Reviewerly/exHarmony huggingface dataset:
Prior Gradient Mask Guided Pruning-Aware Fine-Tuning

We proposed a Prior Gradient Mask Guided Pruning-aware Fine-Tuning (PGMPF) framework to accelerate deep Convolutional Neural Networks (CNNs). In detail, the proposed PGMPF selectively suppresses the gradient of those ”unimportant” parameters via a prior gradient mask generated by the pruning criterion during fine-tuning. PGMPF has three charming characteristics over previous works: (1) Pruning-aware network fine-tuning. A typical pruning pipeline consists of training, pruning and fine-tuning, which are relatively independent, while PGMPF utilizes a variant of the pruning mask as a prior gradient mask to guide fine-tuning, without complicated pruning criteria. (2) An excellent tradeoff between large model capacity during fine-tuning and stable convergence speed to obtain the final compact model. Previous works preserve more training information of pruned parameters during fine-tuning to pursue better performance, which would incur catastrophic non-convergence of the pruned model for relatively large pruning rates, while our PGMPF greatly stabilizes the fine-tuning phase by gradually constraining the learning rate of those ”unimportant” parameters. (3) Channel-wise random dropout of the prior gradient mask to impose some gradient noise to fine-tuning to further improve the robustness of final compact model. Experimental results on three image classification benchmarks CIFAR10/ 100 and ILSVRC-2012 demonstrate the effectiveness of our method for various CNN architectures, datasets and pruning rates. Notably, on ILSVRC-2012, PGMPF reduces 53.5% FLOPs on ResNet-50 with only 0.90% top-1 accuracy drop and 0.52% top-5 accuracy drop, which has advanced the state-of-the-art with negligible extra computational cost.
Modular Parity Quantum Approximate Optimization

The performance of a ``quantum approximate optimization algorithm'' is improved in a hybrid method, which keeps the required circuit depth constant while reducing the number of explicitly enforced parity constraints.
A Survey on Implicit Aspect Detection for Sentiment Analysis: Terminology, Issues, and Scope

Sentiment analysis or opinion mining has come forth as an attractive research field in the past few years. Sentiment analysis extracts sentiments from the text for analysis and aggregation at different levels of detail. In aspect-level sentiment analysis, we aggregate sentiment for different aspects of entities. The bulk of the research work executed so far focuses on detecting explicit aspects but ignored implicit aspects, which are insinuated by other existing words and articulates of the sentence. Since a significant percentage of sentences contain implicit aspects, detection of implicit aspects becomes vital for sentiment analysis. This survey concentrates on implicit aspect detection, and a detailed discussion about state of the art is provided. The available methods are categorized depending on the algorithm applied. Quantitative evaluation for different methods as stated by authors is included for comparison purpose. Discussion about terminology, issues, and scope in the detection of implicit aspects is also included. The fine-grained sentiment information collected may be used in many applications in various domains. This survey aims to advocate the need for implicit aspect detection, determine existing efficient solutions, identify complications in implicit aspect detection, and suggest measures to improve performance, which comprise future research trends in implicit aspect detection.
Adaptive Testing and Debugging of NLP Models

Current approaches to testing and debugging NLP models rely on highly variable human creativity and extensive labor, or only work for a very restrictive class of bugs.We present AdaTest, a process which uses large scale language models (LMs) in partnership with human feedback to automatically write unit tests highlighting bugs in a target model.Such bugs are then addressed through an iterative text-fixretest loop, inspired by traditional software development.In experiments with expert and non-expert users and commercial / research models for 8 different tasks, AdaTest makes users 5-10x more effective at finding bugs than current approaches, and helps users effectively fix bugs without adding new bugs.* Equal contribution, author order chosen by casting lots. LM suggests tests User lters and organizes Test suggestionsTest tree
Rotation Left Digits to Enhance the Security Level of Message Blocks Cryptography

