Miracle-dz/UniFAR-FTUs huggingface dataset:
On chaos in Lotka–Volterra systems: an analytical approach

In this paper, we study Lotka–Volterra systems with N species and n resources. We show that the long time dynamics of these systems may be complicated. Depending on parameter choice, they can generate all types of hyperbolic dynamics, in particular, chaotic ones. Moreover, Lotka–Volterra systems can generate Lorenz dynamics. We state the conditions on the strong persistence of Lotka–Volterra systems when the number of resources is less than the number of species.
Research on Feature Extraction and Classification Algorithms for Infrared Targets

Infrared target detection techniques have been widely used in infrared alarming and reconnaissance, and the detection task is often done by machine learning. Feature extraction and classification algorithm are two core components of the machine learning. The selection of features and algorithm have a determinative effect on the detection result. Traditional target classification techniques only focus on only limited combinations of features and classification algorithm, which may result in a poor detection result. Based on this, this paper selects the gray features, statistical features, frequency domain features, and graphic features of the infrared target, and compares the three classification algorithms of KNN, Bayes, and SVM to give the optimal combination of features and classification algorithms by experiment.
Default Prediction Using Consumption Data

Current practice in the development of credit risk assessment models relies on a large amount of client data, such as account balance information, socio-demographic data, tenure features, or credit history of the client. With the increasing number of different data sources, it is possible to employ nonconventional information to assess clients’ credit risk. Consumption data are an example of such a source, as they provide an overview of clients’ spending habits. However, considering this information at a level of individual companies where clients’ purchases took place inherently leads to sparse data, as customers typically shop at only a small subset of the available retailers. We demonstrate that commonly used models suffer from poor performance caused by the sparsity of the consumption data, and applying dimensionality reduction techniques such as PCA does not offer a meaningful improvement. In this paper, we propose two word2vec based models that leverage the contextual information of clients’ spending habits. Both of the developed models achieved significantly higher Gini scores, outperforming the baseline models by as much as 72%.
Fairness Analysis in IRS assisted C-RAN with Imperfect CSI

Intelligent reflecting surfaces (IRS) are being considered as a potential technology for the future genereations of wireless networks. IRS is a low cost and energy efficient technology to boost the performance of existing wireless systems by providing some control over the propagation channel. In this paper, we focus on fairness in cloud radio access network (C-RAN) and investigate the impact of integrating IRS into the system. In particular, as attaining the full channel state information (CSI) is difficult in IRS systems, we evaluate the performance of IRS assisted C-RAN with imperfect CSI. To ensure the fairness amongst all users, we choose maximizing the minimum expected user rate as the optimization problem. The problem is shown to be stochastic and non-convex which is computationally prohibitive. We propose an algorithm that jointly optimizes the beamformers and the IRS phase shifts. A statistical coordinated descent (SCD) optimization is used to maximize the minimum ergodic user rate. To deal with stochasticity of the optimization problem, we utilize the sample average approximation (SAA) along with weighted minimum mean square error (WMMSE) methods. Finally, the numerical results are presented. They show that particularly at low signal to noise ratio (SNR) regimes, deploying IRS can help increase the maximized minimum rate significantly.
Simulation of waveguiding and emitting properties of semiconductor nanowires with hexagonal or circular sections

Nanowires as an active medium are promising for getting high brightness light emitting diodes. Semiconductors such as GaN and ZnO grow as nanowires with a hexagonal cross section and their optical properties have been little explored. We study important properties for nanowire LED optimization. In particular we review the modal properties of hexagonal nanowires and compare them with those of circular nanowires. We also study the modal reflectivity of guided modes at nanowire end facets and explore the relevance of a comparison with plane-wave reflectivity. Finally we analyze the distribution of the emitted power in three different cases: a single point emitter and an axial and a radial nanowire heterostructure.
Wire-Surface Wave NoC Architecture for Seamless on/off-chip Interconnect

The increasing demands for computation power in cloud computing have driven the need for complex larger data centers and super computers where many-core processors chips are connected through multiple printed boards. This requirement has dramatically increased the complexity and the cost of inter/intra-chip communication between cores in different chips that are running the same applications. In this paper, a seamless hybrid interconnect architecture is proposed that utilizes the power and performance efficient Surface-wave interconnects (SWIs). Consequently, the system level evaluation demonstrates the significant improvements in terms of performance (up to 67%) and power consumption (up to ~1.3 X). Therefore, this proposed on-chip/off-chip hybrid interconnect architecture has created potential for future communication centric cutting edge data centers and/or super-computers.
Determination of stellar ages from asteroseismology

