Timely and accurate identification of tomato leaf disease types can effectively improve the quality and yield of tomatoes, increase farmers' economic returns, and promote intelligent and modernized tomato production. To address the problems of intra- and inter-class multi-scale variation, complex background interference, and difficulty in mobile model deployment faced by tomato leaf disease identification, we propose a lightweight Ghost Dense network (LGDNet) to identify diseases of tomato leaves. First, we replace the standard convolution of the bottleneck layer in DenseNet with the Ghost module, which compresses the network size while maintaining the model's adaptability to the multi-scale variation of tomato leaf diseases. Then, we propose a lightweight and efficient coordinate multidimensional information fusion attention (CMIFA) module that enhances feature extraction for tomato leaves and enables the model to locate the diseased areas more accurately. The experimental results indicate that LGDNet reaches optimal recognition performance in both the PlantVillage dataset with a simple background and the Dataset of Tomato Leaves with natural scenes. Moreover, LGDNet achieves the minimum number of parameters among the compared models. In summary, LGDNet provides an excellent solution to the problem of accurately identifying tomato leaves in complex environments and provides a reference for deployment on mobile.

The flexible job shop scheduling problem (FJSP) is a complex problem with significant applications in modern manufacturing. While various metainspired algorithms are widely used in FJSP, they of-ten converge to local optima, especially as the problem size increases. To overcome this, we propose an adaptive hybrid algorithm with three stages of “explore-exploit-escape” (E3HA). In the first stage, we design a Simplified Variable Neighborhood Search (Sim-VNS) algorithm and introduce a Simplified Nopt1 Neighborhood for extensive exploration of solution spaces. In the second stage, we introduce the crossover operation from genetic algorithms to better exploit the elite solutions obtained in the first stage, and we also use mutation operations to improve the quality of regular solutions. Finally, in the third stage, we design a hybrid mechanism of Reverse Learning with Path Relinking (RLPR) and introduce a critical path neighborhood structure to increase the effectiveness of solutions in avoiding local optima and premature convergence. We perform ablation experiments to confirm the effectiveness of each stage of E3HA and test the algorithm on all BRdata and Fdata instances, comparing its performance to relevant existing state-of-the-art algorithms. The results show the effectiveness and stability of our algorithm for FJSP.

In the author list, "Ferry Sansoto" should be Ferry Susanto.• To reflect more accurately the contribution of the article, the title should be changed to "A permutation and equinumerosity based polynomial-time algorithm for simple undirected graph isomorphism."• In the abstract, the "Pythagorean Triples Theorem" should be removed.• In the abstract, "squared sums of elements" should be "nth power sums."• In Section 2.2, "and the sum of the individual squared elements.By checking two sums," should be ", the sum of the individual squared elements and until the sum of the nth power of the nth element in the array.By checking these sums,"• In Section 2.2, "For both vertex and edge arrays of row/column sum based on the vertex and edge adjacency matrices, if and only if one array is a permutation of another one, the corresponding two graphs are isomorphic."should be "For both the vertex and edge arrays of row/column sum based on the vertex and edge adjacency matrices, if and only if one array is a permutation of another one and the corresponding edge and vertex's adjacent relationship has been preserved, the corresponding two graphs are isomorphic."

Crowd counting and localisation are essential tasks in crowd analysis and are vital to ensure public safety. However, these tasks via UAV bring new obstacles compared with video surveillance (e.g. viewpoint and scale variations, background clutter, and small scales). To overcome the difficulties, this research presents a novel network named PDNet. It employs the multi-task learning approach to combine the point regression and density map regression. PDNet includes a backbone to extract multi-scale features, a Dilated Feature Fusion module (DFF), a Density Map Attention module (DMA), a density map branch and a point branch. Aims of DFF is to address the difficulties of small targets and scale variations by establishing relationships between targets and their surroundings. DMA is created to address the challenges of complicated backgrounds, allowing the PDNet to focus on the target's location. In addition, the density map branch and point branch are designed for density maps regression and point regression, respectively. Experiments on the DroneCrowd dataset demonstrate that our proposed network outperforms state-of-the-art approaches in terms of localisation, L-mAP (53.85%), L-AP@10 (59.14%), L-AP@15 (63.64%), and L-AP@20 (66.21%), and we improved counting performance and significantly reduced inference time. In addition, ablation experiments are conducted to prove the modules' effectiveness.