Due to the availability of several social media platforms and their use in sending text messages, it is necessary to provide an easy and safe way to protect messages from being hacked especially in the presence of intruders and data thieves, and taking into consideration that most of those messages are confidential and personal, it is necessary to provide an easy and safe way to protect messages from being hacked.In this research paper, a simple and easy method of message cryptography will be proposed.The method divides a message into blocks with fixed sizes.The block size ranges from 2 to 60.The method uses a secret color image to generate an array with a size equal to the number of resulted blocks.The array will then be used as a private key.Each element of the private key will be used to calculate the number of rotation digits for the associated block in order to apply block rotation left operation.The proposed method will be examined using the parameter's Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), and throughput.The proposed method will be compared with other standard methods of message cryptography, such as Data Encryption Standard (DES), Triple-DES (3DES), Advanced Encryption Standard (AES), and Blow Fish (BF).Experimental results show that the proposed method is secured enough based on using secret image, block size, and calculated Rotation Left Digits (RLD) for each block.
Discrete Joint Semantic Alignment Hashing for Cross-Modal Image-Text Search

Supervised cross-modal image-text hashing has aroused extensive concentrations in comprehending the correspondence between vision and language for data search tasks. Existing methods learn the compact hash codes by leveraging a given image-text data pairs or supervised information to explore such correspondence. However, they still confront obvious drawbacks. First, there is no engagement between multiple semantic information that yields the suboptimal search performance. Second, most of them adopt continuous relaxation strategy by discarding the discrete constraints, which results in large binary quantization errors. To deal with these problems, we propose a novel supervised hashing method, termed Discrete Joint Semantic Alignment Hashing (DJSAH). Specifically, it builds a connection between semantics (a.k.a. class labels and pairwise similarities) by the joint semantic alignment learning. And thus the high-level discriminative semantics can be preserved into the hash codes. Besides, a well-designed discrete optimization algorithm with linear computation and memory cost is developed to reduce the information loss of the hash codes with no need for relaxation. Extensive experiments and analyses on three benchmark datasets validate the superiority of the proposed DJSAH against several state-of-the-art hashing methods.
The Possibility and Defensibility of Nonstate “Censorship”

In this chapter, we explore conceptual and substantive questions about whether nonstate entities censor. Our prime examples are shopping malls and social media companies. We argue that they can censor and, at least sometimes, they may do so. On our account, one agent censors another when it intentionally suppresses, denies, or withholds some liberty to express or otherwise communicate. Censorship is a success term. Instances show varying scopes and degrees. We defend a position we call stringent private rights to exclude. We do so by canvassing three leading objections to this position: The Town Square Objection, the Argument from Equal Status, and the Appeal to Historic Injustice. We argue that such objections either fail or must meet a high argumentative burden before undermining stringent private rights to exclude. Such objections must overcome the rejoinder that there are substitute communication venues available and/or that restricting rights to exclude is arbitrary. We conclude by discussing emergency exceptions to our position.
Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models

Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.
Learning to Retrieve Passages without Supervision

Dense retrievers for open-domain question answering (ODQA) have been shown to achieve impressive performance by training on large datasets of question-passage pairs.In this work we ask whether this dependence on labeled data can be reduced via unsupervised pretraining that is geared towards ODQA.We show this is in fact possible, via a novel pretraining scheme designed for retrieval.Our "recurring span retrieval" approach uses recurring spans across passages in a document to create pseudo examples for contrastive learning.Our pretraining scheme directly controls for term overlap across pseudo queries and relevant passages, thus allowing to model both lexical and semantic relations between them.The resulting model, named Spider, performs surprisingly well without any labeled training examples on a wide range of ODQA datasets.Specifically, it significantly outperforms all other pretrained baselines in a zero-shot setting, and is competitive with BM25, a strong sparse baseline.Moreover, a hybrid retriever over Spider and BM25 improves over both, and is often competitive with DPR models, which are trained on tens of thousands of examples.Last, notable gains are observed when using Spider as an initialization for supervised training. 1God at Sinai granted Aaron the priesthood for himself and his male descendants, and he became the first High Priest of the Israelites.
Tutorial on Multimodal Machine Learning

Multimodal machine learning involves integrating and modeling information from multiple heterogeneous and interconnected sources of data.It is a challenging yet crucial area with numerous real-world applications in multimedia, affective computing, robotics, finance, HCI, and healthcare.This tutorial, building upon a new edition of a survey paper on multimodal ML as well as previously-given tutorials and academic courses, will describe an updated taxonomy on multimodal machine learning synthesizing its core technical challenges and major directions for future research.
BEST—Blockchain-Enabled Secure and Trusted Public Emergency Services for Smart Cities Environment

