Reviewerly/exHarmony huggingface dataset:
Harris Hawks Optimisation: Using of an Archive

This paper proposes an enhanced variant of the novel and popular Harris Hawks Optimisation (HHO) method. The original HHO algorithm was studied in many research projects, and a lot of hybrid (cooperative) variants of HHO was proposed. In this research study, an advanced HHO algorithm with an archive of the old solutions is proposed (HHO\(_A\)). The proposed method is experimentally compared with the original HHO algorithm on a set of 22 real-world problems (CEC 2011). The results illustrate the superiority of HHO\(_A\) because it outperforms HHO significantly in 20 out of 22 problems, and it is never significantly worse. Four well-known nature-based algorithms were employed to compare the efficiency of the proposed algorithm. HHO\(_A\) achieves the best results in overall statistical comparison. A more detailed comparison shows that HHO\(_A\) achieves the best results in half real-world problems, and it is never the worst-performing method. A newly employed archive of old solutions significantly increases the performance of the original HHO algorithm.
A Simple Algorithm for Quickly Estimating the Sharpness of Mobile Phone Images

The QR code image based on halftone micro-noise information, which has excellent anti-duplication performance and certain application value in the field of anti-counterfeiting and traceability. However, due to the mocro-size of halftone noise dots, it's often difficult to obtain high-sharpness images during the recognition process of ordinary mobile phones. In order to obtain high-sharpness halftone QR code images, this paper proposes the improved algorithm of least squares, which can quickly evaluate the sharpness of the scanned image by calculating the gray gradient of local pixels, and proposes PWM algorithm to evaluate the halftone QR code image after segmentation by calculating the proportion of the local high-frequency components. Proved by experiments, the methods in this paper with less time complexity can effectively improve the sharpness of obtained halftone QR code images.
Dual Gated Learning for Visible-Infrared Person Re-identification

Visible-infrared person re-identification (VI-ReID) is a crucial part of open world ReID task, targeting at cross-modality pedestrian retrieval between visible and infrared images. Its large intra-class variation and cross-modality discrepancy lead to difficulty in discriminated representation learning. In this paper, we investigate adaptive neurons deactivation technique to improve VI-ReID model performance and propose two auxiliary training schemes. First, we present a one-stream module (gated module, GM) and its corresponding training scheme (gated learning, GL), to assist model training by adaptive neuron deactivation. Based on GM and GL, we design two-stream module (dual gated module, DGM) and its corresponding training scheme (dual gated learning, DGL) for further utilizing deactivated neurons in GL. During inference, GL and DGL are abandoned, resulting in no extra computation cost. Extensive experiments are performed on SYSU-MM01 and RegDB dataset to demonstrate the superiority of GL and DGL approach. Experimental results show that our proposed methods achieve significant improvement.
Space-Time Separate Modeling for Efficient Video Classification

Abstract Efficient video classification requires the deep neural network models to be much lightweight. Current deep convolutional networks generally adopt 3D convolutions or similar spatio-temporal computational counterparts to process the 3D signal of videos. However, due to the heavy computational load of those 3D units, they suffer from the problems of hard training and inefficient inferring. To address these problems, this paper proposes a novel yet efficient space-time separate (STS) modelling module. STS splits the feature channels into multiple groups and separately model various types of content information from the channel groups using different convolutional operations. Since the channel splitting mechanism can significantly reduce the model complexity, STS is much more lightweight than the existing video models. Particularly, the joint use of spatial/temporal/spatio-temporal convolutions achieves paralleled information modelling in a single block. We conduct experiments on two benchmarked video datasets to evaluate the performance of STS and demonstrate its effectiveness and efficiency on the video classification task.
Tradeoff relations for two-valued qubit measurements

Quantum measurement is essentially taken to gain information encoded in quantum states, however, it also inevitably disturbs the measured state such that the postmeasurement state loses its quantum information partially or fully. Thus, it is an important issue to characterise such information gain and state disturbance associated with measurement. In this work, we study the relationships between the information gain and measurement disturbance for two-valued qubit measurements. In particular, we employ a powerful tradeoff relation between the measurement strength and reversibility to obtain, generalise, and even strengthen certain well-known tradeoff relations between information gain and measurement disturbance of qubit measurements. Moreover, it is pointed out that our results are useful to understand the measurement effects at the fundamental level as they are state-independent. Hence, our work is helpful for designing quantum information processing tasks aided by measurement.
A Decision Support System for Managing Technical Debt: Towards a Systemic Perspective

