Miracle-dz/UniFAR-FTUs huggingface dataset:
A Lattice-Based Unordered Aggregate Signature Scheme Based on the Intersection Method

An aggregate signature turns multi-message multi-authentication into multi-message single-authentication, reducing the required storage space, transmission cost, and number of verification calculations, and is suitable for fast message authentication in the big data era, particularly in wireless sensor networks and secure email systems. Many aggregate signature schemes have been proposed, including lattice-based ones, which have good resistance to quantum machine attacks. However, the existing lattice-based aggregate signature schemes, which either have strict requirements on the signing order or encounter security risks, are not suitable for the unordered polymerization environment. In this paper, we accordingly propose a lattice-based unordered aggregate signature scheme. The proposed scheme makes use of the intersection method and solves the unordered aggregate problem of lattice signatures with different public keys. Therefore, it avoids both the signing order limitation and the risk of single signature forgery. Furthermore, the scheme follows the improved security model; hence, it is robust against collusion attacks. In addition, the scheme’s security depends on the small integer solution problem, which enables the scheme to resist quantum algorithm attacks.
Online dictionary self-taught learning for hyperspectral image classification

We study self-taught learning for hyperspectral image (HSI) classification with small labeled and unlabeled data sets. Supervised deep learning methods are currently state of the art for many machine learning problems, but these methods require large quantities of labeled data to be effective. Unfortunately, existing labeled HSI benchmarks are too small to train a deep supervised network. Alternatively, self-taught learning methods use sufficiently large quantities unlabeled data to improve the performance on a given image classification task. However, the unlabeled HSI data is also difficult to obtain. To overcome this limitation, we employ an online dictionary learning algorithm for sparse coding to self-taught learning, in which we extract features from much smaller unlabeled data sets. Furthermore, apart from the spectral information we also apply the spatial information to improve the performance of classification. Our results convinced that the proposed approach can extract discriminative features from small unlabeled and labeled data sets for classification. In addition, the results obtained by our approach are better than the results obtained by principal component analysis (PCA).
On dependability of smart applications within edge-centric and fog computing paradigms

Advances of the Internet of Things (IoT) technology resulted in appearance of digital connection and computation boxes, personal mobile devices, and ubiquitous wireless networks. The dependability in such IoT environments suffers due to taking the computation control away from remote servers to local surroundings (Internet edge) and intermediary devices (on network data transfer paths to the servers). In this paper, we consider the two emerging IoT-enabled paradigms: Edge-centric Computing and Fog Computing. We elaborate their potential for development of smart applications with focus on the dependability and using a mobile assistant for e-tourism as a reference application. We analyze possible concept elements for smart application development and provide recommendations in respect to the dependability.
Physically Securing Energy-Based Massive MIMO MAC via Joint Alignment of Multi-User Constellations and Artificial Noise

This paper investigates the artificial noise (AN) aided physical layer security (PLS) for energy-based massive multi-input multi-output multi-access channels with finite-alphabet data inputs, where both the legitimate base station (Bob) and the passive eavesdropper (Eve) are equipped with large antenna arrays and each user has multiple antennas. For such system, the main challenge is how to allocate power between the transmitted constellation of finite size and AN for both interference management and PLS enhancement. To this end, we first characterize a distance-optimal (DO) constellation structure that maximizes the minimum Euclidean distance of the received signals with energy detection. It turns out that the DO multiuser constellations constitute a commonly-used pulse amplitude modulation (PAM) constellation at the receiver side. Then, we specifically design an energy-efficient PAM-constellation alignment to form a received sizeable PAM constellation by adaptively aligning the power of multi-users’ PAM sub-constellations. This design provides us with a new finite-alphabet non-orthogonal multi-access scheme as well as with a stepped water filling (SWF) power allocation between the constellation and AN for each user, which can provide the power-domain freedom for PLS. To spatially exploit this freedom, a SWF-based product-constant AN generation algorithm is developed such that the product of Bob’s channel and the AN is a constant which can be inferred by Bob, and the channel-noise product for Eve cannot be uniquely determined, even for noise-free channels. Simulations indicate that for SWF, Bob’s error rate will vanish when the number of the receiver antennas goes to large, but Eve’s error rate has a non-vanishing lower-bound, even with an unlimited number of antennas. In addition, PAMA, as a new finite-alphabet NOMA scheme, has significant advantages in both security and communication error performance over the time division multi-access.
Dynamic Pricing Mechanism in Smart Grid Communications Is Shaping Up

