Reviewerly/exHarmony huggingface dataset:
Denoising User-aware Memory Network for Recommendation

For better user satisfaction and business effectiveness, more and more attention has been paid to the sequence-based recommendation system, which is used to infer the evolution of users’ dynamic preferences, and recent studies have noticed that the evolution of users’ preferences can be better understood from the implicit and explicit feedback sequences. However, most of the existing recommendation techniques do not consider the noise contained in implicit feedback, which will lead to the biased representation of user interest and a suboptimal recommendation performance. Meanwhile, the existing methods utilize item sequence for capturing the evolution of user interest. The performance of these methods is limited by the length of the sequence, and can not effectively model the long-term interest in a long period of time. Based on this observation, we propose a novel CTR model named denoising user-aware memory network (DUMN). Specifically, the framework: (i) proposes a feature purification module based on orthogonal mapping, which use the representation of explicit feedback to purify the representation of implicit feedback, and effectively denoise the implicit feedback; (ii) designs a user memory network to model the long-term interests in a fine-grained way by improving the memory network, which is ignored by the existing methods; and (iii) develops a preference-aware interactive representation component to fuse the long-term and short-term interests of users based on gating to understand the evolution of unbiased preferences of users. Extensive experiments on two real e-commerce user behavior datasets show that DUMN has a significant improvement over the state-of-the-art baselines.
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction

Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.Existing works either utilize self-training scheme to generate pseudo labels that will cause the gradual drift problem, or leverage metalearning scheme which does not solicit feedback explicitly.To alleviate selection bias due to the lack of feedback loops in existing LRE learning paradigms, we developed a Gradient Imitation Reinforcement Learning method to encourage pseudo label data to imitate the gradient descent direction on labeled data and bootstrap its optimization capability through trial and error.We also propose a framework called GradLRE, which handles two major scenarios in low-resource relation extraction.Besides the scenario where unlabeled data is sufficient, GradLRE handles the situation where no unlabeled data is available, by exploiting a contextualized augmentation method to generate data.Experimental results on two public datasets demonstrate the effectiveness of GradLRE on low resource relation extraction when comparing with baselines.Source code is available 1 .
A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations

Abstract. While the availability and affordability of unmanned aerial systems (UASs) has led to the rapid development of remote sensing applications in hydrology and hydrometry, uncertainties related to such measurements must be quantified and mitigated. The physical instability of the UAS platform inevitably induces motion in the acquired videos and can have a significant impact on the accuracy of camera-based measurements, such as velocimetry. A common practice in data preprocessing is compensation of platform-induced motion by means of digital image stabilisation (DIS) methods, which use the visual information from the captured videos – in the form of static features – to first estimate and then compensate for such motion. Most existing stabilisation approaches rely either on customised tools developed in-house, based on different algorithms, or on general purpose commercial software. Intercomparison of different stabilisation tools for UAS remote sensing purposes that could serve as a basis for selecting a particular tool in given conditions has not been found in the literature. In this paper, we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry. A total of seven tools – six aimed specifically at velocimetry and one general purpose software – were investigated in terms of their (1) stabilisation accuracy in various conditions, (2) robustness, (3) computational complexity, and (4) user experience, using three case study videos with different flight and ground conditions. In an attempt to adequately quantify the accuracy of the stabilisation using different tools, we have also presented a comparison metric based on root mean squared differences (RMSDs) of inter-frame pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying static features in videos, i.e. manual selection of features or automatic. State-of-the-art methods which rely on automatic selection of features require fewer user-provided parameters and are able to select a significantly higher number of potentially static features (by several orders of magnitude) when compared to the methods which require manual identification of such features. This allows the former to achieve a higher stabilisation accuracy, but manual feature selection methods have demonstrated lower computational complexity and better robustness in complex field conditions. While this paper does not intend to identify the optimal stabilisation tool for UAS-based velocimetry purposes, it does aim to shed light on details of implementation, which can help engineers and researchers choose the tool suitable for their needs and specific field conditions. Additionally, the RMSD comparison metric presented in this paper can be used in order to measure the velocity estimation uncertainty induced by UAS motion.
Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications

Our aim was to demonstrate that automated detection and classification of breast microcalcifications, according to Breast Imaging Reporting and Data System (BI-RADS) categorisation, can be improved with the subtraction of sequential mammograms as opposed to using the most recent image only.One hundred pairs of mammograms were retrospectively collected from two temporally sequential rounds. Fifty percent of the images included no (BI-RADS 1) or benign (BI-RADS 2) microcalcifications. The remaining exhibited suspicious findings (BI-RADS 4-5) in the recent image. Mammograms cannot be directly subtracted, due to tissue changes over time and breast deformation during mammography. To overcome this challenge, optimised preprocessing, image registration, and postprocessing procedures were developed. Machine learning techniques were employed to eliminate false positives (normal tissue misclassified as microcalcifications) and to classify the true microcalcifications as BI-RADS benign or suspicious. Ninety-six features were extracted and nine classifiers were evaluated with and without temporal subtraction. The performance was assessed by measuring sensitivity, specificity, accuracy, and area under the curve (AUC) at receiver operator characteristics analysis.Using temporal subtraction, the contrast ratio improved ~ 57 times compared to the most recent mammograms, enhancing the detection of the radiologic changes. Classifying as BI-RADS benign versus suspicious microcalcifications, resulted in 90.3% accuracy and 0.87 AUC, compared to 82.7% and 0.81 using just the most recent mammogram (p = 0.003).Compared to using the most recent mammogram alone, temporal subtraction is more effective in the microcalcifications detection and classification and may play a role in automated diagnosis systems.
Design, Simulation, Analysis and Optimization of PID and Fuzzy Based Control Systems for a Quadcopter

Unmanned aerial vehicles or drones are becoming one of the key machines/tools of the modern world, particularly in military applications. Numerous research works are underway to explore the possibility of using these machines in other applications such as parcel delivery, construction work, hurricane hunting, 3D mapping, protecting wildlife, agricultural activities, search and rescue, etc. Since these machines are unmanned vehicles, their functionality is completely dependent upon the performance of their control system. This paper presents a comprehensive approach for dynamic modeling, control system design, simulation and optimization of a quadcopter. The main objective is to study the behavior of different controllers when the model is working under linear and/or non-linear conditions, and therefore, to define the possible limitations of the controllers. Five different control systems are proposed to improve the control performance, mainly the stability of the system. Additionally, a path simulator was also developed with the intention of describing the vehicle’s movements and hence to detect faults intuitively. The proposed PID and Fuzzy-PD control systems showed promising responses to the tests carried out. The results indicated the limits of the PID controller over non-linear conditions and the effectiveness of the controllers was enhanced by the implementation of a genetic algorithm to autotune the controllers in order to adapt to changing conditions.
AdvHash: Set-to-set Targeted Attack on Deep Hashing with One Single Adversarial Patch

In this paper, we propose AdvHash, the first targeted mismatch attack on deep hashing through adversarial patch. After superimposed with the same adversarial patch, any query image with a chosen label will retrieve a set of irrelevant images with the target label. Concretely, we first formulate a set-to-set problem, where a set of samples are pushed into a predefined clustered area in the Hamming space. Then we obtain a target anchor hash code and transform the attack to a set-to-point optimization. In order to generate a image-agnostic stable adversarial patch for a chosen label more efficiently, we propose a product-based weighted gradient aggregation strategy to dynamically adjust the gradient directions of the patch, by exploiting the Hamming distances between training samples and the target anchor hash code and assigning different weights to discriminatively aggregate gradients. Extensive experiments on benchmark datasets verify that AdvHash is highly effective at attacking two state-of-the-art deep hashing schemes. Our codes are available at: https://github.com/CGCL-codes/AdvHash.
Permissioned Blockchain Frame for Secure Federated Learning

Federated learning is an emerging technology that solves the privacy problem of training data in multi-party machine learning. However, this technology is vulnerable to a series of system security problems. In this letter, we leverage Hyperledger Fabric permissioned blockchain architecture to build a secure and reliable federated learning platform across multiple data owners, where individual local updates are encrypted based on threshold homomorphic encryption and then recorded on a distributed ledger. The security analysis shows that our solution can effectively deal with the existing privacy and security issues in the federated learning system. The numerical results show that the scheme is feasible and efficient.
Fine-grained citation count prediction via a transformer-based model with among-attention mechanism

