Miracle-dz/UniFAR-FTUs huggingface dataset:
A Physically Secure, Lightweight Three-Factor and Anonymous User Authentication Protocol for IoT

Internet of Things has remarkable effects in human’s daily life. It is important for users and sensors to securely access data collected by low-cost sensors via Internet in real-time IoT applications. There exist many authentication protocols for guaranteeing secure communication between users and sensors. However, in some protocols, the privacy of unattended sensors subjected to capture node attacks cannot be guaranteed. Moreover, the sensors subjected to physical tampering attacks can still execute normally the authentication process. Besides, an authentication protocol should be lightweight due to the restricted computing power and storage of the sensors. The idea of designing a more secure and lightweight authentication protocol engender this article. The proposed protocol can provide the physical security through physically unclonable function (PUF), require no additional phase to update challenge-response pairs (CRPs), and store a single CRP for each sensor. At the same time, the proposed protocol utilizes three factors, such as personal biometrics, smartcard and password, to strengthen the security contrasting with two factors, and manipulates some basic cryptographic operations, including bitwise-exclusive-OR (XOR) and hash function, to achieve the lightweight performance. Moreover, both formal security analysis based on Real-Or-Random (ROR) and informal security analysis demonstrate the security of the proposed protocol. Compared with the existing related protocols, the proposed protocol has the advantage in terms of security, functionality and computation costs. Finally, a NS3 simulation on measuring various network performance parameters indicates that the proposed protocol is practical in IoT environment.
Software Vulnerability Analysis and Discovery Using Deep Learning Techniques: A Survey

Exploitable vulnerabilities in software have attracted tremendous attention in recent years because of their potentially high severity impact on computer security and information safety. Many vulnerability detection methods have been proposed to aid code inspection. Among these methods, there is a line of studies that apply machine learning techniques and achieve promising results. This paper reviews 22 recent studies that adopt deep learning to detect vulnerabilities, aiming to show how they utilize state-of-the-art neural techniques to capture possible vulnerable code patterns. Among reviewed studies, we identify four game changers that significantly impact the domain of deep learning-based vulnerability detection and provide detailed reviews of the insights, ideas, and concepts that the game changers have brought to this field of interest. Based on the four identified game changers, we review the remaining studies, presenting their approaches and solutions which either build on or extend the game changers, and sharing our views on the future research trends. We also highlight the challenges faced in this field and discuss potential research directions. We hope to motivate the readers to conduct further research in this developing but fast-growing field.
High Gain Half Wave Dipole Antenna Design for a Passive RFID Tag

Fully incorporated passive RFID tag is the ideal solution to significantly reduce the costs and sizes of RFID tags. In order to achieve this goal, three basic requirements are necessary: high gain antenna, high efficiency RF-DC converter and ultra-low power mixed signal circuitry. This paper proposes a novel high gain dipole antenna with reflector as the candidate for the fully integrated passive RFID tag. The proposed antenna has been compared with the standard patch antenna. All antennas presented in this paper have been investigated by using HFSS simulation software. Simulation results of the prototype antenna demonstrate an over 3dB higher gain than that of a standard half wave dipole antenna operating at 2.45 GHz. Therefore the half wave dipole antenna with reflector is a promising candidate for ultra-low cost and miniature RFID tags.
Learning-based Remote Photoplethysmography for Physiological Signal Feedback Control in Fitness Training

Remote photoplethysmography (rPPG) has attracted much attention in recent years. This research proposes to apply rPPG to the fitness training scenario, enabling non-contact measurement of the subject's heart rate during training. Currently, most existing approaches suffer from a major weakness, i.e. the subject's body needs to remain stably while conducting measurement, which significantly hinders practical applications of the approach. The main purpose of this paper is to build a training system based on rPPG and fitness machines to provide users with better ergonomic exercise experiences. We have built a spinning bike system that combines a camera and an adaptive controller based on heart rate feedback for tracking the desired exercise intensity. Fuzzy control is introduced in the feedback control loop by considering heart rate and heart rate variability simultaneously for better representation of the physical status. Some preliminary results are briefly presented. This research demonstrates promising performance improvement by combining rPPG heart rate estimation and fitness machine control.
Boresight Gain Enhanced with Cylindrical Metasurface Lens

