Reviewerly/exHarmony huggingface dataset:
Resource Matching in the Supply Chain Based on Environmental Friendliness under a Smart Contract

This study aims to solve the problem of environmental pollution caused by industry through the upgrading and transformation of the supply chain, supply chain resource allocation, and related aspects. Specifically, environmental friendliness is added to the resource-matching problem of the cloud platform supply chain. Additionally, learning theory and dynamic evaluation systems are introduced when creating a preference sequence. The deferred-acceptance algorithm is used for matching. Finally, the automatic matching of blockchain smart contracts ensures the interests of both matching parties. Through the analysis of the example at the end of the study, we found that (1) the deviation table of demand side 5 and supply side 7 in the example shows that the deviation between demand side 5 as demand side and supply side 7 is only 11.55186, and the deviation between supply side 7 as demand side and demand side 5 is only 6.56778, and both sides form a high-quality pairing when matched with other partners. No excessive waste of its resources occurs. (2) Effectively ensure the openness and transparency of the supply chain production process; (3) The impact of environmental factors on enterprises is fully considered. In the analysis of the calculation cases, it can be found that demand side 10 has extremely high requirements for the environmental friendliness of its partners, and although supplier 2 has a very high preference for demand side 10, it is not successfully matched because the environmental friendliness of its own enterprise is not up to the standard, while supplier 1 has an environmental friendliness of up to 92 and is finally matched with Demand side 10; (4) Through the comparison test in the appendix, it can be found that the improved GS algorithm achieves the distinction between positive and negative partners. After multiple rounds of scoring, positive demand side 1, 3 was matched with positive supply side 2, 4, which can strengthen the enthusiasm of both partners and avoid negative cooperation.
Cognitive Architecture for Co-evolutionary Hybrid Intelligence

This paper questions the feasibility of a strong (general) data-centric artificial intelligence (AI). The disadvantages of this type of intelligence are discussed. As an alternative, the concept of co-evolutionary hybrid intelligence is proposed. It is based on the cognitive interoperability of man and machine. An analysis of existing approaches to the construction of cognitive architectures is given. An architecture that seamlessly incorporates a human into the loop of intelligent problem solving is considered. The article is organized as follows. The first part contains a critique of data-centric intelligent systems. The reasons why it is impossible to create a strong artificial intelligence based on this type of intelligence are indicated. The second part briefly presents the concept of co-evolutionary hybrid intelligence and shows its advantages. The third part gives an overview and analysis of existing cognitive architectures. It is concluded that many of them do not consider humans as part of the intelligent data processing process. The next part discusses the cognitive architecture for co-evolutionary hybrid intelligence, providing integration with humans. It finishes with general conclusions about the feasibility of developing intelligent systems with humans in the problem solving loop.
Purely Symbolic Induction of Structure

Techniques honed for the induction of grammar from text corpora can be extended to visual, auditory and other sensory domains, providing a structure for such senses that can be understood in terms of symbols and grammars. This simultaneously solves the classical “symbol grounding problem” while also providing a pragmatic approach to developing practical software systems that can articulate the world around us in a symbolic, communicable fashion.
Image enhancement method in high-dust environment based on deep learning and atmospheric scattering model

In view of the image degradation caused by a large number of suspended particles such as coal dust and water mist during underground mining, this paper proposes an image enhancement method based on depth learning and atmospheric scattering model, combined with the characteristics of inconsistent illumination components and inconsistent dust concentration of the collected image. Firstly, the input image is decomposed into reflection component and incident component by neural network; Secondly, referring to the characteristic values of intensity, standard deviation and area, the incident component is decomposed into image blocks by quadtree decomposition method, and the consistency of illumination components in image blocks is achieved; Then, based on the decomposition of the incident component and referring to the dust concentration value, the reflection component is further decomposed into image blocks, realizing the consistency between the illumination component and the dust concentration in the image block; Finally, based on the prior knowledge such as saturation and information entropy, the transmission estimation is completed, and the image enhancement under the high dust environment in the coal mine is realized by combining the principle of atmospheric scattering model. Experimental analysis shows that the image enhancement method proposed in this paper has achieved good results in adding visible edge ratio, contrast restoration, image clarity and so on, and provides a new idea for image enhancement in the high dust environment of coal mines with uneven illumination and uneven concentration of suspended particles.
Towards the Detection of Hidden Familial Type Correlations in Java Code

