Reviewerly/exHarmony huggingface dataset:
Provably Secure Linearly Homomorphic Aggregate Signature Scheme for Electronic Healthcare System

In recent years, deploying Internet of Things (IoT) in electronic healthcare systems (EHS) has made great progress in healthcare detection. It is extremely important to reduce the cost of communication and ensure the authenticity and integrity of data. A linearly homomorphic signature scheme can solve the above problems. However, when the scale of EHS is too large, the transmission, storage and verification of signatures need a high cost. An aggregate signature can combine many signatures generated by many different users into a short one. Therefore, only one aggregate signature needs to be processed during verification, transmission and storage. Combining the advantages of aggregate signature and linearly homomorphic signature, this paper proposes an aggregate signature scheme based on a linearly homomorphic signature for EHS, which has both linear homomorphism and aggregation, and realizes double data compression. Moreover, our scheme can resist a potential real attack, named a coalition attack. The security of this scheme is rigorously demonstrated based on the computational Diffie–Hellman assumption in the random oracle model.
Over-Generation Cannot Be Rewarded: Length-Adaptive Average Lagging for Simultaneous Speech Translation

Simultaneous speech translation (SimulST) systems aim at generating their output with the lowest possible latency, which is normally computed in terms of Average Lagging (AL).In this paper we highlight that, despite its widespread adoption, AL provides underestimated scores for systems that generate longer predictions compared to the corresponding references.We also show that this problem has practical relevance, as recent SimulST systems have indeed a tendency to over-generate.As a solution, we propose LAAL (Length-Adaptive Average Lagging), a modified version of the metric that takes into account the over-generation phenomenon and allows for unbiased evaluation of both under-/over-generating systems.
Compositional generalization with a broad-coverage semantic parser

We show how the AM parser, a compositional semantic parser (Groschwitz et al., 2018), can solve compositional generalization on the COGS dataset.It is the first semantic parser that achieves high accuracy on both naturally occurring language and the synthetic COGS dataset.We discuss implications for corpus and model design for learning human-like generalization.Our results suggest that compositional generalization can be best achieved by building compositionality into semantic parsers.
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation

In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging selfsupervised approach.One of the difficulties is how to learn task discrimination in the absence of target labels.Unlike previous literature which directly aligns cross-domain distributions or leverages reverse gradient, we propose Domain Confused Contrastive Learning (DCCL) to bridge the source and the target domains via domain puzzles, and retain discriminative representations after adaptation.Technically, DCCL searches for a most domainchallenging direction and exquisitely crafts domain confused augmentations as positive pairs, then it contrastively encourages the model to pull representations towards the other domain, thus learning more stable and effective domain invariances.We also investigate whether contrastive learning necessarily helps with UDA when performing other data augmentations.Extensive experiments demonstrate that DCCL significantly outperforms baselines.Recent advances in self-supervised learning (SSL), such as contrastive learning (CL), have been proven effective at instance level by leveraging raw data to define surrogate tasks that help learn repre-
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems

Persuasion is an intricate process involving empathetic connection between two individuals.Plain persuasive responses may make a conversation non-engaging.Even the most wellintended and reasoned persuasive conversations can fall through in the absence of empathetic connection between the speaker and listener.In this paper, we propose a novel task of incorporating empathy when generating persuasive responses.We develop an empathetic persuasive dialogue system by fine-tuning a Maximum Likelihood Estimation (MLE)-based language model in a Reinforcement Learning (RL) framework.To design feedbacks for our RLagent, we define an effective and efficient reward function considering consistency, repetitiveness, emotion and persuasion rewards to ensure consistency, non-repetitiveness, empathy and persuasiveness in the generated responses.Due to lack of emotion annotated persuasive data, we first annotate the existing PERSUAION-FORGOOD dataset with emotions, then build transformer based classifiers to provide emotion based feedbacks to our RL agent.Experimental results confirm that our proposed model increases the rate of generating persuasive responses as compared to the available state-ofthe-art dialogue models while making the dialogues empathetically more engaging and retaining the language quality in responses.
Towards Automatic Generation of Messages Countering Online Hate Speech and Microaggressions

