Reviewerly/exHarmony huggingface dataset:
Group multi-role assignment with conflicting roles and agents

Group role assignment ( GRA ) is originally a complex problem in role-based collaboration ( RBC ) . The solution to GRA provides modelling techniques for more complex problems. GRA with constraints ( GRA + ) is categorized as a class of complex assignment problems. At present, there are few generally efficient solutions to this category of problems. Each special problem case requires a specific solution. Group multi-role assignment ( GMRA ) and GRA with conflicting agents on roles ( GRACAR ) are two problem cases in GRA + . The contributions of this paper include: 1 ) The formalization of a new problem of GRA + , called group multi-role assignment with conflicting roles and agents ( GMAC ) , which is an extension to the combination of GMRA and GRACAR; 2 ) A practical solution based on an optimization platform; 3 ) A sufficient condition, used in planning, for solving GMAC problems; and 4 ) A clear presentation of the benefits in avoiding conflicts when dealing with GMAC. The proposed methods are verified by experiments, simulations, proofs and analysis.
Multi-Model Standard for Bitstream-, Pixel-Based and Hybrid Video Quality Assessment of UHD/4K: ITU-T P.1204

The paper presents a series of three new video quality model standards for the assessment of sequences of up to UHD/4K resolution. They were developed in a competition within the International Telecommunication Union (ITU-T), Study Group 12, in collaboration with the Video Quality Experts Group (VQEG), over a period of more than two years. A large video quality test set with a total of 26 individual databases was created, with 13 used for training and 13 for validation and selection of the winning models. For each database, video quality laboratory tests were run with at least 24 subjects each. The 5-point Absolute Category Rating (ACR) scale was used for rating, calculating Mean Opinion Scores (MOS) as ground-truth. To represent today's commonly applied HTTP-based adaptive streaming context, the test sequences comprise a variety of encoding settings, bitrates, resolutions and framerates for the three codecs H.264/AVC, H.265/HEVC and VP9, applied to a wide range of source sequences of around 8 s duration. Processing was carried out with an FFmpeg-based processing chain developed specifically for the competition, and via upload and encoding through exemplary online streaming services. The resulting data represents the largest, lab-test-based dataset used for video quality model development to date, with a total of around 5,000 test sequences. The paper addresses the three models ultimately standardized in the P.1204 Recommendation series, resulting in different model types and for different applications: (i) Rec. P.1204.3, no-reference bitstream-based, with access to encoded bitstream information; (ii) P.1204.4, pixel-based, using information from the reference and the processed video, and (iii) P.1204.5, no-reference hybrid, using both bitstream-and pixel-information without knowledge of the reference. The paper outlines the development process and provides holistic details about the statistical evaluation, test databases, model algorithms and validation results, as well as a performance comparison with state-of-the-art models.
The DeepFake Detection Challenge (DFDC) Dataset

Deepfakes are a recent off-the-shelf manipulation technique that allows anyone to swap two identities in a single video. In addition to Deepfakes, a variety of GAN-based face swapping methods have also been published with accompanying code. To counter this emerging threat, we have constructed an extremely large face swap video dataset to enable the training of detection models, and organized the accompanying DeepFake Detection Challenge (DFDC) Kaggle competition. Importantly, all recorded subjects agreed to participate in and have their likenesses modified during the construction of the face-swapped dataset. The DFDC dataset is by far the largest currently and publicly available face swap video dataset, with over 100,000 total clips sourced from 3,426 paid actors, produced with several Deepfake, GAN-based, and non-learned methods. In addition to describing the methods used to construct the dataset, we provide a detailed analysis of the top submissions from the Kaggle contest. We show although Deepfake detection is extremely difficult and still an unsolved problem, a Deepfake detection model trained only on the DFDC can generalize to real in-the-wild Deepfake videos, and such a model can be a valuable analysis tool when analyzing potentially Deepfaked videos. Training, validation and testing corpuses can be downloaded from this https URL.
An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling

