Reviewerly/exHarmony huggingface dataset:
A Scale-Adaptive Super-Resolution Algorithm for Single Remote Sensing Image

Single image super-resolution (SISR) algorithm is to recover a high-resolution image from a single low-resolution one and has been widely applied in remote sensing (RS) reconstruction. Numerous SISR models have been proposed for RS ap-plications. However, most existing methods suffer from an inability to reconstruct multi-scale RS images using one fixed pre-trained model. Here, we design a scale-adaptive SISR algorithm for RS images. The main contributions are three-fold: (1) to be applied for the multi-scale reconstruction, we first employ the bicubic interpolation to stretch the images before import so that our convolutional neural network can focus on refining the details of reconstruction images; (2) to extract the deep information of ground surface from RS images, we design multi-scale residual network to recover the image details; (3) we adopt the least-absolute-error loss to constraint our network for reconstructing the RS images. It is demonstrated that our model achieves excellent performance for super-resolution of RS images.
A Survey on the Impact of Chatbots on Marketing Activities

Recently, chatbot technology has been employed in several customer services and support roles. These conversational agents have become valuable assets to companies and organizations because of their ability to communicate with humans and process large amounts of data. Furthermore, since most customers are using online shopping to fulfill their requirements instead of traditional methods, they expect to get a full 24-hour service. This survey provides an overview of different studies that used chatbots as a tool to provide customer service and analyzes the techniques applied to it. In this survey, we divided the studies into two parts, the algorithms, techniques used in creating a chatbot, and the influence of the chatbot's use on marketing. Research shows most of the studies used Deep learning techniques for building chatbots. In addition, chatbot provides services that enable customers to have better communication with the brand and affect their long-term relationships.
Design a Multi-layer Supply Chain Optimization Model Based on Federated Learning and Blockchain

As enterprises have different data security and requirements, data governance between enterprises has become a problem. The traditional relationship between supply and demand can easily lead to the “bullwhip effect” in the supply chain (SC). In order to optimize the interests of most enterprises in the SC, this study designs a multi-layer SC network optimization model. Specifically, first of all, using blockchain technology, a supply and sales framework that conforms to the SCN is constructed, and the chain information flow is transformed into a decentralized information interaction method. Subsequently, the network is optimized from two aspects: on the basis of ensuring the privacy of enterprise data, a time-series federated learning scheme is proposed to predict the needs of multiple parties; relying on the framework of this paper, the enterprise inventory is stabilized. The supply and demand optimization framework proposed in this paper provides information forecasting and sharing services for enterprises from a new perspective. Under the circumstance of ensuring the self-interest of the enterprise, the overall cost reduction and efficiency increase of the SC can be realized.
An Improved YOLOX with C_Focus Module and Spatial Attention Mechanism for Traffic Sign Detection

With the development of social economy, the number of cars is increasing year by year. Road congestion, vehicle violations and other phenomena are becoming more and more frequent. Therefore, the research on Intelligent violation evidence collection system is of great significance to improve road traffic. As one of the basic tasks of intelligent violation forensics system, traffic sign detection has great challenges due to its small target, complex background and various types. This paper uses yolox as the object detection framework. Firstly, a multi output spatial attention mechanism (MO_SA) is proposed to enable the network to extract attention information from the feature map. Secondly, the focus module of large-scale convolution is proposed, which uses the large-scale convolution layer to strengthen the feature extraction ability of focus (C_Focus). The method proposed in this paper is verified on tt100k dataset. The experimental results show that our work improves the recognition accuracy by 2.5% without increasing the amount of model parameters.
IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES

Association Rule Mining is an important field in knowledge mining that allows the rules of association needed for decision making. Frequent mining of objects presents a difficulty to huge datasets. As the dataset gets bigger and more time and burden to uncover the rules. In this paper, overhead and time-consuming overhead reduction techniques with an IPOC (Incremental Pre-ordered code) tree structure were examined. For the frequent usage of database mining items, those techniques require highly qualified data structures. FIN (Frequent itemset-Nodeset) employs a node-set, a unique and new data structure to extract frequently used Items and an IPOC tree to store frequent data progressively. Different methods have been modified to analyze and assess time and memory use in different data sets. The strategies suggested and executed shows increased performance when producing rules, using time and efficiency.
Low-light Image Enhancement Network via Pyramid and Residual Attention