This Letter shows that measurements of the stellar analog of the solar five minute oscillations can permit the determination of the radius and age of isolated stars. The key frequencies of oscillation correspond to pairs of modes differing by two in the degree of the spherical harmonic describing the angular dependence of the motion and by one in the overtone order of the modes. The frequency pairs are very nearly degenerate, and adequate frequency resolution will require a nearly unbroken time sequence extending over 15 days. 21 references.
A Full-Stack Neuromorphic Prototype Architecture for Low-Power Wireless Sensors

One emerging trend in the evolution of sensor technology is to perform Artificial Intelligence (AI) based data processing already on the sensor. The benefits are in terms of reduced upstream communication load or reduced reaction times when the sensor is applied for example, in some time critical control loop. Such developments, however, may easily get in conflict with the equally important requirement of energy efficiency and low power operation. The brain inspired neuromorphic technology may resolve this conflict, if we can put all computation tasks of the sensor on neuromorphic principles, including the communication related processing as well. For this purpose, we need to rebuild the entire communication stack, including information encoding, protocols, radio algorithms on neuromorphic basis. In this paper we show a neuromorphic native design of an end-to-end sensor application and we exemplify such a communication stack with a prototype implementation, where the radio algorithm and protocol components have been replaced by neuromorphic algorithms implemented on a real neuromorphic chip combined with Software Defined Radio (SDR) hardware.
Rear Wheel Independent Drive Electric Vehicle Yaw Stability Control System Design and Analysis

This paper introduces rear wheel traction based fully electrical vehicle lateral stability controller design with independently driven electric motors. The proposed controller consists of two stages: lower and upper controllers. The upper controller is the proportional-integral (PI) controller, which calculates the required corrective yaw moment based on the desired and actual yaw rate of the vehicle. The proposed PI controller design method relies on the parameter space approach (PSA) considering D-stability. The time domain and frequency domain requirements are visualized by contour plotting method on the proportional-integral gain parameter plane. This multi-objective visualization assists designers while selecting an appropriate PI controller gain pairs. The lower controller distributes tire forces based on the vehicle's actual and desired situation using a torque distribution algorithm. The double-track vehicle model is employed to validate the proposed control system under different driving scenarios in MATLAB/Simulink. The results show that by using only the traction forces generated independently on the rear wheels, the lateral control of the vehicle considering D-stability, settling time, overshoot, bandwidth, rise time, integral-time-absolute-error and phase margin criteria could be accurately achieved under beforehand defined driving conditions.
Personality classification via Weibo based on deep learning model

With the coming of the information age and the popularity of Weibo, people will publish their daily behavior records on Weibo. These Weibo data are linked to individual personality, which can be accurately predicted through Weibo text. But this work only remains a theoretical target in the field of medical psychology, psychiatry, etc. and has not been implemented. The project firstly marks the personality of Weibo users, and builds over ten thousands of Chinese Weibo personality classification data base. We uses word2vec to veetorize the Weibo text, extracts the features of the Transformer encoder, builds a model by lstm to train the personality classification, and establishes Personality classification model combined the transformer with lstm. Finally, by comparing with the conventional model, the results show that the model has better personality detection results in terms of precision, recall and F1 score.
Intrusion Detection System in IoT Network by using Metaheuristic Algorithm with Machine Learning Dimensional Reduction Technique

The network security is a main problem in any disseminated framework. To providing the safe and secure network we have been proposed anomaly detection system against from the suspicious attacks. The essential goal of this exploration is to plan effective IDS for IoT organization. The interruption discovery assumes a fundamental part in recognizing various assaults on IoT and upgrades the IoT performance. In this paper, one of the metaheuristic algorithms GSO (glow-worm swarm optimization) algorithm add with machine learning technique PCA (principal component analysis) used for providing the IDS. Therefore, the metaheuristic-based intrusion discovery model to recognize malicious activities by utilizing the NSL-KDD dataset. Dimensional reduction technique Principal component analysis is utilized for extraction of features and GSO algorithm is utilized to classify the various class of malicious attacks in the dataset. We have considered some metrics like precision, detection rate, recall, accuracy and FAR are computed. The suggested method evaluated better in each parameter compared with other existing techniques.
A Review on Oxygen Consumption and Heart Rate Monitoring Methods & Applications

This study presents a systematic review on the methods used to acquire Oxygen uptake (VO2) and heart rate (HR) for monitoring cardiovascular fitness during daily activities. VO2 and HR play a significant role especially during daily activities such as walking, exercise, or housekeeping. Several studies have been undertaken in recent years to evaluate VO2 and HR in a variety of target populations, mainly in two divisions; Direct Measurements and Estimations. Direct measurements are considered as the gold standard for VO2 and HR measurements. However, despite its high level of accuracy, there are practical limitations associated with direct measurement systems. Direct measurements are expensive and sophisticated laboratory equipment is needed to measure VO2 and HR. On the other hand, estimations via wearable devices using mathematical equations or machine learning are more efficient and low-cost. Utilizing various sets of predictor variables and a variety of machine learning and statistical approaches, several types of measurement models have been formed, including the Support Vector Machine (SVM), Generalized Regression Neural Networks (GRNN), Radial Basis Function Networks (RBFN), multilayer perceptron regressor (MLR) and Decision Tree Forest (DTF). Several studies proves that machine learning is the future direction for estimating VO2 and HR for monitoring cardiovascular fitness during daily activities.
A text analysis model based on Probabilistic-KG