In this paper, a transmission-type electromagnetic metasurface lens which can be conformal to a cylindrical surface is designed and employed to enhance the boresight gain of a feeding antenna. The objective gradient of the transmission phase is formulated and implemented with four-layer slot unit cells. Focused electric field strength and power density are observed for a TM-polarized plane electromagnetic wave illumination at the designed focal point. Using a traditional probe-fed microstrip antenna as feeding, the cylindrical lens antenna can enhance its boresight gain by 11.4 dB for TM-polarized radiation at 10 GHz.
INTSMC-Based Fault Tolerant Control Approach for a 2-DOF Robotic System

This paper proposes a Fault Tolerant Control (FTC) scheme for a 2-DoF robotic system. The design is based on a new integral non-singular terminal sliding mode control (INTSMC) framework and aims at mitigating actuator faults whilst guaranteeing finite time convergence of the states. The design includes a comprehensive actuator fault model that encompasses different scenarios of bias and loss of effectiveness faults. The stability of the proposed FTC module is verified based on the Lyapunov stability criteria. The overall scheme was implemented to a second-order robotic system subject to different faulty scenarios. The INTSMC-based FTC approach was shown to guarantee system stability and ensure good tracking performance under various faulty conditions.
A Tunable Vector-Sum Filtering Power Divider With Continuously Tuned Frequency and Arbitrary Output Phase Difference

A novel tunable vector-sum filtering power divider (FPD) with arbitrary output phase difference is proposed. It is constructed by a tunable FPD [which has reconfigurable in-/out-of-phase output and arbitrary power division ratios (PDRs)] on the left and a wideband branch-line coupler (BLC) on the right. The output wave from the left FPD forms two orthogonal vectors at each output port of the BLC. The orthogonal vectors are summed up and result in a new vector with phase that is purely determined by the amplitude ratio of the orthogonal vectors. As a result, the phase difference between the new vectors at the outputs of the BLC can be continuously tuned by adjusting the PDR of the left FPD. Detailed theoretical analysis has been given. It shows that the phase difference between the output ports can be flexibly tuned from 0 °C to 360 °C within the full frequency tuning range.
Quadratic Approximation of Nonlinear Models of the Synchronous Machine Using the Bilinear Representation

The widespread use of new power electronic devices and the integration of renewable energy sources have increased the non-linearity and non-stationarity of modern electric power systems. Taking into account and modeling these nonlinear effects with sufficient accuracy requires the use of quadratic and higher order approximations of nonlinear power system models. However, sufficiently accurate bilinear models of synchronous machine are not easy to find in the literature. Therefore, in this paper we develop a quadratic approximation for the conventional sixth-order model of a synchronous machine using the Carleman bilinearization procedure. The developed bilinear model for one generator can further serve as a basic building block for constructing nonlinear approximations for general power system models.
How Far Have We Come in Detecting Anomalies in Distributed Systems? An Empirical Study with a Statement-level Fault Injection Method

Anomaly detection in distributed systems has been a fertile research area, and a range of anomaly detectors have been proposed for distributed systems. Unfortunately, there is no systematic quantitative study of the efficacy of different anomaly detectors, which is of great importance to reveal the deficiencies of existing anomaly detectors and shed light on future research directions. In this paper, we investigate how various anomaly detectors behave on anomalies of different types and the reasons for the same, by extensively injecting software faults into three widely-used distributed systems. We use a statementlevel fault injection method to observe the anomalies, characterize these anomalies, and analyze the detection results from anomaly detectors of three categories. We find that: (1) the distributed systems’ own error reporting mechanisms are able to report most of the anomalies (from 82.1% to 92.8%) but they incur a high false alarm rate of 26.6%. (2) State-of-the-art anomaly detectors are able to detect the existence of anomalies with 99.08% precision and 90.60% recall, but there is still a long way to go to pinpoint the accurate location of the detected anomalies, and (3) Log-based anomaly detection techniques outperform other anomaly detection techniques, but not for all anomaly types.
Reduction of Conducted Emissions in DC/DC Converters with FPGA-based Random Modulation