Previous studies have confirmed that citation mention and location reveal different contributions of the cited articles, and that both are significant in scientific research evaluation. However, traditional citation count prediction only focuses on predicting citation frequency. In this paper, we propose a novel fine-grained citation count prediction task (FGCCP), which aims to predict in-text citation count from each structural function of a paper separately. Specifically, we treated this task as a “sequence to sequence” issue and a multi-task learning job, in which both the inputs and the outputs are based on the sequence pattern of citations from different structural functions. To fulfill FGCCP, we proposed a transformer-based model (i.e. MTAT) in which a novel among-attention mechanism is employed. Based on an empirical study of full-text documents from PubMed Central Open Access Subset, our model achieves satisfactory prediction accuracy, and surpasses common machine learning and deep learning models on FGCCP. Moreover, we also discuss the potential role of the among-attention mechanism and the reason why our proposed model outperforms state-of-the-art strategies. FGCCP may provide more detailed decision-making evidence and evaluation basis for researchers in scientific research evaluation. In addition, MTAT is a general model which can be easily deployed in other multi-task learning jobs.
Fault-Tolerant Qubit from a Constant Number of Components

With gate error rates in multiple technologies now below the threshold required for fault-tolerant quantum computation, the major remaining obstacle to useful quantum computation is scaling, a challenge greatly amplified by the huge overhead imposed by quantum error correction itself. We propose a fault-tolerant quantum computing scheme that can nonetheless be assembled from a small number of experimental components, potentially dramatically reducing the engineering challenges associated with building a large-scale fault-tolerant quantum computer. Our scheme has a threshold of 0.39% for depolarising noise, assuming that memory errors are negligible. In the presence of memory errors, the logical error rate decays exponentially with $\sqrt{T/\tau}$, where $T$ is the memory coherence time and $\tau$ is the timescale for elementary gates. Our approach is based on a novel procedure for fault-tolerantly preparing three-dimensional cluster states using a single actively controlled qubit and a pair of delay lines. Although a circuit-level error may propagate to a high-weight error, the effect of this error on the prepared state is always equivalent to that of a constant-weight error. We describe how the requisite gates can be implemented using existing technologies in quantum photonic and phononic systems. With continued improvements in only a few components, we expect these systems to be promising candidates for demonstrating fault-tolerant quantum computation with a comparatively modest experimental effort.
A Comprehensive Review of Recent Automatic Speech Summarization and Keyword Identification Techniques

Speech has been the most popular form of human communication. A keyboard or a mouse, on the other hand, is the most common way of entering data into a computer. It would be wonderful if computers could understand and carry out human commands. The method of obtaining the transcription (word sequence) of an utterance from the speech waveform is known as automatic speech recognition (ASR). Over the last few decades, speech technology and systems in human-computer interaction have progressed progressively and significantly. This chapter suggests a comprehensive review of automatic speech recognition systems (ASR) and their most recent developments. This research aims to outline and explain some of the popular approaches in speech recognition systems at various stages and highlight selected systems’ unique and innovative characteristics.
Joint resource management for mobility supported federated learning in Internet of Vehicles

In recent years, the powerful combination of Multi-access Edge Computing (MEC) and Artificial Intelligence (AI), called edge intelligence, promotes the development of Intelligent Transportation Systems (ITS). However, there is a mismatch between the ever-increasing consumer privacy awareness and the data leakage risk in centralized AI training solutions in vehicular edge scenarios, which has become a new obstacle to satisfying the user experience. As a promising privacy-preserving paradigm, federated learning synthesizes a global model only with the parameters of decentralized trained local models, avoiding the exposure of sensitive data. Given this, we introduce federated learning into the proposed two-level MEC-assisted vehicular network framework. This paper aims to address the challenges posed by adopting federated learning into the Internet of Vehicles (IoV) scenario. Firstly, as the entity of the participant (the local model training node of federated learning), vehicles have high mobility. We design a mobility supported federated learning participant decision algorithm to pick out participants from candidate vehicles. Secondly, federated learning is rather resource-consuming, inevitably incurring considerable costs to participants. We focus on the joint resource allocation problem to optimize the federated learning cost. Finally, considering the limitations of centralized resource allocation, we propose a fully distributed resource allocation method inspired by multi-agent deep reinforcement learning. Simulation results are presented to demonstrate the feasibility and effectiveness of the proposed schemes.
Multimedia Prototyping for Early-Stage Startups Endurance: Stage for New Normal?