Warning: This paper discusses and contains content that may be deemed offensive or upsetting.With the widespread use of social media, online hate is increasing, and microaggressions, unintentional offensive remarks in everyday life (Sue et al., 2007), are receiving attention.We explore the possibility of using pre-trained language models to automatically generate messages that combat the associated offensive texts.Specifically, we focus on using prompting to steer model generation as it requires less data and computation than fine-tuning and shows the potential for using prompting in the proposed generation task.We also propose a human evaluation perspective; offensiveness, stance, and informativeness.After obtaining 306 counterspeech and 42 micro intervention messages generated by GPT-2, textscGPT-Neo, and textscGPT-3, we conducted a human evaluation using Amazon Mechanical Turk and found that GPT-3 produces messages of the highest quality among three systems.Also, We discuss the pros and cons of using our evaluation perspectives.We release a corpus of countering hate speech and microaggressions (CHASM), annotated machine-generated counternarratives along with the annotation to promote further research on automatic counternarrative generation and its evaluation.
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning

Conventionally, generation of natural language for dialogue agents may be viewed as a statistical learning problem: determine the patterns in human-provided data and generate appropriate responses with similar statistical properties.However, dialogue can also be regarded as a goal directed process, where speakers attempt to accomplish a specific task.Reinforcement learning (RL) algorithms are designed specifically for solving such goal-directed problems, but the most direct way to apply RLthrough trial-and-error learning in human conversations, -is costly.In this paper, we study how offline reinforcement learning can instead be used to train dialogue agents entirely using static datasets collected from human speakers.Our experiments show that recently developed offline RL methods can be combined with language models to yield realistic dialogue agents that better accomplish task goals.
Development of a Chatbot App for Interactive German Grammar Learning

This study aims to develop the Gramabot application as a means of online learning during the pandemic and to examine its effectiveness as a medium for German grammar learning. The design-based research uses learning experience design methodology. Beside an introduction of the state of the art of chatbots for language learning, the paper describes the concept of the chatbot application and its evaluation. Prior experiences with chatbots, implementation experiences and evaluations insights were obtained from 36 Indonesian students with beginner level German language skills. The data were described using qualitative and simple descriptive statistical analysis. The results of the analysis show that most of the respondents are familiar with several chatbot applications, the use of chatbots is not as a learning medium, but as a means of asking questions about products. In addition, String Matching is a suitable method used to develop Gramabot. Based on the results of the evaluation, it can be concluded that Gramabot can help students understand basic level grammatical material.
A Review on forecasting the photovoltaic power Using Machine Learning

Abstract In this review paper on different forecasting method of the solar power output for effective generation of the power grid and proper management of transfer rate of energy per unit area occurred into the solar PV system. Essential part in focusing the prediction of solar power is irradiance and temperature. The irradiance can be forecasted by many algorithm and method is applied in prediction of generation of Short-term photovoltaic power and long term solar power forecasting. And many papers describes on numerical weather forecasting and some algorithm like neural networks or support vector regression for two step approach for predicting the PV power. In this review shown that methods like Bagging Model, deep learning, genetic algorithm, random forest, gradient boosting and artificial neural network. We found that for enhancing the performance of predicting PV power many authors proposed the ensemble method that is the hybrid models of different algorithm. And I found that on this review process ensemble methods show that good results and improve the forecasting solar PV power.
Property Invariant Embedding for Automated Reasoning