FPGA became a low-cost platform providing the possibility to design new "EMC friendly" control techniques for power electronics. However, some of the functions required in the power electronic control system are difficult to implement in FPGA. The main challenges are connected to the fixed-point operation, and the need to adjust the dynamic range of numbers. To provide design insights on this topic, the paper presents an FPGA-based random modulation technique intended for DC/DC converter for harmonic reduction. The presented random technique is compared with deterministic PWM in terms of harmonics reduction and FPGA hardware resources. The theoretical algorithms, hardware details, and experimental results are presented and discussed.
A Stigmergy Learning Approach at the Edge: Securely Cooperative Caching for Fog Radio Access Networks

To support the rapid development of multimedia services under the Internet of Things (IoT), fog radio access network (F-RAN) has become an emerging architecture in the 5G era. In this paper, content caching in a cloud and fog heterogeneous cooperative manner for F-RAN is investigated. More specifically, we jointly consider cache placement and file transmission in F-RAN, where fog access points (F-APs) serving as collaborative caching agents to provide caching for popular files, thus reducing the traffic from cloud and improving content delivery efficiency. A file download latency minimization problem subject to the storage capacities of F-APs is formulated. A distributed learning algorithm based on a swarm collaboration framework, i.e., stigmergy which enables an F-AP to expand its influence to other F-APs is proposed to improve caching resource utilization. In addition, a double-masking protocol is proposed to guarantee the security of F-APs' locations during stigmergy learning. Extensive simulations are conducted to show the effectiveness and reliability of our proposed scheme.
Radio tracking based on Time-Modulated Array

A novel radio tracking system is proposed by a two-element time modulated array (TMA). By estimating the spectrum of incident signal after time modulation, the system produces control signal and rotates the platform to tracking the source. The signal processing of the proposed method relies on two-point discrete Fourier transform (DFT). Its system complexity is low since only one RF channel is required. Simulation is conducted to verify its effectiveness.
Use of Deep Learning to Analyze Social Media Discussions About the Human Papillomavirus Vaccine

Key Points Question Can public perceptions of the human papillomavirus (HPV) vaccine be accessed from the perspective of behavior change theories by mining social media data with machine learning algorithms? Findings This cohort study included 1 431 463 English-language posts about the HPV vaccine from 486 116 unique usernames from a social media platform. An increase in HPV vaccine–related discussions was found, and the results suggest temporal and geographic variations in public perceptions of the HPV vaccine. Meaning The findings of this study suggest that social media and machine learning algorithms can serve as a complementary approach to inform public health surveillance and understanding and help to design targeted educational and communication programs that increase HPV vaccine acceptance.
Enhancements and Challenges in CoAP—A Survey

The Internet of Engineering Task (IETF) developed a lighter application protocol (Constrained Application Protocol (CoAP)) for the constrained IoT devices operating in lossy environments. Based on UDP, CoAP is a lightweight and efficient protocol compared to other IoT protocols such as HTTP, MQTT, etc. CoAP also provides reliable communication among nodes in wireless sensor networks in addition to features such as resource observation, resource discovery, congestion control, etc. These capabilities of CoAP have enabled the implementation of CoAP in various domains ranging from home automation to health management systems. The use of CoAP has highlighted its shortcomings over the time. To overcome shortcomings of CoAP, numerous enhancements have been made in basic CoAP architecture. This survey highlights the shortcomings of basic CoAP architecture and enhancements made in it throughout the time. Furthermore, existing challenges and issue in the current CoAP architecture are also discussed. Finally, some applications with CoAP implementation are mentioned in order to realize the viability of CoAP in real world use cases.
Circulating exosomes express α4β7 integrin and compete with CD4+ T cells for the binding to Vedolizumab