Maintaining a software system in operation requires resources to keep it reasonably bug-free, appropriate for the business needs, and still changeable. However, it is challenging to evaluate resource allocation strategies while considering both current and future needs. This paper presents a decision support system, using technical debt concepts, built based on the system dynamics approach that aims to assist decision-makers in evaluating different maintenance investment strategies. The tool can help evaluate future impacts by simulating possible scenarios, generating knowledge and insights for decision-makers, and helping to communicate them among different stakeholders.
Modulation Signal Denoising Based on Auto-encoder

This paper proposed a denoising method for modulated signals based on the autoencoder. The auto-encoder is a cascade structure, which is composed of multiple convolution layers and multiple pooling layers. It is mainly divided into a feature encoder and a generation decoder. We use the features of the modulated signal with noise as the input of the auto-encoder and the features of the clean signal as the label. At the same time, back-propagation algorithm and gradient descent method are used to optimize and update the parameters in the auto-encoder model to minimize the reconstruction error, so as to realize the denoising function of the modulated signal. For a variety of modulation types, this method can improve the modulation signal about 3–9 dB in different SNR environment. The denoising model can generate high-level features of different modulation signals without any artificial feature extraction and prior knowledge and has strong feature representation ability. It has the advantages of strong versatility, low complexity, good denoising effect and good stability.
AUTOMATION OF SOURCE DATA GENERATION FOR LABORATORY WORK IN THE DISCIPLINE «ALGORITHMS AND DATA STRUCTURES»

The article presents the results of the software development for automated graph generation, and corresponding adjacency and incidence tables. The program is tested in the educational process within the laboratory work "Representation of a graph in a computer in the form of an adjacency matrix and a list of edges" in the discipline "Algorithms and data structures" for students of the 1st year studying Information Systems and Technologies.
Short text similarity computation method based on feature expansion and Siamese network

Text similarity computation issues is a widely studied problem in natural language processing (NLP). Short text similarity computation is a new and more challenging problem, which cannot be effectively solved by using previous regular text similarity computation approach. The main reason is that, a short text generally contains limited number of words and fewer features can be extracted. In this paper, we propose a short text similarity computation method based on feature expansion and Siamese neural network. Firstly, a latent Dirichlet allocation (LDA) based model is constructed to expand the features of a short text. Then, deep features are extracted by using Siamese neural networks model which contains both convolutional neural networks (CNN) and Bi-directional long short-term memory (BiLSTM). Finally, the similarity of two short texts can be achieved by computing the Manhattan distance between generated feature vectors of these two texts. Experimental results show that, based on the data set of Ant Financial NLP Challenge, our method achieves higher accuracy and F1 score.
MorphSet: Improving Renal Histopathology Case Assessment Through Learned Prognostic Vectors

Computer Aided Diagnosis (CAD) systems for renal histopathology applications aim to understand and replicate nephropathologists’ assessments of individual morphological compartments (e.g. glomeruli) to render case-level histological diagnoses. Deep neural networks (DNNs) hold great promise in addressing the poor intra- and interobserver agreement between pathologists. This being said, the generalization ability of DNNs heavily depends on the quality and quantity of training labels. Current “consensus” labeling strategies require multiple pathologists to evaluate every compartment unit over thousands of crops, resulting in enormous annotative costs. Additionally, these techniques fail to address the underlying reproducibility issues we observe across various diagnostic feature assessment tasks. To address both of these limitations, we introduce MorphSet, an end-to-end architecture inspired by Set Transformers which maps the combined encoded representations of Monte Carlo (MC) sampled glomerular compartment crops to produce Whole Slide Image (WSI) predictions on a case basis without the need for expensive fine-grained morphological feature labels. To evaluate performance, we use a kidney transplant Antibody Mediated Rejection (AMR) dataset, and show that we are able to achieve 98.9% case level accuracy, outperforming the consensus label baseline. Finally, we generate a visualization of prediction confidence derived from our MC evaluation experiments, which provides physicians with valuable feedback.
Key Clips and Key Frames Extraction of Videos Based on Deep Learning

Abstract The surveillance camera network covering the city, while protecting the safety of people’s lives and property, generate a large amount of surveillance video data every day, but few videos contain useful information. Surveillance cameras in sparsely crowded areas may capture most of the video in the background. A large number of surveillance videos bring greater storage pressure, and also increase the difficulty of the staff’s work. In this paper, we use the auto-encoder network to extract features of video frames. By comparing the feature differences between video frames, we can automatically select key video clips and key frame images that contain useful information to slim down the surveillance video. Through experiments on actual cameras, we found that this method can achieve the purpose of reducing the pressure of storage and traversal.
Intelligent Module of Information Processing for the Applied Software System to Provide Administrative Services

