Reviewerly/exHarmony huggingface dataset:
A Lightweight Fusion Distillation Network for Image Deblurring and Deraining

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from a high computational burden. We propose a lightweight fusion distillation network (LFDN) for image deblurring and deraining to solve the above problems. The proposed LFDN is designed as an encoder-decoder architecture. In the encoding stage, the image feature is reduced to various small-scale spaces for multi-scale information extraction and fusion without much information loss. Then, a feature distillation normalization block is designed at the beginning of the decoding stage, which enables the network to distill and screen valuable channel information of feature maps continuously. Besides, an information fusion strategy between distillation modules and feature channels is also carried out by the attention mechanism. By fusing different information in the proposed approach, our network can achieve state-of-the-art image deblurring and deraining results with a smaller number of parameters and outperform the existing methods in model complexity.
Sports Action Recognition Based on GB-BP Neural Network and Big Data Analysis

In recent years, the application of the gradient boosting-back propagation (GB-BP) neural network algorithm in many industries has brought huge benefits, so how to combine the GB-BP neural network algorithm with sports has become a research hotspot. Based on this, this paper studies the application of the GB-BP neural network algorithm in wrestling, designs the sports athletes action recognition and classification model based on the GB-BP neural network algorithm, first analyzes the research status of wrestling action recognition, and then optimizes and improves the shortcomings of action recognition and big data analysis technology. The GB-BP neural network algorithm can realize the accurate recognition and classification of wrestlers' training actions and carry out big data mining analysis with known action recognition, so as to achieve accurate classification. The experimental results show that the model can play a good role in wrestling and effectively improve the efficiency of wrestlers in training.
Few-Shot Partial-Label Learning

Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label. A basic promise of existing PLL solutions is that there are sufficient partial-label (PL) samples for training. However, it is more common than not to have just few PL samples at hand when dealing with new tasks. Furthermore, existing few-shot learning algorithms assume precise labels of the support set; as such, irrelevant labels may seriously mislead the meta-learner and thus lead to a compromised performance. How to enable PLL under a few-shot learning setting is an important problem, but not yet well studied. In this paper, we introduce an approach called FsPLL (Few-shot PLL). FsPLL first performs adaptive distance metric learning by an embedding network and rectifying prototypes on the tasks previously encountered. Next, it calculates the prototype of each class of a new task in the embedding network. An unseen example can then be classified via its distance to each prototype. Experimental results on widely-used few-shot datasets demonstrate that our FsPLL can achieve a superior performance than the state-of-the-art methods, and it needs fewer samples for quickly adapting to new tasks.
Quantum Key Distribution (QKD) and the Quantum Internet: The challenges facing this new technology

We describe the status of Quantum Key Distribution and highlight the developments required for this technology and describing a full QKD network. We set out the major differences between QKD and the Quantum Internet.
Developing the E-Commerce Model a Consumer to Consumer Using Blockchain Network Technique

E-commerce has increased recently because of the development of the internet and has become a new concept that is applicable to trade transaction and services providing, using information technology. This is known as e-commerce that is a means of communicating information،products or services through technical tools. This research proposed model is able to take advantage of Blockchain technology to develop e-commerce especially consumer to consumer. The proposed model adds some advantages to ecommerce operations, and the possibility of developing them to reach a high percentage of profits by using blockchain technology which led to verify the information of products offered for sale. In addition, to the possibility of distributing feedback to all Blockchain users, through which it develops the mechanism of trust and cooperation between consumers, it is considered a reference point to explore the behaviour of commercial consumers which is stored in the data file of consumers. This model facilitates business processes between consumer andconsumer, eliminates the central role of large business companies in controlling and setting restrictions, and to the development and expansion of this type of trade.
Small object detection using deep convolutional networks: applied to garbage detection system