Mispronunciation detection and diagnosis (MDD) is a core component of computer-assisted pronunciation training (CAPT). Most of the existing MDD approaches focus on dealing with categorical errors (viz. one canonical phone is substituted by another one, aside from those mispronunciations caused by deletions or insertions). However, accurate detection and diagnosis of non-categorial or distortion errors (viz. approximating L2 phones with L1 (first-language) phones, or erroneous pronunciations in between) still seems out of reach. In view of this, we propose to conduct MDD with a novel end- to-end automatic speech recognition (E2E-based ASR) approach. In particular, we expand the original L2 phone set with their corresponding anti-phone set, making the E2E-based MDD approach have a better capability to take in both categorical and non-categorial mispronunciations, aiming to provide better mispronunciation detection and diagnosis feedback. Furthermore, a novel transfer-learning paradigm is devised to obtain the initial model estimate of the E2E-based MDD system without resource to any phonological rules. Extensive sets of experimental results on the L2-ARCTIC dataset show that our best system can outperform the existing E2E baseline system and pronunciation scoring based method (GOP) in terms of the F1-score, by 11.05% and 27.71%, respectively.
Rethinking the Value of Labels for Improving Class-Imbalanced Learning

Real-world data often exhibits long-tailed distributions with heavy class imbalance, posing great challenges for deep recognition models. We identify a persisting dilemma on the value of labels in the context of imbalanced learning: on the one hand, supervision from labels typically leads to better results than its unsupervised counterparts; on the other hand, heavily imbalanced data naturally incurs in the classifier, where the decision boundary can be drastically altered by the majority classes. In this work, we systematically investigate these two facets of labels. We demonstrate, theoretically and empirically, that class-imbalanced learning can significantly benefit in both semi-supervised and self-supervised manners. Specifically, we confirm that (1) positively, imbalanced labels are valuable: given more unlabeled data, the original labels can be leveraged with the extra data to reduce label bias in a semi-supervised manner, which greatly improves the final classifier; (2) negatively however, we argue that imbalanced labels are not useful always: classifiers that are first pre-trained in a self-supervised manner consistently outperform their corresponding baselines. Extensive experiments on large-scale imbalanced datasets verify our theoretically grounded strategies, showing superior performance over previous state-of-the-arts. Our intriguing findings highlight the need to rethink the usage of imbalanced labels in realistic long-tailed tasks. Code is available at this https URL.
The use of machine translation algorithm based on residual and LSTM neural network in translation teaching

With the rapid development of big data and deep learning, breakthroughs have been made in phonetic and textual research, the two fundamental attributes of language. Language is an essential medium of information exchange in teaching activity. The aim is to promote the transformation of the training mode and content of translation major and the application of the translation service industry in various fields. Based on previous research, the SCN-LSTM (Skip Convolutional Network and Long Short Term Memory) translation model of deep learning neural network is constructed by learning and training the real dataset and the public PTB (Penn Treebank Dataset). The feasibility of the model’s performance, translation quality, and adaptability in practical teaching is analyzed to provide a theoretical basis for the research and application of the SCN-LSTM translation model in English teaching. The results show that the capability of the neural network for translation teaching is nearly one times higher than that of the traditional N-tuple translation model, and the fusion model performs much better than the single model, translation quality, and teaching effect. To be specific, the accuracy of the SCN-LSTM translation model based on deep learning neural network is 95.21%, the degree of translation confusion is reduced by 39.21% compared with that of the LSTM (Long Short Term Memory) model, and the adaptability is 0.4 times that of the N-tuple model. With the highest level of satisfaction in practical teaching evaluation, the SCN-LSTM translation model has achieved a favorable effect on the translation teaching of the English major. In summary, the performance and quality of the translation model are improved significantly by learning the language characteristics in translations by teachers and students, providing ideas for applying machine translation in professional translation teaching.
In Data We Trust: A Critical Analysis of Hate Speech Detection Datasets

