Reviewerly/exHarmony huggingface dataset:
Lessons from the <i>Evolutionary Computation Bestiary</i>

Abstract The field of metaheuristics has a long history of finding inspiration in natural systems, starting from evolution strategies, genetic algorithms, and ant colony optimization in the second half of the 20th century. In the last decades, however, the field has experienced an explosion of metaphor-centered methods claiming to be inspired by increasingly absurd natural (and even supernatural) phenomena—several different types of birds, mammals, fish and invertebrates, soccer and volleyball, reincarnation, zombies, and gods. Although metaphors can be powerful inspiration tools, the emergence of hundreds of barely discernible algorithmic variants under different labels and nomenclatures has been counterproductive to the scientific progress of the field, as it neither improves our ability to understand and simulate biological systems nor contributes generalizable knowledge or design principles for global optimization approaches. In this article we discuss some of the possible causes of this trend, its negative consequences for the field, and some efforts aimed at moving the area of metaheuristics toward a better balance between inspiration and scientific soundness.
Enhancing identification of digital rock images using super-resolution deep neural network

Identification of pore and minerals from digital rock images plays an important role in characterization of oil and gas reservoirs. Traditional methods usually require high costs of time and labor. With the rapid development of artificial intelligence, deep learning methods have been applied to geosciences. Among them, semantic segmentation neural networks are used to identify rock images, but traditional semantic segmentation networks require high-resolution input images to improve performance and can only generate masks with the same resolutions as the input images, which again requires high-performance equipment and increases computational and equipment costs. In this paper, we propose an approach to enhancing identification of digital rock images with super-resolution neural network (SSRN) which enables low-resolution input images to generate high-resolution representations. We use SSRN to train the 10-classification thin-section images, and compare it with traditional deep learning methods. It indicates that SSRN can effectively improve the performance of the generated high-resolution masks.
Path planning algorithm of robot arm based on improved RRT* and BP neural network algorithm

To address the issues of slow motion planning, low efficiency, and high path calculation cost of the six-degrees of freedom manipulator in three dimensional multi-obstacle narrow space, a path planning method of the manipulator based on Back Propagation (BP) neural network and improved Rapidly expanding Random Tree* (RRT*) algorithm is proposed (referred to as BP-RRT*). Due to the spherical envelope of the obstacle, this method evaluates the connection between the path and obstacle in space using the triangular function and identifies the collision-free path in 3D space. Then, using the sampling space division, obstacles discretization, and distance weight function, the adaptive node sampling probability method of RRT* algorithm in space is proposed, to reduce unnecessary sampling nodes and optimize the sampling efficiency; because the sampling nodes might fall into the area with dense obstacles, which results in significant increase in the search time. A stepwise sampling method is proposed to modify the global search into a phased local search, train the BP neural network model, forecast the number of node samples in the local search at each stage, automatically guide the algorithm into the next stage to complete the search, and improve the path optimization efficiency. Finally, the simulation experiment of the improved BP-RRT* algorithm is executed on the Python and Robot Operating System, and the physical experiment is done on the Baxter manipulator. The effectiveness and superiority of the improved algorithm are determined by comparing it with the existing algorithms.
Information-diffused graph tracking with linear complexity

Mainstream tracking approaches have achieved remarkable performance by adopting transformer structures. However, transformer structures’ inherent design of dot-product with softmax normalization incurs quadratic computation complexity regarding sequence length. This issue is further complicated when vision tasks employ softmax attention, as sequence length scales with the square of images’ sizes. Even though sparse attention and low-rank decomposition can alleviate over-inflated computation, it is still laborious to balance trackers’ accuracy, computation cost, and inference speed. To tackle the above problems, we propose an Information-Diffused Graph tracking pipeline with linear complexity (IDGtrack). As the feature constraint relationship in the physical world is an important cue for vision tasks, graph modules are constructed with information-diffused adjacency matrices to substitute softmax attention, which is not only efficient for linear computations but also maintains the non-negativity and global distribution of the attention matrix. Distinct from traditional linear attention methods exclusive to self-attention, a self-integrated and cross-context graph module with linear complexity is explored where a complete bipartite graph is established between the target and search region, facilitating a comprehensive perception of local and background information. Extensive experiments are conducted on public tracking benchmarks, demonstrating that our method achieves state-of-the-art (SOTA) performance with 111 FPS on GPU RTX3090.
Values@Runtime: An Adaptive Framework for Operationalising Values