The article is devoted to the issue of functioning efficiency improvement of the information system of administrative services center through implementation of mathematical tools and software of an intelligent information processing module. Scientific novelty of the work is the architecture of applied software system for administrative services centers of municipality level. The software system contains the intelligent module for information processing while providing services.
Video Enhancement Based on Unpaired Learning

Distortion of real video is affected by many factors. Many existing old videos n have the problem of low definition. However, most video enhancement based on paired learning is trained for specific degradation problem, lack of the ability to enhance real low-definition video with unknown distortion. In this paper, we propose a joint video enhancement algorithm based on unpaired learning, which uses a high-definition video to enhance a low-definition video with similar contents. In order to train the network, we also build three pairs of unpaired video datasets with chimpanzee, city night view and military figures as contents. In experiments, we compare our method with enhancement algorithm based on paired learning and other unpaired framework and find that our method achieves a higher performance,
Image-Based Visual Servoing Control of Quadrotor with MsQL Method

In this paper, a multi-step Q-learning (MsQL) method is employed to design an image-based visual servoing controller of the quadrotor, where image moment of the target image is used as the image feature for information extraction. The virtual camera and virtual image plane are introduced to construct the image dynamics model to realize the decoupling of the quadrotor. Combined with the quadrotor dynamics model, the quadrotor-image dynamics is proposed. The MsQL method is used to design the optimal control law for the horizontal motion of quadrotor. The control policy is updated through the dataset, such that the control gain can be solved and the desired trajectory of roll angle and pitch angle can be calculated. Simulation studies are conducted on the Matlab platform and good results are obtained.
Autonomous Obstacle Avoidance Algorithm for UAV in 3D Space Based on Improved Potential Field and Fluid Disturbance

To solve the obstacle avoidance problem of Unmanned Aerial Vehicle (UAV) flying in three-dimensional space, an effective obstacle avoidance algorithm is proposed. Firstly, a standard convex body modeling method is adopted according to the known obstacle edge information, and the obstacles are roughly covered by the standardized model to generate the obstacle information about the current flight environment. Then, a force field of repulsion and gravity is generated by using the three-dimensional artificial potential field (APF) method, which is suitable for the global search. Finally, when approaching the surface of the obstacle, a fluid disturbance potential field is generated by using the fluid disturbance algorithm, which can achieve high precision obstacle avoidance. The experiments show that in the three-dimensional space obstacle avoidance trajectory, the combination of artificial potential field and fluid disturbance algorithm can realize UAV obstacle avoidance with better response speed and accuracy.
Learning intents behind interactions with high-order graph for session-based intelligent recommendation

Session-based recommendation is an overwhelming task owing to the inherent ambiguity in anonymous behaviors. Graph convolutional neural networks are receiving wide attention for session-based recommendation research for the sake of their ability to capture the complex transitions of interactions between sessions. Recent research on session-based recommendations mainly focuses on sequential patterns by utilizing graph neural networks. However, it is undeniable that proposed methods are still difficult to capture higher-order interactions between contextual interactions in the same session and has room for improvement. To solve it, we propose a new method based on graph attention mechanism and target oriented items to effectively propagate information, HOGAN for brevity. Higher-order graph attention networks are used to select the importance of different neighborhoods in the graph that consists of a sequence of user actions for recommendation applications. The complementarity between high-order networks is adopted to aggregate and propagate useful signals from the long distant neighbors to solve the long-range dependency capturing problem. Experimental results consistently display that HOGAN has a significantly improvement to 71.53% on precision for the Yoochoose1_64 dataset and enhances the property of the session-based recommendation task.
The demon in the diagram: towards a theory of evolving diagrammatic embodiment

This essay proposes that the intricate but sprawling evolution of diagrams might be looked at through a single graphic system. It is further proposed that this system might reconcile diverse definitions of ontological and temporal minima, in a sequence of 25 types of historical diagram that concludes with a kind of meta-diagram of diagrams. The ‘demon’, a historical form of hypostatic abstraction, is introduced as an eidetic body useful for exploring this diagrammatic space. The complete sequence was exhibited in a complex installation, titled ‘The Demon in the Diagram’, which is also discussed.
Predictive Analysis of Damage Occurred Due to Natural Disasters Using Whale-Optimization Algorithm-Based Hybrid Computation

