Reviewerly/exHarmony huggingface dataset:
Who Says What to Whom: A Survey of Multi-Party Conversations

Multi-party conversations (MPCs) are a more practical and challenging scenario involving more than two interlocutors. This research topic has drawn significant attention from both academia and industry, and it is nowadays counted as one of the most promising research areas in the field of dialogue systems. In general, MPC algorithms aim at addressing the issues of Who says What to Whom, specifically, who speaks, say what, and address whom. The complicated interactions between interlocutors, between utterances, and between interlocutors and utterances develop many variant tasks of MPCs worth investigation. In this paper, we present a comprehensive survey of recent advances in text-based MPCs. In particular, we first summarize recent advances on the research of MPC context modeling including dialogue discourse parsing, dialogue flow modeling and self-supervised training for MPCs. Then we review the state-of-the-art models categorized by Who says What to Whom in MPCs. Finally, we highlight the challenges which are not yet well addressed in MPCs and present future research directions.
Contrastive Vicinal Space for Unsupervised Domain Adaptation

Recent unsupervised domain adaptation methods have utilized vicinal space between the source and target domains. However, the equilibrium collapse of labels, a problem where the source labels are dominant over the target labels in the predictions of vicinal instances, has never been addressed. In this paper, we propose an instance-wise minimax strategy that minimizes the entropy of high uncertainty instances in the vicinal space to tackle the stated problem. We divide the vicinal space into two subspaces through the solution of the minimax problem: contrastive space and consensus space. In the contrastive space, inter-domain discrepancy is mitigated by constraining instances to have contrastive views and labels, and the consensus space reduces the confusion between intra-domain categories. The effectiveness of our method is demonstrated on public benchmarks, including Office-31, Office-Home, and VisDA-C, achieving state-of-the-art performances. We further show that our method outperforms the current state-of-the-art methods on PACS, which indicates that our instance-wise approach works well for multi-source domain adaptation as well. Code is available at https://github.com/NaJaeMin92/CoVi .
Metaverse Shape of Your Life for Future: A bibliometric snapshot

The metaverse was first introduced in 1992. Many people saw Metaverse as a new word but the concept of Metaverse is not a new term. However, Zuckerberg's press release drew all the attention to the Metaverse. This study presents a bibliometric evaluation of metaverse technology, which has been discussed in the literature since the nineties. A field study is carried out especially for the metaverse, which is a new and trendy subject. In this way, descriptive information is presented on journals, institutions, prominent researchers, and countries in the field, as well as extra evaluation on the prominent topics in the field and researchers with heavy citations. In our study, which was carried out by extracting the data of all documents between the years 1990-2021 from the Web of Science database, it was seen that there were few studies in the literature in the historical process for the metaverse, whose popularity has reached its peak in recent months. In addition, it is seen that the subject is handled intensively with virtual reality and augmented reality technologies, and the education sector and digital marketing fields show interest in the field. Metaverse will probably have entered many areas of our lives in the next 15-20 years, shape our lives by taking advantage of the opportunities of developing technology.
Blockchain Tree Powered Green Communication for Efficient and Sustainable Connected Autonomous Vehicles

Connected and Autonomous Vehicles (CAV) is a system of inter-connectivity and communication between smart automated vehicles. Blockchain has emerged as a technology focusing on data security with a decentralized architecture and has been successful to a very high degree. Unfortunately, this technology was not originally meant for low-power IoT devices and is very computationally expensive with high power requirements and a tendency to introduce delays. With the explosion of the Internet of Things (IoT) enabled autonomous vehicles, boosted further by the advent of smart cities, energy requirements have become an issue that can no longer be ignored. As a consequence, research into green technologies has been gaining popularity. With this area of focus, blockchain with its exponential power consumption levels is heavily flawed and thus presents itself as a prime candidate for improvement. In this paper, a framework is proposed to further evolve blockchain technology to integrate it with a semi-centralized data storage mechanism, while keeping network control decentralized. The proposed Blockchain Tree (BCT) approach entails a combination of a tree structure network composed of blockchains, based on a time-based upward data propagation mechanism that optimizes the architecture to reduce communication delay, cut down its high power consumption levels and ensure sustainability in the long run. As is evident in the comparative study, such a blockchain-based network structure offers drastic improvements in terms of speed, energy savings, storage and computation. The proposed model reduces the storage requirement of the blockchain by a factor of 0.17. The energy requirements and time complexity have been optimized by approximately 195% in the early stages of optimization. All the parameters get further optimized by orders of magnitude with more transactions and users.
MeSH Term Suggestion for Systematic Review Literature Search

