Reviewerly/exHarmony huggingface dataset:
Region Attention Network For Single Image Super-resolution

The task of single image super-resolution (SISR) is a highly inverse problem because it is very challenging to reconstruct rich details from blurry images. Most previous super-resolution (SR) methods based on Convolution Neural Network (CNN) tend to design a more complex network structure to directly learn the mapping between low-resolution images and high-resolution images. Nevertheless, it is not the best choice for blindly increasing the depth of the network since the performance improvement may not increase but the computing cost. In order to tackle this problem, we propose an effective method, which integrates the image prior to the model to enhance image reconstruction. In fact, the role of categorical prior as an important image feature has been widespreadly used in several high challenge computer vision tasks. In this work, we propose a region attention network (RAN) to recovery clear SR images with the assistance of categorical prior. The proposed RAN can be divided into two branches: the rough image reconstruction branch and the subtle image reconstruction branch. Meanwhile, we also propose a coupling group to make full use of the feature of two branches. Extensive experiments demonstrate that our RAN obtains satisfactory performance with the help of image categorical prior.
Quantum message-passing algorithm for optimal and efficient decoding

Recently, Renes proposed a quantum algorithm called belief propagation with quantum messages (BPQM) for decoding classical data encoded using a binary linear code with tree Tanner graph that is transmitted over a pure-state classical-quantum channel [1]. The algorithm presents a genuine quantum counterpart to decoding based on the classical belief propagation algorithm, which has found wide success in classical coding theory when used in conjunction with LDPC or Turbo codes. Here we significantly expand the understanding, formalism, and applicability of the BPQM algorithm with the following contributions. First, we prove analytically that BPQM realizes optimal decoding for any binary linear code with tree Tanner graph. We also provide the first formal description of the BPQM algorithm in full detail and without any ambiguity. In so doing, we identify a key flaw overlooked in the original algorithm which causes quantum circuit realizations to be exponentially large in the code size. We remedy this problem by formulating a truly message-passing algorithm which approximates BPQM and has circuit complexity, ${\mathcal{O}}\left({{\text{ poly }}n{\text{, polylog }}\frac{1}{ \in }}\right)$, where n is the code length and ϵ is the approximation error. Finally, we also propose a novel method for extending BPQM to factor graphs containing cycles by making use of approximate cloning.
An Effective Cloud Broker Framework for Knowledge Discovery in Multi-Cloud Environment

Industries make use of multi-cloud strategy which allows them to select different services from different providers because of reasons like flexibility, availability of computing resources and data storage, risk management, etc. A cloud broker also called as cloud agent is required in this case for cost-effective work distribution between cloud service providers. The broker also provides services such as deduplication, encryption, and data life cycle management. The abovementioned operations performed by a cloud broker necessitate the importance of a broker with desirable properties and smart functionalities for capturing, management, and sharing of knowledge. Therefore, an architectural procedure for textual-based information extraction that uses data mining technique to classify the documents is presented. It also discusses about multi-cloud-based data broadcasting services and computing that can improve the outcome of the system.
An Anonymous Editable Blockchain Scheme Based on Certificateless Aggregate Signature

Blockchain technology has gradually replaced traditional centralized data storage methods, and provided people reliable data storage services with its decentralized and non-tamperable features. However, the current blockchain data supervision is insufficient and the data cannot be modified once it is on the blockchain, which will cause the blockchain system to face various problems such as illegal information cannot be deleted and breach of smart contract cannot be fixed in time. To address these issues, we propose an anonymous editable blockchain scheme based on the reconstruction of the blockchain structure of the SpaceMint combining with the certificateless aggregate signature algorithm. Users register with their real identities and use pseudonyms in the system to achieve their anonymity. If the number of users who agree to edit meets the threshold, the data on the blockchain can be modified or deleted, and our scheme has the function of accountability for malicious behavior. The security analysis show that the proposed certificateless aggregate signature algorithm enjoys the unforgeability under the adaptive selected message attack. Moreover, the method of setting the threshold of related users is adopted to guarantee the effectiveness and security of editing blockchain data. At last, we evaluate the performance of our certificateless aggregate signature algorithm and related schemes in theoretical analysis and experimental simulation, which demonstrates our scheme is feasible and efficient in storage, bandwidth and computational cost.
Sentiment Analysis Implementation For Detecting Negative Sentiment Towards Indihome In Twitter Using Bidirectional Long Short Term Memory

Sentiment analysis is the method of extracting opinions from texts written in human language. Sentiment analysis can be used to analyze and evaluate the customer experience of the services that have been provided. With easy access to social media, sentiment analysis can be applied from people's comments on social media. One of the social media that is suitable for sentiment analysis is Twitter. In this paper, we focus on negative sentiment detection using tweets on Twitter by Indihome consumers. The system is designed to apply sentiment analysis using the BiLSTM method. Using BiLSTM, the accuracy 88 % is achieved.
Multi-relational EHR representation learning with infusing information of Diagnosis and Medication

Medical concept embedding which aims at learning interpretable low-dimensional representations of medical codes has become one of the key technologies to enable the machine (deep) learning models to imitate the doctor’s cognitive reasoning process in a variety of clinical tasks. Most existing works focus on leveraging the medical ontology to get the representations but remains ineffective in dealing with 1) the inconsistency between the knowledge of the medical ontology and the observations in health records, and 2) the deficiency of discovering the relations among multi-types of medical concepts. To address these challenges, this paper proposes MrER(Multi-relational EHR representation learning method). It’s a heterogeneous graph convolutional network with a self-adaptive adjacency matrix, to infer the multi-relations among different types of medical concepts and align them in the same subspace for the complex knowledge inference. Moreover, an temporal convolutional network is introduced to capture the dependency patterns in the sequence of medical records. The entire framework is trained in an end-to-end fashion. The experimental results show that MrER achieves competitive performance advantages in sequential diagnosis prediction task in comparison with state-of-the-art methods and the learned embeddings have good interpretability regarding the relationship between medical codes.
BTGAN: Training GAN with Balanced Triplet Loss and Two-Branch Architecture

TripletGAN is a variant of Generative Adversarial Network (GAN) by replacing the classification loss of discriminator with a triplet loss. Although TripletGAN delivers better mode coverage than vanilla GAN thanks to the characteristics of adversarial triplet loss that maximizes the embedding distance between generated samples, its adversarial training method suffers from the drawback that some generated images tend to deviate from the real sample distribution and noisy images are produced as we increase the number of iterations of training. In this paper, we propose an adversarially balanced triplet loss with four dynamic coefficients to achieve a trade-off between the quality and the diversity of generated samples. We also design a novel network architecture to provide GANs with an auto-encoding ability. Extensive experiments demonstrate the effectiveness of our proposed methods in terms of alleviating the problem in TripletGAN and the superiority in terms of reconstruction over some methods that directly train generator and encoder such as O-GAN.
Prediction of pediatric activity intensity with wearable sensors and bi-directional LSTM models

Assessing activity intensity has its clinical importance to the treatment of diseases such as obesity. The metabolic equivalent of task (MET) is the objective numerical measure for assessing the intensity of general activities. Because daily activities vary, an activity cannot be easily mapped to a MET value, which makes intensity quantification of daily activities more challenging than monotonous activities. In this article, we use the data from wearable inertial measurement unit (IMU) sensors and a calorimetry machine to map the relationship between activity motions and intensities. In detail, we describe an end-to-end approach for predicting METs of preschoolers. Based on the collection of data from the two devices, we present a systematic approach to address the aforementioned challenges and to predict physical activity intensity of preschoolers. Specifically, a dynamic synchronization method is first proposed to deal with the displaced data series, which takes the dynamic time warping (DTW) as an evaluation criterion. Second, additional features are designed to reinforce the ability of intensity prediction. Third, proposed methods are tested on a two-layer bidirectional long short term memory (LSTM) network model to predict MET values. Our experimental results reveal the effectiveness of the end-to-end approach.
Robust Graph Collaborative Filtering Algorithm Based on Hierarchical Attention

Learning the embedding representation of users and items is the core of the collaborative filtering algorithm. In recent years, the graph neural network (GNN) has been applied to the recommendation field due to its excellent performance. However, in the process of GNN iteratively aggregating neighbor information, the occasional noise in the graph structure will transmit errors to neighbor nodes along with the aggregation process, which will worsen the embedding representation of other nodes. Noise information is ubiquitous in real life. Therefore, while mining high-order collaborative information in depth, improving the robustness of the GNN model is also an important factor that needs to be considered in the recommendation task. Based on the above problems, this paper proposes a robust graph collaborative filtering algorithm based on hierarchical attention, which includes node-level and graph-level attention. Node-level attention performs preference aggregation and occasional noise information filtering on different neighbor nodes by learning the attention coefficients of different neighbor nodes; graph-level attention performs fusion and secondary filtering of occasional noise information on different deep graph embeddings by learning the attention coefficients of different dimensional nodes. The node-level and graph-level attention can fully realize the noise reduction of the graph structure in the deep propagation process. While ensuring that the nodes encode high-order collaborative information, it minimizes the noise information carried. Extensive experimental results on three data sets show that the recommendation algorithm is better than the existing mainstream recommendation algorithm in all evaluation indicators.
Research on the Current Situation and Teaching Strategies of “Digital Learning and Innovation” Literacy of Senior High School Students

With the development of the digital age, “digital learning and innovation” is becoming more and more important in the core literacy of information technology discipline. Through the elaboration of relevant concepts, this paper explores the connotation and concrete performance of “digital learning and innovation” literacy of senior high school students, and then obtains its three manifestations: the collection and management of digital learning resources, the adaptation of digital environment, the application and innovation of digital learning resources. On this basis, a questionnaire survey was designed to understand the status of “digital learning and innovation” literacy of high school students in S Middle School of Anhui Province, and the data results were analysed. It was found that senior high school students generally have this literacy, but some students still have some problems and need teachers’ timely guidance. Therefore, this study further proposes strategies for achieving high school students’ “digital learning and innovation” literacy to assist teachers in reminding students of “digital learning and innovation literacy” in teaching.
An Optical Flow Based Approach to Detect Movement Epenthesis in Continuous Fingerspelling of Sign Language

In this paper, the movement epenthesis problem in continuous fingerspelling is addressed. Movement epenthesis caused due to unwanted but unavoidable hand movement in between two sign gestures in continuous signing is one of the major problems in automatic sign language recognition. A novel method based on calculating the 2-norm values of the magnitude matrices of optical flow has been proposed in this paper to detect the movement epenthesis containing video frames. We used Horn-Schunck method to compute the optical flow and estimate the speed of hands in the the continuous fingerspelling videos. The 2-norm values of the magnitude matrix of optical flow provides a discriminative feature to distinguish of movement epenthesis frames from sign frames and hence mean of the 2-norm values is used as the threshold value to detect the movement epenthesis frames in a gesture video. We tested our method on continuous fingerspelling videos of Indian sign language. Experimental results show that the performance of our proposed method is 100% accurate in detecting the movement epenthesis frames in continuous fingerspelling.
Robust End-to-End Offline Chinese Handwriting Text Page Spotter with Text Kernel

Offline Chinese handwriting text recognition is a long-standing research topic in the field of pattern recognition. In previous studies, text detection and recognition are separated, which leads to the fact that text recognition is highly dependent on the detection results. In this paper, we propose a robust end-to-end Chinese text page spotter framework. It unifies text detection and text recognition with text kernel that integrates global text feature information to optimize the recognition from multiple scales, which reduces the dependence of detection and improves the robustness of the system. Our method achieves state-of-the-art results on the CASIA-HWDB2.0-2.2 dataset and ICDAR-2013 competition dataset. Without any language model, the correct rates are 99.12% and 94.27% for line-level recognition, and 99.03% and 94.20% for page-level recognition, respectively. Code will be available at GitHub.
Cyberbullying-Mediated Depression Detection in Social Media Using Machine Learning

Mental distress is one of the most paramount causes of disability worldwide. People using social media at an exponential rate becomes prey to cyberbullying victimization, eventually leading to mental health problems. This study focused on assessing the association of social media, cyberbullying, and mental depression via the use of supervised learning techniques. In this work, learning techniques namely support vector machines, random forests, Naïve Bayes, multilayer perceptron, convolution neural network, and recurrent neural network have been applied on the data (taken from Twitter and Reddit) for predicting cyberbullying-mediated depression detection. Higher accuracy is observed with deep learning as compared to baseline machine learning. Also, among the applied classical machine learning techniques, random forests reported the highest accuracy.
Oblivious Data Structure for Secure Multiple-Set Membership Testing

Searchable encryption (SE) assists untrusted cloud to provide various private query functionalities in an efficient way. Deploying SE schemes on the cloud directly can leak the locations of the data needed, namely, access pattern leakages. Statistical attacks have demonstrated the importance of sealing access-pattern leakages. To protect against such attacks, several works were made to design oblivious data structures by combining oblivious random access machine (ORAM) and SE. However, prior designs only work for private SQL or keywords queries such as range or boolean queries not for secure multiple-set membership testing which is also a fundamental service of the cloud. In this paper, we firstly design a new oblivious data structure, called OBTree, to overcome the problem of oblivious multiple-set membership testing over encrypted data. Specifically, OBTree is built from ORAM, bloom tree and the secure features of Intel SGX. Based on this novel oblivious data structure, we present a search algorithm that allows the users to do membership testing efficiently and securely.
Efficient algorithms for Risk-Sensitive Markov Decision Processes with limited budget