Automated reasoning and theorem proving have recently become major challenges for machine learning. In other domains, representations that are able to abstract over unimportant transformations, such as abstraction over translations and rotations in vision, are becoming more common. Standard methods of embedding mathematical formulas for learning theorem proving are however yet unable to handle many important transformations. In particular, embedding previously unseen labels, that often arise in definitional encodings and in Skolemization, has been very weak so far. Similar problems appear when transferring knowledge between known symbols. We propose a novel encoding of formulas that extends existing graph neural network models. This encoding represents symbols only by nodes in the graph, without giving the network any knowledge of the original labels. We provide additional links between such nodes that allow the network to recover the meaning and therefore correctly embed such nodes irrespective of the given labels. We test the proposed encoding in an automated theorem prover based on the tableaux connection calculus, and show that it improves on the best characterizations used so far. The encoding is further evaluated on the premise selection task and a newly introduced symbol guessing task, and shown to correctly predict 65% of the symbol names.
Breast Cancer Histopathological Image Recognition Based on Pyramid Gray Level Co-Occurrence Matrix and Incremental Broad Learning

In order to recognize breast cancer histopathological images, this article proposed a combined model consisting of a pyramid gray level co-occurrence matrix (PGLCM) feature extraction model and an incremental broad learning (IBL) classification model. The PGLCM model is designed to extract the fusion features of breast cancer histopathological images, which can reflect the multiresolution useful information of the images and facilitate the improvement of the classification effect in the later stage. The IBL model is used to improve the classification accuracy by increasing the number of network enhancement nodes horizontally. Unlike deep neural networks, the IBL model compresses the training and testing time cost greatly by making full use of its single-hidden-layer structure. To our knowledge, it is the first attempt for the IBL model to be introduced into the breast cancer histopathological image recognition task. The experimental results in four magnifications of the BreaKHis dataset show that the accuracy of binary classification and eight-class classification outperforms the existing algorithms. The accuracy of binary classification reaches 91.45%, 90.17%, 90.90% and 90.73%, indicating the effectiveness of the established combined model and demonstrating the advantages in breast cancer histopathological image recognition.
DISTANCE EDUCATION IN A HIGH SCHOOL EDUCATIONAL ACTIVITY: CHALLENGES OF TIME

The article notes that the modern transition of education from traditional to distance makes changes in the format of educational activities of higher education institutions. New methods and ways of its organization are introduced; teachers meet new requirements concerning the effective realization of their skills and the increase of their professional mobility. Updated tasks that require teachers training to use information technology in the professional activities of higher education institutions in the context of distance education are emphasized, especially: a) studying of the general principles of information technology, the development of teacher’s key level of information competence; b) mastering of the information technologies which are specific to the subject area that corresponds to the teacher’s specialty; c) knowing of general theory and methodology of information technology using in professional pedagogical activities; d) preparing for using in the process of teaching the information technology which is specially focused on a particular subject. There is a modular principle in the basis of distance learning programs, which means that each discipline consists of several independent learning modules (topics) due to its content. Accordingly, each discipline contains the following parts: content-related (electronic version of the lectures); training (practical classes); controlling (tests and creative tasks). At the same time, the teachers learn to effectively develop author's distance courses, to place teaching materials (lectures, tasks for practical / laboratory and independent work; additional materials, as well as video, audio and presentation materials in various formats and through additional plug-ins, to automatically generate tests, add new course resources). In this research it was revealed that scientific and pedagogical workers need to: form knowledge, skills and abilities in the field of network culture; teach students strategies and tactics of interaction with carriers and sources of new knowledge, master skills of two-side communication with a large number of users, whose activities are monitored and controlled; study new information technologies that are specific to the subject area that corresponds to teacher’s specialty; create space for improving their professional activities and developing innovative methods; develop varied methodology for distance learning of people with different levels of abilities, age and needs
Next Steps in Quantum Computing: Computer Science's Role