High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for relevant medical literature is a key phase in constructing systematic reviews and often involves domain (medical researchers) and search (information specialists) experts in developing the search queries. Queries in this context are highly complex, based on Boolean logic, include free-text terms and index terms from standardised terminologies (e.g., MeSH), and are difficult and time-consuming to build. The use of MeSH terms, in particular, has been shown to improve the quality of the search results. However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query. Naturally, the full value of the MeSH terminology is often not fully exploited. This paper investigates methods to suggest MeSH terms based on an initial Boolean query that includes only free-text terms. These methods promise to automatically identify highly effective MeSH terms for inclusion in a systematic review query. Our study contributes an empirical evaluation of several MeSH term suggestion methods. We perform an extensive analysis of the retrieval, ranking, and refinement of MeSH term suggestions for each method and how these suggestions impact the effectiveness of Boolean queries.
Machine learning approaches for sentiment analysis

Sentiment Analysis or Opinion Mining is popular task of Natural Language Processing (NLP) performed on textual data generated by users to know the orientation or sentiment of the text. To perform Sentiment Analysis, it is critical to create an accurate and precise model, machine learning techniques are heavily utilized to build an accurate model. Deep learning and transfer learning techniques have been found to have increased utilization and better results, making them one of the most popular research areas around the world. Hotel and restaurant industries analyze reviews to obtain a deeper understanding of their client’s needs, likes and dislikes, whereas specialists use Twitter data and stock market news items to forecast stock market trends. Machine Learning algorithms are most essential part of a Sentiment Analysis model, this survey paper analyze all the widely used Machine Learning Approaches for Sentiment Analysis. A brief introduction on Methodology for Sentiment Analysis is given along with conclusion and future scope and in the field of Sentiment Analysis.
How Do Organizations Seek Cyber Assurance? Investigations on the Adoption of the Common Criteria and Beyond

Cyber assurance, which is the ability to operate under the onslaught of cyber attacks and other unexpected events, is essential for organizations facing inundating security threats on a daily basis. Organizations usually employ multiple strategies to conduct risk management to achieve cyber assurance. Utilizing cybersecurity standards and certifications can provide guidance for vendors to design and manufacture secure Information and Communication Technology (ICT) products as well as provide a level of assurance of the security functionality of the products for consumers. Hence, employing security standards and certifications is an effective strategy for risk management and cyber assurance. In this work, we begin with investigating the adoption of cybersecurity standards and certifications by surveying 258 participants from organizations across various countries and sectors. Specifically, we identify adoption barriers of the Common Criteria through the designed questionnaire. Taking into account the seven identified adoption barriers, we show the recommendations for promoting cybersecurity standards and certifications. Moreover, beyond cybersecurity standards and certifications, we shed light on other risk management strategies devised by our participants, which provides directions on cybersecurity approaches for enhancing cyber assurance in organizations.
SphereFace Revived: Unifying Hyperspherical Face Recognition

This paper addresses the deep face recognition problem under an open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. To this end, hyperspherical face recognition, as a promising line of research, has attracted increasing attention and gradually become a major focus in face recognition research. As one of the earliest works in hyperspherical face recognition, SphereFace explicitly proposed to learn face embeddings with large inter-class angular margin. However, SphereFace still suffers from severe training instability which limits its application in practice. In order to address this problem, we introduce a unified framework to understand large angular margin in hyperspherical face recognition. Under this framework, we extend the study of SphereFace and propose an improved variant with substantially better training stability - SphereFace-R. Specifically, we propose two novel ways to implement the multiplicative margin, and study SphereFace-R under three different feature normalization schemes (no feature normalization, hard feature normalization and soft feature normalization). We also propose an implementation strategy - "characteristic gradient detachment" - to stabilize training. Extensive experiments on SphereFace-R show that it is consistently better than or competitive with state-of-the-art methods.
Quantum Signal Processing for simulating cold plasma waves

Numerical modeling of radio-frequency waves in plasma with sufficiently high spatial and temporal resolution remains challenging even with modern computers. However, such simulations can be sped up using quantum computers in the future. Here, we propose how to do such modeling for cold plasma waves, in particular, for an X wave propagating in an inhomogeneous one-dimensional plasma. The wave system is represented in the form of a vector Schr\"odinger equation with a Hermitian Hamiltonian. Block-encoding is used to represent the Hamiltonian through unitary operations that can be implemented on a quantum computer. To perform the modeling, we apply the so-called Quantum Signal Processing algorithm and construct the corresponding circuit. Quantum simulations with this circuit are emulated on a classical computer, and the results show agreement with traditional classical calculations. We also discuss how our quantum circuit scales with the resolution.
Squeezing Water from a Stone: A Bag of Tricks for Further Improving Cross-Encoder Effectiveness for Reranking

