Reviewerly/exHarmony huggingface dataset:
Efficient approximate approach for graph edit distance problem

Graph Edit Distance (GED) problem is a well-known tool used to measure the similarity/dissimilarity between two graphs. It searches for the best set of edit operations (in terms of cost) that transforms one graph into another. Due to the NP-hardness nature of the problem, the search space increases exponentially making exact approaches impossible to use for large graphs. In this context, there is a huge need for approaches that give near-optimal results in reasonable time. In this paper, we propose a tree-based approximate approach for dealing with GED problem. It operates on a search tree that models all possible solutions of the problem. Since exploring the whole tree is impractical; this approach keeps only the best k nodes at each level of the tree for further exploration. This reduces enormously the execution time without scarifying the solution quality. Experiments using small and medium size data-sets show the low deviation of our results as compared to the optimal results of a Depth First Search algorithm. Moreover, our approach show a strong scalability potential by dealing with large data-sets in low execution time.
An Improved Multioperator-Based Constrained Differential Evolution for Optimal Power Allocation in WSNs

Optimal power allocation (OPA), which can be transformed into an optimization problem with constraints, plays a key role in wireless sensor networks (WSNs). In this paper, inspired by ant colony optimization, an improved multioperator-based constrained adaptive differential evolution (namely, IMO-CADE) is proposed for the OPA. The proposed IMO-CADE can be featured as follows: (i) to adaptively select the proper operator among different operators, the feedback of operators and the status of individuals are considered simultaneously to assign the selection probability; (ii) the constrained reward assignment is used to measure the feedback of operators; (iii) the parameter adaptation is used for the parameters of differential evolution. To extensively evaluate the performance of IMO-CADE, it is used to solve the OPA for both the independent and correlated observations with different numbers of sensor nodes. Compared with other advanced methods, simulation results clearly indicate that IMO-CADE yields the best performance on the whole. Therefore, IMO-CADE can be an efficient alternative for the OPA of WSNs, especially for WSNs with a large number of sensor nodes.
Multi-Actor-Attention-Critic Reinforcement Learning for Central Place Foraging Swarms

Multiple agents with relatively low cost, decentralized control, and robustness have the advantages of completing a foraging task more efficiently than a single advanced robot. Despite many foraging algorithms are efficient in multiple robot systems, most are pre-designed or not very adaptive to different environments since they have to evolve the parameters of the foraging algorithm in each different environment. Besides, designing an efficient collision avoidance strategy for multiple agents is a challenge. Addressing these issues, we introduce the multi-actor-attention-critic(MAAC) reinforcement learning method into the multiple foraging agents. We train the foraging strategy for multiple simulated agents. We compare our approach with existing foraging algorithms for multiple robots, the Central Place Foraging Algorithm (CPFA) and the Distributed Deterministic Spiral Algorithm (DDSA). Experimental results demonstrate that our approach outperforms the two algorithms. Also, we illustrate that our approach has a better performance in avoiding obstacles and adapting to different environments.
A Case-Based Approach for the Selection of Explanation Algorithms in Image Classification

Research on eXplainable AI (XAI) is continuously proposing novel approaches for the explanation of image classification models, where we can find both model-dependent and model-independent strategies. However, it is unclear how to choose the best explanation approach for a given image, as these novel XAI approaches are radically different. In this paper, we propose a CBR solution to the problem of choosing the best alternative for the explanation of an image classifier. The case base reflects the human perception of the quality of the explanations generated with different image explanation methods. Then, this experience is reused to select the best explanation approach for a given image.
Balance-Aware Grid Collage for Small Image Collections

Grid collages (GClg) of small image collections are popular and useful in many applications, such as personal album management, online photo posting, and graphic design. In this article, we focus on how visual effects influence individual preferences through various arrangements of multiple images under such scenarios. A novel balance-aware metric is proposed to bridge the gap between multi-image joint presentation and visual pleasure. The metric merges psychological achievements into the field of grid collage. To capture user preference, a bonus mechanism related to a user-specified special location in the grid and uniqueness values of the subimages is integrated into the metric. An end-to-end reinforcement learning mechanism empowers the model without tedious manual annotations. Experiments demonstrate that our metric can evaluate the GClg visual balance in line with human subjective perception, and the model can generate visually pleasant GClg results, which is comparable to manual designs.
Vulnerability Analysis of Wireless LAN Networks Using Penetration Testing Execution Standard: A Case Study of Cafes in Palembang