Bio-inspired computing algorithms are inspired by nature that can provide flexible, dynamic, and effective solutions to hard and complex real-life problems. Many bio-inspired algorithms such as ant colony algorithm, firefly algorithm, whale optimization algorithm, grey-wolf algorithm, etc. proved their efficiency in solving various nonlinear, high-dimensional, and complex problems. On the other hand, artificial intelligence techniques can help to build intelligent models to implement the bio-inspired computing algorithms successfully and provide solutions to real-life problems that occurred due to natural disasters. Natural disasters like hurricanes, earthquakes, floods, tsunami, landslides, forest fires, etc. are the inspiration for solving hard and complex problems in computer science since they exhibit robust phenomena. It is necessary for the government agencies to know the accurate measurements of the damage that occurred due to natural disasters. However, these disasters are also called “ACTS OF GOD” because they cannot be found in fixed locations and time. So, here the predictive analysis is necessary to save the economy as well as human and animal lives. The combination of bio-inspired computing algorithms and artificial intelligence techniques will provide flexible solutions in those situations to predict the damage that occurred due to natural disasters. In this study, we proposed a bio-inspired computing-based hybrid approach, which is a combination of the modified version of popular bio-inspired algorithm whale optimization algorithm (MWOA) with significant artificial intelligent technique multi-layer perceptron (MLP) neural network to predict the damage of buildings that occurred due to the natural disasters using the satellite images.
Multi-UUV Path Planning Study with Improved Ant Colony Algorithm and DDQN Algorithm

For the Unmanned Undersea Vehicle (UUV) path planning problem, an improved ant colony algorithm is proposed to solve the global static path planning and an improved DDQN algorithm is proposed to solve the local dynamic collision avoidance problem. The traditional ant colony algorithm is optimized by adding the constraints of collision avoidance among UUVs to improve the accuracy of the algorithm. The Q algorithm idea is introduced to improve the DDQN algorithm to realize the UUV’s anticipation of future environmental state changes. This algorithm can not only solve the local collision avoidance problem, but also realize the accurate prediction of future environment. Finally, the algorithm is validated with a real UUV. The experimental results show that the proposed method solves the problems of collision avoidance and path finding for multiple UUVs in complex environments, and can effectively avoid collisions for obstacles.
LenC: A Redundancy-Aware Length Control Framework for Extractive Summarization

While extractive summarization is an important approach of the NLP text summarization task, redundancy in the generated extractive summary is always a problem. Previous works usually set the length of the output summary to a fixed number, which might only be appropriate for some of the documents while too long for others. At the same time, though extractive summarization possesses high readability as it directly selects sentences from the document, the unimportant parts within sentences are also selected. These two scenarios result in redundancy in the extractive summaries. To solve this problem, we propose a length control framework for extractive summarization, named LenC, in a two-stage pipeline. We first use a pretrained BERT-based summarizer to select smaller units (i.e. EDUs) than original sentences to abandon the insignificant parts of a sentence. Then a portable length controller is implemented to prune the output summary to an appropriate length, and it can be attached to any extractive summarizer. Experiments show that the proposed model outperforms the state-of-the-art baseline models and successfully reduces the redundancy in the extractive summaries.
The Proposition of Balanced and Explainable Surrogate Method for Network Intrusion Detection in Streamed Real Difficult Data

Handling the data imbalance problem is one of the crucial steps in a machine learning pipeline. The research community is well aware of the effects of data imbalance on machine learning algorithms. At the same time, there is a rising need for explainability of AI, especially in difficult, high-stake domains like network intrusion detection. In this paper, the effects of data balancing procedures on two explainability procedures implemented to explain a neural network used for network intrusion detection are evaluated. The discrepancies between the two methods are highlighted and important conclusions are drawn.
Usis: Unsupervised Semantic Image Synthesis

Semantic Image Synthesis (SIS) is a subclass of I2I (I2) translation where a photorealistic image is synthesized from a segmentation mask. SIS has mainly been addressed as a supervised problem. However, state-of-the-art methods depend on a massive amount of labeled data and cannot be applied in an unpaired setting. On the other hand, generic unpaired I2I frameworks underperform in comparison. In this work, we propose a new framework, Unsupervised Semantic Image Synthesis USIS), as a first step toward closing the performance gap between paired and unpaired settings. We design a simple and effective learning scheme that combines the fragmented benefits of cycle losses and relationship preservation constraints. Then, we make the discovery that, contrary to I2I translation, discriminator design is crucial for label-to-image translation. To this end, we design a new discriminator with a wavelet-based encoder and a decoder to reconstruct the real images. The self-supervised reconstruction loss in the decoder prevents the encoder from overfitting on a few wavelet coefficients. We test our methodology on 3 challenging datasets and set a new standard for unpaired SIS. The generated images demonstrate significantly better diversity, quality and multimodality.