Small object detection is always a hot research direction in the field of computer vision, and widely used in traffic, production, industry, and other fields. A neural network for detecting small objects based on original Cascade RCNN is proposed. First, we modify the traditional feature pyramid network and introduce multi-branch dilated convolutions to enhance the feature information of the small target. Second, the feature extraction networks in original Cascade RCNN are replaced with multi-layer deformable convolution networks, which can better adapt to the geometric variations of detection target and huge size span of the objects in the same scene. Finally, Soft non-maximum suppression is also integrated into the network to avoid problems in dense object detection. To verify the practicability of our proposed network, we apply it to the garbage detection system. The experimental results show that compared with original Cascade RCNN and other commonly used target detection networks, our proposed method, with a recall rate >0.98 as well as mean average precision up to 0.964, performs better not only in small object detection but also in industrial applications.
Semi-supervised Anomaly Detection with Imbalanced Data for Failure Detection in Optical Networks

We proposed an autoencoder-based anomaly-detection for optical failure detection with imbalanced data (&lt;3%), which identifies implicit failure and achieves detection accuracy of 96.8% and F1 value of 0.9224.
A highly efficient framework for outlier detection in urban traffic flow

The outliers in traffic flow represent the anomalies or emergencies in the road. The detection and research of outliers will help to reveal the mechanism of such events. Aiming at the problem of outlier detection in urban traffic flow, this paper innovatively proposes a highly efficient traffic outlier detection framework based on the study of road traffic flow patterns. The main research works are as follows: (1) data pre-processing, the road traffic flow matrix of the roads is calculated based on the collected GPS data, the non-negative matrix factorisation algorithm is chosen to reduce the dimension of the matrix. (2) Road traffic flow pattern extraction, the fuzzy C-means clustering algorithm with the Optimal k-cluster centre (K-FCM) is adopted to cluster the roads with the same road traffic flow pattern. (3) Outlier detection model training and evaluation, kernel density estimation is introduced to fit the probability density of roads traffic flow matrices which are used to train the back propagation neural network based on particle swarm optimisation to obtain the outlier detection and evaluation model, and a threshold is introduced to optimise the precision and recall of the model. The experimental results show that: the average precision and recall of the proposed method in this paper are 95.38% and 96.23%, respectively, and the average detection time is 28.4 seconds. The method has high accuracy, high efficiency and good practical significance.
The Construction of Chinese Online Resource Database and Teaching Implementation in Higher Vocational Education

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Effect of Augmented Reality-Based Reading Activities on Some Reading Variables and Participation in Class

The aim of this study was to examine the effect of augmented reality-based reading activities on reading comprehension, reading motivation, attitude towards reading and class participation of fourth grade elementary students, and to obtain the students’ views regarding this. The research was conducted with 54 fourth grade students attending two different classes of a state school with a medium socioeconomic level located in the city centre of the province of Kars during the autumn semester of the 2018-2019 academic year. The quantitative dimension of the study, in which a sequential explanatory mixed design was used, was carried out with a quasi-experimental design with pretest-posttest control group. In the qualitative dimension, however, opinions were obtained by means of semi-structured interviews following the implementation. The semi-structured process lasted for a total of 7 weeks, including a 1-week awareness programme and a 6-week implementation, and during this period, the implementation, which was related to augmented reality-based reading exercises, was carried out with the experimental group. In the control group, however, instruction based on the standard Turkish subject curriculum was carried out. The findings obtained from the quantitative dimension of the study revealed that there was a significant difference between scores of the experimental and control groups in reading comprehension, attitude towards reading, reading motivation and class participation, in favour of the experimental group. Moreover, in the qualitative dimension of the study, the interviewed students in the experimental group generally expressed positive views related to the augmented reality-based reading exercises. The students stated that they were able to use augmented reality easily, and that it had real-like, three-dimensional, entertaining and participative features. Furthermore, the students reported that as well as reading comprehension and reading attitude, it had an effect on their thinking skills, academic development and communication skills.
An Intelligent Diagnosis System for English Writing Based on Data Feature Extraction and Fusion