Images taken under weak illumination conditions usually suffer from low brightness and complex degradation. Most existing low-light image enhancement approaches cannot effectively eliminate the complicated degradation. In this paper, we propose a novel two-stage low-light image enhancement network based on Pyramid Architecture and Residual Attention. In the first stage, we employ a pyramid brightness enhancement module to capture the illumination differences in the low-light image through the receptive field range of different scales. This stage can avoid the problems of over-exposure and under-exposure and produce satisfactory brightness-enhanced images. In the second stage, we propose a degradation repair module that combines U-Net and residual attention block. This stage can effectively deal with multiple degradation factors after enhancing the contrast of low-light images. Experiments conducted on the LOL real-world and LIME, DICM datasets demonstrate that our proposed model outperforms the state-of-the-art approaches in terms of brightness enhancement and degradation recovery.
Categorization of Threatening Speech Acts

The paper explores the notion of threatening speech act which is defined as an intentional speech action aimed at suppressing the will of other people and deterioration of their mental state. Based on semantic and pragmatic features of threatening speech acts described in the literature we build a dataset out of randomly collected messages containing verbalized threats presented open access on different Internet social networks sites, Internet forums and Internet messengers. A total number of 68 messages dated from 2015 to 2021 in Russian language are manually processed using contextual analysis and methods of Forensic Linguistics. Categorization method based on (1) type of addressor’s intention, (2) direction of threatening speech act, and (3) type of threatening speech act gave a matrix proving our research hypothesis, that among the analyzed messages the most frequent type of threatening speech acts are threats-punishments towards addressees aiming at harming their lives or health. At the same time the prevalent communicative intention of the addressor in such threatening speech acts is the realization of psycho-emotional affect in the form of a threat and the desire to demonstrate the “power” in communication.
Multilayer Online Self-Acquired Knowledge Distillation

Online knowledge distillation has been proposed as an auspicious approach for circumventing the flaws of the conventional offline distillation (i.e., complex, and computationally and memory demanding process). In this work, a novel online self-distillation method, named Multilayer Online Self-Acquired Knowledge Distillation (MOSAKD), is proposed, aiming to develop fast-to-execute and effective models that can comply with applications with memory and computational restrictions, e.g., robotics applications. The MOSAKD method is able to mine additional knowledge both from the intermediate and the output layers of a deep neural model in an online fashion. To achieve this goal, k-nn non-parametric density estimation for estimating the unknown probability distributions of the data samples in the feature space generated by any neural layer is used. This enables us to compute the soft labels that explicitly express the similarities of the data with the classes, by directly estimating the posterior class probabilities of the data samples. The experimental evaluation on four datasets, including a dataset of synthetic images, indicates the effectiveness of the MOSAKD method and the superiority over existing online distillation methods.
Remote Sensing Image Super-Resolution Via Attentional Feature Aggregation Generative Adversarial Network

The extraction of high-frequency details is generally neglected in single image super-resolution (SISR) for remote sensing images. In this paper, we propose an attentional feature aggregation generative adversarial network (AFA-GAN) with the capability of strong feature extraction and attentional feature fusion to generate high-resolution remote sensing images. We adopt the residual feature aggregation framework for the feature extraction to make full use of the hierarchical features on the residual branches. To better fuse global and local features with inconsistent scales, an attentional feature fusion mechanism is utilized in residual feature aggregation modules. The comprehensive experiments with state-of-the-art SISR methods on the UC Merced dataset demonstrate the effectiveness and superiority of our AFA-GAN.
Autoencoder Attractors for Uncertainty Estimation

The reliability assessment of a machine learning model’s prediction is an important quantity for the deployment in safety critical applications. Not only can it be used to detect novel sceneries, either as out-of-distribution or anomaly sample, but it also helps to determine deficiencies in the training data distribution. A lot of promising research directions have either proposed traditional methods like Gaussian processes or extended deep learning based approaches, for example, by interpreting them from a Bayesian point of view. In this work we propose a novel approach for uncertainty estimation based on autoencoder models: The recursive application of a previously trained autoencoder model can be interpreted as a dynamical system storing training examples as attractors. While input images close to known samples will converge to the same or similar attractor, input samples containing unknown features are unstable and converge to different training samples by potentially removing or changing characteristic features. The use of dropout during training and inference leads to a family of similar dynamical systems, each one being robust on samples close to the training distribution but unstable on new features. Either the model reliably removes these features or the resulting instability can be exploited to detect problematic input samples. We evaluate our approach on several dataset combinations as well as on an industrial application for occupant classification in the vehicle interior for which we additionally release a new synthetic dataset.
Using Design Questions to Analyze System Adaptation Potential