Bandwidth-sensitive applications rely on the accurate estimation of the bottleneck bandwidth. The real-time bandwidth prediction enables the application to cope with bandwidth fluctuation and adjust the transmission strategy to improve the Quality of Experience (QoE) of user. The traditional bandwidth prediction model hardly considers the bandwidth characteristics in various scenarios, making it challenging to achieve high accuracy. In this paper, we propose ALSTM model, which is based on the Long Short Term Memory (LSTM) recurrent neural network and the attention mechanism for multi-scenario bandwidth prediction. Firstly, we conduct the bandwidth trajectories feature analysis, and then we adopt the Support Vector Machine (SVM) to classify scenarios based on the bandwidth characteristics. Secondly, we apply an attention mechanism to assign weights to the input of the bandwidth series, and the attention feature is utilized to effectively select the feature sequences as input to the LSTM model for the prediction. The experimental results show that the ALSTM reduces the Root Mean Square Error (RMSE) by 20%, and the Mean Average Error (MAE) is improved by 26%. For practical applications, we adopt the pre-trained SVM model for real-time scenario detection, dynamic switch the corresponding ALSTM model, and the switching success rate is up to 86%. In addition, by deploying the proposed bandwidth prediction model ALSTM, the DASH's QoE has increased by more than 25%.

Localization is a crucial prerequisite for automated valet parking, in which a vehicle is required to navigate itself in a GPS-denied parking lot. Traditional visual localization methods usually build a feature map and use it for future localizations. However, the feature map is not robust to changes in illumination, appearance, and viewing perspective. To deal with this issue, we need a more stable map. In this paper, we propose to use the parking lot’s HD vector map directly for localization. The vector representation is ultimately stable but brings challenges in data association as well. To this end, we present a novel data association method to match the surround-view images with the vector map. In addition, we also propose a closed-form relocalization strategy by exploiting distinctive road mark combinations in the vector map. Experiments show that the proposed method is able to achieve centimeter-level localization accuracy in a multi-floor parking lot.

Summary The graph isomorphism problem is to determine two finite graphs that are isomorphic which is not known with a polynomial‐time solution. This paper solves the simple undirected graph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial‐time solution to check if two simple undirected graphs are isomorphic or not. Three new representation methods of a graph as vertex/edge adjacency matrix and triple tuple are proposed. A duality of edge and vertex and a reflexivity between vertex adjacency matrix and edge adjacency matrix were first introduced to present the core idea. Beyond this, the mathematical approval is based on an equivalence between permutation and bijection. Because only addition and multiplication operations satisfy the commutative law, we propose a permutation theorem to check fast whether one of two sets of arrays is a permutation of another or not. The permutation theorem was mathematically approved by Integer Factorization Theory, Pythagorean Triples Theorem, and Fundamental Theorem of Arithmetic. For each of two n ‐ary arrays, the linear and squared sums of elements were respectively calculated to produce the results.

Summary Individuals' right to privacy includes control over access to their location information. With the advent of location‐based services and personal transport services (such as ridesharing), the risk of location privacy breaches is increased greatly. The potential negative effects of location privacy leakages include spam location‐based service flooding, threats to personal safety (such as physical attacks), and intrusion related to access to private places (such as homes and hospitals). Therefore, protecting the privacy of users' real locations is becoming increasingly important. This is often achieved using a pseudo‐location near the real location, but existing pseudo‐location generators, such as NRand and the uniform random method, suffer from statistical inference, which can infer the obfuscation domain to cover the real location. In this paper, we propose an intelligent pseudo‐location recommendation (IPLR) method to reduce the risk of a statistical inference attack. In IPLR, we generate a random substitute of the real location to attract the adversary and thus hide the real location. Then, the pseudo‐location is generated in the neighborhood of the random substitute location following a normal distribution; the random substitute location is changed frequently to confuse attackers. In particular, we define three levels of location privacy, ie, address level, street level, and district level, to evaluate the effectiveness of the IPLR method. Our experimental study using simulation data demonstrates that the proposed IPLR method achieves lower risk of location privacy leakage and higher probabilities of safety in all three levels of location privacy than NRand and the random method. It also demonstrates the effectiveness of the proposed IPLR to balance location privacy and service quality.