Reviewerly/exHarmony huggingface dataset:
A Multi-Population Exploration-only Exploitation-only Hybrid on CEC-2020 Single Objective Bound Constrained Problems

Many meta-heuristics attempt to “transition” a single algorithm from exploration to exploitation. Conversely, previous research has shown that it can be better for the two distinct tasks of exploration and exploitation to instead be performed by two distinct algorithms/mechanisms. This has led to the development of Exploration-only, Exploitation-only Hybrid search techniques. This paper presents a Multi-Population Exploration only Exploitation-only Hybrid in which exploitation occurs in one population while a global search strategy performs exploration in another population. Unlike a sequential hybrid, this hybridization allows the exploratory technique (in this case Unbiased Exploratory Search) to delay convergence (up to indefinitely) which allows the hybrid system to benefit from a large budget of function evaluations. The new hybrid is evaluated on the CEC2020 test suite in the special session and competition on single objective bound constrained numerical optimization.
Genetic Improvement of Genetic Programming

GISMOE BNF grammar based GI is applied to optimise run time of the tree interpreter in the fastest single computer floating point genetic programming system, GPavx. Up to two fold speed up is obtained. Performance varies with tree size. The GI version of Singleton's C++ GPquick is demonstrated on random trees of up to 79 million opcodes on Intel AVX512 SIMD parallel compute servers.
Harmonious attention network for person re-identification via complementarity between groups and individuals

Person re-identification (Re-ID) is of important capability for artificial intelligence and human–computer interaction. The main challenge of person Re-ID lies in limited data to precisely capture a wide range of appearance variations over multiple viewpoints. Furthermore, compared to the Re-ID between the single person, the person groups contain information about the relationship between pedestrians that can potentially help identify certain identities. The Re-ID combining groups and individuals remain to be a promising task under rare study. In this paper, we propose a harmonious attention network for person re-identification, in which we jointly consider the complementarity between person groups and individuals. Concretely, first we propose a two-stream attentive network (TSAN) to respectively learn the information from the person groups and individuals. TSAN consists of a spatial–temporal fusion network for the group Re-ID, as well as a deep network for the traditionally individual person Re-ID. To jointly consider the contributions of the groups and individuals, then we propose a novel re-ranking algorithm (GIRK) based on the learned features to associate the group and individual information. We also propose a new group Re-ID dataset DukeGroupVid to evaluate the performance of our approach. Comprehensive experimental results on the proposed dataset and other Re-ID datasets demonstrate the effectiveness of our model.
Energy-Efficient Hardware for Language Guided Reinforcement Learning

Reinforcement learning (RL) has shown great performance in solving sequential decision-making problems. While a lot of works have done on processing state information such as images, there has been some effort towards integrating natural language instructions into RL. In this paper, we propose an energy-efficient architecture which is designed to receive both images and text inputs as a step towards designing RL agents that can understand human language and act in real-world environments. Different configurations are proposed to illustrate the trade off between the number of parameters and the model accuracy, and a custom low power hardware is designed and implemented on FPGA based on the best configuration. The hardware designed to be configurable with different parameters such as number of processing elements, so that it can easily balance power and performance. The high throughput configuration achieves 217 frames per second throughput with 1.2 mJ energy consumption per classification on Xilinx Artix-7 FPGA, while the low power configuration consumes less than 139 mW for 30 frames per second classification. Compared to the similar works using FPGA for hardware implementation, our design is more energy efficient and need less energy for generating each output.
Interdisciplinary Teaching Using Satellite Images as a Way to Introduce Remote Sensing in Secondary School

This article aims to meet two needs: (i) the need for skilled workers and students in the area of remote sensing and (ii) the need to make school science interesting for students. This article addresses both needs by proposing a project for high school students entitled “the Colors of Earth”. The main aim for students was to distinguish between different types of land cover via the creation of various false color band compositions from the satellite Sentinel-2. Achieving this goal requires knowledge from various subjects and enables their practical application via work performed using real data. The project was presented to 39 high-school teachers and 184 high-school students (K-9 and K-10) in the summer semester of the 2019/2020 school year, and their opinions about the project were collected. Overall, both students and teachers judged the project to be interesting, worth introducing to the school, and capable of influencing student opinions of science. In addition, introducing remote sensing elements during pre-university education can help meet the demands for students and workers to study Earth observation.
Explaining Symbolic Regression Predictions