About 90% of machine learning applications are based on supervised machine learning drives, and a relatively important task is how to obtain labels for the data. For data with clear rules and structure, a fairly satisfactory label can be obtained by simple processing and analysis. In contrast, the processing of unstructured data is more tedious and complicated for you to go home, such as text-based data, reports, images, etc. There is no predefined data model for this type of data, and the value density itself is low and ambiguous, making it very difficult to extract high- quality information from it. To this end, this paper proposes a text data annotation model that fuses domain knowledge graphs with Bayesian inference networks. The knowledge graph is used as the extraction of semantic classes and the relationship between upper and lower concept entities, and then mapping inference is performed using plain Bayes, while considering the context of the text, as a way to remove the uncertainty of fuzzy concepts. Compared with the traditional text data annotation model, this model introduces concept probability quantification to eliminate the ambiguity of inference results of knowledge graphs while fully considering the influence of human domain knowledge.
SoftChain: Dynamic Resource Management and SFC Provisioning for 5G using Machine Learning

Network automation is an area of interest which further incurs in Zero-touch network and Service Management (ZSM). Machine Learning (ML) is acting as a key tool for the realization of such intelligent reformations. Adopting all new technologies inside the network needs enhancement of current orchestration frameworks. Even though existing works are contributing enough to technologically superior countries, this work exclusively aims at developing countries. In this work, a dynamic VNF allocation and embedding problem is addressed on the shared network slices (NSs) using softwarized Service Function Chaining (SFC). The ML techniques are specially designed to make the placement decisions more dynamic and resilient. The proposed VNF-CAR (VNF-Creation, Allocation, and Release) approach is portable among heterogeneous slices and backed by a systematic performance evaluation over a real network topology (INDIA-SDNlib). Accurate VNF selection and embedding are done by supporting OSM MANO decisions (Open Source Management and Orchestration). Through eventful actions, we have shown 20-30% of overall improved VFN selection efficiency against other existing benchmarks.
A Dual-Frequency Antenna of Microstrip Characteristics Having with π–shaped Slot for Wireless Communication Application

In this paper, an inset-fed antenna with microstrip characteristics has been proposed which resonates at dual-frequencies of 1.59 GHz and 1.94 GHz due to a π-shaped slot created in the rectangular element of radiation which has a physical size of 50×75 mm2. The antenna which is proposed demonstrates correct gain characteristics and appropriate pattern of radiation at the wireless application such as PCS 1900 (1.94 GHz).
X-ray and ultraviolet observations of the Seyfert galaxy MCG 8-11-11

MCG 8-11-11 was observed in the 0.5-8 keV band with the EXOSAT observatory at 9 epochs in 1983-1986. Quasi-simultaneous UV observations were obtained in all but one case. The X-ray spectra are well fitted by absorbed power laws. Small variations of the spectral shape are found with softening of the source at brighter states. No correlation between UV and X-ray fluxes is apparent, suggesting an independent origin of the UV, consistent with the absence of indication of a soft X-ray excess. 32 refs.
Adaptive motion control design of robot manipulators: an input-output approach

An input-output approach to adaptive motion control design of robot manipulators is presented. The main technical device in our approach is the passivity theory. This formulation provides a framework suitable for the design of new control and adaptation laws. A new control law which consists of a computed torque part and a feedforward compensation part is analysed using this approach.
AI Based IoT Framework for Soil Analysis and Fertilization Recommendation for Smart Coconut Farming

India is identified as one of the top two farm producers in the world, and the agriculture sector plays a key role in the Indian economy. The major revenue of Kerala state is from coconut farming. Kerala is the top-ranked producer of coconut in India in terms of both area and production. It is observed that coconut farmers are going through a tough situation due to the decline of coconut production over time. They find it difficult to manage the crop efficiently. Proper irrigation and fertilization will help farmers improve the crop yield. In this paper, we have developed analytical models for predicting the fertilizer requirements based on the soil analysis and macronutrients present in the soil. This research work proposes an IoT framework for improving the yield of coconut production by effectively predicting fertilizer recommendations based on the crop’s current fertilization requirements, weather forecasts, and environmental conditions. Based on this analysis an efficient ML model is derived that helps in predicting fertilization requirements based on soil parameters. A model called Linear Regression is built to predict the amount of fertilizer needed using the values of soil parameters such as soil pH, potassium, nitrogen, phosphorus, boron, zinc, soil carbon, manganese, and so on as input features. This model demonstrates promising performance with an accuracy of 93.5% in predicting the fertilizer and can be used by farmers as a tool for scientific farming for enhancing crop production.
Optimized Random Forest Classifier Basedon Genetic Algorithm for Heart Failure Prediction