Vedolizumab (VDZ) is a therapeutic monoclonal antibody approved for the treatment of inflammatory bowel diseases (IBD). VDZ selectively binds to the α4β7 integrin and blocks trafficking of a specific subset of gastrointestinal-homing T-lymphocytes to inflamed tissue. Although VDZ has shown promising results in numerous clinical studies a subgroup of patients do not respond adequately. Mechanistic insights and prognostic biomarkers able to predict which patients might benefit from VDZ therapy are currently lacking. Circulating exosomes were isolated from serum of blood donors and VDZ-treated patients by polymer-based precipitation. The surface expression of α4β7 integrin was evaluated by flow cytometry and the levels of exosome-bound VDZ were investigated by Promonitor-VDZ ELISA kit. The capacity of exosomes to interfere with the adhesion of VDZ-treated CD4+ T cells was assessed by adhesion assay. In this study, we showed that serum exosomes isolated from both blood donor and ulcerative colitis patients express on their surface the VDZ target α4β7 integrin. We observed an increased exosomal sequestration of VDZ in anti-TNF exposed patients compared to anti- TNFα naïve patients, according to a greater expression of α4β7 integrin on vesicles surface. Circulating exosomes could compete for VDZ binding with CD4+ T cells since we found that the amount of VDZ bound to T cells was impaired in the presence of exosomes. In addition, we demonstrated that exosomes bind VDZ, which consequently becomes unable to block MadCAM-1-mediated adhesion of lymphocytes. Circulating exosomes might contribute to drug sequestration, possibly affecting the therapeutic efficacy of VDZ in IBD patients. Our data suggest that previous biologic therapy may have altered the sequestration capacity of circulating exosomes, thus reducing the efficacy of VDZ in patients who failed anti-TNF agents.
Interactive associations of sex and hyperlipidemia with calcific tendinitis of the shoulder in Taiwanese adults

Abstract Calcific tendinitis (CT) of the shoulder is a painful disorder usually identified in individuals aged 40 and 60 years. The estimated global prevalence of CT is 2.7% to 36%. We examined the association of hyperlipidemia and sex with CT of the shoulder using Taiwan Biobank (TWB) and the National Health Insurance Research Database (NHIRD). Data were available for 9903 TWB participants who were recruited between 2008 and 2015. We used multiple logistic regression analysis to estimate the odds ratios (OR) and 95% confidence intervals (CI) for CT of the shoulder. Overall, 1564 women, and 1491 men were identified with hyperlipidemia. Women, compared to men, had higher odds of CT of the shoulder (OR, 1.53; 95% CI, 1.08–2.16). Hyperlipidemia, compared to no hyperlipidemia, was associated with an increased risk of CT (OR, 1.40; 95% CI, 1.02–1.93). The test for interaction was significant for sex and hyperlipidemia (P = .006). After stratification, the odds ratio for CT was 1.95 (95% CI, 1.30–2.92) in women and 0.82 (95% CI, 0.48–1.39) in men, respectively. Compared to men with no hyperlipidemia, the odds ratio was 0.86 (95% CI, 0.53–1.38) for men with hyperlipidemia and 2.00 (95% CI, 1.29–3.10) for women with hyperlipidemia. Importantly, our findings indicated that the risk for CT of the shoulder was higher among Taiwanese women with hyperlipidemia. However, CT risk among their male counterparts with hyperlipidemia was not significant.
Nano Cocrystals: Crystal Engineering from a Nanotechnology Perspective.