Family polymorphism is an object-oriented programming feature which facilitates the definition of groups of classes (families) that are allowed to be used together while statically forbidding them to be mixed with classes outside their families. Unfortunately, this feature has not been yet adopted by mainstream industrial-strength programming languages. Consequently, in Java, the idea of non-mixable families is prone to be implemented in a statically unsafe fashion, affecting the programs' intelligibility. In order to support program comprehension, we present an approach to detect code fragments where types of references are correlated within a family; nonetheless, these correlations are hidden behind the references' declarations. We obtained promising results during the initial design and evaluation iterations, based on the analysis of a software system where the presence of families was previously reported.
Semi-Automatic Refactoring to C++20 Modules: A Semi-Success Story

The component-based design of software projects is a desired property both for development and ease of code comprehension. Programming languages have long allowed component-based development (e.g., Java packages, Python modules); however, other languages, especially C and C++, had stuck to the “translation unit” model where every source file is individually compiled. The Modules system of C++20 was expected to allow cleaner encapsulation of concern. In this paper, we investigate the effort of a (semi-)automatic modularisation of existing C++ projects. Based on our investigation, upgrading existing software systems to the new Modules feature is extremely hard due to coupling issues arising from necessarily legacy design. Implementing real transition requires a significant redesign of both project-internal and user-facing programming interfaces.
S-VSUM: Static Video Content SUMmarization using CNN

Due to complex structure of video data, it is very challenging to find the important part of the video and to understand the content present in it. There are different types of videos like sports, scenery, movies available online and offline. There is no prior knowledge for understanding video's important content, so there is need to do video summarization with precise and abstract way. The research work presented here proposes new enhanced system of static video summarization for the video data that creates static storyboards. A static video content summarization using modified Convolution Neural Network (CNN) is proposed here. First, video segmentation is done to separate video frames from a video input signal. The features are selected using proposed modified CNN architecture considering frame level importance of each frame available in a particular scene and combines the features from the three pre-trained models. Here along with significant frames near duplicate frames are also extracted. Then these redundant near duplicate frames are eliminated using thresholding. The whole implementation is performed using the standard benchmark public video dataset Open Video Project (OVP) and SumMe. The performance evaluation of the proposed static video content summarization (S-VSUM) is done using accuracy. The result shows that the proposed static video content summarization system outperforms traditional feature-based approaches like DT, VSUMM, k-means.
Gesture Control System: Using CNN based Hand Gesture Recognition for Touch-less Operation of Kiosk Machine

Kiosk machines have gained good popularity among the general public as they are easy to operate and provide a good interactive interface. As a result, multiple users use the kiosk machine throughout the day to find the information they are looking for. Users interact with the kiosk machine by the means of touching its screen or using the buttons. Due to this, it is observed that throughout the day hundreds or even thousands of people end up touching the surface of the kiosk machine. Because of this hygiene cannot be maintained as it is not possible to sanitize the kiosk machine after each use. This has become a serious issue considering the effects that the Covid-19 pandemic had on the world. Multiple people touching the same surface is one of the most common ways through which the virus can spread. To help deal with this problem we have designed a gesture control system using deep learning techniques through which kiosk machines can be operated in a touch-less way.
A Distributed Multi-Robot Path Planning Algorithm for Searching Multiple Hidden Targets

This paper proposes a new distributed multi-robot path planning algorithm based on fuzzy logic control and reinforcement learning, which can navigate for robots searching for hidden targets in unknown environment. The algorithm is composed of two controllers, a fuzzy logic controller based on multiple behavior coordination strategy and a policy controller based on deep reinforcement learning. The first controller is used for roaming search to find the hidden targets, and the role of the second controller is to navigate from the current position to the targets. To check the performance of the algorithm, we simulate it in simulation environment built in the CoppeliaSim simulation software and implemented by e-Puck robots. The simulation results demonstrate the effectiveness of the proposed method.
Feature Representation Techniques for Hate Speech Detection on Social Media: A Comparative Study

Past decade has shown an overwhelming use of social media. Inspite of multi-fold benefits of using social media platforms, it is being misused due to increasing prevalence of inappropriate content in form of hate speech and abusive language shared on these platforms. Hate speech content is one such form of content that has shown dramatic increase in recent years. Automated techniques like AI and NLP have reported significant performance in detection of hate speech content. In order to extract the textual properties from online content that help in the detection of hate speech, feature extraction and representation using NLP are essential. There is an increasing trend in academia and industry to the use of pretrained neural network language models for hate speech and offensive content detection. This paper presents the performance of important feature extraction techniques: TD-IDF, GloVe, FastText and BERT for binary classification and multiclass classification of hate speech with Convolutional Neural Network (CNN) architecture as the base network for all the techniques.
Multiple Recurrent Attention Convolutional Neural Network For fine-grained image recognition