We tackle the problem of finding optimal policies for Markov Decision Processes, that minimize the probability of the cumulative cost exceeding a given budget. Such task falls under the umbrella of Risk-Sensitive Markov Decision Processes, which optimize a non-additive, non-linear function of cumulative cost that incorporates the user's attitude towards risk. Current algorithms for solving that task, for any budget equal or smaller than an user-defined budget, scale poorly when the support of the cost function is large, since they operate in an augmented state space which enumerates all possible remaining budgets. To circumvent this issue, we develop (i) an improved version of the Topological Value Iteration with Dynamic Programming algorithm (tvi-dp), and (ii) the first symbolic dynamic programming algorithm for this class of problems, called rs-spudd, that exploits conditional independence in the transition function in the augmented state space. The proposed algorithms improve efficiency by pruning irrelevant states and terminating early, without sacrificing optimality. Empirical results show that rs-spudd is able to solve problems up to 103 times larger than tvi-dp.
Structure-Aware Multi-scale Hierarchical Graph Convolutional Network for Skeleton Action Recognition

In recent years, graph convolutional neural network (GCNN) has achieved the most advanced results in skeleton action recognition tasks. However, existing models mainly focus on extracting local information from joint-level and part-level, but ignore the global information of frame-level and the relevance between multiple levels, which lead to the loss of hierarchical information. Moreover, these models consider the non-physical connection relationship between nodes but neglect the dependence between body parts. The lose of topology information directly results in poor model performance. In this paper, we propose a structure-aware multi-scale hierarchical graph convolutional network (SAMS-HGCN) model, which includes two modules: a structure-aware hierarchical graph pooling block (SA-HGP Block) and a multi-scale fusion module (MSF module). Specifically, SA-HGP Block establishes a hierarchical network to capture the topological information of multiple levels by using the hierarchical graph pooling (HGP) operation and model the dependence among parts via the structure-aware learning (SA Learning) operation. MSF module fuses information of different scales in each level to obtain multi-scale global structural information. Experiments show that our method achieves comparable performances to state-of-the-art methods on NTU-RGB+D and Kinetics-Skeleton datasets.
Online Clustering Technique with Adaptable Threshold and Radius for Evolving Data Stream

In today's technological communication age, it has become an emerging challenge to cluster online data streams that are generated continuously and evolve over time. Several methods of data mining have been proposed over the years for managing and clustering these massive amounts of data. Although the variety of fixed radius, buffer based and two-stage-based techniques were developed for the clustering data streams, these are not without their issues. However, this paper presents an online clustering technique with an adaptive threshold and radius for evolving data stream named OCATRED. This technique allows user-defined value both for micro-cluster threshold and radius which outperforms the existing fixed threshold and radius-based algorithms. In this proposed OCATRED, a weight adaptation-based mechanism is developed by analyzing the evolving of data streams which ensures the minimum and maximum threshold value. An additional data counter variable is defined for estimating the size of data that will be inserted in the micro-cluster which mainly secured the purity and accuracy of the cluster. It also eliminates growing macro-cluster which reduces both exceed amount of memory utilization and cluster processing time. Moreover, this adaptive method dynamically defines the threshold and radius value for the volume of the cluster over time which constructs a new arbitrary shape of the micro- cluster. An experiment based on several data sets was performed, and the results were compared to other algorithms for specific purposes. The proposed methods outperformed the current methods, according to the less memory and processing time consumption for the micro-cluster and also more cluster purity and accuracy. As a result, the proposed OCATRED method is more sophisticated and precise.
Route Optimization for Medication Delivery of Covid-19 Patients with Drones

With the developments in information technologies and the intense use of online commerce, the use of drones in distribution process has gained importance. In order to transport products to more than one location, drones can perform the distribution by following a specific route, as in the traveling salesman problem. Drones provide advantages over land transportation since they are not affected by the traffic congestion and can be used autonomously. However, the limited battery durations increase the importance of using the optimum route in distribution processes. In this study, it is aimed to use drones in drug distribution. Nowadays, due to the Covid-19 pandemic, it is aimed to distribute the drugs for the patients in an optimum way with drones. In this study, it is aimed to find the optimized routes for drones in drug distribution since Covid-19 medicine distribution is a time-critic mission. Since the number of patients in a certain area may increase very quickly, it is ensured that the patients are divided into clusters and the optimum route is determined for each cluster. We propose a hybrid model consisting of a combination of K-means clustering and Ant Colony algorithms. In particular, Covid-19 patients use the mobile part of the developed application on their smartphones and transmit their medication requests to our central server. We have compared the performance of Ant Colony, Artificial Bee and Genetic algorithm metaheuristics at the stage of determining the most suitable route according to the demands collected dynamically on the central server. In the process of determining the most suitable route, Ant Colony algorithm yields the closest to optimum results for different location groups. We have developed the mobile and web site of the application to validate the proposed drug delivery model.