We present an adaptive framework to assist users in making more value-sensitive decisions during their (runtime) use of software. The framework enables users to (i) represent, instantiate, and monitor their values and behaviour; (ii) understand mismatches between stated values and their observed behaviour; and (iii) recommend ways to align users’ values and behaviour. We built a values shopping basket tool to illustrate and demonstrate the adaptive framework in the food consumption domain, a sector that is rich in values and regularly undergoes reflection and debate.
Make Your Tools Sparkle with Trust: The PICSE Framework for Trust in Software Tools

The day to day of a software engineer involves a variety of tasks. While many of these tasks are collaborative and completed as such, it is not always possible or feasible to engage with other engineers for task completion. Software tools, such as code generators and static analysis tools, aim to fill this gap by providing additional support for developers to effectively complete their tasks. With a steady stream of new tools that emerging to support software engineers, including a new breed of tools that rely on artificial intelligence, there are important questions we should aim to answer regarding the trust engineers can, and should, put into their software tools and what it means to build a trustworthy tool. In this paper, we present findings from an industry interview study conducted with 18 engineers across and external to the Microsoft organization. Based on these interviews, we introduce the PICSE (pronounced "pixie") framework for trust in software tools to provide preliminary insights into factors that influence engineer trust in their software tools. We also discuss how the PICSE framework can be considered and applied in practice for designing and developing trustworthy software tools.
A Graph-Incorporated Latent Factor Analysis Model for High-Dimensional and Sparse Data

A High-dimensional and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> parse (HiDS) matrix is frequently encountered in Big Data-related applications such as e-commerce systems or wireless sensor networks. It is of great significance to perform highly accurate representation learning on an HiDS matrix due to the great desires of extracting latent knowledge from it. <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> atent <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</u> actor <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> nalysis (LFA), which represents an HiDS matrix by learning the low-rank embeddings based on its observed entries only, is one of the most effective and efficient approaches to this issue. However, most existing LFA-based models directly perform such embeddings on an HiDS matrix without exploiting its hidden graph structures, resulting in accuracy loss. To aid this issue, this paper proposes a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</u> raph-incorporated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</u> atent <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</u> actor <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> nalysis (GLFA) model. It adopts two-fold ideas: 1) a graph is constructed for identifying the hidden <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</u> igh- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</u> rder <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</u> nteraction (HOI) among nodes described by an HiDS matrix, and 2) a recurrent LFA structure is carefully designed with the incorporation of HOI, thereby improving the representation learning ability of a resultant model. Experimental results on three real-world datasets demonstrate that GLFA outperforms six state-of-the-art models in predicting the missing data of an HiDS matrix, which evidently supports its strong representation learning ability to HiDS data.
Low-light image enhancement based on virtual exposure

Under poor illumination, the image information captured by a camera is partially lost, which seriously affects the visual perception of the human. Inspired by the idea that the fusion of multiexposure images can yield one high-quality image, an adaptive enhancement framework for a single low-light image is proposed based on the strategy of virtual exposure. In this framework, the exposure control parameters are adaptively generated through a statistical analysis of the low-light image, and a virtual exposure enhancer constructed by a quadratic function is applied to generate several image frames from a single input image. Then, on the basis of generating weight maps by three factors, i.e., contrast, saturation and saliency, the image sequences and weight images are transformed by a Laplacian pyramid and Gaussian pyramid, respectively, and multiscale fusion is implemented layer by layer. Finally, the enhanced result is obtained by pyramid reconstruction rule. Compared with the experimental results of several state-of-the-art methods on five datasets, the proposed method shows its superiority on several image quality evaluation metrics. This method requires neither image calibration nor camera response function estimation and has a more flexible application range. It can weaken the possibility of overenhancement, effectively avoid the appearance of a halo in the enhancement results, and adaptively improve the visual information fidelity.
HyperGraph based human mesh hierarchical representation and reconstruction from a single image