Classifying fine-grained categories is a popular task nowadays since it can be applied in many daily tasks, such as helping people distinguish different species of animals or different model of vehicles. There are some existing approaches to deal with the task, while few take advantage of generating multiple attention in the image to better recognize the details. In this paper, we propose a novel network: Multiple Recurrent Attention Convolutional Neural Network (MRA-CNN), which uses a Multiple Attention Proposal Network (MAPN) to localize multiple key features and classify the subcategories according to them. The process of localizing attention and classifying each sub-picture is a mutual reinforcement. There will be a specialized loss function for MAPN, which consists of two losses, allowing the network to generate key features that are different from each other and have key information. We conduct our experiments mainly on the dataset CUB-Birds (CUB-200-2011). Our model achieves an overall accuracy of 85.6% which is pretty satisfying.
Quantity Estimation Method of Warehousing Cargo Based on CSwin-Unet and Pixel Position Weight

The quantity estimation of cargo is of great significance for the scientific management of storage. The traditional quantity statistics of cargo is based on RFID technology to obtain the information of cargo in circulation, which is time-consuming, laborious, and complicated. Due to the unsatisfactory accuracy, computer vision-based statistical methods are not widely used in the field of cargo quantity statistics. To solve the above problems, this paper proposes a method for estimating the quantity of cargo in storage based on CSwin-Unet and XGBoost, which improves the accuracy while reducing equipment dependency. Firstly, the CSwin-Unet model is pre-trained on the COCO Stuff dataset using transfer learning. It achieves 48.8% mIoU, which is 1.6% higher than Swin-Unet. Secondly, this paper designs a pixel position weight (PPW) loss function to fine tune the CSwin-Unet in local dataset to obtain better feature extraction effect. Estimation of the cargo quantity through PPW-XGboost achieves an MSE of 0.86, which is 0.13 lower than the result without using the PPW loss function.
LCE-Net: Local-Aware and Context Enhancement based YOLOv5 for object detection in remote sensing images

Remote sensing image target detection has been a research hotspot in the field of remote sensing. Aiming at the problems of complex background of remote sensing images, few pixels and large scale variability of remote sensing targets, a Local-Aware and Context Enhancement network(LCE-Net) is proposed with YOLOv5m as the baseline model. Firstly, the context enhancement module is designed in the network extraction layer to increase the perceptual field to fully extract feature information. Secondly, a cascade Swin Transformer block is added at the detection to capture feature information of object in similar environments. Thirdly, Alpha-CIoU to improve the localization accuracy. We validate the remote sensing image target detection algorithm on the DOTA dataset and the Plane dataset. The experimental results show that our algorithm increases the overall mAP from 69.4% to 73% compared to the YOLOv5m algorithm, which improves the remote sensing image target detection performance.
Contactless Mask Monitoring and Biometric Attendance System

This paper attempts to give an overview of the system which is designed keeping social distancing guidelines in mind. Our system will detect in real-time, if the person in the captured live video is wearing a mask properly or not using a mask detecting algorithm developed using deep learning and neural networks with an accuracy of 96.05%. If and only if the person is wearing a mask, they will be allowed to scan the iris and hence record their attendance, which can be stored in excel or CSV formats. The location of iris biometric is translated to a real-life position in the 3D space with the resolution of 0.lmm. To scan the located biometric this system comprises a robotic arm. End effector of this robotic arm traverses to the translated position of the person's eye to scan iris with an iris scanner. The system employs a ‘four degrees of motion’ robotic arm that can autonomously align itself to the iris with an accuracy of 96.86%. It is battery operated and has a cylindrical workspace with maximum range of 300mm, hence it is easily deployable in institutions requiring secure authorization while monitoring COVID-19 safety norms.
TS-D3D: A novel two-stream model for Action Recognition

Action recognition is a fundamental task of computer vision. The most widely used model in this task is Resnet. However, we want to study the capabilities of DenseNet in action recognition tasks. In this paper, we propose a new deep learning model named Two-Stream Densenet-3D (TS-D3D) for action recognition, taking the advantages of both 3D DenseNet and two-stream network. Specifically, we design an RGB pathway to extract spatial features and an RGB DIFF pathway to extract temporal features of continuous video frames, each of which is based on 3D DenseNet. In particular, we modify the convolution kernels of the RGB DIFF pathway for better modeling temporal correlations and reducing the computation of 3D convolution. Then, a Transition Layer is proposed to effectively fuse the features of the two pathways. Numerical results demonstrate that our TS-D3D performs well on widely adopted action recognition datasets.
Effective Generation of Visual Questions