The problem of designing a self-adaptive system (SAS) is often approached from the perspective of adding an adaptive control loop to a technology or system that is not explicitly adaptive on its own. Although the control loop is a separate component, its design must be compatible with the features of the base system. In practice, the two designs can be interdependent. The objective of this paper is to define a set of design questions that can be used to pinpoint important attributes in a base system in terms of how it constrains or enables adaptation capabilities and influences the design of an adaptive control loop. To allow for meaningful analysis across a variety of problem domains, the design questions are specific but require open-ended, qualitative responses. Examples are given of how these design questions can be used to analyze technologies that have appeared in SAS literature.
A Model for Research of Transitions among Different Care Settings in Smart Lifetime Neighbourhoods

Neighbourhoods in EU member states are ageing, increasingly becoming the subjects of geo-gerontology and supply network analyses. Geo-gerontological aspects of the care and supply networks are becoming essential in the processes of deinstitutionalisation of long-term care and services and are crucial for the successful development and implementation of Ambient Assisted Living technologies (AAL_T). A growing number of older adults suffer from declining functional capacities and, thereby, experience decreased mobility and increasing frailty, which can lead to difficulties in carrying out essential activities of daily living as well as increased exposure to the risk of falls. Aid in managing one's daily life needs to be provided on time and in a properly built environment. Ambient intelligence for real-time monitoring of daily activities and supporting older residents with AAL_T will become essential means by which ageing cities will be able to maintain and improve the quality of life for older adults with declining functional capacities. This development will further influence the delineation of service areas and the timing of services. AAL_T and ambient intelligence will enable older adults to live longer in their dwellings and specialised facilities in the community and postpone or even avoid relocation to a nursing home altogether. Development in AAL_T has raised the possibility of developing more efficient services if the functional area of service networks is optimised. Based on these developments, we propose the adaptation of a multi-state competing risk model based on time to failure as modelled in reliability engineering to model and control the trajectories of declining functional capacities and related events that lead to the ill-health and disabilities. In this model, the built environment and timing in service networks are supposed to be embedded. The final goal of such a model is to support decision making that would extend the physically and mentally active life of seniors and enable them to live autonomously and be engaged with their communities. This paper aims to review the development of condition monitoring systems in ambient technologies, shape the model and propose a future research agenda.
Combining CNN and Transformer as Encoder to Improve End-to-End Handwritten Mathematical Expression Recognition Accuracy

The attention-based encoder-decoder (AED) models are increasingly used in handwritten mathematical expression recognition (HMER) tasks. Given the recent success of Transformer in computer vision and a variety of attempts to combine Transformer with convolutional neural network (CNN), in this paper, we study 3 ways of leveraging Transformer and CNN designs to improve AED-based HMER models: 1) Tandem way, which feeds CNN-extracted features to a Transformer encoder to capture global dependencies; 2) Parallel way, which adds a Transformer encoder branch taking raw image patches as input and concatenates its output with CNN's as final feature; 3) Mixing way, which replaces convolution layers of CNN's last stage with multi-head self-attention (MHSA). We compared these 3 methods on the CROHME benchmark. On CROHME 2016 and 2019, Tandem way attained the ExpRate of 54.85% and 58.56%, respectively; Parallel way attained the ExpRate of 55.63% and 57.39%; and Mixing way achieved the ExpRate of 53.93% and 55.64%. This result indicates that Parallel and Tandem ways perform better than Mixing way, and have little difference between each other.
A survey on VLSI architecture and design for data compression

In today's world data processing is very important. Also, the size of the data should be made as small as possible while considering the size of the device, area utilization, power consumption, etc. So we go for data compression, where after data compression it requires less space. Data can be an image or text. The compression techniques are different for image and text. Along with compression, the security of data is essential in today's applications. After the compression of input image compression, some of the data will be reduced in a lossless or lossy way. This paper summarizes different algorithms for data compression that are implemented in hardware, mainly FPGA.
An Intelligent System for Managing Uncertain Temporal Flood Events

During the analysis and management of flood events, the issue of time is crucial. In fact, such events require accurate knowledge of the time of their occurrence and their temporal relationships. However, in most cases, temporal information about flood events are uncertain. In this paper, we propose an intelligent system for managing the temporal uncertainty of these events. The modeling of this uncertainty is based on the belief functions theory and Allen's Interval Algebra. In fact, an uncertain flood event is represented using temporal intervals. Each interval is associated with a belief mass. If there is different uncertain information provided from different sources, a reasoning phase is carried out on these data in order to obtain more reliable information.
Research on Medical Question Answering System Based on Joint Model