Reconstructing 3D human mesh from monocular images has been extensively studied. However, the existing non-parametric reconstruction methods are inefficient when modeling vertex relationship concerning human information due to they generally adopt an uniform template mesh. To this end, this paper proposes a novel hypergraph-based human mesh hierarchical representation that enables the expression of vertices at body, parts, and vertices perspectives, corresponding to global, local and individual, respectively. And then a novel HyperGraph Attention-based human mesh reconstruction network (HGaMRNet) is put forward in turn, which mainly consists of two modules and can efficiently capture human information from different granularities of the mesh. Specifically, the first module, Body2Parts, decouples a body into local parts, and leverages Mix-Attention (MAT) based feature encoder to learn visual cues and semantic information of the parts for capturing complex human kinematic relationships from monocular images. The second module, Part2Vertices, transfers part features to the corresponding vertices through an adaptive incidence matrix, and utilizes a HyperGraph Attention network to update the vertex features. This is conductive to learning the fine-grained morphological information of a human mesh. All in one, supported by the hypergraph-based hierarchical representation of the human mesh, HGaMRNet balances the effects of neighbor vertex from different levels properly and eventually promotes the reconstruction accuracy of human mesh. Experiments conducted on both Human3.6M and 3DPW datasets show that HGaMRNet outperforms most of the existing image-based human mesh reconstruction methods.
Differential Analysis of ARX Block Ciphers Based on an Improved Genetic Algorithm

Differential cryptanalysis is one of the most critical analysis methods to evaluate the security strength of cryptographic algorithms.This paper first applies the genetic algorithm to search for differential characteristics in differential cryptanalysis.A new algorithm is proposed as the fitness function to generate a high-probability differential characteristic from a given input difference.Based on the differential of the differential characteristic found by genetic algorithm, Boolean satisfiability (SAT) is used to search all its differential characteristics to calculate the exact differential probability.In addition, a penalty-like function is also proposed to guide the search direction for the application of the stochastic algorithm to differential cryptanalysis.Our new automated cryptanalysis method is applied to SPECK32 and SPECK48.As a result, the 10-round differential probability of SPECK32 is improved to 2 -30.34 , and a 12-round differential of SPECK48 with differential probability 2 -46.78 is achieved.Furthermore, the corresponding differential attacks are also performed.The experimental results show our method's validity and outstanding performance in differential cryptanalysis.
EST-NAS: An evolutionary strategy with gradient descent for neural architecture search

Using weight-sharing and continuous relaxation strategies, the gradient descent-based differential architecture search has achieved great success in automatically designing neural network architectures. However, unresolved issues, i.e., the local optimum dilemma of the gradient descent method, and the network performance collapse of the searched architecture with too many unreasonable operations, are still frustrating for researchers and practitioners. To address these two issues, a novel and efficient neural architecture search approach based on a hybrid evolutionary strategy, termed EST-NAS, is proposed in this paper. In particular, we propose using a new evolutionary strategy to explore various search directions based on the gradient descent-based neural network architecture search, aiming at obtaining a more excellent architecture. In the proposed EST-NAS, the gradient descent architecture search is performed first, and then the best architecture obtained is utilized to design an efficient initialization for the following evolutionary strategy-based architecture search. By hybridizing evolutionary strategy with gradient descent-based search, EST-NAS can improve the performance of the searched architecture with better search efficiency. Meanwhile, the validation accuracy is applied to directly measure the importance of operations, which reduces the error in the relationship between operation and task performance. Extensive experimental results in the various datasets on different search spaces show that the proposed EST-NAS achieves remarkably competitive performance with less search cost, compared to other state-of-the-art NAS approaches.
BRPPNet: Balanced privacy protection network for referring personal image privacy protection

Traditional personal image privacy protection usually suffers from the overprotection problem, where one or more undesired persons in an image may be inevitably shielded, yielding unnecessary information loss. Motivated by this, this paper explores a novel task “Referring Personal Image Privacy Protection” (RP-IPP) to protect the designated person in an image according to a user’s text or voice input. We propose a lightweight yet effective personal protection network “Balanced Referring Personal PrivacyNet” (BRPPNet), which introduces a Multi-scale Feature Fusion Module (MFFM) with a proper “Balanced-BCE loss” to effectively localize the referring person. Technically, MFFM adopts a lightweight CNN backbone to filter noise information as well as complement visual features for high-quality mask generation. What is more, we have theoretically proven the insufficiency of binary cross-entropy (BCE) loss and its variants for RP-IPP, which suffers from the serious imbalance problem during gradient propagation, and thus formulate “Balanced-BCE loss” to alleviate the gradient propagation bias caused by unequal positive and negative pixels. To verify the effectiveness of BRPPNet, we manually construct a dataset “Referring Personal COCO” (RPCOCO). The experimental results demonstrate that BRPPNet outperforms the advanced approaches for RP-IPP, and the proposed “Balanced-BCE loss” embedded into several existing approaches consistently boosts performance, yielding remarkable improvements on all the metrics.
On Codex Prompt Engineering for OCL Generation: An Empirical Study