Currently, Wireless LAN has been widely used in public places, one of which is cafe. Wireless LAN that are increasingly developing still have problems when implementing this network, one of which is security issues. Thus, this study aims to evaluate the security of WLAN networks in cafe. This can be done using the Vulnerability Assessment and Penetration Testing methods. Penetration Testing Execution Standard (PTES) was used as a standard for this experiment. Penetration testing is conducted by simulating several attacks to the network, namely Unauthorized Access Attack, DoS Attack, and Packet Sniffing. The test results showed that WLAN security in each cafe still has vulnerabilities that can be exploited, where these results state that all attacks carried out were successful.
Multi-Agent Combat in Non-Stationary Environments

Multi-agent combat is a combat scenario in multiagent reinforcement learning (MARL). In this combat, agents use reinforcement learning methods to learn optimal policies. Actually, policy may be changed, which leads to a non-stationary environment. In this case, it is difficult to predict opponents' policies. Many reinforcement learning methods try to solve nonstationary problems. Most of the previous works put all agents into a frame and model their policies to deal with non-stationarity of environments. But, in a combat environment, opponents can not be in the same frame as our agents. We group opponents and our agents into two frames, only considering opponents as a part of the environment. In this paper, we focus on the problem of modelling opponents' policies in non-stationary environments. To solve this problem, we propose an algorithm called Additional Opponent Characteristics Multi-agent Deep Deterministic Policy Gradient (AOC-MADDPG) with the following contributions: (1) We propose a new actor-critic framework to deal with nonstationarity of environments in MARL, so that agents can adapt to more complex environments. (2) A model for opponents' policies is built by introducing observations and actions of the opponents into the critic network as additional characteristics. We evaluate our AOC-MADDPG algorithm in two multi-agent combat environments. As a result, our approach significantly outperforms the baseline. Agents trained by our method can get higher rewards in non-stationary environments.
Natlog: a Lightweight Logic Programming Language with a Neuro-symbolic Touch

We introduce Natlog, a lightweight Logic Programming language, sharing Prolog's unification-driven execution model, but with a simplified syntax and semantics. Our proof-of-concept Natlog implementation is tightly embedded in the Python-based deep-learning ecosystem with focus on content-driven indexing of ground term datasets. As an overriding of our symbolic indexing algorithm, the same function can be delegated to a neural network, serving ground facts to Natlog's resolution engine. Our open-source implementation is available as a Python package at https://pypi.org/project/natlog/ .
Soft Upper-bound Minimal Complexity LP SVMs

The minimal complexity linear programming support vector machine (MLP SVM) was proposed to solve the problem of unbounded non-unique solutions of the minimal complexity machine (MCM). The MLP SVM minimizes the maximum margin that is the maximum distance between training data and the separating hyperplane as well as maximizes the minimum margin. Therefore, the generalization ability may be worsened if outliers are included and they affect the slope and the location of the separating hyperplane. To solve this problem, in this paper, we propose the soft upper-bound MLP SVM (SLP SVM), in which the outliers that affect the hyperplane are suppressed by introducing the slack variables. This introduction leads to the increase of hyperparameters. We discuss how to reduce the number of hyperparameters to speed up model selection. By computer experiments we compare the generalization ability and training time of the SLP SVM with those of the MLP SVM, MCM, and other SVM based classifiers using two-class and multiclass problems.
Utterance-Level Neural Confidence Measure for End-to-End Children Speech Recognition

Confidence measure is a performance index of particular importance for automatic speech recognition (ASR) systems deployed in real-world scenarios. In the present study, utterance-level neural confidence measure (NCM) in end-to-end automatic speech recognition (E2E ASR) is investigated. The E2E system adopts the joint CTC-attention Transformer architecture. The prediction of NCM is formulated as a task of binary classification, i.e., accept/reject the input utterance, based on a set of predictor features acquired during the ASR decoding process. The investigation is focused on evaluating and comparing the efficacies of predictor features that are derived from different internal and external modules of the E2E system. Experiments are carried out on children speech, for which state-of-the-art ASR systems show less than satisfactory performance and robust confidence measure is particularly useful. It is noted that predictor features related to acoustic information of speech play a more important role in estimating confidence measure than those related to linguistic information. N-best score features show significantly better performance than single-best ones. It has also been shown that the metrics of EER and AUC are not appropriate to evaluate the NCM of a mismatched ASR with significant performance gap.
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning

In the real world, many tasks require multiple agents to cooperate with each other under the condition of local observations. To solve such problems, many multi-agent reinforcement learning methods based on Centralized Training with Decentralized Execution have been proposed. One representative class of work is value decomposition, which decomposes the global joint Q-value Q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">jt</inf> into individual Q-values Q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</inf> to guide individuals' behaviors, e.g. VDN (Value-Decomposition Networks) and QMIX. However, these baselines often ignore the randomness in the situation. We propose MMD-MIX, a method that combines distributional reinforcement learning and value decomposition to alleviate the above weaknesses. Besides, to improve data sampling efficiency, we were inspired by REM (Random Ensemble Mixture) which is a robust RL algorithm to explicitly introduce randomness into the MMD-MIX. The experiments demonstrate that MMD-MIX outperforms prior baselines in the StarCraft Multi-Agent Challenge (SMAC) environment.
Evaluation of the quality indicators in dehazed images: Color, contrast, naturalness, and visual pleasingness

Hazy images suffer from low quality due to blurring, veiling effects, and low contrast. To improve their visibility, dehazing methods attempt to restore them to their corresponding clear scenes, often by focusing more on obtaining an accurate estimate based on a known ground truth. The perceptual quality of dehazed images, which can be described by means of objective and subjective quality assessments, is often not considered. This paper provides a quality assessment of dehazed images, focusing on aspects, e.g., color, image structure, and naturalness. Four image dehazing methods are considered, i.e., Contrast Limited Adapted Histogram Equalization (CLAHE), Dark Channel Prior and Refinement (DCP-R), Perception Inspired Deep Dehazing Network with Refinement (PDR-Net) and Conditional Generative Adversarial Network (CGAN) Pix2pix. The dehazing results are then put through objective and subjective assessments, for a comprehensive evaluation on image quality. Overall, Pix2pix shows the best results objectively, excelling in the recovery of color and image structure. Although it is outperformed by DCP-R in terms of naturalness, our subjective assessment shows that Pix2pix is also most preferred by human observers.
Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.
Designing a tourism recommendation system using a hybrid method (Collaborative Filtering and Content-Based Filtering)

A recommendation system is an application model based on observations of the customer’s circumstances and desires. The recommendation system can be applied in various fields, one of which is tourism. By utilizing a recommendation system, tourism can introduce and promote its tourism to the wider community. In the recommendation system, several methods are used to support how the system works in producing information. This paper’s uses the Hybrid (Collaborative Filtering & Content-Based Filtering) method and the Nearest Neighbor (NN) algorithm. This method has a weakness when an item does not have a rating at all. Therefore, it needs a method that can overcome the weaknesses of the method. We implement a hybrid method, which combines several methods in the recommendation system to produce recommendation items under the user’s wishes. In addition, this system will use Whatsapp Messenger as a promotional medium. This paper aims to propose a recommendation system that will help several historical tours become the foremost destinations when visiting Palembang. The result of developing this recommendation system is a website that can recommend tours desired by users.
Secure Aerial Surveillance using Split Learning

Personal monitoring devices such as cyclist helmet cameras to record accidents or dash cams to catch collisions have proliferated, with more companies producing smaller and compact recording gadgets. As these devices are becoming a part of citizens' everyday arsenal, concerns over the residents' privacy are progressing. Therefore, this paper presents SASSL, a secure aerial surveillance drone using split learning to classify whether there is a presence of a fire on the streets. This innovative split learning method transfers CCTV footage captured with a drone to a nearby server to run a deep neural network to detect a fire's presence in real-time without exposing the original data. We devise a scenario where surveillance UAVs roam around the suburb, recording any unnatural behavior. The UAV can process the recordings through its on-mobile deep neural network system or transfer the information to a server. Due to resource limitations of mobile UAVs, the UAV does not have the capacity to run an entire deep neural network on its own. This is where the split learning method comes in handy. The UAV runs the deep neural network only up to the first hidden layer and sends only the feature map to the cloud server, where the rest of the deep neural network is processed. By ensuring that the learning process is divided between the UAV and the server, the privacy of raw data is secured while the UAV does not overexert its minimal resources.
The Application on distributed geospatial data management based on Hadoop and the application in WebGIS