Low aqueous solubility and poor bioavailability are the major hurdles during drug development for the oral route. A large number of the newly discovered drug molecules fall under BCS II class and have solubility related issues and hence poses low oral bioavailability, which in turn render them as non-suitable candidates for further development. A multitude of solubility enhancement approaches are available, notable among them are salt formation, solid dispersions, inclusion complexes, cocrystallization, nanonization etc. Cocrystallization and Nanonization are among the most widely used approaches in the pharmaceutical field that offer multiple enhancements to the active pharmaceutical ingredients. This review endeavours to cover the recent work, important finding, advantages offered by nano sized cocrystals and their future aspects and challenges in the implementation of this newer approach in pharmaceutical research and development.
HUMAN - Hierarchical Clustering for Unsupervised Anomaly Detection & Interpretation

The automatic detection and interpretation of network traffic anomalies through machine learning is a well-known problem, for which no general solution is available. Both supervised and unsupervised (i.e., anomaly detection) approaches require prior knowledge on the monitoring data, either in terms of normal operation profiles or on the specific anomalies to detect. As a consequence, both approaches have clear limitations when it comes to detecting, and in particular interpreting, previously unseen events. We present HUMAN, a general hierarchical-clustering-based approach for unsupervised network traffic analysis, which can both detect and interpret anomalous behaviors in a completely black-box manner, without relying on ground-truth on the system under analysis. HUMAN can detect and interpret complex patterns in the analyzed data, using a structural approach which exploits hierarchical cluster relationships and correlation among features. We describe the building blocks of HUMAN and explain its functioning in detail, using as case study the detection and interpretation of performance issues in major cloud platforms, through the unsupervised analysis of distributed active cloud latency measurements. The HUMAN approach can be applied to the unsupervised analysis of any kind of nested or hierarchically structured multi-dimensional data, showing the potential of hierarchical clustering for general unsupervised data analytics.
Eicosanoids in Skin Wound Healing

Wound healing is an important process in the human body to protect against external threats. A dysregulation at any stage of the wound healing process may result in the development of various intractable ulcers or excessive scar formation. Numerous factors such as growth factors, cytokines, and chemokines are involved in this process and play vital roles in tissue repair. Moreover, recent studies have demonstrated that lipid mediators derived from membrane fatty acids are also involved in the process of wound healing. Among these lipid mediators, we focus on eicosanoids such as prostaglandins, thromboxane, leukotrienes, and specialized pro-resolving mediators, which are produced during wound healing processes and play versatile roles in the process. This review article highlights the roles of eicosanoids on skin wound healing, especially focusing on the biosynthetic pathways and biological functions, i.e., inflammation, proliferation, migration, angiogenesis, remodeling, and scarring.
Admittance Control Based on a Stiffness Ellipse for Rapid Trajectory Deformation

This study proposes an admittance control method based on a stiffness ellipse. Some assembly tasks in factories are difficult to automate because discontinuous transitions between contact and noncontact phases generate issues. In the contact phase, a control approach is required to handle assembly parts with compliance; in the noncontact phase, a control method follows a trajectory. To achieve an assembly task, it is often necessary to switch control during the contact phase; however, such switchovers often degrade performance. This study aims to solve this problem by adjusting the stiffness ellipse by admittance control. By tilting the stiffness ellipse, external force can be applied in the desired direction during contact without switching the control or adjusting the position/force command. Using the proposed method, tasks having contact and noncontact phases can be achieved with continuous control. Experimental results from a drawing task and simulation results from drawing and peg-in-hole tasks demonstrate the advantages of the proposed method.
Outage Performance Analysis of Full-Correlated Rayleigh MIMO Channels

The outage performance of multiple-input multiple-output (MIMO) technique has received intensive attention to meet the stringent requirement of reliable communications for 5G applications, e.g., mission-critical machine-type communication (cMTC). To account for spatial correlation effects at both transmit and receive sides, the full-correlated Rayleigh MIMO fading channels are modeled according to Kronecker correlation structure in this paper. The outage probability is expressed as a weighted sum of the generalized Fox's H functions. The simple analytical result empowers asymptotic outage analysis at high signal-to-noise ratio (SNR), which not only reveal helpful insights into understanding the behavior of fading effects, but also offer useful design guideline for MIMO configurations. Particularly, the negative impact of the spatial correlation on the outage probability is revealed by using the concept of majorization, and the asymptotic outage probability is proved to be a monotonically increasing and convex function of the transmission rate. In the end, the analytical results are validated through extensive numerical experiments.