Recently, a few studies have discussed the limitations of datasets collected for the task of detecting hate speech from different viewpoints. We intend to contribute to the conversation by providing a consolidated overview of these issues pertaining to the data that debilitate research in this area. Specifically, we discuss how the varying pre-processing steps and the format for making data publicly available result in highly varying datasets that make an objective comparison between studies difficult and unfair. There is currently no study (to the best of our knowledge) focused on comparing the attributes of existing datasets for hate speech detection, outlining their limitations and recommending approaches for future research. This work intends to fill that gap and become the one-stop shop for information regarding hate speech datasets.
A Flexible Recurrent Residual Pyramid Network for Video Frame Interpolation

Video frame interpolation (VFI) aims at synthesizing new video frames in-between existing frames to generate smoother high frame rate videos. Current methods usually use the fixed pre-trained networks to generate interpolated-frames for different resolutions and scenes. However, the fixed pre-trained networks are difficult to be tailored for a variety of cases. Inspired by classical pyramid energy minimization optical flow algorithms, this paper proposes a recurrent residual pyramid network (RRPN) for video frame interpolation. In the proposed network, different pyramid levels share the same weights and base-network, named recurrent residual layer (RRL). In RRL, residual displacements between warped images are detected to gradually refine optical flows rather than directly predict the flows or frames. Owing to the flexible recurrent residual pyramid architecture, we can customize the number of pyramid levels, and make trade-offs between calculations and quality based on the application scenarios. Moreover, occlusion masks are also generated in this recurrent residual way to solve occlusion better. Finally, a refinement network is added to enhance the details for final output with contextual and edge information. Experimental results demonstrate that the RRPN is more flexible and efficient than current VFI networks but has fewer parameters. In particular, the RRPN, which avoid over-reliance on datasets and network structures, shows superior performance for large motion cases.
Improvement of public distribution system efficiency applying blockchain technology during pandemic outbreak (COVID-19)

Purpose The purpose of this paper is to analytically examine the viability of using blockchain technology (BT) in a public distribution system (PDS) supply chain to overcome issues of shrinkage, misplacement and ghost demand. Design/methodology/approach The authors use a standard news vendor model with two objectives, the first of which includes a reduction of the total cost of stock, while the second includes minimization of the negative impact of human suffering due to the nonavailability of subsidized food supplies to the needy people. Findings The authors applied the model to a real-life case to draw meaningful insights. The authors also analyzed the cost/benefit tradeoff of adopting BT in a PDS supply chain. The results show that the adoption of BT in a charitable supply chain can reduce pilferage and ghost demand significantly. Originality/value The paper is positioned for utilizing inventory visibility via consistent and tamper-resistant data stream flow capability of BT to enhance the overall efficiency of PDS. Notably, Indian PDS faces three major challenges in terms of its supply chain efficiency.
Multi-key Fully-Homomorphic Encryption in the Plain Model

The notion of multi-key fully homomorphic encryption (multi-key FHE) [López-Alt, Tromer, Vaikuntanathan, STOC’12] was proposed as a generalization of fully homomorphic encryption to the multiparty setting. In a multi-key FHE scheme for n parties, each party can individually choose a key pair and use it to encrypt its own private input. Given n ciphertexts computed in this manner, the parties can homomorphically evaluate a circuit C over them to obtain a new ciphertext containing the output of C, which can then be decrypted via a decryption protocol. The key efficiency property is that the size of the (evaluated) ciphertext is independent of the size of the circuit. Multi-key FHE with one-round decryption [Mukherjee and Wichs, Eurocrypt’16], has found several powerful applications in cryptography over the past few years. However, an important drawback of all such known schemes is that they require a trusted setup. In this work, we address the problem of constructing multi-key FHE in the plain model. We obtain the following results:
Two-Layered Blockchain Architecture for Federated Learning Over the Mobile Edge Network

Federated learning (FL) is seen as a road toward privacy-preserving distributed artificial intelligence while keeping raw training data on local devices. By leveraging blockchain, this article puts forward a blockchain and FL fusioned framework to manage the security and trust issues when applying FL over mobile edge networks. First, a two-layered architecture is proposed that consists of two types of blockchains: local model update chain (LMUC) assisted by device-to-device (D2D) communication and global model update chain (GMUC) supporting task sharding. The D2D-assisted LMUC is designed to chronologically and efficiently record all of the local model training results, which can help to form long-term reputations of local devices. The GMUC is proposed to provide both security and efficiency by preventing mobile edge computing nodes from malfunctioning and dividing them into logically isolated FL task-specific chains. Then a reputation-learning-based incentive mechanism is introduced to make participating local devices more trustful with a reward implemented by a smart contract. Finally, a case study is given to show that the proposed framework performs well in terms of FL learning accuracy and blockchain time delay.
DGTN: Dual-channel Graph Transition Network for Session-based Recommendation