An efficient smart grid communication system is a key enabling technology for modernized utility that can adjust electricity usage to balance generation and demand in real time. However, the utility industry is not able to meet the power demand for the wholesale market during overloading or emergency situations. To mitigate power shortage pressures, utility providers need to employ a dynamic pricing policy to enforce higher pricing than pre-estimated statistic pricing to motivate consumers to reduce their power consumption. In this letter, the time of use (TOU) approach is proposed to regulate price variances considering desired power demand. In this mechanism, the consumers deliver their TOU electricity demands and subsequent control signals to utility providers through communication network infrastructure. The utility provider creates an hourly demand profile for each consumer by predicating individual requests over short- and long-time frames. The control signaling exchanged and prior arrangement of services enable utility providers to evaluate the status of wholesale market demand and assign prices considering dynamic changes in electricity demand. Numerical analysis study was carried out to validate the advantages of the proposed mechanism.
Stress in the Educational System as a Potential Source of Epigenetic Influences on Children's Development and Behavior

Despite current advances on the relevance of environmental cues and epigenetic mechanisms in biological processes, including behavior, little attention has been paid to the potential link between epigenetic influences and educational sciences. For instance, could the learning environment and stress determine epigenetic marking, affecting students' behavior development? Could this have consequences on educational outcomes? So far, it has been shown that environmental stress influences neurological processes and behavior both in humans and rats. Through epigenetic mechanisms, offspring from stressed individuals develop altered behavior without any exposure to traumatizing experiences. Methylated DNA and noncoding RNAs regulate neurological processes such as synaptic plasticity and brain cortex development in children. The malfunctioning of these processes is associated with several neurological disorders, and these findings open up new avenues for the design of enriched environments for education and therapy. In this article, we discuss current cases of stress and behavioral disorders found in youngsters, and highlight the importance of considering epigenetic processes affecting the development of cognitive abilities and learning within the educational environment and for the development of teaching methodologies.
Information Diffusion and Opinion Leader Mathematical Modeling Based on Microblog

Public emergency occurs every day, and the information diffusion of an online social network (OSN) shows the characteristics of large capacity, a wide range of far-reaching effects. In order to analyze the role of opinion leader in OSN and the life cycle of Sina Microblog information diffusion, the public emergency information diffusion model and a new method grading opinion leader model are proposed. Firstly, the people of OSN are divided into three kinds: supporter, neutral, and objector. Information interaction formula of opinion leader is built up according to the properties of all kinds of subjects (public people, rumor maker, and opinion leader), and the statistical analysis of the microblog information data is obtained by the computer technology. Second, the communication power is constructed as a function related to the three factors (the number of forwarding, the degree of activity, and the number of fans), and then a weight calculation method based on the analytic hierarchy process is established. Communication power is combined with the new proposed model, and the information diffusion of opinion leader and microblog life cycle is researched. The simulation results show that the established public emergency information diffusion model can truly reflect the actual situation. Opinion leaders have a strong communication in microblog information diffusion. Opinion leaders can influence those who are unsteady about certain public emergency. The value of error is 13.7% and 5.1% by comparing the true data. It is proved that the new proposed models are reasonable and effective for the communication life of the analysis opinion leader in microblog information diffusion.
A Size-Reduced Tri-Band Gysel Power Divider With Ultra-Wideband Harmonics Suppression Performance

In this paper, a size-reduced tri-band Gysel power divider (PD) with ultra-wideband harmonics suppression performance, simple structure, high-power handling capability, and independently controllable bandwidth is first presented. The three passbands are introduced by two tri-band biasing networks whose impedances can also be utilized to control the bandwidths of the three passbands independently. With the introduction of slow-wave transmission lines and coupled lines with series open-circuited stubs, a reduced size and ultra-wide stopband can be obtained, respectively. Detailed closed-form design equations are derived and validated with a design of PD operating at 2.0, 2.5, and 3.0 GHz. Also, two additional circuits are designed, simulated, and fabricated. One is utilized to obtain the optional range of the three passbands, and the other is to make a comparison with the proposed PD. All the measured results agree well with the corresponding simulated ones. Proper impedance matching, isolation, and a wider 3-dB fractional bandwidth can be obtained, which is 18.5%, 20.7%, and 13.0%, respectively, at each passband. In addition, more bands can be easily extended to the proposed PD by introducing more open-circuited stubs in the tri-band biasing networks.
Optical solution for particulate distribution estimation