The Object Constraint Language (OCL) is a declarative language that adds constraints and object query expressions to Meta-Object Facility (MOF) models. OCL can provide precision and conciseness to UML models. Nevertheless, the unfamiliar syntax of OCL has hindered its adoption by software practitioners. LLMs, such as GPT-3, have made significant progress in many NLP tasks, such as text generation and semantic parsing. Similarly, researchers have improved on the downstream tasks by fine-tuning LLMs for the target task. Codex, a GPT-3 descendant by OpenAI, has been fine-tuned on publicly available code from GitHub and has proven the ability to generate code in many programming languages, powering the AI-pair programmer Copilot. One way to take advantage of Codex is to engineer prompts for the target downstream task. In this paper, we investigate the reliability of the OCL constraints generated by Codex from natural language specifications. To achieve this, we compiled a dataset of 15 UML models and 168 specifications from various educational resources. We manually crafted a prompt template with slots to populate with the UML information and the target task in the prefix format to complete the template with the generated OCL constraint. We used both zero- and few-shot learning methods in the experiments. The evaluation is reported by measuring the syntactic validity and the execution accuracy metrics of the generated OCL constraints. Moreover, to get insight into how close or natural the generated OCL constraints are compared to human-written ones, we measured the cosine similarity between the sentence embedding of the correctly generated and human-written OCL constraints. Our findings suggest that by enriching the prompts with the UML information of the models and enabling few-shot learning, the reliability of the generated OCL constraints increases. Furthermore, the results reveal a close similarity based on sentence embedding between the generated OCL constraints and the human-written ones in the ground truth, implying a level of clarity and understandability in the generated OCL constraints by Codex.
Burt: A Chatbot for Interactive Bug Reporting

This paper introduces Burt,a web-based chatbot for interactive reporting of Android app bugs. Burtis designed to assist Android app end-users in reporting high-quality defect information using an interactive interface. Burtguides the users in reporting essential bug report elements, i.e., the observed behavior, expected behavior, and the steps to reproduce the bug. It verifies the quality of the text written by the user and provides instant feedback. In addition, Burtprovides graphical suggestions that the users can choose as alternatives to textual descriptions. We empirically evaluated Burt,asking end-users to report bugs from six Android apps. The reporters found that Burt's guidance and automated suggestions and clarifications are useful and Burtis easy to use. Burtis an open-source tool, available at github.com/sea-lab-wm/burt/tree/tool-demo. A video showing the full capabilities of Burtcan be found at https://youtu.be/SyfOXpHYGRo
Urban feature shadow extraction based on high-resolution satellite remote sensing images

With the continuous increase in the resolution of high-resolution remote sensing satellite images, shadows have increasingly impacted feature target recognition and classification in images, urban reconstruction, and image interpretation. Although various shadow detection methods exist for different features, undetected small shadowed areas, misclassified dark areas, and highlighted non-shadowed areas remain. Moreover, it is still difficult to distinguish waters from shadowed regions owing to their similar characteristics. To further solve these problems, we propose a novel mixed shadow detection index (MSDI) for extracting urban feature shadows, using the original information of the first principal component that contains the differences between most of the different objects in the image, as well as the difference features in the shadowed areas and water bodies. We evaluated the effectiveness and robustness of our method by conducting comparison experiments using Gaofen-1, Gaofen-2, and WorldView-3 images from different scenes, times, and acquisition locations. Through visual analysis and data analysis of our method, we found that the method achieved excellent shadow detection results, with an average total accuracy 94% for shadow detection. The proposed shadow detection algorithm could accurately recognize waters, and it could also accurately recognize easily missed small shadowed areas and easily confused non-shadowed areas.