The outgrowing application of machine learning methods has raised a discussion in the artificial intelligence community on model transparency. In the center of this discussion is the question of model explanation and interpretability. The genetic programming (GP) community has systematically pointed out as one of the major advantages of GP the fact that it produces models that can be interpreted by humans. However, as other interpretable supervised models, the more complex the model becomes, the less interpretable it is. This work focuses on post-hoc interpretability of GP for symbolic regression. This approach does not explain the process followed by a model to reach a decision. Instead, it justifies the predictions it makes. The proposed approach, named Explanation by Local Approximation (ELA), is simple and model agnostic: it finds the nearest neighbors of the point we want to explain and performs a linear regression using this subset of points. The coefficients of this linear regression are then used to generate a local explanation to the model. Results show that the errors of ELA are similar to those of the regression performed with all points. It also shows that simple visualizations can provide insights to the users about the most relevant attributes.
A New Feature Descriptor for Multimodal Image Registration Using Phase Congruency

Images captured by different sensors with different spectral bands cause non-linear intensity changes between image pairs. Classic feature descriptors cannot handle this problem and are prone to yielding unsatisfactory results. Inspired by the illumination and contrast invariant properties of phase congruency, here, we propose a new descriptor to tackle this problem. The proposed descriptor generation mainly involves three steps. (1) Images are convolved with a bank of log-Gabor filters with different scales and orientations. (2) A window of fixed size is selected and divided into several blocks for each keypoint, and an oriented magnitude histogram and the orientation of the minimum moment of a phase congruency-based histogram are calculated in each block. (3) These two histograms are normalized respectively and concatenated to form the proposed descriptor. Performance evaluation experiments on three datasets were carried out to validate the superiority of the proposed method. Experimental results indicated that the proposed descriptor outperformed most of the classic and state-of-art descriptors in terms of precision and recall within an acceptable computational time.
Ultra-Lightweight Mutual Authentication in the Vehicle Based on Smart Contract Blockchain: Case of MITM Attack

For quite a long time, purchasers have come to depend on the convenience and security and included security that a passive vehicle immobilizer framework offers. These frameworks comprise of a Passive Entry Passive Start (PEPS) innovation, conveyed by the driver, and a Smart Key Unit (SKU), mounted in the vehicle with regards to the principal parts of these frameworks. This paper for the first time shows the cyber vulnerability of PEPS in the vehicle against the man-in-the middle attack (MITM). It enables the hacker to open and begin the vehicle, although there was still a distance between the vehicle and the key. To stop this physical cyber attack, we propose a hearty ultra-lightweight common confirmation technique dependent on the radio frequency identification (RFID) convention that cooperates with a decentralized database to verify the PEPS. A secure blockchain based on smart contract is also developed which is compatible with the mutual authentication in the vehicle. A detailed security analysis is performed on an undefined vehicle to prove the high security of the proposed communication approach when facing MITM.
RGB-D salient object detection via cross-modal joint feature extraction and low-bound fusion loss

RGB-D salient object detection aims at identifying attractive objects in a scene by combining the color image and depth map. However, due to the differences between RGB-D image pairs, it is a key issue to utilize cross-modal data effectively. In this paper, we propose a novel RGB-D salient object detection method via cross-modal joint feature extraction and low-bound fusion loss. A two-stream framework is designed to generate the saliency maps for the RGB image and depth map. During the feature extraction, a cross-modal joint feature extraction module (CFM) is proposed to capture valuable joint features from the two streams. The CFM explores complementary information from the feature extraction and feeds the joint features to the aggregation stage of the network. Then, the fusion block (FB) is utilized to aggregate the multi-scale features of each stream and the joint features to generate the updated features. In addition, a low-bound fusion loss is designed to constrain the predictions of the two streams, to improve the lower bound of saliency values and generate a distinct saliency map. Experimental results on five datasets demonstrate that the proposed method achieves superior performances.
Adaptive Differential Evolution Based on Successful Experience Information