This paper describes a simple sensor which is designed to sample the air and extract the distribution of particulate it contains. The proposed system employs a normal fiberglass filter, a small video camera and a RaspberryPI to process the image and extract the particulate distribution. The fiberglass filter is capable of stopping particulate down to sizes of 2.5 μm, i.e. what is usually refereed to as PM2.5 particulate and employs a small pump to increase the sensitivity. A motor is used to change the filter interested by the particulate and to restart the measurement. Depending on the amount of particulate in the air, the sampling can be restarted every day or more frequently. The proposed system has been characterized by observing the filter using a field-emission electronic microscope showing that the measured distribution is correct. The overall cost of the proposed system is of less than 100$.
Model Predictive Virtual Flux Control to Improve Performance Under Distorted Input Voltage Conditions

This paper proposes a model predictive virtual flux control (MPVFC) method that can mitigate the degradation of the total harmonic distortion of rectifier input currents experienced by active front-end (AFE) rectifiers when input voltages are distorted by harmonics. The proposed method utilizes a cost function based on virtual fluxes, which are robust to input voltage distortions, to control the rectifiers. For the calculation of the proposed cost function which consists of a predictive value of the rectifier virtual flux and a reference of the rectifier virtual flux, the dynamics of AFE rectifiers and the definition of the integration in the discrete time domain are used. Also, the phase of the reference current which is needed when calculating the reference of the rectifier virtual flux is obtained by the input virtual flux. The proposed MPVFC method shows better input current waveforms with lower total harmonic distortion than those obtained with the conventional model predictive control method under input voltage distortion conditions, which was proved via simulations and experiments.
Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery

This paper proposes a new signal feature extraction and fault diagnosis method for fault diagnosis of low-speed machinery. Statistic filter (SF) and wavelet package transform (WPT) are combined with moving-peak-hold method (M-PH) to extract features of a fault signal, and special bearing diagnostic symptom parameters (SSPs) in a frequency domain that are sensitive to bearing fault diagnosis are defined to recognize fault types. The SF is first used to adaptively cancel noises, and then fault detection is performed by exploiting the optimum symptom parameters in a time domain to identify a normal or fault state. For precise diagnosis, the SSPs are calculated after the signals are processed by M-PH and WPT. A decision tree is used to structure intelligent diagnosis rules in each step until the states are fully and automatically detected. The efficacy of this method was confirmed by applying it to an experimental low-speed rotation machine.
A Dual-Parameter Sensor Based on the Asymmetry of Alcohol Filling the Photonic Crystal Fiber in Sagnac Loop

This paper proposes a novel temperature and stress sensor based on the asymmetric filling of alcohol into the photonic crystal fiber in a Sagnac loop. First, we etched the photonic crystal fiber and collapsed the air. Then, we filled alcohol into the asymmetric photonic crystal fiber. Next, we connected this structure into a Sagnac interference ring. When the ambient temperature changed, the thermal expansion effect of filled alcohol would lead to the change of birefringence of photonic crystal fiber, which made the interference spectrum of the sensor drift with the change of temperature. Furthermore, after adding transverse stress to the photonic crystal fiber, the fiber’s strain changed. With its strain changed, the birefringence of photonic crystal fiber also changed, which made the interference spectrum drift. The experimental results showed that when the temperature or the lateral stress is added, a red shift would occur in the interference spectrum. The temperature sensitivity was found to be 0.1864 nm/°C, and the lateral stress sensitivity was 0.6201 nm/N. The sensor achieved the highly sensitive measurement of temperature and stress. Concurrently, the structure has the advantages of high stability, anti-electromagnetic interference, and easy construction, and it provides a new method for obtaining birefringence of ordinary photonic crystal fiber. In addition, an application system of the sensor has been built. Monochromatic light source with 1554-nm wavelength was used in the experiment. The drift of interference spectrum is transformed into the change of light intensity. The relative error is less than 3%, which indicates a good application prospect.
Scaling Analysis for Massive MIMO Systems With Hardware Impairments in Rician Fading

