Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptable Reduced-Complexity Approach Based on State Vector Machine for Identification of Criminal Activists on Social Media

Security agencies face an emerging challenge of identifying and counter the malicious contents spread on the social media by the terrorists. However, text classification techniques are limited by visualization, pre-processing, features extraction, and larger features space. Additionally, change in criminal content require the learning models to identify altered malicious textual contents which poses extra challenge. This study proposes simplified yet adaptable framework that uses a novel features extraction algorithm for extracting features from the textual part of social media contents. The feature extraction considers selective features from only 8 dimensions and follows a six step process. The extracted features are suitably used to train the state vector machine for the classification of the malicious content. The performance of the proposed method is evaluated against other popular feature selection/extraction algorithms like term frequency-inverse document frequency, Gini Index (GI), Chi square statistics, and PCA. Additionally, machine learning classifiers like decision tree, random forest, and Naïve Bayes are also used for classification. Results suggest that the proposed approach consumes less energy on text visualization, pre-processing, and dimensionality reduction. It also reduces the time-space complexity of the features extraction process and is capable to steer according to the changing strategies of the active criminal groups. In addition, it can effectively analyze the propaganda material published by the extremists. It automatically identifies the radical text on social media platforms allowing understanding of the behaviors, characteristics and subsequent blockage of such content.
A Many-Objective Artificial Bee Colony Algorithm Based on Adaptive Grid

Many-objective optimization problems are widely applied and complex to solve. For most many-objective evolutionary algorithms, maintaining the balance of solution convergence and diversity is a challenging problem. Considering the convergence and diversity at the same time, a many-objective artificial bee colony algorithm based on adaptive grid(MOABCAG) is proposed. By searching for the ideal target point in the grid, the solution selection pressure is enhanced, the solution is positioned under the designed grid, and the grid is adaptively divided to improve the convergence and diversity of the solution. The algorithm improves the position sharing mechanism of the leader and follower bees in the artificial bee colony algorithm, and it sets the variable neighborhood search method of the follower bee to improve the precision of the solution vectors. MOABCAG is compared with five well-known evolutionary algorithms on thirteen benchmark test functions. The results show that the proposed MOABCAG algorithm obtains better performance than other related state-of-the-art algorithms in solving such many-objective optimization problems.
An Efficient Hybrid Metaheuristic Algorithm for QoS-Aware Cloud Service Composition Problem

Cloud computing has a great ability to store and manage remote access to services in a term of software as a service (SaaS). Recently, many organizations have moved to use outsourcing over the cloud to reduce the local resource burden. The stored services over the cloud are too scalable and complex, so an optimization method is more desirable to select appropriate services that satisfy the clients’ request. To do so, the quality of service (QoS) parameters that associated with each service are the best resources for choosing and optimizing the appropriate services over the cloud. Therefore, the cloud service composition aims to select and integrate services over the cloud to satisfy the clients’ request. In this work, a hybrid algorithm is introduced, which combines ant colony optimization (ACO) and genetic algorithm (GA) to efficiently compose the services over the cloud. The GA is used to tune the ACO’s parameters automatically and the ACO adapts its performance based on the parameters tuning. The main contribution of this work is to help the ACO algorithm to avoid stagnation problem and enhance the performance of the ACO where this performance is affected by the value of the ACO’s parameters. The experimental results on 15 different real datasets have shown the effectiveness of the proposed algorithm to search comparable solutions compared to five competitors.
Apple Leaf Disease Recognition and Sub-Class Categorization Based on Improved Multi-Scale Feature Fusion Network

Apple diseases cause a lot of economic losses to fruit growers in China. Early diagnosis and accurate recognition of apple diseases can control the spread of disease and reduce production costs. However, the significance of disease characteristic of apple leaves in complex environment is relatively weak, and the fine-grain among different diseases of apple leaves is high, and the conventional feature extraction methods will lose the discrimination information. To solve these problems, an apple disease classification model based on multi-scale feature fusion is proposed in this paper. Firstly, the information flow of conventional residual network (ResNet) was improved to achieve efficient information circulation through changing the position of batch normalization and rectified linear unit (ReLU). Secondly, in order to solve the problem of serious loss of information in ResNet downsample, the channel projection and spatial projection of downsample were separated. Lastly, the $3\times 3$ conv in ResBlocks was replaced by pyramid convolution, and the dilated convolution with different dilation rate was introduced into pyramid convolution to enhance the output scale of feature maps and improve the robustness of the model. The optimized model was verified on the dataset of this paper, and the optimized model had stronger anti-noise ability and better robustness, excellent learning effect and fast convergence speed. The classification accuracy on the original dataset is 94.24%, and that on the preprocessed dataset is 94.99%. The results demonstrate that the optimal model has a high accuracy, which can provide a reference for the prevention and control of apple leaf diseases.
Hierarchical Control of Trajectory Planning and Trajectory Tracking for Autonomous Parallel Parking