In the last few years, the Internet of things (IoT) has recently gained attention in developing various smart city applications such as smart healthcare, smart supply chain, smart home, smart grid, etc. The existing literature focuses on the smart healthcare system as a public emergency service (PES) to provide timely treatment to the patient. However, little attention is given to a distributed smart fire brigade system as a PES to protect human life and properties from severe fire damage. The traditional PES are developed on a centralised system, which requires high computation and does not ensure timely service fulfilment. Furthermore, these traditional PESs suffer from a lack of trust, transparency, data integrity, and a single point of failure issue. In this context, this paper proposes a Blockchain-Enabled Secure and Trusted (BEST) framework for PES in the smart city environment. The BEST framework focuses on providing a fire brigade service as a PES to the smart home based on IoT device information to protect it from serious fire damage. Further, we used two edge computing servers, an IoT controller and a service controller. The IoT and service controller are used for local storage and to enhance the data processing speed of PES requests and PES fulfilments, respectively. The IoT controller manages an access control list to keep track of registered IoT gateways and their IoT devices, avoiding misguiding the PES department. The service controller utilised the queue model to handle the PES requests based on the minimum service queue length. Further, various smart contracts are designed on the Hyperledger Fabric platform to automatically call a PES either in the presence or absence of the smart-home owner under uncertain environmental conditions. The performance evaluation of the proposed BEST framework indicates the benefits of utilising the distributed environment and the smart contract logic. The various simulation results are evaluated in terms of service queue length, utilisation, actual arrival time, expected arrival time, number of PES departments, number of PES providers, and end-to-end delay. These simulation results show the effectiveness and feasibility of the BEST framework.
Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine

Abstract Fennel contains many antioxidant and antibacterial substances, and it has very important applications in food flavoring and other fields. The kinds and contents of chemical substances in fennel vary from region to region, which can affect the taste and efficacy of the fennel and its derivatives. Therefore, it is of great significance to accurately classify the origin of the fennel. Recently, origin detection methods based on deep networks have shown promising results. However, the existing methods spend a relatively large time cost, a drawback that is fatal for large amounts of data in practical application scenarios. To overcome this limitation, we explore an origin detection method that guarantees faster detection with classification accuracy. This research is the first to use the machine learning algorithm combined with the Fourier transform-near infrared (FT-NIR) spectroscopy to realize the classification and identification of the origin of the fennel. In this experiment, we used Rubberband baseline correction on the FT-NIR spectral data of fennel (Yumen, Gansu and Turpan, Xinjiang), using principal component analysis (PCA) for data dimensionality reduction, and selecting extreme learning machine (ELM), Convolutional Neural Network (CNN), recurrent neural network (RNN), Transformer, generative adversarial networks (GAN) and back propagation neural network (BPNN) classification model of the company realizes the classification of the sample origin. The experimental results show that the classification accuracy of ELM, RNN, Transformer, GAN and BPNN models are above 96%, and the ELM model using the hardlim as the activation function has the best classification effect, with an average accuracy of 100% and a fast classification speed. The average time of 30 experiments is 0.05 s. This research shows the potential of the machine learning algorithm combined with the FT-NIR spectra in the field of food production area classification, and provides an effective means for realizing rapid detection of the food production area, so as to merchants from selling shoddy products as good ones and seeking illegal profits.
Autonomous Path Identity-Based Broadcast Proxy Re-Encryption for Data Sharing in Clouds

Cloud computing with massive storage and computing capabilities has become widespread in actual applications.It is critical to ensure secure data sharing in cloud-based applications.Currently, numerous identity-based broadcast proxy re-encryption (IB-BPRE) schemes have been proposed to resolve the privacy issue.However, the existing IB-BPRE schemes cannot reach the transformation of the decryption right for outsourced encrypted data between the broadcast receiver sets (data user sets) delegated by the data owner (Alice) because it is difficult for the IB-BPRE to hold the character of multi-hop.Consequently, a new cryptographic primitive called autonomous path identity-based broadcast proxy re-encryption (APIB-BPRE) is presented to address the above issue.In an APIB-BPRE scheme, the delegator establishes an autonomous path involving preferred multiple broadcast receiver sets and the proxy can convert the decryption right for the broadcast receiver set into the decryption right for the next broadcast receiver set by the re-encryption key from the delegator.This solution is convenient and flexible for cloud users and utilizes the benefits of cloud computing.The evaluation and comparison indicate that our APIB-BPRE system is effective and practical.
Theory of Acceleration of Decision-Making by Correlated Time Sequences

