The majority of people are looking for vehicles. To find their desired vehicle, people search on the most popular websites. The sellers post their ads on multiple sites to reach out to a larger audience. This means buyers have to find their desired vehicle on multiple sites to compare the prices because buyers always look for lower prices. This search needs to visit and find ads on multiple vehicle entertaining websites, which requires much time. In this paper, we are proposing a unique “Automobile Search Engine” (ASE) which grabs all the automobile ads posted on different websites. Our solution will give a single search for automobile ads. The user can get all the ads based on his/her search criteria. ASE will find all the ads for different websites and will display the results. The user can then compare the searched ads on one single view. ASE supported Multi Thread Crawler.

Although consciousness has been difficult to define, most researchers in artificial intelligence would agree that AI systems to date have not exhibited anything resembling consciousness. But is a conscious machine possible in the near future? I suggest that a new definition of consciousness may provide a basis for developing a conscious machine. The key is pattern recognition of correlated events in time, leading to the identification of a unified self-agent. Such a conscious system can create a simplified virtual environment, revise it to reflect updated sensor inputs, and partition the environment into self, other agents, and relevant objects. It can track recent time sequences of events, predict future events based on models and patterns in memory, and attribute causality to events and agents. It can make rapid decisions based on incomplete data, and can dynamically learn new responses based on appropriate measures of success and failure. The central aspect of consciousness is the generation of a dynamic narrative, a real-time model of a self-agent pursuing goals in a virtual reality. A conscious machine of this type may be implemented using an appropriate neural network linked to episodic memories. Near-term applications may include autonomous vehicles and online agents for cybersecurity. Paper presented at virtual IEEE International Conference on Rebooting Computing (ICRC), Nov. 2021. To be published in conference proceedings 2022.

A key encapsulation mechanism (KEM) that takes as input an arbitrary string, i.e., a tag, is known as tag-KEM, while a scheme that combines signature and encryption is called signcryption. In this paper, we present a code-based signcryption tag-KEM scheme. We utilize a code-based signature and a CCA2 (adaptive chosen ciphertext attack) secure version of McEliece’s {encryption} scheme. The proposed scheme uses an equivalent subcode as a public code for the receiver, making the NP-completeness of the equivalent subcode problem be one of our main security assumptions. We then base the signcryption tag-KEM to design a code-based hybrid signcryption scheme. A hybrid scheme deploys an asymmetric- as well as a symmetric-key encryption. We give security analyses of both our schemes in the standard model and prove that they are secure against IND-CCA2 (indistinguishability under adaptive chosen ciphertext attack) and SUF-CMA (strong existential unforgeability under chosen message attack).

Every person has an equal right to information, therefore, impairments shouldn't restrict people from gaining this knowledge from any form of source. Social Networking applications have tremendously grown their popularity among all kinds of age groups for providing socialising opportunities, entertainment and exchange of knowledge. Hence, the motive of this paper is to propose a social networking application pipeline with a strong Machine Learning backend which makes it more accessible to the blind, deaf and dumb section of the society who otherwise do not enjoy the features of social networking platforms.

In this work, we propose a new formulation for low-rank tensor approximation, with tunable outlier-robustness, and present a unified algorithmic solution framework. This formulation relies on a new generalized robust loss function (Barron loss), which encompasses several well-known loss-functions with variable outlier resistance. The robustness of the proposed framework is corroborated by the presented numerical studies on synthetic and real data.

The work proposes static and dynamic input-based event-triggered controllers for a network resource-constrained environment. The controller is designed for a discrete-time system using a low-gain approach, where feedback gain is designed as a function of a user-defined parameter. Depending on the event density, the low-gain parameter can be adjusted to increase the inter-event time between two consecutive events at a particular instant. Thus the demand for computational and network resources can be reduced

This paper designs parameter adaptation algorithms for online simultaneous identification of a two-parameter sigmoid inverter model for compensating inverter nonlinearity to reduce the voltage error in flux estimation for a position sensorless motor drive. The inverter model has two parameters, a2 and a3, where a2 is “plateau voltage”, and a3 is a shape parameter that mainly accounts for the stray capacitor effect. Parameter a3 is identified by the (6k ± 1)-th order harmonics in measured current. Parameter a2 is identified by the amplitude mismatch of the estimated active flux. It is found that the classic linear flux estimator, i.e., the hybrid of voltage model and current model, cannot be used for a2 identification. This paper proposes to use a saturation function based nonlinear flux estimator to build an effective indicator for a2 error. The coupled identifiability of the two parameters is revealed and analyzed, which was not seen in literature. The concept of the low current region where the two-way coupling between a2 and a3 occurs is established. In theory, it is suggested to stop the inverter identification in the low current region. However, the experimental results in which dc bus voltage variation and load change are imposed, have shown the effectiveness of the proposed online inverter identification and compensation method, even in low current region.

High speed arbitrary waveform generator enabled by photonic digital to analog converter where the bandwidth limitation arising due to interconnect and device parasitics in its electronic counterparts is circumvented. Leveraging the silicon photonic integration technology for this purpose provides a potential high resolution, high bandwidth, and energy efficient solution for signal transmitters.

Searching digitized detector signals for piled-up delayed components with distinct energy and delay time signatures is a smart method to provide common NaI(Tl) detectors with additional neutron detection capabilities at no extra cost. This technique nicely complements the idea of neutron detection by analyzing events with high energy depositions above the range of common gamma-ray energies. In combination, both approaches can provide half of the neutron sensitivity offered by a commercial 6Li co-doped NaI(Tl) (NaIL™) scintillator of the same size, at the price of higher and load-dependent background contributions. Delayed-coincidence techniques are most suitable for neutron monitoring or long-term measurements, where the statistics of the acquired delay-time distributions allows separate fitting of the effect and background contributions. In this case, the thermal neutron flux can be quantified in parallel to gamma-ray spectroscopy at overall detector loads exceeding 10 kcps.