For the parallel parking problem in narrow space, this paper proposes a trajectory tracking control method with a novel trajectory planning layer for autonomous parallel parking based on a numerical optimization algorithm and model predictive control. In the trajectory planning layer, the vehicle kinematics model suitable for the low-velocity parking scene is established. Considering the vehicle physical constraints, boundary condition constraints, and obstacle avoidance constraints during the parking process, the parking trajectory planning task is described as an optimal control problem, further transformed into a nonlinear programming problem by Gauss pseudo-spectral method. Taking the shortest parking completion time as the optimization objective function, the parking trajectories of the large, medium and small parking spaces are obtained, respectively. A parking trajectory tracking controller based on the model predictive control algorithm is designed in the trajectory tracking control layer. The linear error model is used as the prediction model, and the quadratic programming is adopted as the rolling optimization algorithm in the tracking controller. The velocity and front-wheel swing angle are obtained as control signals for parking trajectory tracking. Through CarSim and Simulink’s co-simulation, the feasibility and effectiveness of the proposed parallel parking trajectory planning and tracking control method are verified. The co-simulation results show that the maximum tracking errors of horizontal and longitudinal positions are less than 0.15m. The maximum tracking errors of heading angle are less than 2° under three different parking spaces. Real vehicle tests are carried out to verify the effectiveness of the proposed hierarchical control method. The test results show that the vehicle can park in the parking space safely, quickly and accurately when the actual parking space is detected. The proposed method can plan the parking trajectory with the constraints and the shortest time and control the vehicle to complete the parking operation accurately along the planned trajectory.
Transfer Subspace Learning for Unsupervised Cross-Corpus Speech Emotion Recognition

In many practical applications, a speech emotion recognition model learned on a source (training) domain but applied to a novel target (testing) domain degenerates even significantly due to the mismatch between the two domains. Aiming at learning a better speech emotion recognition model for the target domain, the paper investigates this interesting problem, i.e., unsupervised cross-corpus speech emotion recognition (SER), in which the training and testing speech signals come from two different speech emotion corpora. Meanwhile, the training speech signals are labeled, while the label information of the testing speech signals is entirely unknown. To deal with this problem, we propose a simple yet effective method called transfer subspace learning (TRaSL). TRaSL aims at learning a projection matrix with which we can transform the source and target speech signals from the original feature space to the label space. The transformed source and target speech signals in the label space would share similar feature distributions. Consequently, the classifier learned on the labeled source speech signals can effectively predict the emotional states of the unlabeled target speech signals. To evaluate the performance of the proposed TRaSL method, we carry out extensive cross-corpus SER experiments on four speech emotion corpora including IEMOCAP, EmoDB, eNTERFACE, and AFEW 4.0. Compared with recent state-of-the-art cross-corpus SER methods, the proposed TRaSL can achieve more satisfactory overall results.
E-Textile Technology Review–From Materials to Application

Wearable devices are ideal for personalized electronic applications in several domains such as healthcare, entertainment, sports and military. Although wearable technology is a growing market, current wearable devices are predominantly battery powered accessory devices, whose form factors also preclude them from utilizing the large area of the human body for spatiotemporal sensing or energy harvesting from body movements. E-textiles provide an opportunity to expand on current wearables to enable such applications via the larger surface area offered by garments, but consumer devices have been few and far between because of the inherent challenges in replicating traditional manufacturing technologies (that have enabled these wearable accessories) on textiles. Also, the powering of e-textile devices with battery energy like in wearable accessories, has proven incompatible with textile requirements for flexibility and washing. Although current e-textile research has shown advances in materials, new processing techniques, and one-off e-textile prototype devices, the pathway to industry scale commercialization is still uncertain. This paper reports the progress on the current technologies enabling the fabrication of e-textile devices and their power supplies including textile-based energy harvesters, energy storage mechanisms, and wireless power transfer solutions. It identifies factors that limit the adoption of current reported fabrication processes and devices in the industry for mass-market commercialization.
AltaiPony - Flare science in Kepler, K2 and TESS light curves

Flares are unmistakeable signs of stellar magnetic activity, and a key to our understanding of stellar properties and evolution. They are violent explosions that penetrate all layers of a star’s atmosphere, and enhance its overall brightness by up to orders of magnitude within minutes. We observe flares as distinct signatures in time series of stellar photometric observations that we call light curves. Flaring rates and energies provide unique insights into the nature of the stars that produce them.
Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