Heart failure is a serious long-term condition that usually gets worse over time. On the other hand, some people do not aware to check their heart health regularly. In this study, the Random Forest will be optimized using the Genetic Algorithm to obtain the best parameters and will be applied to the heart failure dataset from Kaggle. We experimented with two iterations for every nine combinations of the parameters. We compared the results of optimized random forest, stand-alone random forest, decisiontree, and Naïve Bayes algorithms. Our finding is that the optimized method is slightly better than the other algorithms. The best F1-score is obtained atthe second iteration which is 0.90789 compared to0.89404 obtained with the sole random forest, 0.85034 obtained with the decision tree, and 0.86195 obtained with Naive Bayes. The best recall value is 0.91925obtained in the first iteration, and in the second iteration. The best recall is also obtained with the sole random forest algorithm. The best precision valueis 0.89937 which was obtained in the first. By these results, the optimized random forest algorithm could be used to result in reliable predictions about heart failure.
Phase-Noise-Resisting Modulation and Demodulation for Sub-Terahertz Communications

Sub-terahertz (sub-THz) communications have promising prospects for future beyond-5G or 6G applications due to the sufficient spectrum resources. However, the bit-error-rate (BER) performance of sub-THz is seriously deteriorated by phase noise because of the defective high-frequency oscillators. In this paper, a novel modulation and demodulation method with low peak-to-average power ratio (PAPR) is proposed to resist phase noise, where the signal waveform is generated in the form of Gray-mapping amplitude and differential phase shift keying (Gray-ADPSK). As phase noise varies slowly in time, the amplitude and differential phase between adjacent symbols almost remain after transmission with Gray-ADPSK, and hence the BER performance is barely affected by phase noise. In order to support decoding effectively, a soft-decision demodulation method with low complexity is also put forward. Numerical simulations demonstrate that the proposed Gray-ADPSK can resist phase noise significantly, and the high-spectral-efficiency transmission even in the order of 4096 is feasible under high-intensity phase noise.
Regulatory Support to Prevent Health Data Breaches

Digitalization of health data is one of the advances in the era of information technology. It is very useful for both the patient and the paramedic. Unfortunately, it also has a hole because there are more breaches due to the leakage of health data nowadays. Electronic health data is particularly vulnerable, as it is most often targeted by attackers or hackers. Health data were considered to have the most sensitive personal data. In Indonesia, there have been several leaks of health data. The breached data are from government-owned applications including BPJS Kesehatan, eHAC, and vaccine certificates. The health data needed to be protected, so there is no misuse of it. The breached health data can be harmful to the person affected. This study is to capture the situation and problem of health data breaches on the government-owned application in Indonesia. The result of this paper is to propose a recommendation to minimize the occurrence of health data breaches. Strategic moves are needed in anticipating the occurrence of health data breaches. One of the steps that can be taken is the preparation of regulations to secure health data. With the relevant regulations, it is projected that the breach of health data can be minimized.
The MX Northern Abell Cluster Redshift Survey

The results from the COBE satellite show the existence of structure on scales ~10% or more of the horizon scale of the universe. Rich clusters of galaxies from the Abell/Abell, Corwin, & Olowin catalogs show evidence of structure on scales of 100 h-1 Mpc and hold the promise of confirming structure on the scale of the COBE results. An impediment to that promise has been that redshift information has been unavailable for a large percentage of these clusters, so knowledge of their three-dimensional distribution has had large uncertainties. We have been working to greatly expand the sample of Abell clusters with reliable redshifts. Our approach in this effort, through the MX Northern Abell Cluster Redshift Survey, has been to measure redshifts of at least 25 galaxies in each of 95 R ≥ 1 Abell cluster fields with m10 ≤ 16.8 and zero or one previously measured redshift. Of these 95 observed Abell clusters, 88 new cluster redshifts were obtained with an average of nine cluster member galaxy redshifts per field. Two clusters were found to be chance projections of galaxies along the line of sight, while five cluster observations did not provide enough galaxy redshifts to make a positive identification. This work has resulted in a deeper, 98% complete, and more reliable sample of three-dimensional positions of rich Abell clusters in the northern hemisphere. The primary intent of this survey has been to produce a larger and more complete sample of rich Abell clusters that can be used as tracers for large-scale structure. Through analyses with tools such as the two-point correlation function, power spectrum, and velocity dispersions, this sample can be used to constrain theoretical models better for the formation of structure we see in the universe today.