GIS spatial big data presents the characteristics of massive, multi-source, heterogeneous, spatiotemporal, multiscale. The traditional GIS data storage technology can not meet the needs of GIS spatial big data management. In this paper, for the construction of lightweight WebGIS management system of Oilfield Engineering, aiming at the application requirements of 2D and 3D vector data in security, fault tolerance and scalability, the application strategy of distributed spatial data management technology in cloud environment is proposed, which has made progress in the aspects of high concurrent access of spatial data, fast data loading and high efficiency operation.
Learning from interpretation transition using differentiable logic programming semantics

The combination of learning and reasoning is an essential and challenging topic in neuro-symbolic research. Differentiable inductive logic programming is a technique for learning a symbolic knowledge representation from either complete, mislabeled, or incomplete observed facts using neural networks. In this paper, we propose a novel differentiable inductive logic programming system called differentiable learning from interpretation transition (D-LFIT) for learning logic programs through the proposed embeddings of logic programs, neural networks, optimization algorithms, and an adapted algebraic method to compute the logic program semantics. The proposed model has several characteristics, including a small number of parameters, the ability to generate logic programs in a curriculum-learning setting, and linear time complexity for the extraction of trained neural networks. The well-known bottom clause positionalization algorithm is incorporated when the proposed system learns from relational datasets. We compare our model with NN-LFIT, which extracts propositional logic rules from retuned connected networks, the highly accurate rule learner RIPPER, the purely symbolic LFIT system LF1T, and CILP++, which integrates neural networks and the propositionalization method to handle first-order logic knowledge. From the experimental results, we conclude that D-LFIT yields comparable accuracy with respect to the baselines when given complete, incomplete, and mislabeled data. Our experimental results indicate that D-LFIT not only learns symbolic logic programs quickly and precisely but also performs robustly when processing mislabeled and incomplete datasets.
QPlane

Reinforcement Learning (RL) is a fast-growing field of research that is mostly applied in the realm of video games due to the compatibility of RL and game tasks. AI Gym has established itself as the gold standard toolkit for Reinforcement Learning research. Unfortunately, toolkits like AI Gym are very optimized for benchmark purposes and may not always be suitable for real world type problems. Additionally, fixed wing flight simulation has specific requirements and may need other solutions. In this paper, we propose QPlane as an alternative toolkit for RL training of fixed wing aircraft. QPlane was developed in an effort to create a RL toolkit for fixed wing aircraft simulation that is easily modifiable for different scenarios. QPlane is replicable and flexible for ease of implementation to high performance computing, and is modular for quick environment and algorithm replacement. In this paper we present and discuss details of QPlane, as well as proof of concept results.
Boolean and fuzzy logic operators and multivariate linear regression applied to airborne gamma-ray spectrometry data for regolith mapping in granite-greenstone terrain in Midwest Brazil

Recent studies have proposed several possibilities of combining K, Th and U airborne gamma-ray spectrometry channels to generate predictive algorithms maps. These algorithms can be used for mapping regolith in deeply weathered terrains with residual, erosion, and deposition surfaces helping to developed strategies to better understand the regolith landscape and to improve geomorphology interpretation, and to identify mineral exploration target sites for primary (bedrock or saprolite) or supergene (hosted in lateritic duricrust) ore deposits. With the goal to easily map the regolith, two mathematical procedures were used on airborne gamma-ray spectrometry data in GIS software, validated by fieldwork on granite-greenstone belts in Midwest Brazil: 1. airborne gamma-ray spectrometry and altimetric data integrated in Boolean and fuzzy logic allowed segregating the areas with ferruginous and manganese residual lateritic duricrusts from erosional surface with rocks and saprolite with 90% of accuracy (κBoolean = 0.69 and κFAPO = 0.66) and 2. airborne gamma-ray spectrometry and altimetric data integrated with weathering stages in multivariate linear regression (basic statistic) helped establish the regional weathering intensity index, with acceptable error (r2 adjusted >0.6 and p-value < 5%). These two modeling techniques provide useful, accurate, rapidly and complementary regolith maps and can be applied in large regions for preliminary interpretations.