Mobile device cloud (MDC) is a collaborative cloud computing platform over which neighboring smart devices form an alliance of shared resources to mitigate resource-scarcity of an individual user device for running compute-intensive applications. A major challenge of such a platform is maximizing user quality-of-experience (QoE) at minimum cost while providing attractive incentives to workers’ mobile devices. In state-of-the-art works, either a voluntary task execution or merely resource-cost driven mechanism has been applied to minimize the task execution time while overlooking payment of any additional incentive to the worker devices for their quality services. In this paper, we develop a computational framework for MDC where the afore-mentioned challenging problem is formulated as a multi-objective linear programming (MOLP) optimization function that exploits reverse-auction bidding policy. Due to the NP-hardness of MOLP, we offer two greedy worker selection algorithms for maximizing user QoE or minimizing execution cost. In both algorithms, the amount of incentive awarded to a worker is determined following the QoE offered to a user. Theoretical proofs of desirable properties of the proposed incentive mechanisms are presented. Simulation results illustrate the effectiveness of our incentive algorithms compared to the state-of-the-art approaches.
Taxol in Combination with Doxorubicin or Etoposide

hours). Results. When corrected for taxol-induced cytotoxic- ity, doxorubicin and etoposide caused less cell killing in the presence of taxol compared with control incubations of doxorubicin and etoposide alone. To determine if a dif- ferent schedule of drug application resulted in a similar finding, MCF7, A549, and OVGl cells were exposed to doxorubicin for
Protocols for Transferring Bulk Data Over Internet: Current Solutions and Future Challenges

Big Data has gained interests in effectively capturing, storing, analysis and visualisation from wide range of scientific, economic and business communities and is frequently communicated over internet for various purposes among government and enterprise sectors sited at different locations. Several experiments and analyses have shown that currently employed applications and transport protocols in internet are not suitable for transferring such voluminous data because of not addressing requirements of low-access latency. This paper presents issues associated with the basic mechanism of legacy protocols in the context of high speed networks for transferring Big Data e.g. conservative TCP congestion control mechanism may result in minute utilisation of high bandwidth provisioning networks. We present state-of-art alternatives proposed in the literature to solve these problems in high speed networks. We compare several underlying emerging alternatives of TCP, UDP and multi-TCP-streams protocols over a number of comparison criteria e.g. protocol convergence, responsiveness etc., to handle communication of huge data. We note that these protocol alternatives have significant importance over fulfilling requirements of emerging data-intensive applications in high-speed networks. In addition, we discuss open research issues and challenges that can be explored as a source of motivation towards development and deployment of data-intensive applications in emerging networking technologies.
Progressive and Corrective Feedback for Latent Fingerprint Enhancement Using Boosted Spectral Filtering and Spectral Autoencoder

The objective of this research is to design an efficient algorithm that can successfully enhance a targeted latent fingerprint from various complex backgrounds under an uncontrolled environment. Most algorithms in literature exploited dictionary learning schemes and deep learning architectures to capture latent fingerprints from complicated backgrounds and noise. However, an algorithm learned from other high-quality fingerprint images may not solve all possible cases within a given unseen image. We propose a new feedback framework to distinguish latent fingerprints from complex backgrounds and gradually improve friction-ridge quality using the information provided inside the given unseen image. We combine two efficient mechanisms. The first mechanism enhances high-quality areas in priority and feeds the enhanced areas back to improve the quality of latent fingerprints in the nearby area. The second mechanism is to verify that the first mechanism works correctly by detecting anomalously enhanced fingerprint patterns. The second mechanism employs a spectral autoencoder that learns from good fingerprint spectra in the frequency domain. The anomalous fingerprint area is sent back to the first mechanism for further improving the enhanced result. We benchmark the proposed algorithm against available state-of-the-art algorithms using two fingerprint matching systems (one commercial off-the-shelf and one open-source) on two public latent fingerprint databases. The experimental results show that the proposed algorithm outperforms most state-of-the-art algorithms in the literature.
A survey on indoor 3D modeling and applications via RGB-D devices

With the fast development of consumer-level RGB-D cameras, real-world indoor three-dimensional (3D) scene modeling and robotic applications are gaining more attention. However, indoor 3D scene modeling is still challenging because the structure of interior objects may be complex and the RGB-D data acquired by consumer-level sensors may have poor quality. There is a lot of research in this area. In this survey, we provide an overview of recent advances in indoor scene modeling methods, public indoor datasets and libraries which can facilitate experiments and evaluations, and some typical applications using RGB-D devices including indoor localization and emergency evacuation.
Load Balancing Model under Multiple Failures with Workload-Dependent Failure Probability