With the global outbreak of virulent infectious diseases such as COVID-19, the lack of medical resources has become a serious social problem. More and more online users who encounter physical discomfort will first choose to look up the relevant symptoms on the Internet. Some online platforms have been able to understand the symptoms entered by users and provide auxiliary diagnosis suggestions. However, the professionalism and accuracy of online health and medical Question Answering (QA) systems are very insufficient. How to obtain and utilize massive medical symptom data from multiple data sources such as the Internet, medical symptom libraries and medical professional electronic books has become a difficult problem. The development of big data, artificial intelligence, and especially knowledge graph technology has provided ideas and methods to solve this problem. In this paper, a medical knowledge graph NWNU-KG is constructed on the basis of multi-source data, and the BiLSTM-CRF-CNN-Dict (BCCD) joint model is used for entity recognition and relationship extraction of user-asked questions to implement a healthcare knowledge QA system. Numerous experiments have found that the joint model BCCD proposed in this paper has a higher accuracy rate compared with the best available models, and can filter the answers to questions and return them to users in multi-source, heterogeneous and massive healthcare data, which has some practical value.
WebQMon.ai: Gateway-Based Web QoE Assessment Using Lightweight Neural Networks

Users’ perception of their experience accessing web pages greatly affects users’ willingness to continue browsing the website. However, it is difficult to assess user perception through a generic Quality of Experience (QoE) model. Web content consists of a large variety of static as well as dynamic objects, with some of them coming from the remote sites. This makes QoE assessment a challenge for the traditional methods. To build a generic QoE model, we introduce WebQMon.ai, a lightweight Web QoE assessment architecture using machine learning methods without setting any specific formula or threshold. WebQMon.ai can evaluate web-browsing QoE using mostly network-layer data with only one piece of application-layer information, the referer in the HTTP header, which is used to aggregate the packets associated with the same web page. The distribution of the arriving packets requested by the web page is used to construct WebQMon.ai. WebQMon.ai requires little storage space (80KB~6MB). More importantly it can be deployed directly at edge routers/gateways, due to the weak dependence on the application-layer payload. We further improved our algorithm by ensemble learning combining multiple orthogonal features, to generate a stronger classifier. We evaluated WebQMon.ai on three popular websites. It shows that the QoE assessment results for 4,800 unknown samples can be obtained within just 0.07 s and reach an average accuracy of 97%.
Contrast enhancement of scintigraphic image using fuzzy intensification

The objective of this study was to see the effect of fuzzy intensification (INT) operator on enhancement of scintigraphic image.Nuclear medicine physician (NMP) provided 25 scintigraphic images that required enhancement. The image pixels value was converted into fuzzy plane and was subjected to contrast INT operator with parameters of INT operator i.e., cross-over = 0.5 and number of iterations = 1 and 2. The enhanced image was again brought back into spatial domain (de-fuzzification) whose intensity value was in the range 0-255. NMP compared the enhanced image with its input image and labeled it as acceptable or unacceptable. The quality of enhanced image was also accessed objectively using four different image metrics namely: Entropy, edge content, absolute mean brightness error and saturation metrics.Most of the enhanced images (18 out of 25 images) obtained at cross-over = 0.5 and number of iterations = 1 are acceptable and found to have overall better contrast compared to the corresponding input image. Four images (two brain positron emission tomography scan and two I-131 scan) obtained at cross-over = 0.5 and with iteration = 2 are acceptable. Three input images (one dimercaptosuccinic acid (DMSA), one I-131 and one I-131- metaiodo-benzyl-guanidine (MIBG) scan) were better than their enhanced images.The enhancement produced by fuzzy INT operator was encouraging. Majority of enhanced images were acceptable at cross-over = 0.5 and number iterations = 1.
Efficient Search for Efficient Architecture

Differentiable architecture search (DARTS) has achieved success in searching powerful network architectures but suffers from unstable search, high search cost from repetitive attempts and unawareness of computational cost. In this paper, we propose a novel approach, namely Efficient and Stable Differentiable Architecture Search (ES-DARTS), that leverages decoupled search strategy and variational proxy pruning to achieve efficient search for efficient neural networks. Specifically, the decoupled search strategy stabilizes the search via bridging the gap between search and evaluation, and achieves acceleration with decoupled optimization, while the variational proxy pruning introduces structural pruning into DARTS to accommodate varying constraints on computational cost. ES-DARTS can be a plug-and-play module for DARTS-based approaches to achieve improved performance with reduced search cost and FLOPS. Experimental results demonstrate that ES-DARTS reduces top-1 error rate to 2.71% with $10 \times$ acceleration of DARTS on CIFAR10. ES-DARTS finds an architecture of 2.80% top-1 error rate with only 2.27 MB parameters on CIFAR-10 and of 29.4% top-1 error rate with only 322M FLOPS when transferred to ImageNet.