Objective. The aim of this study is to evaluate the hypolipidemic effect of hemp seed oil (HSO) obtained from a local ecotype called “Beldiya. ” Methods. The extraction of HSO was carried out by cold press method. Then, the fatty acid and tocopherol composition was analyzed, respectively, by GC-FID and HPLC. The hypolipidemic activity of HSO at a dose of 3.5 and 7 mg/kg body weight was evaluated in Triton WR-1339-induced hyperlipidemic mice by measuring plasma cholesterol (total lipid, HDL, and LDL), plasma triglycerides, and atherogenic index using enzymatic methods. Fenofibrate was used as a standard hypolipidemic drug at a dose of 3.5 mg/kg body weight. Results. Analyzed HSO shows a high unsaturated fatty acids’ content with the dominance of linoleic acid (48.85%), oleic acid (21.82%), as well as α- and γ-linolenic acid (14.72%). The result demonstrates that this typical vegetable oil contains a high concentration of γ-tocopherol (456 mg·kg−1 oil). Furthermore, the administration of HSO decreases plasma total cholesterol, triglycerides, and LDL-cholesterol while increases HDL-cholesterol. Consequently, the HSO reduces the atherogenic index and LDL/HDL ratio. The hypolipidemic effect of fenofibrate is relatively more marked comparatively to that of HSO especially concerning total cholesterol and its LDL fraction. Conclusions. The local ecotype HSO has an interesting effect on plasma lipid parameters and might be beneficial for the treatment of hyperlipidemia and prevention of atherosclerosis.
Design and Development of Intelligent Electrodes for Future Digital Health Monitoring: A Review

Electrodes are sensors used in electrocardiography (ECG) monitoring system to diagnose heart diseases. Over the years, diverse types of electrodes have been designed and developed to improve ECG monitoring system. However, more recently, with the technological advances and capabilities from the Internet of Things (IoT), cloud computing and data analytics in personalized healthcare, researchers are attempting to design and develop more effective as well as flexible ECG devices by using intelligent electrodes. This paper reviews previous works on electrodes used in electrocardiography (ECG) monitoring devices to identify the key ftures for designing and developing intelligent electrodes in digital health monitoring devices.
Impact of Motor-Cognitive Interventions on Selected Gait and Balance Outcomes in Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background Efficient performance of most daily activities requires intact and simultaneous execution of motor and cognitive tasks. To mitigate age-related functional decline, various combinations of motor and cognitive training have shown promising results. The aim of this systematic review and meta-analysis of randomized controlled trials (RCTs) was to evaluate the efficacy of different types of motor-cognitive training interventions (e.g., sequential and simultaneous) on selected functional outcomes in healthy older adults. Methods Six online academic databases were used to retrieve eligible RCTs up to April 2021, following PRISMA guidelines and PICO criteria. A random-effects model was used for all meta-analyses conducted on selected functional outcomes: single- and dual-task gait speed, the Timed Up and Go Test (TUG), and Berg Balance Scale (BBS) score. Effect size (ES) was calculated as Hedges' g and interpreted as: trivial: <0.20, small: 0.20–0.60, moderate: 0.61–1.20, large: 1.21–2.00, very large: 2.01–4.00 or extremely large >4.00. Results From 2,546 retrieved records, 91 RCTs were included for meta-analysis (n = 3,745 participants; 64.7–86.9 years). The motor-cognitive interventions included differed according to the type of training (e.g., sequential, simultaneous with additional cognitive task or exergame training. The results showed that motor-cognitive interventions can improve gait speed under single-task conditions (small ES = 0.34, P = 0.003). The effect of the intervention was moderated by the type of control group (Q = 6.203, P = 0.013): passive (moderate ES = 0.941, P = 0.001) vs. active controls (trivial ES = 0.153, P = 0.180). No significant effect was found for dual-task walking outcomes (P = 0.063). Motor-cognitive intervention had a positive effect on TUG (small ES = 0.42, P < 0.001), where the effect of intervention was moderated by control group [passive (moderate ES = 0.73, P = 0.001) vs. active (small ES = 0.20, P = 0.020)], but not by the type of training (P = 0.064). Finally, BBS scores were positively affected by motor-cognitive interventions (small ES = 0.59, P < 0.001) with however no significant differences between type of control group (P = 0.529) or intervention modality (P = 0.585). Conclusions This study provides evidence for the effectiveness of various types of motor-cognitive interventions on performance-based measures of functional mobility in healthy older adults. With respect to significant effects, gait speed under single-task condition was improved by motor-cognitive interventions, but the evidence shows that this type of intervention is not necessarily more beneficial than motor training alone. On the other hand, motor-cognitive interventions are better at improving multicomponent tasks of dynamic balance and mobility function, as measured by the TUG. Because of substantial heterogeneity and the current limited availability of different types of interventions, the conclusions should be interpreted with caution.
Expression of PTEN Protein and Its Relationship with Immunophenotype in Children with Myeloid Leukemia*