- A novel approach is presented to reduced annotation based on Deep Active Learning for Arabic text detection in Natural Scene Images. - A new Arabic text images dataset (7k images) using the Google Street View service named TSVD. - A new semi-automatic method for generating natural scene text images from the streets. - Training samples is reduced to 1/5 of the original training size on average. - Much less training data to achieve better dice index : 0.84

The electroencephalogram (EEG) has been extensively used to record brain signals for the analysis of neurological diseases over several decades. Epilepsy is the one of the most common neurological disorder in the world. It is associated with seizures, which are a clinical manifestation of abnormal, excessive excitation and synchronisation of a population of cortical neurons, resulting in abrupt changes in consciousness, movement, behaviour, and feelings. Our goal in this letter is to propose a computationally efficient EEG based system that can detect seizures with improved detection performance. To deeply optimize the electrodes and frequency bands, we proposed a fast computational algorithm by finding the critical electrodes and optimal sub-bands of EEG signals. We used wavelet packet decomposition (WPD) to find optimal sub-bands and maximum relevance minimum redundancy (mRMR) method for electrodes selection. It is found that the portrait of the features in the phase space depicted promising discriminating ability to detect seizure efficiently. Experimental results show that the proposed method outperformed the state-of-art methods and provided 100% and 94.79% of detection accuracy on UBonn and CHB-MIT datasets, respectively, at a significantly reduced computational cost.

Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search space toward potential solutions, while the second Beta-2 function is used for convergence enhancement. This new system is tested on 36 benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective Optimization Competition. Comparatives with the state-of-the-art methods are done using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best results on most tested problems.

Code smells are structures in code that often have a negative impact on its quality. Manually detecting code smells is challenging and researchers proposed many automatic code smell detectors. Most of the studies propose detectors based on code metrics and heuristics. However, these studies have several limitations, including evaluating the detectors using small-scale case studies and an inconsistent experimental setting. Furthermore, heuristic-based detectors suffer from limitations that hinder their adoption in practice. Thus, researchers have recently started experimenting with machine learning (ML) based code smell detection. This paper compares the performance of multiple ML-based code smell detection models against multiple traditionally employed metric-based heuristics for detection of God Class and Long Method code smells. We evaluate the effectiveness of different source code representations for machine learning: traditionally used code metrics and code embeddings (code2vec, code2seq, and CuBERT). We perform our experiments on the large-scale, manually labeled MLCQ dataset. We consider the binary classification problem – we classify the code samples as smelly or non-smelly and use the F1-measure of the minority (smell) class as a measure of performance. In our experiments, the ML classifier trained using CuBERT source code embeddings achieved the best performance for both God Class (F-measure of 0.53) and Long Method detection (F-measure of 0.75). With the help of a domain expert, we perform the error analysis to discuss the advantages of the CuBERT approach. This study is the first to evaluate the effectiveness of pre-trained neural source code embeddings for code smell detection to the best of our knowledge. A secondary contribution of our study is the systematic evaluation of the effectiveness of multiple heuristic-based approaches on the same large-scale, manually labeled MLCQ dataset.

The Industrial Internet of Things (IoT) has gained a lot of momentum thanks to the introduction of Time Slotted Channel Hopping (TSCH) in IEEE 802.15.4. At last, we can enjoy collision-free, low-latency wireless communication in challenging environments. Nevertheless, the fixed size of time slots in TSCH provides an opportunity for further enhancements. In this paper, we propose an enhanced centralized TSCH scheduling (ECTS) algorithm with simple packet aggregation while collecting data over a tree topology. Having in mind that the payload of a sensor node is rather short, we attempt to put more than one payload in one packet. Thus, we occupy just one cell to forward them. We investigated the schedule compactness of ECTS in Matlab, and we evaluated its operation, after implementing it in Contiki-NG, using Cooja. Our results show that ECTS with packet aggregation outperforms TASA in terms of slotframe duration and imposes fairness among the nodes in terms of latency. A validation exercise using real motes confirms its successful operation in real deployments.

This paper rethinks the basic assumptions often used in analytically modeling parasitic capacitance in inductors. These assumptions are classified in two commonly-used physics-based analysis methods: the lumped capacitor network method and the energy conservation method. The lumped-capacitor network method is not the proper solution for calculating the equivalent parasitic capacitance in inductors at the first resonant frequency, but rather represents the equivalent parasitic capacitance above the last resonant frequency. The energy-conservation based method is shown to be more accurate and a reasonable solution to model the equivalent parasitic capacitance at the first resonant frequency. Multiple case studies of inductors are used for verifying the theory.

Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of the wireless channel characteristics in a computationally efficient way. However, most of the existing research on ML-based propagation models focuses on outdoor propagation modelling, while indoor data-driven propagation models remain site-specific with limited scalability. In this paper we present an efficient and credible ML-based radio propagation modelling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder-decoder can be trained to replicate the results of a ray-tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decode it as the path-loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. Additionally, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its pre-estimate weights, allowing it to make predictions that are consistent with measurement data.

Comparative study of the performance of different ITU model in predicting Malaysia tropical climate properties.

Products continuously evolve over time. Realizing the pattern of product family evolution along with proper estimation of features for future products has been regarded as a critical issue for business success. Focusing on this issue, a dynamic model for product family evolution combined with forecasting is proposed in this research work. The proposed model considers the influence of market demand, customer needs and technological requirements that are time-dependent. The methodology is a four-phase model. For each phase, the effectiveness of the developed approach is demonstrated with numerical simulation results and validated with a case study of Apple's iPhone product family.