While much recent work has demonstrated that hard negative mining can be used to train better bi-encoder models, few have considered it in the context of cross-encoders, which are key ingredients in modern retrieval pipelines due to their high effectiveness. One noteworthy exception comes from Gao et al. [13], who propose to train cross-encoders by adapting the well-known NCE loss and augmenting it with a "localized" selection of hard negative examples from the first-stage retriever, which they call the Localized Contrastive Estimation (LCE) loss. In this work, we present a replication study of LCE on a different task and combine it with several other "tricks" (e.g., replacing $$\text {BERT}_{\text {Base}}$$ with $$\text {ELECTRA}_{\text {Base}}$$ and replacing BM25 with TCT-ColBERTv2) to substantially improve ranking effectiveness. We attempt to more systematically explore certain parts of the hyperparameter space, including the choice of losses and the group size in the LCE loss. While our findings, for the most part, align with those from the original paper, we observe that for MS MARCO passage, orienting the retriever used for hard negative mining with the first-stage retriever used for inference is not as critical for improving effectiveness across all settings. Our code and documentation can be found in: https://github.com/castorini/replicate-lce .
Multi-behavior Recommendation with Two-Level Graph Attentional Networks

Multi-behavior recommendation learns accurate embeddings of users and items with multiple types of interactions. Although existing multi-behavior recommendation methods have been proven effective, the following two insights are often neglected. First, the semantic strength of different types of behaviors is ignored. Second, these methods only consider the static preferences of users and the static feature of items. These limitations motivate us to propose a novel recommendation model AMR (Attentional Multi-behavior Recommendation) in this paper, which captures hidden relations in user-item interaction network by constructing multi-relation graphs with different behavior types. Specifically, the node-level attention aims to learn the importance of neighbors under specific behavior, while the behavior-level attention is able to learn the semantic strength of different behaviors. In addition, we learn the dynamic feature of target users and target items by modeling the dependency relation between them. The results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art recommendation methods.
Extract the Degradation Information in Squeezed States with Machine Learning

In order to leverage the full power of quantum noise squeezing with unavoidable decoherence, a complete understanding of the degradation in the purity of squeezed light is demanded. By implementing machine-learning architecture with a convolutional neural network, we illustrate a fast, robust, and precise quantum state tomography for continuous variables, through the experimentally measured data generated from the balanced homodyne detectors. Compared with the maximum likelihood estimation method, which suffers from time-consuming and overfitting problems, a well-trained machine fed with squeezed vacuum and squeezed thermal states can complete the task of reconstruction of the density matrix in less than one second. Moreover, the resulting fidelity remains as high as 0.99 even when the antisqueezing level is higher than 20 dB. Compared with the phase noise and loss mechanisms coupled from the environment and surrounding vacuum, experimentally, the degradation information is unveiled with machine learning for low and high noisy scenarios, i.e., with the antisqueezing levels at 12 dB and 18 dB, respectively. Our neural network enhanced quantum state tomography provides the metrics to give physical descriptions of every feature observed in the quantum state with a single scan measurement just by varying the local oscillator phase from 0 to 2π and paves a way of exploring large-scale quantum systems in real time.
Hardening the security analysis of browser extensions

Browser extensions boost the browsing experience by a range of features from automatic translation and grammar correction to password management, ad blocking, and remote desktops. Yet the power of extensions poses significant privacy and security challenges because extensions can be malicious and/or vulnerable. We observe that there are gaps in the previous work on analyzing the security of browser extensions and present a systematic study of attack entry points in the browser extension ecosystem. Our study reveals novel password stealing, traffic stealing, and inter-extension attacks. Based on a combination of static and dynamic analysis we show how to discover extension attacks, both known and novel ones, and study their prevalence in the wild. We show that 1,349 extensions are vulnerable to inter-extension attacks leading to XSS. Our empirical study uncovers a remarkable cluster of "New Tab" extensions where 4,410 extensions perform traffic stealing attacks. We suggest several avenues for the countermeasures against the uncovered attacks, ranging from refining the permission model to mitigating the attacks by declarations in manifest files.
Efficient yet Competitive Speech Translation: FBK@IWSLT2022