English writing is conducive to the online communication and communication of language; the current diagnosis system of English writing is difficult to accurately find and diagnose the wrong words, which leads to a low diagnosis rate of wrong words in English writing system. To solve this problem, this paper designs an intelligent diagnosis system for English writing based on data feature extraction and fusion. First of all, B/S architecture is introduced on the basis of the conventional intelligent diagnosis system structure of English writing, which makes up for the problem that the C/S mode is prone to diagnostic errors. Secondly, the features of English lexical data are extracted and fused to provide better input for the diagnostic model, which effectively solves the problems of complex vocabulary and feature redundancy in English writing. The simulation results show that the proposed intelligent diagnosis system for English writing has higher diagnostic accuracy and faster query speed.
Documenting evidence of a reuse of ‘“why should I trust you?”: explaining the predictions of any classifier’

We report here the following example of reuse. LIME is a local instance-based explanation generation framework that was originally proposed by Ribeiro et al. in their paper "'Why Should I Trust You?': Explaining the Predictions of Any Classifier". The framework was reused by Peng et al. in their paper "Defect Reduction Planning (using TimeLIME)". The paper used the original implementation of LIME as one of the core components in the proposed framework.
Dropping Sensation for Development of Lower Limb Force Feedback Device

In this study, we evaluate the dropping sensation for the development of a wearable lower limb force feedback device that can render both dropping and walking sensations. The developed device can render the dropping sensation at a smaller height than in reality by decelerating and stopping descent during the rendering of the drop image. Considering the user will be walking with the device, a smaller device height leads to better safety. The purpose of this study is to clarify the required specifications of the height of the vertical range of motion of the platform part, and the feasibility of the concept of rendering the dropping sensation. For this purpose, the dropping sensation for the difference in human acceleration time and human deceleration acceleration was evaluated. The results showed that the rendering of the dropping sensation required more than 0.41 s of descent at an acceleration of approximately 1377 mm/s<sup>2</sup>. Moreover, the dropping sensation and sense of reality were not impaired, even when the platform part of the foot was decelerated. This result indicates that the device can be made smaller.
A new key protocol design for cryptocurrency wallet

The majority of hacking accidents in cryptocurrency occur when the information of a cryptocurrency wallet is stolen. Since the cryptocurrency wallet is simply used for a key storage, when connecting to a transaction network, it is vulnerable for a key theft. Blockchain is not traceable, but it should communicate to applicate data of blockchain. To communicate to applicate data of blockchain, this study proposes a key protocol design to secure cryptocurrency transactions for user privacy of cryptocurrency to resolve the drawback of decentralized exchange. The key protocol includes a session key for a blockchain data structure and the Federated Byzantine Agreement (FBA) for the key-exchange agreement among users. In F-measure model, the values of Key Cluster Mode, Test Session key Mode and Original Session key Mode resulted in True Positive Ratio greater than 0.5 and False Positive Ratio lesser than 0.5. Therefore, the key protocol model has optimal security. In addition, computation costs of the protocol improve by compared with former studies. It may be played an important role in the cryptocurrency hacking accident and supported robust cryptocurrency market The study guarantees the security of cryptocurrency users without decentralized exchange, and it is scalable to other areas by using secure distributed networks.
ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search

Users of Web search engines reveal their information needs through queries and clicks, making click logs a useful asset for information retrieval. However, click logs have not been publicly released for academic use, because they can be too revealing of personally or commercially sensitive information. This paper describes a click data release related to the TREC Deep Learning Track document corpus. After aggregation and filtering, including a k-anonymity requirement, we find 1.4 million of the TREC DL URLs have 18 million connections to 10 million distinct queries. Our dataset of these queries and connections to TREC documents is of similar size to proprietary datasets used in previous papers on query mining and ranking. We perform some preliminary experiments using the click data to augment the TREC DL training data, offering by comparison: 28x more queries, with 49x more connections to 4.4x more URLs in the corpus. We present a description of the dataset's generation process, characteristics, use in ranking and suggest other potential uses.
Closure-interior duality over complete local rings

We define a duality operation connecting closure operations, interior operations, and test ideals, and describe how the duality acts on common constructions such as trace, torsion, tight and integral closures, and divisible submodules. This generalizes the relationship between tight closure and tight interior demonstrated by Epstein and Schwede (2014) and allows us to extend commonly used results on tight closure test ideals to operations such as those above.
Healthcare Data Storage Options Using Cloud