This paper proposes a load balancing model with preventive recovery priority setting to minimize the maximum unavailable probability with a workload-dependent failure probability. The workload-dependent failure probability is a non-decreasing function which reveals the relationship between the workload and the failure probability. With the individual workload-dependent failure probabilities for each node, a service provider is able to provide each service by minimizing unavailability among functions. We introduce a recovery strategy which is determined at the operation start time and can be applied for each failure pattern with the given maximum number of failures, Γ. Once a failure pattern occurs, the recoveries are automatically operated according to the priority setting to promptly recover the failed function. The workload variation after detecting the failures and activating the backup resource is considered in the proposed model against multiple failures. The numerical results observe that the proposed model reduces the maximum unavailable probability compared to the baselines, while it does not require the computation for reallocation of the failed functions after failure detection.
Effect of an organic residue on herbicide field dissipation.

The aim of this work was to study the effect of the application of a solid waste from olive oil production (alperujo) on the movement and persistence of the herbicide diuron [3-(3,4-dichlorophenyl)-1,1-dimethylurea]. The application of alperujo, fresh or composted, to the land has been shown to be an extremely effective contribution to increasing crop yields and to maintaining or improving soil fertility. An experimental olive grove located in Mengibar (Jaen, Spain) was divided into two plots: 1. Plot without organic amendment (blank) and 2. Plot treated with alperujo during 3 years at a rate of 17920 kg of alperujo ha. Diuron was applied to both plots at a rate of 2 kg haa.i. Triplicates from each plot were sampled at three depths (0-10, 10-20 and 20-30 cm), air-dried, remains of olive leaves, grass roots, and stones removed and sieved through a 5 mm mesh sieve. Diuron was extracted with methanol 1:2 weight:volume ratio, the extracts were evaporated to dryness, resuspended in 2 mL of methanol, filtered and analyzed by HPLC. Higher amounts of diuron were detected at each sampling depth in plots treated with alperujo. The increase in soil organic matter content upon amendment with alperujo slightly increased sorption, suggesting that other factors beside sorption affect diuron degradation rate in organic amended soils.
Interplay between extreme drift and selection intensities favors the fixation of beneficial mutations in selfing maize populations

Population and quantitative genetic models provide useful approximations to predict long-term selection responses sustaining phenotypic shifts, and underlying multilocus adaptive dynamics. Valid across a broad range of parameters, their use for understanding the adaptive dynamics of small selfing populations undergoing strong selection intensity (thereafter High Drift-High selection regime, HDHS) remains to be explored. Saclay Divergent Selection Experiments (DSEs) on maize flowering time provide an interesting example of populations evolving under HDHS, with significant selection responses over 20 generations in two directions. We combined experimental data from Saclay DSEs, forward individual-based simulations, and theoretical predictions to dissect the evolutionary mechanisms at play in the observed selection responses. We asked two main questions: How do mutations arise, spread, and reach fixation in populations evolving under HDHS ? How does the interplay between drift and selection influence observed phenotypic shifts ? We showed that the long-lasting response to selection in small populations is due to the rapid fixation of mutations occurring during the generations of selection. Among fixed mutations, we also found a clear signal of enrichment for beneficial mutations revealing a limited cost of selection. Both environmental stochasticity and variation in selection coefficients likely contributed to exacerbate mutational effects, thereby facilitating selection grasp and fixation of small-effect mutations. Together our results highlight that despite a small number of polymorphic loci expected under HDHS, adaptive variation is continuously fueled by a vast mutational target. We discuss our results in the context of breeding and long-term survival of small selfing populations.
Intelligent control of quad-rotor aircrafts with a STM32 microcontroller using deep neural networks

Purpose Deployment of deep neural networks on embedded devices is becoming increasingly popular because it can reduce latency and energy consumption for data communication. This paper aims to give out a method for deployment the deep neural networks on a quad-rotor aircraft for further expanding its application scope. Design/methodology/approach In this paper, a design scheme is proposed to implement the flight mission of the quad-rotor aircraft based on multi-sensor fusion. It integrates attitude acquisition module, global positioning system position acquisition module, optical flow sensor, ultrasonic sensor and Bluetooth communication module, etc. A 32-bit microcontroller is adopted as the main controller for the quad-rotor aircraft. To make the quad-rotor aircraft be more intelligent, the study also proposes a method to deploy the pre-trained deep neural networks model on the microcontroller based on the software packages of the RT-Thread internet of things operating system. Findings This design provides a simple and efficient design scheme to further integrate artificial intelligence (AI) algorithm for the control system design of quad-rotor aircraft. Originality/value This method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.