Multimedia prototypes of varying degrees of fidelity and interactivity could be a powerful tool for early stage startups to validate their business model assumptions especially product value proposition. This grounds on the prototype's ability to enhance user experience (UX) thereby making their feedback more accurate and reliable. This article reports practical experiences with consultancy projects involving European early stage startups focusing on the cocreation process of value proposition innovation amid pandemic. This includes the challenges faced in interacting with onsite customers, multimedia prototyping as a tool to foster remote customer involvement, and different challenges involved in prototype-driven cocreation like intellectual property rights, federal laws, motivation rewards, tool selection, and balancing customer expectations. The involvement of remote customers is fostered by the combination of varying degrees of fidelity and interactivity. Higher fidelity and interactivity prototypes are more applicable when a startup has a continuously increasing customer base, better market understanding, and customers having better product understanding. The practical experience indicates that resource-constrained startups could be greatly benefitted from the adoption of multimedia prototyping in their market research. The multimedia prototype development technologies could be easily adopted in startup business practices owing to zero purchase cost, ease of use, and usefulness. Their adoption is expected to become a norm for the “new normal” as the role of multimedia prototypes is trivial for lean startups enhancing the value of design thinking as well as customer development processes. However, there is an urgent need to define a systematic process for the selection of prototype development tools meeting the business needs and their seamless integration in startup business practices, thereby leveraging across the wider spectrum of prototyping tools market.
End-to-end entity-aware neural machine translation

Accurate translation of entities (e.g., person names, organizations, geography) is important in neural machine translation (briefly, NMT), as they are usually more difficult to translate than other words, and an incorrect translation of them will greatly hurt user experiences. In previous works, entities are either treated in the same way as other words, which leads to inaccurate translation, or handled by multiple steps (including named entity recognition, translation, and replacing entities back), which significantly increase the inference latency. In this work, we propose an end-to-end algorithm that carefully handles the translation of entities. There are mainly two novel parts compared to conventional NMT model: (1) The encoder and the decoder are attached with entity classifiers, which are used to verify whether the input token is a named entity. In this way, the encoder and decoder are capable to treat named entities differently; (2) The translation loss of each target token is adaptively increased by the probability that the target token is a named entity, which results in more accurate translation of entities. During inference time, these two parts will be removed so that the translation model maintains the same inference speed as conventional NMT models. Empirical results on six translation tasks demonstrate the effectiveness of our methods of improving the translation quality. Specifically, we improve 1.7 BLEU scores on Japanese to English translation and 4.6 entity $$F_{1}$$ scores on English to Chinese translation, without additional inference cost.
Argumentation profiles and the manipulation of common ground. The arguments of populist leaders on Twitter

The detection of hate speech and fake news in political discourse is at the same time a crucial necessity for democratic societies and a challenge for several areas of study. However, most of the studies have focused on what is explicitly stated: false article information, language expressing hatred, derogatory expressions. This paper argues that the explicit dimension of manipulation is only one – and the least problematic – of the risks of political discourse. The language of the unsaid is much more dangerous and incomparably more difficult to detect, hidden in different types of fallacies and inappropriate uses of emotive language. Through coding scheme developed by integrating instruments drawn from argumentation theory and pragmatics, a corpus of argumentative tweets published by 4 politicians (Matteo Salvini, Donald Trump, Jair Bolsonaro, and Joseph Biden) within 6 months from their taking office is analyzed, detecting the types of argument, the fallacies, and the uses and misuses of “emotive words.” This coding results in the argumentation profiles of the speakers, which are compared statistically to show their different implicit strategies and deceptive tactics.
C²-CRS

Conversational recommender systems (CRS) aim to recommend suitable items to users through natural language conversations. For developing effective CRSs, a major technical issue is how to accurately infer user preference from very limited conversation context. To address issue, a promising solution is to incorporate external data for enriching the context information. However, prior studies mainly focus on designing fusion models tailored for some specific type of external data, which is not general to model and utilize multi-type external data. To effectively leverage multi-type external data, we propose a novel coarse-to-fine contrastive learning framework to improve data semantic fusion for CRS. In our approach, we first extract and represent multi-grained semantic units from different data signals, and then align the associated multi-type semantic units in a coarse-to-fine way. To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarse-grained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion. Such an approach can be extended to incorporate more kinds of external data. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach in both recommendation and conversation tasks.