Modern healthcare systems are more and more complex today as they involve EHR applications, mobile applications, and IoT system integration. They typically generate a large volume of data on a daily basis, whereby the storage of the healthcare big data becomes challenging especially data from IoT connected devices. Traditional on-premise storage is not scalable enough. Cloud computing is a good option for storing healthcare big data as it is scalable, secure, reliable, provides ubiquitous access, and is highly available. In this chapter, the different storage media used to store data are described and the different storage mechanisms such as file storage, block storage, and object storage are discussed. Object storage is being increasingly adopted for storing healthcare Big data as it is more scalable, cost-effective, secure and reliable, and more suitable for data analytics. Blockchain technology is also being investigated for the healthcare industry. The use of blockchain for storing data on the cloud is also discussed, and the security of the data stored on cloud storage is described.
Robot Chat System (Chatbot) to Help Users “Homelab” based in Deep Learning

Homelab is a discussion platform on course materials and assignments for students and is packed in an Android application product and website.The Homelab website is built using Laravel.For Android-based Homelab application development, a special Application Programming Interface (API) with JWT security is made in this research.In Homelab, besides the question and answer feature, a virtual conversation agent (chatbot) based on deep learning with a retrieval model that uses multilayer perceptron and a special text dataset for conversations about Homelab products is also created.The virtual conversation agent at Homelab is made by utilizing the Sastrawi library and natural language processing to facilitate the processing of user messages in Indonesian.The output of this research is the response from the chatbot and the probability value from the classification results of the available response classes.The system made has an accuracy rate of 96.43 percent with an average processing time of 0.3 seconds to get a response.
Learning Asymmetric and Local Features in Multi-Dimensional Data Through Wavelets With Recursive Partitioning

Effective learning of asymmetric and local features in images and other data observed on multi-dimensional grids is a challenging objective critical for a wide range of image processing applications involving biomedical and natural images. It requires methods that are sensitive to local details while fast enough to handle massive numbers of images of ever increasing sizes. We introduce a probabilistic model-based framework that achieves these objectives by incorporating adaptivity into discrete wavelet transforms (DWT) through Bayesian hierarchical modeling, thereby allowing wavelet bases to adapt to the geometric structure of the data while maintaining the high computational scalability of wavelet methods-linear in the sample size (e.g., the resolution of an image). We derive a recursive representation of the Bayesian posterior model which leads to an exact message passing algorithm to complete learning and inference. While our framework is applicable to a range of problems including multi-dimensional signal processing, compression, and structural learning, we illustrate its work and evaluate its performance in the context of image reconstruction using real images from the ImageNet database, two widely used benchmark datasets, and a dataset from retinal optical coherence tomography and compare its performance to state-of-the-art methods based on basis transforms and deep learning.
Polynomial multiplication on embedded vector architectures

High-degree, low-precision polynomial arithmetic is a fundamental computational primitive underlying structured lattice based cryptography. Its algorithmic properties and suitability for implementation on different compute platforms is an active area of research, and this article contributes to this line of work: Firstly, we present memory-efficiency and performance improvements for the Toom-Cook/Karatsuba polynomial multiplication strategy. Secondly, we provide implementations of those improvements on Arm® Cortex®-M4 CPU, as well as the newer Cortex-M55 processor, the first M-profile core implementing the M-profile Vector Extension (MVE), also known as Arm® Helium™ technology. We also implement the Number Theoretic Transform (NTT) on the Cortex-M55 processor. We show that despite being singleissue, in-order and offering only 8 vector registers compared to 32 on A-profile SIMD architectures like Arm® Neon™ technology and the Scalable Vector Extension (SVE), by careful register management and instruction scheduling, we can obtain a 3× to 5× performance improvement over already highly optimized implementations on Cortex-M4, while maintaining a low area and energy profile necessary for use in embedded market. Finally, as a real-world application we integrate our multiplication techniques to post-quantum key-encapsulation mechanism Saber
Contrast and Synthetic Multiexposure Fusion for Image Enhancement

Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.