As a powerful optimization algorithm for solving nonlinear, complex and tough global optimization problems, differential evolution (DE) has been widely applied in various science and engineering fields. In this article, considering that the evolution direction of each individual is not fully exploited to guide the search process in most DE algorithms, a new DE variant (named ADEwSE), which incorporates the successful experience of evolved individuals into classic “current-to-pbest/1” mutation strategy to reduce the randomness of search direction, is proposed. Moreover, crossover matrix sorting scheme based on real crossover rate, opposition learning of crossover rate and adaptive adjustment of top p% values are combined with the new mutation strategy to improve the global search ability. In addition, to improve the searching ability of ADEwSE further, an ADEwSE variant by introducing the linear reduction of population size is proposed. In order to verify and analyze the performance of ADEwSE, numerical experiments on a set of 29 test problems from CEC2017 benchmark for 30, 50 and 100 dimensions are executed. And the experimental results are compared with that of 21 state-of-art DE-based algorithms. Comparative analysis indicates that the ADEwSE and its improved version are competitive with these state-of-art DE variants in terms of solution quality obtained.
A Systematic Literature Review of Medical Image Analysis Using Deep Learning

We review literature in top journals and conferences on the usage of deep learning for medical image analysis in modern healthcare. As a result it is shown that deep learning offers unique capabilities and breakthroughs in identifying, classifying and segmenting different kinds of medical images, especially related to cancer in the breast, lung, and brain.
Cross-view classification by joint adversarial learning and class-specificity distribution

Despite the promising preliminary results, none of existing deep learning based cross-view classification methods simultaneously takes into account both view consistency learning and class-specificity distribution of the extracted features, resulting in unstable classification performance. Moreover, most existing cross-view classification methods are sensitive to scale due to the scale issue of view representations, resulting in unstable view-consistent representations. In this paper, we propose a new deep adversarial network for cross-view classification that attempts to learn robust view-consistent representations by combing the thought of adversarial learning and metric learning in Fisher criterion. Meanwhile, a class-specificity distribution term, which is measured by ℓ12-norm, is employed to make the view-consistent representations with the same label to further have a common distribution in dimension space while view-representations with different labels have different distribution in the intrinsic dimension space. We formulate the aforementioned two concerns into a unified optimization framework. Extensive experiments on several real-world datasets indicate the effectiveness of our method over the other state-of-the-arts.
Improvement of English key competences based on machine learning and artificial intelligence technology

How to apply artificial intelligence technology to help education reform is a problem that teaching researchers need to solve urgently. Using artificial intelligence technology to improve the key competences of English subjects is the new direction of current English teaching development. This research combines machine learning technology to analyze the key competences assessment of English teaching disciplines and builds an evaluation model corresponding to the threshold. Moreover, on the basis of orderly mutual information, this study combines the maximum correlation and minimum redundancy theory to select the attribute algorithm to optimize the key competences assessment function of English subjects. In addition, in this study, the performance of the research model is analyzed through a comparative test, and the results are analyzed through actual numerical comparison and error comparison. The research results show that the recognition accuracy of this research model is closer than that of the real score, has higher accuracy, and has certain practical effects.
A novel selective encryption scheme for H.264/AVC video with improved visual security

As cloud storage becomes more popular, concerns about data leakage have been increasing. Encryption techniques can be used to protect privacy of videos stored in the cloud. However, the recently proposed sketch attack for encrypted H.264/AVC video, which is based on the macroblock bitstream size (MBS), can generate the outline images of both intra-frames and inter-frames from a video encrypted by most existing encryption schemes; thus, the protection of the original video may be considered a failure. In this paper, a novel selective encryption scheme for H.264/AVC video with improved visual security is presented. Two different scrambling strategies that do not destroy the format compatibility are proposed to change the relative positions between macroblocks in intra-frames and inter-frames respectively, which in turn substantially distort the sketched outline images so that they do not disclose meaningful information. Moreover, the sign bits of non-zero DCT coefficients are encrypted to contribute to the visual security of our scheme, and an adaptive encryption key related to the intra prediction mode and the DCT coefficient distribution of each frame is employed to provide further security. The experimental results show that our encryption scheme can achieve a better visual scrambling effect with a small adverse impact on the video file size. Furthermore, the security analysis demonstrates that our scheme can successfully resist the MBS sketch attack compared with other related schemes. The proposed method is also proven secure against some other known attacks.
Research on FOD Detection for Airport Runway based on YOLOv3