The primary goal of this FBK's systems submission to the IWSLT 2022 offline and simultaneous speech translation tasks is to reduce model training costs without sacrificing translation quality. As such, we first question the need of ASR pre-training, showing that it is not essential to achieve competitive results. Second, we focus on data filtering, showing that a simple method that looks at the ratio between source and target characters yields a quality improvement of 1 BLEU. Third, we compare different methods to reduce the detrimental effect of the audio segmentation mismatch between training data manually segmented at sentence level and inference data that is automatically segmented. Towards the same goal of training cost reduction, we participate in the simultaneous task with the same model trained for offline ST. The effectiveness of our lightweight training strategy is shown by the high score obtained on the MuST-C en-de corpus (26.7 BLEU) and is confirmed in high-resource data conditions by a 1.6 BLEU improvement on the IWSLT2020 test set over last year's winning system.
Entanglement generation in a quantum network at distance-independent rate

Abstract We develop a protocol for entanglement generation in the quantum internet that allows a repeater node to use n -qubit Greenberger-Horne-Zeilinger (GHZ) projective measurements that can fuse n successfully entangled links , i.e., two-qubit entangled Bell pairs shared across n network edges, incident at that node. Implementing n -fusion, for n ≥ 3, is in principle not much harder than 2-fusions (Bell-basis measurements) in solid-state qubit memories. If we allow even 3-fusions at the nodes, we find—by developing a connection to a modified version of the site-bond percolation problem—that despite lossy (hence probabilistic) link-level entanglement generation, and probabilistic success of the fusion measurements at nodes, one can generate entanglement between end parties Alice and Bob at a rate that stays constant as the distance between them increases. We prove that this powerful network property is not possible to attain with any quantum networking protocol built with Bell measurements and multiplexing alone. We also design a two-party quantum key distribution protocol that converts the entangled states shared between two nodes into a shared secret, at a key generation rate that is independent of the distance between the two parties.
Contrastive Learning for Prompt-based Few-shot Language Learners

The impressive performance of GPT-3 using natural language prompts and in-context learning has inspired work on better fine-tuning of moderately-sized models under this paradigm.Following this line of work, we present a contrastive learning framework that clusters inputs from the same class for better generality of models trained with only limited examples.Specifically, we propose a supervised contrastive framework that clusters inputs from the same class under different augmented "views" and repel the ones from different classes.We create different "views" of an example by appending it with different language prompts and contextual demonstrations.Combining a contrastive loss with the standard masked language modeling (MLM) loss in prompt-based few-shot learners, the experimental results show that our method can improve over the state-of-the-art methods in a diverse set of 15 language tasks.Our framework makes minimal assumptions on the task or the base model, and can be applied to many recent methods with little modification.
CE-text: A context-Aware and embedded text detector in natural scene images

• A novel deep and context-aware CNN structure for accurate and fast text detection • Hierarchically channel wise attention scheme with channel wise and multilayer features • Adopts frequency-based deep compression method to build a lightweight text detector With the significant power of deep learning architectures, researchers have made much progress on effectiveness and efficiency of text detection in the past few years. However, due to the lack of consideration of unique characteristics of text components, directly applying deep learning models to perform text detection task is prone to result in low accuracy, especially producing false positive detection results. To ease this problem, we propose a lightweight and context-aware deep convolutional neural network (CNN) named as CE-Text, which appropriately encodes multi-level channel attention information to construct discriminative feature map for accurate and efficient text detection. To fit with low computation resource of embedded systems, we further transform CE-Text into a lighter version with a frequency based deep CNN compression method, which expands applicable scenarios of CE-Text into variant embedded systems. Experiments on several popular datasets show that CE-Text not only has achieved accurate text detection results in scene images, but also could run with fast performance in embedded systems.
Spectral single photons characterization using generalized Hong–Ou–Mandel interferometry

AbstractWe propose new methods to characterize the spectral-temporal distribution of single photons. The presented protocols take advantage of spectral filtering, frequency entanglement between two single photons, the one of interest and a reference, followed by the generalized Hong–Ou–Mandel interferometer. The measurement of the coincidence probability in these different schemes reveals the chronocyclic Wigner distribution, the pseudo-Wigner one and the spectrogram at the single photon level.Keywords: Quantum opticstime–frequency continuous variables of single photonsspectral tomography of single photons AcknowledgmentsN. Fabre acknowledges useful discussion with Simone Felicetti, Marco Liscidini, Arne Keller and Perola Milman.Disclosure statementNo potential conflict of interest was reported by the author.Additional informationFundingN. Fabre acknowledges support from the project 'Quantum Optical Technologies' (Project No. MAB/2018/4) carried out within the International Research Agendas programme of the Foundation for Polish Science, co-financed by the European Union under the European Regional Development Fund.