摘要目的:探讨髓细胞性白血病(AML)患儿 PTEN蛋白表达及其与免疫表型的关系。 方法:选择 AML患儿 143例,根据免疫表型 的不同分为两组:免疫分型为 LY+AML型 59例,LY-AML84例。 所有患儿都给予免疫表型分析,检测 PTEN蛋白表达情况并进 行相关性分析。 结果:LY+AML患儿 CD34阳性、CD117阳性比例显著高于 LY-AML患儿(P <0.05),染色体核型异常比例显著 低于 LY-AML患儿(P <0.05)。 LY+AML患儿的 PTEN蛋白表达量为(65.33± 2.34)%,阳性表达率为 94.9%;而 LY-AML分别为 (20.11± 4.11)%和 13.1%,与 LY+AML患儿对比差异都有统计学意义(P <0.05)。 在 AML患儿中,Spearman相关分析显示 PTEN 蛋白表达水平与免疫分型呈现显著相关性 (r =0.653,P =0.000)。 多因素 logistic 回归方法显示 PTEN 蛋白表达、CD34阳性、 CD117阳性、染色体核型异常为影响 AML患儿免疫表型的主要独立危险因素 (OR=1.098、1.045、1.092、0.294,P <0.05)。 结论: AML患儿骨髓单个核细胞的 PTEN蛋白表达上调使得 LY+AML型发生风险显著增加,可作为 AML患儿病情判断与预后预测 的参考指标之一。 关键词:髓细胞性白血病;PTEN蛋白;免疫表型;相关性 中图分类号:R733.7 文献标识码:A 文章编号:1673-6273(2019)10-1953-05
The Use of Real Options to Evaluate Business-To-Business The Use of Real Options to Evaluate Business-To-Business Companies Companies

The purpose of this work is to show that the use of real options can contribute to a better evaluation of companies devoted to Electronic Commerce – Business-to-Business. For such, usual company -evaluation techniques are presented and the advantages and disadvantages of each one are discussed, with emphasis to the real -option technique. Additionally, the simi larity between asset investment and call options is shown, and internal and external real options existing in a Business -to-Business company are identified. Finally, this work describes how to evaluate a company acting in Business -to-Business by employing the concepts of real-option theory.
Channelling actions in motor cortex : how I h gates cortical control of movement

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Directionally Resolved Measurement and Modeling of THz Band Propagation Channels

Design and assessment of THz communications systems, which will form an essential part of 6G, require an understanding of the propagation channels the systems will operate in. This paper presents investigations of the channel characteristics in various scenarios at 145 GHz, which is the band currently envisioned for the first round of deployments. In particular, we review several extensive measurement campaigns performed by the University of Southern California in both outdoor and indoor environments. We present the measurement and evaluation methodology and sample results that illustrate the dominant propagation effects in different environments. We then summarize the parameters of the statistical channel models for path loss, delay dispersion, and angular dispersion. Based on these results, we find that even in NLoS (non-line-of-sight) situations, Gbit/s communications can be sustained over a 100 m distance; that (for an antenna gain of 20 dB), there is considerable delay dispersion, requiring tens of equalizer taps, and that angular dispersion is significant in both LoS and NLoS situations. The channel parameters can be thus used as a basis for system design and evaluation under realistic operating conditions.
Checked Coverage for Test Suite Reduction – Is It Worth the Effort?