In this paper, we propose a framework for rate analysis in the multicell massive multiple-input multiple-output (MIMO) system which models the channel within one cell as Rician fading and considers different kinds of hardware impairments. Closed-form achievable uplink rates with common and separate oscillators are derived, respectively. Based on them, we find that line-of-sight (LoS) components can benefit the rate performance under any configurations, but this improvement decays as the phase drift aggravates. Utilizing the excessive degrees-of-freedom offered by massive MIMO, we can tolerate the hardware constraints without rate reduction. In particular, as the number of base station (BS) antennas (<inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>) grows, we can relax the hardware constraint on additive noise by <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> in LoS-Rayleigh mixed channel, which is different from the relaxation by <inline-formula> <tex-math notation="LaTeX">$\sqrt {M}$ </tex-math></inline-formula> in pure Rayleigh channel, whereas the hardware constraint on phase drift can only be relaxed logarithmically with <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>. Furthermore, we also utilize the benefit of LoS components to reduce the required antenna size and transmit powers. Via simulation, we find that with sufficient LoS propagations, both the BS antenna and transmit powers can be cut down remarkably while maintaining a non-reduction rate performance as the Rayleigh fading environment, which is useful for the ultra-dense network in future.
Robust Student’s  $t$ -Based Cooperative Navigation for Autonomous Underwater Vehicles

A robust leader–slave cooperative navigation (CN) algorithm for autonomous underwater vehicles (AUVs) based on the Student’s <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> extended Kalman filter (SEKF) is proposed. Compared with the conventional EKF based on a Gaussian distributed noise assumption, which has been widely used in the field of CN, the Student’s <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-based filtering algorithms show an improved robustness against outliers existing in the process and measurement noises. The utilization of the Student’s <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> distribution can minimize the negative effect induced by the outliers existing in the underwater acoustic communication system and the cheap but unreliable dead reckoning sensors equipped on the slave AUVs. After a detailed derivation of the robust CN algorithm based on the SEKF, two approximation methods that are required in the Student’s <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-based filter are discussed and compared. Simulation results show the efficiency and superiority of the robust SEKF-based leader–slave CN algorithm as compared with the conventional EKF-based CN algorithm. The validity of the proposed CN algorithm is also evaluated on field trial data, and the performance of different degrees of freedom (DOF) values, which determine the tail behavior of the Student’s <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> distribution is compared and analyzed, and then the link between the DOF value and the robust effect of the Student’s <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> distribution is revealed, which will act as a guide when applying the Student’s <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-based filter in the field of CN.
Assessment of extended aggregated association rules

In this paper we discuss some issues related to generating aggregated association rules which implies the fact that features can take on values from finite sets with any number of elements, not only binary ones. We consider changes in the structure of a rule and the influence of these changes on some association rule parameters. We note that considering rules with low values of the main rule parameters can be of a great importance for resolving security problems. These results can be used by specialists in Big Data processing.
Multi-Objective Optimum Design of High-Speed Backplane Connector Using Particle Swarm Optimization

This paper outlines a new procedure for computer modeling and optimum design for the dynamic mechanical and electrical study of a high-speed backplane connector, which is a key electrical interconnection technology in large communications equipment, ultra-high performance servers, supercomputers, industrial computers, high-end storage devices, and so on. The optimum structure design of contact pairs is important for a backplane connector in meeting multiple challenges in terms of minimizing the maximum insertion force and the contact resistance. Current optimization schemes, such as the quadrature method, are relatively complex. Therefore, we designed the connector contact pairs for simultaneously obtaining the proper insertion force and the contact resistance through a multi-objective particle swarm optimization (MCDPSO) method with simpler settings and faster convergence speed. In this paper, the required insertion force was minimized during the entire process, and the minimum contact resistance was maintained after insertion. To this end, an MCDPSO algorithm was proposed for the connector design. A dynamic weight coefficient was developed to calculate the interval values of the reserved solutions for the selection of the operator, and an external archive update based on roulette wheel selection and gbest selection strategies was developed to increase the diversity of the solutions. A set of optimal structure solutions of the contact pairs was obtained for the subsequent design optimization. The feasibility and effectiveness of the proposed method were verified by comparing with the results from ANSYS finite element simulation.
Multi-GNSS time transfer with CGGTTS-V2E

A software tool was developed at the Royal Observatory of Belgium for building the CGGTTS products from the RINEX observation and navigation files provided by the GNSS receivers. This software has been completely re-built in order to increase the robustness, the computation speed, and to include GPS, GLONASS, BeiDou and Galileo. The first part of the paper describes this new software tool. Then some time transfer experiments are realized for a short and a long baseline, comparing the results obtained from single-constellation with the multi-constellation solutions. We show that even with an incomplete constellation, Galileo already provides time transfer results with nearly the same quality as GPS for the short baseline, and 1.5 times better for the long baseline time link. Combining GPS and Galileo satellites furthermore improves the time transfer link with respect to the GPS-only or Galileo-only solutions, reducing the noise by a factor of about 1.4. Adding moreover BeiDou satellites in the combination gives a solution similar to the solution obtained from GPS+Galileo.