The task of asking questions based on visual input like images is considered to be very intelligent. It is quite a recent addition in the range of multi-modal problems. It needs the ability to understand the image and then translate it in the form of a question. While many models are able to generate questions successfully, there are certain simple optimizations that when applied to any question generation model, can upgrade the correctness of newly generated questions. We apply a few of these like Beam search to encoder-decoder family of visual question generation models and show that they help in boosting the accuracy and hence quality of the questions.
Image Retrieval using Otsu and Thepade's Sorted Block Truncation Coding Based Color Features

Because of the tremendous growth in digital imaging and enhanced communication and storage technology, trillions of pictures are clicked, stored, and exchanged daily. Finding and searching for an image in an enormous collection is becoming challenging. The query by reference image retrieval technique aims to close the semantic gap between the query image and the retrieved images while improving performance. The primary goal of the work proposed here is to develop discriminative and descriptive features of the image with the minimum possible size. Here, the weighted feature fusion-based image retrieval method is proposed using Thepade's Sorted Block Truncation Coding (SBTC) and Otsu Multilevel thresholding (OMT) methods. The technique is tested for two standard datasets with mean square error (MSE) as a distance measure and average retrieval accuracy (ARA) as a performance metric. The technique has contributed to the enhancement of ARA with the small and fixed-size image feature vector. The feature vector generated is much smaller than the image dimension and is used to represent the image for retrieval. Results prove that the proposed technique of SBTC level 8 with 0.5 weight and OMT - 8 thresholds with 0.5 weight feature fusion gives better ARA than other methods studied.
Oracle Bone Inscriptions Detection Based On Standard Evaluation Metric

Just like ImageNet in its time, MS COCO has become the standard for object detection today. Naturally, the evaluation metric of MS COCO also become the standard. However, the previous evaluation metrics for the detection of Oracle Bone Inscriptions (OBIs) are the precision, recall and F-measure. So, they will fail to evaluate one detector comprehensively and fairly. This paper uses the standard evaluation metric of object detection in MS COCO to evaluate the performance of various models for detecting Oracle Bone Inscriptions (OBIs).
Topic Enhanced Affective Response Generation in multi-Turn Dialogues

Current generation models of emotional dialogue pay more attention to integrating emotion into the process of response generation, but not enough attention to the semantic content of response. In order to enhance the quality of semantic content in response generation, this paper proposes an emotional dialogue generation model incorporating topic information (T-ECCM), which can enhance the relevance between response and context. Firstly, the model trains the topic module to obtain the input topic probability distribution and encodes it into vector representation at the context level. Then in the independent coding module, recurrent neural network is used to capture semantic signals and emotional signals respectively. In the fusion module, theme information is used to enhance the representation ability of semantic vector. Finally, in the generation module, the information representation learned in the context is input to generate semantically coherent and emotionally appropriate responses. Experimental results on a publicly available dataset show that the T -ECCM can generate emotional responses that are more appropriate to the current conversation content than the baselines. We also analyze the effects of different context lengths on the performance of the T-ECCM through experiments, and prove the robustness of the T-ECCM.
Cryptanalysis of Magpie Block Cipher

Block cipher is one of the most important methods to safeguard data confidentiality. The design and analysis of dynamic block ciphers are the frontier of current cryptography research. Magpie is a lightweight dynamic block cipher proposed in the “Acta Electronics Sinica” in 2017. Its block length is 64 bits, and the key length is 96 bits. The first 64-bit key is used as the round key to participate in encryption and decryption, the middle 16-bit key is used to control the selection of the S-boxes, and the last 16-bit key controls the execution sequence of the round function. In this paper, we evaluate the security of Magpie in two dimensions: the single-key model and the related-key model. In the single-key model, we research on the ability of Magpie against the meet-in-the-middle attack. Our results show that we are able to recover the master key with the time complexity of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">88</sup> encryptions and memory complexity of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">92</sup> bytes. In the related-key model, we research on the ability of Magpie against the differential attack, and our results show that the time complexity of recovering the full-round Magpie is 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">78</sup> encryptions. The results of this paper provide strong support for accurately characterizing the security performance of Magpie.