As the size of software projects increases, their test suites usually grow accordingly. Test suite size, however, has a direct impact on the efficiency of software testing. Hence, test suite reduction (TSR) procedures aim at removing redundant test cases while maintaining the suites fault detection capabilities (FDC). This paper explores checked coverage as a coverage metric for TSR; checked coverage not only investigates if a part of code was executed but also if it was checked by a test oracle. Previously, this metric has been applied successfully as an indicator for oracle quality. To assess how suitable checked coverage is in comparison to traditional metrics, such as line or method coverage, we developed a TSR tool for Java programs. In an empirical evaluation, we performed TSR based on different reduction algorithms, coverage metrics, and open-source Java projects. Our study investigates both the efficiency of the TSR as well as effectiveness in regard to the FDC and size of the reduced test suites. CCS CONCEPTS • Software and its engineering → Software testing and debugging. ACM Reference Format: Roxane Koitz-Hristov, Lukas Stracke, and Franz Wotawa. 2022. Checked Coverage for Test Suite Reduction–Is It Worth the Effort? In IEEE/ACM 3rd International Conference on Automation of Software Test (AST'22), May 17-18, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3524481.3527216
Sensitivity-Enhanced Fiber-Optic Sensor Based on a Drilled PDMS Diaphragm for Ultrasound Imaging of Seismic Physical Model

An optical fiber Fabry–Perot interferometer (FPI) ultrasound sensor, based on a polydimethylsiloxane (PDMS) diaphragm with dual circular holes, is proposed and experimentally demonstrated. Two holes with central axis symmetry were drilled in the <inline-formula> <tex-math notation="LaTeX">$20 \mu \text{m}$ </tex-math></inline-formula>-thick PDMS diaphragm using femtosecond micromachining. The dual holes eliminated the pressure difference between the two interfaces of the diaphragm, allowed the strain of the diaphragm vibration to concentrate around the center of the diaphragm, and increased the ultrasound sensing sensitivity. The center distance of dual holes was optimized to enhance high ultrasound response. The ultrasound response peak-to-peak voltage (PPV) of the PDMS diaphragm with a dual-holes center distance of <inline-formula> <tex-math notation="LaTeX">$45.0 \mu \text{m}$ </tex-math></inline-formula> increased by 35% compared to that of the PDMS film without drilling. The FPI sensor was packaged using an acoustic plano-concave lens to facilitate the acoustic-to-sensor coupling efficiency and realize underwater ultrasound imaging. Experimental results showed that the proposed sensor realized the ultrasound imaging of an uplifted multilayer physical model.
Novel Method for Measuring High Temperature Hygroscopic Swelling

Electronic packaging reliability is known to be affected by humidity from the environment. Absorbed moisture can cause serious assembly issues such as warpage, delamination, and even pop-corning. However, to this day, material moisture properties are not readily available - especially at high temperatures. This paper introduces a novel method of measuring high temperature material swelling to extend the characterization capability beyond typical measurement limits. This new measurement concept has yielded some promising results and presents opportunities for future work.
Towards A Methodological Framework for Production-ready AI-based Software Components

AI-based software systems have seen more widespread production deployment in recent years. However, many challenges currently complicate the integration of AI-based software components into production systems, and their operation and maintenance remains complex. In this research proposal, we summarize these challenges and propose a framework that enables the efficient design, integration, and operation of AI components.
Record and Replay of Online Traffic for Microservices with Automatic Mocking Point Identification

Using recorded online traffic for the regression testing of web applications has become a common practice in industry. However, this “record and replay” on microservices is challenging because simply recorded online traffic (i.e., values for variables or input/output for function calls) often cannot be successfully replayed because microservices often have various dependencies on the complicated online environment. These dependencies include the states of underlying systems, internal states (e.g., caches), and external states (e.g., interaction with other microservices/middleware). Considering the large size and the complexity of industrial microservices, an automatic, scalable, and precise identification of such dependencies is needed as manual identification is time-consuming. In this paper, we propose an industrial grade solution to identifying all dependencies, and generating mocking points automatically using static program analysis techniques. Our solution has been deployed in a large Internet company (i.e., Ant Group) to handle hundreds of microservices, which consists of hundreds of millions lines of code, with high success rate in replay (99% on average). Moreover, our framework can boost the efficiency of the testing system by refining dependencies that must not affect the behavior of a microservice. Our experimental results show that our approach can filter out 73.1% system state dependency and 71.4% internal state dependency, which have no effect on the behavior of the microservice.
Engineering a Platform for Reinforcement Learning Workloads

