Reviewerly/exHarmony huggingface dataset:
A pipeline for medical literature search and its evaluation

One database commonly used by clinicians for searching the medical literature and practicing evidence-based medicine is PubMed. As the literature grows, it has become challenging for users to find the relevant material quickly because most of the time the relevant results are not on the top. In this article, we propose a search and ranking pipeline to improve the search results based on relevancy. We first propose an ensemble model consisting of three classifiers: bidirectional long-short-term memory conditional random field (bi-LSTM-CRF), support vector machine and naive Bayes to recognise PICO (patient, intervention, comparison, outcome) elements from abstracts. The ensemble was trained on an annotated corpus consisting of 5000 abstracts split into 4000 and 1000 training and testing data, respectively. The ensemble recorded an accuracy of 93%. We then retrieved around 927,000 articles from PubMed for the years 2017–2021 (access date 16 April 2021). For every abstract, we extracted and grouped all P, I and O terms, and stored these groups along with the article ID in a separate database. During the search, every P, I and O term of the query is searched only in its corresponding group of every abstract. The scoring method simply counts the number of matches between the query’s P, I and O elements and the words in P, I and O groups, respectively. The abstracts are sorted by the number of matches and the top five abstracts are listed using their pre-stored abstract IDs. A comprehensive user study was conducted where 60 different queries were formulated and used to generate ranked search results using both PubMed and our proposed model. Five medical professionals assessed the ranked search results and marked every item as relevant or non-relevant. Both models were compared using precision@K and mean-average-precision@K metrics where K is 5. For most of the queries, our model produced higher precision@K values than PubMed. The mean-average-precision@K value of our model is also higher than PubMed (0.83 versus 0.67).
Introducing an adaptive model for<scp>auto‐scaling</scp>cloud computing based on workload classification

Summary With the increasing expansion of cloud computing services, one of the main goals of researchers is to solve its major challenges. Cloud service providers must satisfy the service level agreement for customers and prevent resource wastage as much as possible. Without a precise, optimal, and dynamic policy, this is unattainable. The key idea is the ability to acquire resources as you need them and release resources when you no longer need them, named “Cloud Elasticity.” Elasticity is a trade‐off between resource acquisition and release, and if this optimization is done best, the service level agreement will be fully achieved and the cloud provider will have the least waste of resources. The researchers used machine learning techniques to predict user workload and decide to scale up/out the resources. A challenging issue is the different characteristics of the users' workloads. The results show that each prediction algorithm works well on a class of users' workloads not all. Hence, in this study, a new architecture has been suggested to automatically classify the workloads based on their sequential statistical characteristics. First, the sequential statistical characteristics of the users' workload are extracted and then a trained neural network classifies the user's workload. The developed adaptive model chooses the best suitable algorithm among LR, SVM, and ARIMA to predict the workload. The results indicate a 10% improvement in forecast error.
MTCSNet: Mean Teachers Cross-Supervision Network for Semi-Supervised Cloud Detection

Cloud detection methods based on deep learning depend on large and reliable training datasets to achieve high detection accuracy. There will be a significant impact on their performance, however when the training data are insufficient or when the label quality is low. Thus, to alleviate this problem, a semi-supervised cloud detection method, named the mean teacher cross-supervision cloud detection network (MTCSNet) is proposed. This method enforces both consistency and accuracy on two cloud detection student network branches, which are perturbed with different initializations, for the same input image. For each of the two student branches, the respective teacher branches, used to generate high-quality pseudo labels, are constructed using an exponential moving average method (EMA). A pseudo one-hot label, produced by one teacher network branch, supervises the other student network branch with the standard cross-entropy loss, and vice versa. To incorporate additional prior information into the model, the presented method uses near-infrared bands instead of red bands as model inputs and injects strong data augmentations on unlabeled images fed into the student model. This induces the model to learn richer representations and ensure consistency constraints on the predictions of the same unlabeled image across different batches. To attain a more refined equilibrium between the supervised and semi-supervised loss in the training process, the proposed cloud detection network learns the optimal weights based on homoscedastic uncertainty, thus effectively exploiting the advantages of semi-supervised tasks and elevating the overall performance. Experiments on the SPARCS and GF1-WHU public cloud detection datasets show that the proposed method outperforms several state-of-the-art semi-supervised algorithms when only a limited number of labeled samples are available.
HAFNet: Hierarchical Attentive Fusion Network for Multispectral Pedestrian Detection

Multispectral pedestrian detection via visible and thermal image pairs has received widespread attention in recent years. It provides a promising multi-modality solution to address the challenges of pedestrian detection in low-light environments and occlusion situations. Most existing methods directly blend the results of the two modalities or combine the visible and thermal features via a linear interpolation. However, such fusion strategies tend to extract coarser features corresponding to the positions of different modalities, which may lead to degraded detection performance. To mitigate this, this paper proposes a novel and adaptive cross-modality fusion framework, named Hierarchical Attentive Fusion Network (HAFNet), which fully exploits the multispectral attention knowledge to inspire pedestrian detection in the decision-making process. Concretely, we introduce a Hierarchical Content-dependent Attentive Fusion (HCAF) module to extract top-level features as a guide to pixel-wise blending features of two modalities to enhance the quality of the feature representation and a plug-in multi-modality feature alignment (MFA) block to fine-tune the feature alignment of two modalities. Experiments on the challenging KAIST and CVC-14 datasets demonstrate the superior performance of our method with satisfactory speed.
An empirical analysis of sentencing of “Access to Information” computer crimes

Abstract There is a widespread perception that computer crime sentencing is too harsh. But this criticism has occurred in the absence of comprehensive, multi‐year data on how computer crimes are actually sentenced and how those sentences compare to other, purportedly similar crimes, such as trespass, burglary, or fraud. This article uses an analysis of real‐world sentencing data to examine how the computer crimes are actually sentenced. We combined court filings and U.S. Sentencing Commission data files to build a custom data set of 1095 Computer Fraud and Abuse Act (CFAA) sentences from 2005 through 1998. Our results show that CFAA sentences are sentenced differently from trespass, burglary, or non‐CFAA fraud crimes; that sentences in which the defendant exceeded authorized access have declined over the years; and that the “sophisticated means” and “special skills” enhancements have been less routinely applied than has been assumed. These results have policy implications for how CFAA crimes are sentenced.
Label distribution learning with high‐order label correlations

Summary Label distribution learning (LDL) is an emerging learning paradigm, which can be used to solve the label ambiguity problem. In spite of the recent great progress in LDL algorithms considering label correlations, the majority of existing methods only measure pairwise label correlations through the commonly used similarity metric, which is incapable of accurately reflecting the complex relationship between labels. To solve this problem, a novel label distribution learning method—based on high‐order label correlations (LDL‐HLC) is proposed. By virtue of the ‐regularization sparse reconstruction of the label space, the high‐order label correlations matrix is firstly obtained. Then, a new regular term can be constructed to fit the final prediction label distribution via the correction matrix. Furthermore, efficient classification performance and complete feature selection are guaranteed by common features learning via ‐regularization. Finally, the performance and effectiveness of the proposed algorithm are well illustrated through extensive experiments on 14 label distribution datasets and comparisons with some existing algorithms.
Attribute-based multi-user collaborative searchable encryption in COVID-19

With the outbreak of COVID-19, the government has been forced to collect a large amount of detailed information about patients in order to effectively curb the epidemic of the disease, including private data of patients. Searchable encryption is an essential technology for ciphertext retrieval in cloud computing environments, and many searchable encryption schemes are based on attributes to control user's search permissions to protect their data privacy. The existing attribute-based searchable encryption (ABSE) scheme can only implement the situation where the search permission of one person meets the search policy and does not support users to obtain the search permission through collaboration. In this paper, we proposed a new attribute-based collaborative searchable encryption scheme in multi-user setting (ABCSE-MU), which takes the access tree as the access policy and introduces the translation nodes to implement collaborative search. The cooperation can only be reached on the translation node and the flexibility of search permission is achieved on the premise of data security. ABCSE-MU scheme solves the problem that a single user has insufficient search permissions but still needs to search, making the user's access policy more flexible. We use random blinding to ensure the confidentiality and security of the secret key, further prove that our scheme is secure under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. Security analysis further shows that the scheme can ensure the confidentiality of data under chosen-keyword attacks and resist collusion attacks.
Virtual Assistance using Python

The paper explores the potential of utilizing new technology to develop an intelligent Virtual Assistant that is capable of using natural language processing and user-based data. It examines existing intelligent programs with different categories of support and evaluates the potential usefulness of a particular software as a Virtual Assistant. The proposed Virtual Assistant should be able to communicate socially through natural language processing, store and analyze user data, and operate without the need for human input or programming. The paper suggests that with advancements in technology, creating virtual personal assistants could become a reality. The authors conducted experiments on a specific software and performed user testing, which demonstrated that a basic program with natural language processing algorithms could already be viable. Overall, the paper presents the idea of an intelligent Virtual Assistant that could revolutionize the way we interact with technology.
User Cold Start Recommendation System Based on Hofstede Cultural Theory

The main function of recommendation systems is to help users select satisfactory services from many services. Existing recommendation systems usually need to conduct a questionnaire survey of the user or obtain the user's third-party information in the case of cold start users; this operation often infringes on the user's privacy. This article is aimed at providing accurate recommendations for cold start users without infringement on user privacy. Therefore, in response to this problem, this manuscript per the authors proposes a recommendation algorithm based on Hofstede's cultural dimensions theory. The algorithm uses Hofstede's cultural dimensions theory to establish a connection between two cold start users, thus ensuring the stability of QoS prediction accuracy. Then, the prediction results and the dynamic combination of the matrix factorization algorithm are used to obtain a more accurate prediction. The verification results on the real dataset WS-Dream show that the prediction algorithm proposed in this paper effectively alleviates the user cold start problem.
Patient and wearable device authentication utilizing attribute-based credentials and permissioned blockchains in smart homes

Purpose Coronavirus Disease 2019 (COVID-19) necessitated the need for “Hospital-at-home” improvisations that involve wearable technology to classify patients within households before visiting health institutions. Do-It-Yourself wearable devices allow for the collection of health data leading to the detection and/or prediction of the prevalence of the disease. The sensitive nature of health data requires safeguards to ensure patients’ privacy is not violated. The previous work utilized Hyperledger Fabric to verify transmitted data within Smart Homes, allowing for the possible implementation of legal restrictions through smart contracts in the future. This study aims to explore privacy-enhancing authentication schemes that are operated by multiple credential issuers and capable of integration into the Hyperledger ecosystem. Design/methodology/approach Design Science Research is the methodology that was used in this study. An architecture for ABC-privacy was developed and evaluated. Findings While the privacy-by-design architecture enhances data privacy through edge and fog computing architecture, there is a need to provide an additional privacy layer that limits the amount of data that patients disclose. Selective disclosure of credentials limits the number of information patients or devices divulge. Originality/value The evaluation of this study identified Coconut as the most suitable attribute-based credentials scheme for the Smart Homes Patients and Health Wearables use case Coconut user-centric architecture Hyperledger integration multi-party threshold authorities public and private attributes re-randomization and unlinkable revelation of selective attribute revelations.
Radio Frequency Fingerprint Identification Based on Metric Learning

With the popularization of the internet of things (IoT), its security has become increasingly prominent. Radio-frequency fingerprinting (RFF) is used as a physical-layer security method to provide security in wireless networks. However, the problems of poor performance in a highly noisy environment and less consideration of calculation resources are urgent to be resolved in a practical RFF application domain. The authors propose a new RFF identification method based on metric learning. They used power spectrum density (PSD) to extract the RFF from the nonlinearity of the RF front end. Then they adopted the large margin nearest neighbor (LMNN) classification algorithm to identify eight software-defined radio (SDR) devices. Different from existing RFF identification algorithms, the proposed LMNN method is more general and can learn the optimal metric from the wireless communication environment. Furthermore, they propose a new training and test strategy based on mixed SNR, which significantly improves the performance of conventional low-complexity RFF identification methods. Experimental results show that the proposed method can achieve 99.8% identification accuracy with 30dB SNR and 96.83% with 10dB SNR. In conclusion, the study demonstrates the effectiveness of the proposed method in recognition efficiency and computational complexity.
LoNAS: Low-Cost Neural Architecture Search Using a Three-Stage Evolutionary Algorithm [Research Frontier]

Neural architecture search (NAS) has been widely studied to design high-performance network architectures automatically. However, existing approaches require more search time and substantial resource consumption due to their intensive architecture evaluations. Moreover, recently developed NAS algorithms are noncompetitive when combining multiple competing and conflicting objectives, e.g., the test accuracy and the number of parameters. In this paper, a low-cost NAS (LoNAS) method is proposed to address these problems. First, a variable-architecture encoding strategy based on a novel Reg Block is designed to construct high accuracy network architectures with few parameters. Second, a training-free proxy based on the neural tangent kernel (NTK) is proposed to accelerate the search process efficiently. Finally, a three-stage evolutionary algorithm (EA) based on multiple-criteria environmental selection and a set of block-based mutation operators are designed to balance exploration and exploitation better. The experimental results show that LoNAS finds network architectures with competitive performance compared to the state-of-the-art architectures in test accuracy and the number of parameters. Moreover, LoNAS uses less search time and fewer computational resources, consuming only 0.02 GPU Days with one GPU on CIFAR-10 and CIFAR-100. Furthermore, the architectures found by LoNAS on CIFAR-10 and CIFAR-100 exhibit good transferability to ImageNet-16-120, with the test accuracy surpassing that of the state-of-the-art network architectures.
Design and Optimization Method of Intelligent Interconnection Decision System Based on Blockchain Technology