Photonic accelerators have been intensively studied to provide enhanced information processing capability to benefit from the unique attributes of physical processes. Recently, it has been reported that chaotically oscillating ultrafast time series from a laser, called laser chaos, provides the ability to solve multi-armed bandit (MAB) problems or decision-making problems at GHz order. Furthermore, it has been confirmed that the negatively correlated time-domain structure of laser chaos contributes to the acceleration of decision-making. However, the underlying mechanism of why decision-making is accelerated by correlated time series is unknown. In this study, we demonstrate a theoretical model to account for accelerating decision-making by correlated time sequence. We first confirm the effectiveness of the negative autocorrelation inherent in time series for solving two-armed bandit problems using Fourier transform surrogate methods. We propose a theoretical model that concerns the correlated time series subjected to the decision-making system and the internal status of the system therein in a unified manner, inspired by correlated random walks. We demonstrate that the performance derived analytically by the theory agrees well with the numerical simulations, which confirms the validity of the proposed model and leads to optimal system design. This study paves the way for improving the effectiveness of correlated time series for decision-making, impacting artificial intelligence and other applications.
Blockchain technology applications in the health domain: a multivocal literature review

Blockchain technology has been changing the nature of several businesses, from supply chain management to electronic record management systems and copyright management to healthcare applications. It provides a resilient and secure platform for modifications due to its distributed and shared nature and cryptographic functions. Each new technology, however, comes with its challenges alongside its opportunities. Previously, we performed a systematic literature review (SLR) to explore how blockchain technology potentially benefits health domain applications. The previous SLR included 27 formal literature papers from 2016 to 2020. Noticing that blockchain technology is rapidly growing, we extended the previous SLR with a multivocal literature review (MLR) approach to present the state of the art in this study. We focused on understanding to what degree blockchain could answer the challenges inherited in the health domain and whether blockchain technology may bring new challenges to health applications. The MLR consists of 78 sources of formal literature and 23 sources of gray literature from 2016 to 2021. As a result of this study, we specified 17 health domain challenges that can be categorized into four groups: (i) meeting regulatory requirements and public health surveillance, (ii) ensuring security and privacy, (iii) ensuring interoperability, and (iv) preventing waste of resources. The analysis shows that blockchain makes significant contributions to the solutions of these challenges. However, 10 new pitfalls come with adopting the technology in the health domain: the inability to delete sensitive data once it is added to a chain, limited ability to keep large-scale data in a blockchain, and performance issues. The data we extracted during the MLR is available in a publicly accessible online repository.
The weighted Bitcoin Lightning Network

The Bitcoin Lightning Network (BLN) was launched in 2018 to scale up the number of transactions between Bitcoin owners. Although several contributions concerning the analysis of the BLN binary structure have recently appeared in the literature, the properties of its weighted counterpart are still largely unknown. The present contribution aims at filling this gap, by considering the Bitcoin Lightning Network over a period of 18 months, ranging from 12th January 2018 to 17th July 2019, and focusing on its weighted, undirected, daily snapshot representation - each weight representing the total capacity of the channels the two involved nodes have established on a given temporal snapshot. As the study of the BLN weighted structural properties reveals, it is becoming increasingly ‘centralized’ at different levels, just as its binary counterpart: (1) the Nakamoto coefficient shows that the percentage of nodes whose degrees/strengths ‘enclose’ the 51% of the total number of links/total weight is rapidly decreasing; (2) the Gini coefficient confirms that several weighted centrality measures are becoming increasingly unevenly distributed; (3) the weighted BLN topology is becoming increasingly compatible with a core–periphery structure, with the largest nodes ‘by strength’ constituting the core of such a network, whose size keeps shrinking as the BLN evolves. Further inspection of the resilience of the weighted BLN shows that removing such hubs leads to the network fragmentation into many components, an evidence indicating potential security threats — as the ones represented by the so called ‘split attacks’.