Reinforcement Learning (RL) is an area of machine learning concerned with teaching intelligent agents to take desired actions in a specific environment. The teaching part can be performed in a simulated environment where the agent can learn how to react to the (simulated) current state in order to reach a desired state. Offering Reinforcement Learning as a service with stringent reliability and scalability requirements, entails a set of challenges at both the architectural and implementation level. In this paper we present the Bonsai platform for RL workloads. We discuss the requirements, design and implementation of the Bonsai platform. CCS CONCEPTS • Software and its engineering → organization and properties → Software system structures
Convergence Rate Comparison of Proximal Algorithms for Non-Smooth Convex Optimization With an Application to Texture Segmentation

In this paper we provide a theoretical and numerical comparison of convergence rates of forward-backward, Douglas-Rachford, and Peaceman-Rachford algorithms for minimizing the sum of a convex proper lower semicontinuous function and a strongly convex differentiable function with Lipschitz continuous gradient. Our results extend the comparison made in [1], when both functions are smooth, to the context where only one is assumed differentiable. Optimal step-sizes and rates of the three algorithms are compared theoretically and numerically in the context of texture segmentation problem, obtaining very sharp estimations and illustrating the high efficiency of Peaceman-Rachford splitting.
Trust in AI-based conversational agents in the customer service - Trust in AI-based conversational agents in the customer service - a theoretical framework a theoretical framework

In the context of AI-based conversational agents, existing trust theories are fragmented. Researchers may get lost and feel confused in reading existing literature with diverse perspectives. Based on prior literature, there is a need to expand existing frameworks by integrating different views into one comprehensive model. In this paper, we aim to clarify all different views in trust antecedents and identify a comprehensive framework that can be generalized to various contexts. Our research question is: What factors affect the trust beliefs of users to interact with AI-based conversational agents (AICA) in customer service? Grounded on the theory of ABI (i.e., Ability, Benevolence, Integrity) (Mayer et al. 1995) for trusting beliefs, we take unique characteristics of AI-based conversational agents for customer service into account to study trust antecedents. Specifically, we propose a theoretical framework to empirically examine the trust antecedents on trusting beliefs in AI-based conversational agents, and the impact of trusting beliefs on the trusting intentions towards AI-based conversational agents. Our proposed framework differs from previous articles in several ways: 1) We clarify definitions and categorization for trust-related concepts; 2) We incorporate existing literature in trust antecedents into a comprehensive framework; 3) We apply the comprehensive framework to the context of AI-based conversational agents in the customer service. The proposed research model is illustrated in Figure 1.
Peritoneal Macrophages Lipopolysaccharide-Stimulated Mouse Cytokine and Chemokine Production in IL-4 Pretreatment Selectively Enhances

Although well recognized for its anti-inﬂammatory effect on gene expression in stimulated monocytes and macrophages, IL-4 is a pleiotropic cytokine that has also been shown to enhance TNF- (cid:1) and IL-12 production in response to stimulation with LPS. In the present study we expand these prior studies in three areas. First, the potentiating effect of IL-4 pretreatment is both stimulus and gene selective. Pretreatment of mouse macrophages with IL-4 for a minimum of 6 h produces a 2- to 4-fold enhancement of LPS-induced expression of several cytokines and chemokines, including TNF- (cid:1) , IL-1 (cid:1) , macrophage-inﬂammatory protein-2, and KC, but inhibits the production of IL-12p40. In addition, the production of TNF- (cid:1) by macrophages stimulated with IFN- (cid:2) and IL-2 is inhibited by IL-4 pretreatment, while responses to both LPS and dsRNA are enhanced. Second, the ability of IL-4 to potentiate LPS-stimulated cytokine production appears to require new IL-4-stimulated gene expression, because it is time dependent, requires the activation of STAT6, and is blocked by the reversible protein synthesis inhibitor cycloheximide during the IL-4 pretreatment period. Finally, IL-4-mediated potentiation of TNF- (cid:1) production involves speciﬁc enhancement of mRNA translation. Although TNF- (cid:1) protein is increased in IL-4-pretreated cells, the level of mRNA remains unchanged. Furthermore, LPS-stimulated TNF- (cid:1) mRNA is selectively enriched in actively translating large polyribosomes in IL-4-pretreated cells compared with cells stimulated with LPS alone. The Journal of Immunology, 2002, 168: