Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Improvement of Navic Signal Using Real-time Filtering Techniques

The Indian Regional Navigational Satellite System [IRNSS] is India's own indigenous navigation system. Its purpose is to offer precise positional information to users in India as well as the surrounding region, which extends up to 1500 kilometers outside the country's borders. Satellite geometry, signal reception delays induced by the ionosphere and troposphere, multipath doppler effect produced by relative motion clock drift, and receiver noise all affect the positioning accuracy of a satellite-based navigation system. The ionosphere's influence surpasses all the preceding factors since even a slight delay in the signal has a significant impact on the user's position. Different methods, such as dual frequency methods, differential correction approaches, and various single frequency ion delay models, are used to alleviate ionosphere delays. In the proposed work Kalman filter is applied to correct the positioning errors [1]. The suggested method's performance in an urban setting is examined using real-time data obtained by a Global Navigation Satellite System (GNSS) receiver. Both static and dynamic data is used to analyse the performance of the proposed method. In the position domain the results are evaluated using the parameters - Percentage Fit Error and Mean Absolute Error [2]. The lost path during reception is regenerated using linear interpolation function. The simulation results obtained show that the proposed Kalman filter algorithm in combination with interpolation significantly improves accuracy of tracking in real-time.
μ Viz: Visualization of Microservices

Microservice architectures have become very popular and widely adopted by the industry, because of the benefits they bring to the software development process and resulting systems, such as parallel development, modularity and scalability. However, as interfaces become more fine-grained and systems grown in size, complexity is moved from the component services to their interactions, eventually leading to intricate workflows that are hard to observe, visualize, and understand. This problem is compounded by the typically high workloads that produce intractable amounts of observation data. To deal with these challenges, operators need support from tools able to take in observation data, in particular tracing, and provide a fast and intuitive understanding of which components or workflows require attention and how are they affecting a module, service, instance, or the whole application. In this paper, we present the design of a microservice visualization application that can fill a gap that exists in leveraging tracing data, aggregating and navigating it in ways that are actionable for operators. Our application provides multiple views of the system and uses spatial and hierarchical navigation using flip zoom to simplify their exploration, while preserving context. Our application can provide a better understanding of the system than existing applications that lack navigability and do not preserve context when switching between different services, layers or views.
Stacked Bidirectional Long Short-Term Memory for Stock Market Analysis

Stock market prediction is a difficult task as it is extremely complex and volatile. Researchers are exploring methods to obtain good performance in stock market prediction. In this paper, we propose a Stacked Bidirectional Long Short-Term Memory (SBLSTM) network for stock market prediction. The proposed SBLSTM stacks three bidirectional LSTM networks to form a deep neural network model that can gain better prediction performance in the stock price forecasting. Unlike LSTM-based methods, the proposed SBLSTM uses bidirectional LSTM layers to obtain the temporal information in both forward and backward directions. In this way, the long-term dependencies from the past and future stock market values are encapsulated. The performance of the proposed SBLSTM is evaluated on six datasets collected from Yahoo Finance. Additionally, the proposed SBLSTM is compared with the state-of-the-art methods using root mean square error. The empirical studies on six datasets demonstrates that the proposed SBLSTM outperforms the state-of-the-art methods.
Graph embedding of music structures for machine learning approaches

Several works on representation learning for graph-structured data have been proposed in recent literature. However, most of such techniques have several downsides. On the one hand, graph kernels which use handcrafted features (e.g., shortest paths) are hampered by poor generalization problems. On the other hand, methods for learning representations of whole graphs deal with unattributed or single-attributed graphs.In this work, we propose a novel technique for graph embedding learning able to take into account multi-attribute graphs (from 1 to an arbitrary number). Given a multi-attribute graph, the proposed method generates an embedding vector as follows: (i) the graph is split into several single-attribute graphs; for each of these, one numeric vector is generated by using state-of-the-art graph embedding techniques; (ii) the obtained vectors are concatenated in one representative vector using a multi-view learning integration technique; (iii) the size of such a vector is reduced through deep autoencoders.Experiments have been conducted on the music style recognition problem. We focus on the corpus of 4-voice J. S. Bach’ compositions. First, such a corpus has been decomposed and translated into graph-based structures corresponding to the music scores. Then, the proposed method is applied to generate the embedding vectors from the obtained graphs. Finally, a Random Forest model trained on such obtained vectors is used for generating novels music compositions in the learned style. Results obtained show the effectiveness of the proposed approach.
PM10 Data Assimilation on Real-time Agent-based Simulation using Machine Learning Models: case of Dakar Urban Air Pollution Study

High air pollution is a major health risk. Heavy urbanisation favours the degradation of air quality in large cities such as Dakar. In this city, the annual rate of PM10 exposure in the city is above the threshold recommended by the World Health Organisation (WHO). However, in order to set up a national pollution monitoring network, our approach consists in combining system observations (data from different stations) with a multi-agent simulation. In this paper, we present a model for assimilating PM10 pollution data coupled with a multi-agent realtime simulation. This assimilation model is based on a machine learning method. We performed several simulations to show that the autoregressive ARIMA model is better suited for predicting PM10 pollution data. Then we discussed the relevance of studying other parameters of the model.
Improved Shape-Based Distance Method for Correlation Analysis of Multi-Radar Data Fusion in Self-Driving Vehicle

Multi-radar data fusion techniques are important for self-driving vehicles to better perceive the environment. In the process of obtaining targets location and constructing environmental models, multi-radar data fusion can be regarded as a homogeneous time-series data fusion problem which is commonly solved by weighted fusion methods. However, when most sensors data are biased, especially when the data are all larger or smaller than the accurate data at the same time, the weights of sensors defined by sensor consistency and sensor stability can not reflect the real reliability of sensors. Thus, in this study, the correlation change between different observed parameters of one sensor, which is calculated based on the shape-based distance, together with the sensor consistency and sensor stability is leveraged to judge the reliability of the sensors and calculate the weights of the sensors. An experimental study is carried out and the Nuscenes dataset is used where a multi-radar system consists one LIDAR and two millimeter wave radars. In the situation that most sensors data are biased, the relative error and the root-mean-square error of the method proposed in this study are significantly lower than that of the arithmetic averaging method, the trimmed mean method and the weighted fusion method which only considers the sensor consistency and stability. Besides, under other situations, the fusion accuracy is also improved.
Research and implementation of Intelligent Service Platform for Flexible Employment in Internet Sharing Economy

With the development of emerging technologies on the Internet, the dividends brought by the sharing economy are constantly undergoing butterfly change effect, and a new sharing mode-flexible employment arises at the historic moment. In order to solve the pain points of enterprises, freelancers and the government in the process of developing flexible employment in the sharing economy, this paper uses advanced technologies such as cloud computing, blockchain, artificial intelligence, big data and finance technology to research and develop the intelligent service platform for flexible employment in the sharing economy, one-stop SaaS (software as a service)platform for flexible employment and salary settlement based on sharing economy, one-stop completion of flexible employment, salary settlement, batch payment, invoice provision and legal tax payment, and provision of SaaS platform services for flexible employment and salary settlement based on sharing economy for enterprises. This platform research includes four sub platforms: shared employment SaaS platform; salary Settlement SaaS Platform; freelancer entrepreneurship and innovation services SaaS platform; big data collaborative supervision platform. The research results have been successfully transformed and put into operation to solve the pain points for enterprises, freelancers and government supervision. The platform has now served more than 100 enterprises, and nearly 5,000 freelancers who have realized innovation and entrepreneurship in the shared economy, and helped the government increase more tax revenue than traditional model. Finally, Example shows that compared with the traditional employment model, the platform in this paper achieves better performance, which helped enterprises reduce cost by 21.9%, while personal income increased by 12.3%, achieved good economic and social benefits.
Deep Learning Approach to Inverse Grain Pattern of Nanosized Metal Gate for Multichannel Gate-All-Around Silicon Nanosheet MOSFETs

For the first time, a deep learning (DL) algorithm is presented to study the effect of the source of variability on the performance of semiconductor nanodevice. This paper reports the possibility of an alternative solution of device simulation in order to optimize the source variation. It is based on the statistical distribution of work function fluctuation (WKF) on the metal gate depending on the orientation and location of metal grains. It has been revealed that the WKF of a metal gate can lead to different fluctuations in electrical characteristics. Therefore, an emerging DL algorithm, artificial neural network (ANN) is utilized to identify the appropriate WKF patterns on the metal gate that can reduce the impact of characteristic fluctuation, i.e., $\sigma V_{TH}, \sigma I_{ON}$ and $\sigma I_{OFF}$ , simultaneously. The application of the DL-ANN algorithm to multichannel gate-all-around silicon nanosheet MOSFETs is explored to suppress the effect of WKF on the characteristic fluctuation. Consequently, it can be further utilized to investigate the implication of WKF for the process variation, modeling the nanodevices and analysis of circuit design. Notably, this technique can be extended to study the diverse random sources and process variation effects for emerging nano-CMOS devices and can effectively accelerate the device simulation and optimization.
Privacy Considerations for Risk-Based Authentication Systems

Risk-based authentication (RBA) extends authentication mechanisms to make them more robust against account takeover attacks, such as those using stolen passwords. RBA is recommended by NIST and NCSC to strengthen password-based authentication, and is already used by major online services. Also, users consider RBA to be more usable than two-factor authentication and just as secure. However, users currently obtain RBA’s high security and usability benefits at the cost of exposing potentially sensitive personal data (e.g., IP address or browser information). This conflicts with user privacy and requires to consider user rights regarding the processing of personal data. We outline potential privacy challenges regarding different attacker models and propose improvements to balance privacy in RBA systems. To estimate the properties of the privacy-preserving RBA enhancements in practical environments, we evaluated a subset of them with long-term data from 780 users of a real-world online service. Our results show the potential to increase privacy in RBA solutions. However, it is limited to certain parameters that should guide RBA design to protect privacy. We outline research directions that need to be considered to achieve a widespread adoption of privacy preserving RBA with high user acceptance.
Approach for CAD model reconstruction basing on 3D points insertion and surface approximation

Reverse engineering (RE) consists in the reconstruction of a geometric model of a 3D object from a set of points, a mesh or a 3D triangulation. This model is a combination of geometric primitives (cylinders, planes, spheres, etc.) and complex surfaces (B-Spline, NURBS$\dots$) defined by parameters that can be estimated from the 3D data. RE is widely used in different fields such as mechanic, artistic, medical, Building Information Modeling, reality augmentation, etc. In this context, the reconstruction of 3D surface is an important task to obtain the Computer Aided Design (CAD) model in order to visualize 3D objects and approximate their shapes by mathematical formulations. Triangular surface models are now commonly used to model three-dimensional object. Many of these geometric models are obtained from scanning techniques or modeled through CAD software. This paper presents a new approach to rebuild a CAD model basing on the reconstruction of the B-Spline surfaces given a set of points extracted from a deformed mesh. To guarantee a good precision of the fitted surface, new 3D points are inserted to the input mesh using the Walton's method. Given the updated set of points, the B-Spline surface is approximated. To validate the proposed method, reconstruction errors of different complex 3D surfaces before and after the points insertion are calculated. A comparison with the existing methods prove the efficiency of the developed algorithm.
Experimental demonstration of flexible information rate PON beyond 100 Gb/s with probabilistic and geometric shaping

The application of pairwise probabilistic shaping combined with geometric shaping is investigated for a pulse-amplitude-modulation-based intensity modulated and direct detection (IMDD) system. With probabilistically shaped signals, we experimentally demonstrate the flexible information rate beyond 100 Gb/s in a practical passive optical network (PON) link. Similar to the G.hsp 50 Gb/s PON standard, the PON link assumes the existence of a semiconductor optical amplifier (SOA), which boosts the transmitter power to achieve a high link budget. While the optimal signal distributions for the IMDD link with a SOA tend to be unipolar distributions, we consider a practical methodology of accommodating forward error correction (FEC) parity bits using pairwise probabilistic distribution. Since pairwise signaling causes performance degradation when the optimal signal would be strongly shaped, we propose to combine geometric shaping (GS) with probabilistic shaping (PS) to overcome the shortcomings of the pairwise signaling. We present experimental demonstrations of the various achievable rate measurements using PS $+$+ GS modulations with flexible FEC assumption. We also propose a novel descrambling-based decoding technique to evaluate the post-FEC performance based on transmission data that is agnostic of the FEC code. The post-FEC performances are then presented using a fixed and practical low-density parity check code.
Designing, Analysis and Synthesis of 32-Bit Configurable Hack CPU

Designing of a configurable/modular processor based on HACK computer architecture is presented in the paper. The process confused in designing and building compound circuit of computer system that can perform real world computation is explained in the paper. The processor is build using bottom up approach; that is designing from basic components to major components like PC, ALU, ROM, RAM.A configurable processor signifies a processor that is configurable with respect to memory and components. Configurable memory denotes processor memory with 16 bit, 32 bit, 64 bit according to the requirement. And configurable components are in the sense that any specific individual component can be swapped with any other particular component according to the need and can analyze the performance change. The simulation of design is performed on Modelsim -Altera 10.1d (Quartus II 13.0sp1). The analysis and synthesis based on various parameters (i.e area/device utilization, Minimum propagation delay, Maximum frequency, Throughput, RTL schematic, Technology schematic) is done on Quartus II 13.0sp1.
Impact of Low Frequency Noise Source over Tunnel Field Effect Transistor in Nano Regime

As the device shrinks in nano regime the characteristics degrades. The device characteristics depends upon so may factors associated with device internally as well as externally. In semiconductor devices noise is an important parameter in terms of device functionality in nano regime. In this paper, we discussed the impact of low frequency noise sources on Tunnel FET device. The detail discussion has been done for the random telegraph noise, and flicker noise for TFET. In nano regime with accurate selection of device dimension it is observed that the sub threshold swing can be obtained below 60mV/decade with constant low frequency noise.
A Hybrid Approach of ANN-GWO Technique for Intrusion Detection

As informed individuals, while keeping ourselves updated with the whereabouts of the world, we often come across news articles with bold headlines outlining the various cyberattacks happening across the world. In this paper, we've attempted to build our own intrusion detection system (IDS) which we propose as a viable solution for detecting malicious entities in a network. Artificial Neural Networks (ANN) use backpropagation to update their weights which can get stuck in a local minima rather than a global one. This can lead to the weights and biases not reaching the optimal values. We have proposed and built a hybrid model of ANN along with the grey wolf optimization algorithm (GWO), to combine the technological benefits of these two state-of-the-art algorithmic techniques. We have employed the MIT Darpa 1998 intrusion detection dataset in our study, and used four metrics, namely, precision, accuracy, recall, and F1 score to evaluate the performance of our model.
UCAV maneuvering trajectory prediction based on PSO-CNN

To the problem of low accuracy of unmanned combat aircraft maneuver trajectory, a particle swarm optimization convolutional neural network prediction method is proposed. Firstly, establish a three-degree-of-freedom model of Unmanned Combat Aerial Vehicles (UCAV) with constraints to solve the problem of trajectory source. The structure of the convolutional neural network is analyzed, and the particle swarm optimization algorithm (PSO) is used to replace the backpropagation algorithm to update the internal weights and biases. The PSO is compared with multiple algorithms, and the results show that the PSO updates the weights fast and has small errors. Finally, the prediction is made on a relatively complex and cluttered maneuvering trajectory. The method proposed in this paper is compared with three traditional prediction methods, and the result shows that the method proposed in this paper has small prediction errors.
Evaluating System Architecture Safety in Early Phases of Development with MBSE and STPA

Emerging segments such as autonomous driving require new by-wire system architectures for steering and braking. These system architectures are highly safety-critical and currently not commonly used in the automotive industry. This results in challenges for traditional development approaches. One issue is that a well-thought-out architecture selection is already required in early phases of development. Within this paper, a concept is proposed to help consideration of safety in this timely architecture selection, using a safety trade-off concept. An early consideration of system architecture safety is achieved by utilization of a formalized System-Theoretic Process Analysis on a Systems Modeling Language model. This underlying system model was developed with a Model-based System Engineering approach. Additionally, it is explained how classical safety considerations and safety principles can be integrated into this safety trade-off. Finally, the approach is demonstrated in an architecture comparison for a simplified Steer-by-Wire architecture. Results show that it is possible to find relevant safety requirements and use them to compare solution architecture candidates.
Binary Space Topology Features in Applying to Transitional States Generation of Asynchronous Finite State Machine

Several features of n-dimensional binary space topology are considered in the paper. A number of special rules for encoding the signatures of state-transition diagram of FSM according to the binary space features have been formulated. The asynchronous FSM encoding algorithm, according the described rules is implemented as a software module. The algorithm that generating additional transitional states sequences is in accordance with “differs exactly one bit state” switching condition is described in details. Evaluation subsequent with the comparative analysis of obtained encoding results is also presented.
Theoretical Investigation of Mid Infrared Temperature Sensor Based on Sagnac Interferometer Using Chloroform Filled Photonic Crystal Fiber

In this paper, a novel Sagnac Interferometer (SI) based chalcogenide Photonic Crystal Fiber (PCF) temperature sensor is theoretically and numerically analyzed operating in the mid infrared (MIR) region. The PCF cladding air holes are proposed to be filled with a highly temperature sensitive liquid chloroform to enhance the temperature sensitivity. The temperature sensing characteristics of three different infiltration technique is investigated theoretically and compared by Finite Element Method (FEM). Simulation results show that full infiltration performs better than partial infiltration. In the numerical analysis two sensing dips with high linearity is achieved for full infiltration which shows sensitivity of −5.77 nm/°C and 8.49 nm/°C when the temperature increases from 25 to 85 °C for the specific fiber length of 1.532 mm. The R2 values are 0.957446809 and 0.97680771 for the respective two sensing dips. The proposed highly sensitive sensor has broad detection window which can be applied in fingerprint unlocking, MIR radiation detection in human body, biomedicine applications, and environmental temperature monitoring.
Parkinson's disease Diagnosis using Voice Signals by Machine Learning Approach

Parkinson's Disease (PD) is a chronic neurologic deteriorating condition produced by mislaying of dopamine-producing cells within the cerebrum. These dopamine-producing cells are liable for the mastery, adaptive, and grace of movements. When these cells are astrayed, then enough dopamine is not processed which leads to Parkinson's motor symptoms. The proposed method follows data collection, feature selection, train a model, and model prediction. This paper explores, Machine Learning (ML)-based diagnosis of PD using various classifiers and their prediction accuracy is diagnosed with the defined attributes. From the experimental results it was observed that the Gradient Boost algorithm gives the best test accuracy of 91.53%.