Foreign object debris (FOD) on airport runways is a threatening factor to aircraft taking off and landing. Accurately detection of foreign objects debris is important to ensure aircraft flight safety. In this paper, a detection algorithm based on YOLOv3(You Only Look Once) for foreign objects debris is presented. This method employs deep residual network to extract feature and multi-scale feature fusion to detect small-scale FOD. Sample datasets of foreign object debris are established to validate our proposed method. The experiments show that the detection algorithm based on YOLOv3 effectively detect foreign objects debris, and has good accuracy and robustness.
Regional Terrain Complexity Assessment Based on Principal Component Analysis and Geographic Information System: A Case of Jiangxi Province, China

Regional terrain complexity assessment (TCA) is an important theoretical foundation for geological feature identification, hydrological information extraction and land resources utilization. However, the previous TCA models have many disadvantages; for example, comprehensive consideration and redundancy information analysis of terrain factors is lacking, and the terrain complexity index is difficult to quantify. To overcome these drawbacks, a TCA model based on principal component analysis (PCA) and a geographic information system (GIS) is proposed. Taking Jiangxi province of China as an example, firstly, ten terrain factors are extracted using a digital elevation model (DEM) in GIS software. Secondly, PCA is used to analyze the information redundancy of these terrain factors and deal with data compression. Then, the comprehensive evaluation of the compressed terrain factors is conducted to obtain quantitative terrain complexity indexes and a terrain complexity map (TCM). Finally, the TCM produced by the PCA method is compared with those produced by the slope-only, the variation coefficient and K-means clustering models based on the topographic map drawn by the Bureau of Land and Resources of Jiangxi province. Meanwhile, the TCM is also verified by the actual three-dimensional aerial images. Results show that the correlation coefficients between the TCMs produced by the PCA, slope-only, variable coefficient and K-means clustering models and the local topographic map are 0.894, 0.763, 0.816 and 0.788, respectively. It is concluded that the TCM of the PCA method matches well with the actual field terrain features, and the PCA method can reflect the regional terrain complexity characteristics more comprehensively and accurately when compared to the other three methods.
Recognition of classroom student state features based on deep learning algorithms and machine learning

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.
Testing Randomness Using Artificial Neural Network

The vital characteristic of randomness is unpredictability. Thus any regularity will compromise the application of random numbers. Quantum random number generators (QRNGs) can provide intrinsic unpredictable randomness based on the nature of quantum physics, while pseudo-random number generator can not due to the origination of deterministic algorithms or physical processes. However, commonly used traditional test suits are rigorously to test the statistical properties, and the unpredictability of random numbers is still difficult to test. To verify which sources of random numbers are truly unpredictable, in this paper, we propose a new randomness testing method with the artificial neural network (ANN). Random number sequences generated by four different kinds of sources are tested, which are the natural number λ, the linear congruence generator (LCG) pseudo-random algorithm, the Mersenne Twister (MT) pseudorandom algorithm and the QRNG based on vacuum noise, respectively. The testing results indicate that the random sequences from natural number λ and LCG fail to pass our randomness test, while the other two kinds of sequences pass the test successfully due to the relatively simple structure of our ANN. As the complexity of the ANN and the amount of computing power involved increasing, this test method has potential to predict the MT random numbers, and it is also expected that the quantum random numbers can not pass the test with unpredictability no matter how complex the ANN is.
ML2E: Meta-Learning Embedding Ensemble for Cold-Start Recommendation

Cold-start problem has been recognized as the most crucial challenge in recommender systems. Many recommendation algorithms work well when lots of preference information is available but start to degrade in cold-start settings. Inspired by the spirit of meta-learning, we identified that the appeal of cold-start problems and the superiority of meta-learning are congruous where meta-learning aims to learn a model from a small set of labeled examples (users' consuming history), and then this model can be quickly generalized to new tasks (recommendation for new users or new items). Therefore, faced with the extreme cold-start scenario, we proposed a meta-learning embedding ensemble (ML2E) recommendation algorithm to forecast new users' preference and generate desirable initial embedding for new items. It's worth pointing out that the training process of ML2E only uses few first-order gradient information, and ML2E not only has the incremental mining ability for different mini-batches on the same task but also the generalization ability for different tasks. Finally, we validated ML2E on two benchmark datasets, experimental results showed that our algorithm has significantly improved recommendation metric in comparison with three existing baselines.