The task of session-based recommendation is to predict user actions based on anonymous sessions. Recent research mainly models the target session as a sequence or a graph to capture item transitions within it, ignoring complex transitions between items in different sessions that have been generated by other users. These item transitions include potential collaborative information and reflect similar behavior patterns, which we assume may help with the recommendation for the target session. In this paper, we propose a novel method, namely Dual-channel Graph Transition Network (DGTN), to model item transitions within not only the target session but also the neighbor sessions. Specifically, we integrate the target session and its neighbor (similar) sessions into a single graph. Then the transition signals are explicitly injected into the embedding by channel-aware propagation. Experiments on real-world datasets demonstrate that DGTN outperforms other state-of-the-art methods. Further analysis verifies the rationality of dual-channel item transition modeling, suggesting a potential future direction for session-based recommendation.
Randomized error removal for online spread estimation in data streaming

Measuring flow spread in real time from large, high-rate data streams has numerous practical applications, where a data stream is modeled as a sequence of data items from different flows and the spread of a flow is the number of distinct items in the flow. Past decades have witnessed tremendous performance improvement for single-flow spread estimation. However, when dealing with numerous flows in a data stream, it remains a significant challenge to measure per-flow spread accurately while reducing memory footprint. The goal of this paper is to introduce new multi-flow spread estimation designs that incur much smaller processing overhead and query overhead than the state of the art, yet achieves significant accuracy improvement in spread estimation. We formally analyze the performance of these new designs. We implement them in both hardware and software, and use real-world data traces to evaluate their performance in comparison with the state of the art. The experimental results show that our best sketch significantly improves over the best existing work in terms of estimation accuracy, data item processing throughput, and online query throughput.
Diagnostic feature training improves face matching accuracy.

Identifying unfamiliar faces is surprisingly error-prone, even for experienced professionals who perform this task regularly. Previous attempts to train this ability have been largely unsuccessful, leading many to conclude that face identity processing is hard-wired and not amenable to further perceptual learning. Here, we take a novel expert knowledge elicitation approach to training, based on the feature-based comparison strategy used by high-performing professional facial examiners. We show that instructing novices to focus on the facial features that are most diagnostic of identity for these experts-the ears and facial marks (e.g., scars, freckles and blemishes)-improves accuracy on unfamiliar face matching tasks by 6%. This training takes just 6 min to complete and yet accounts for approximately half of experts' superiority on the task. Benefits of training are strongest when diagnostic features are clearly visible and absent when participants are trained to rely on nondiagnostic features. Our data-driven approach contrasts with theory-driven training that is designed to improve holistic face processing mechanisms associated with familiar face recognition. This suggests that protocols which bypass the core face recognition system-and instead reorient attention to features that are undervalued by novices-offer a more promising route to training for unfamiliar face matching. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Deep neural networks for quantum circuit mapping

Abstract Quantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy.
Safeguarding cross-silo federated learning with local differential privacy

Federated Learning (FL) is a new computing paradigm in privacy-preserving Machine Learning (ML), where the ML model is trained in a decentralized manner by the clients, preventing the server from directly accessing privacy-sensitive data from the clients. Unfortunately, recent advances have shown potential risks for user-level privacy breaches under the cross-silo FL framework. In this paper, we propose addressing the issue by using a three-plane framework to secure the cross-silo FL, taking advantage of the Local Differential Privacy (LDP) mechanism. The key insight here is that LDP can provide strong data privacy protection while still retaining user data statistics to preserve its high utility. Experimental results on three real-world datasets demonstrate the effectiveness of our framework.
Biology and medicine in the landscape of quantum advantages

Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.