Blockchain, as the application of distributed computing and data storage, point-to-point transmission, encryption algorithm, consensus mechanism, and other technologies, has become an important hotspot of data technology in banks, securities, and other Internet enterprises. In view of the problems of delay and large throughput in the current intelligent interconnection system of block chain, this paper proposes an optimization method of intelligent interconnection of block chain technology, so as to improve the performance of the system. In this paper, firstly, the development of intelligent interconnection and the related concepts of intelligent interconnection system optimization are put forward. Then, the related algorithms of blockchain are proposed, including the consensus algorithm of blockchain and the throughput delay algorithm of blockchain. Finally, in the experimental part, the blockchain technology is deeply studied, and the advantages and internal structure of blockchain are clearly analyzed. Then, by comparing the consensus of blockchain to optimize intelligent interconnected system and comparing blockchain technology with ordinary technology to optimize intelligent interconnected system, it is concluded that blockchain plays a positive role in optimizing intelligent interconnected system and also promotes the optimization of intelligent system. Therefore, we should introduce the related technologies of blockchain into the optimization of intelligent interconnection system, and its blockchain technology analysis can make up for the shortcomings of intelligent interconnection system. It also shows that blockchain technology is suitable for many information research fields and plays an irreplaceable role. The blockchain system consists of six different hierarchies, mainly network data layer and blockchain consensus layer. The data layer mainly encrypts the data related to blockchain. The consensus layer recognizes the main algorithms of blockchain, which can encapsulate the network information nodes of blockchain. Therefore, when blockchain carries out relevant information analysis algorithms, it mainly relies on blockchain consensus algorithm. It is beneficial to analyze the complex information of the system and promote the optimal development of blockchain.
Challenges and Solutions of the Application of Data-Driven Technology in Network 3D Game Design

The development of online games is in full swing in recent years, and it is developing in the direction of 3D, exquisite, and has attained significant popularity. However, there are many challenges faced while designing 3D games that are targeted to specific user groups and can run stably. Data-driven technology can collect game preferences and identify common technical defects and other issues in game design for effective completion of frameworks. To solve the problem and difficulty of obtaining the data for 3D games and the possibility of flaws in the framework design, this paper adopts data-driven technology to descriptively analyze the obtained data through website log files, web beacons, and JS page tags and constructs a network after experimenting with the mathematical model of 3D modeling for gaming. For this purpose, a 3D game framework was successfully designed. After testing the data of the server for three cases while there are 4000, 8000, and 12000 players online at a given time, where each of which ran for 3 consecutive days, and it was concluded that the average CPU occupancy rate of these three cases was below 50%, especially in the range of about 30% for the majority of these players. Moreover, it can stably support about 10000 simultaneous online players for a relatively long period of time. For game servers and character servers, the results are excellent and have the potential to be adopted in future for 3D gaming industry.
Deep Pixel Restoration Loop Coding Network [Application Notes]

This paper presents a solution to the video prediction problem based on the deep learning paradigm. Predicted frames generated by existing video prediction models are often blurry and have difficulty maintaining accuracy in multi-step prediction. To overcome these limitations, this paper presents a deep learning model, named the deep pixel restoration loop coding network (DPR-LC-Net), which employs the concept of predictive coding and adopts pixels in the real frames. While making a long-term prediction, it can generate clear prediction frames with few errors. The calculation process of DPR-LC-Net is multi-sequential: it completes calculations in the form of an approximate loop from top to bottom and then from left to right. After predicting subsequent steps, DPR-LC-Net both calculates errors and removes them from sequential prediction. Finally, the model includes a unique pixel restoration module that works efficiently on pixels in the preceding real frames to generate predicted frames, thereby improving the clarity of the predicted frames. Extensive experiments using four video datasets illustrate that the prediction performance of DPR-LC-Net is superior to that of the state-of-the-art models.