The computing ecosystem has always had deep impacts on society and technology and profoundly changed our lives in myriads of ways. Despite decades of impressive Moore's Law performance scaling and other growth in the computing ecosystem there are nonetheless still important potential applications of computing that remain out of reach of current or foreseeable conventional computer systems. Specifically, there are computational applications whose complexity scales super-linearly, even exponentially, with the size of their input data such that the computation time or memory requirements for these problems become intractably large to solve for useful data input sizes. Such problems can have memory requirements that exceed what can be built on the most powerful supercomputers, and/or runtimes on the order of tens of years or more. Quantum computing (QC) is viewed by many as a possible future option for tackling these high-complexity or seemingly-intractable problems by complementing classical computing with a fundamentally different compute paradigm. There is a huge gap between the problems for which a quantum computer might be useful and what we can currently build, program, and run. The goal of the QC research community is to close the gap such that useful algorithms can be run in practical amounts of time on reliable real-world QC hardware. In particular, the goal of this Computing Community Consortium (CCC) workshop was to articulate the central role that the computer science (CS) research communities plays in closing this gap. CS researchers bring invaluable expertise in the design of programming languages, in techniques for systems building, scalability and verification, and in architectural approaches that can bring practical QC from the future to the present. This report introduces issues and recommendations across a range of technical and non-technical topic areas.
Ethereum’s Peer-to-Peer Network Monitoring and Sybil Attack Prevention

Public blockchains, like Ethereum, rely on an underlying peer-to-peer (P2P) network to disseminate transactions and blocks between nodes. With the rise of blockchain applications and cryptocurrencies values, they have become critical infrastructures which still lack comprehensive studies. In this paper, we propose to investigate the reliability of the Ethereum P2P network. We developed our own dependable crawler to collect information about the peers composing the network. Our data analysis regarding the geographical distribution of peers and the churn rate shows good network properties while the network can exhibit a sudden and major increase in size and peers are highly concentrated on a few ASes. In a second time, we investigate suspicious patterns that can denote a Sybil attack. We find that many nodes hold numerous identities in the network and could become a threat. To mitigate future Sybil attacks, we propose an architecture to detect suspicious nodes and revoke them. It is based on a monitoring system, a smart contract to propagate the information and an external revocation tool to help clients remove their connections to suspicious peers. Our experiment on Ethereum’s Test network proved that our solution is effective.
A Novel Design Method of High Throughput Blockchain for 6G Networks: Performance Analysis and Optimization Model

Sharing is undoubtedly one of the most important features of 6G networks, and blockchain can provide an extended trust-as-a-service (TAAS) distributed sharing solution for 6G networks. However, as a special distributed technology system, blockchain naturally needs to face the “impossible triangle” problem: in the case of ensuring security and decentralization, scalability will inevitably become the Achilles heel of the blockchain system. This article will design a high-throughput blockchain system for 6G networks to achieve trusted sharing and efficient scheduling of network infrastructure, assist future networks in integrating an open Internet architecture, and realize the combination of openness and distributed control. We adopt a shard blockchain design and incorporate digital twins and federated learning; formulate an optimization model for maximizing the throughput of the blockchain network by getting the optimal number of shards, also analyzing other factors affecting the throughput such as service distribution; propose a security performance analysis model for the blockchain network to describe the measures taken against the Byzantine attacks on the network. Analysis and simulation results show that the transaction throughput of the proposed method can reach more than 30 times larger than that of a nonsharding scheme. They also show that when one-third of the nodes in the system are attacked, the consensus of the system is hardly affected; even if the number of nodes being attacked at the same time reaches half of the total number of nodes, the probability of the occurrence of failed shards is still less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-4}$ </tex-math></inline-formula> , and the system still has good survivability.
Flexible Proof Production in an Industrial-Strength SMT Solver

Abstract Proof production for SMT solvers is paramount to ensure their correctness independently from implementations, which are often prohibitively difficult to verify. Historically, however, SMT proof production has struggled with performance and coverage issues, resulting in the disabling of many crucial solving techniques and in coarse-grained (and thus hard to check) proofs. We present a flexible proof-production architecture designed to handle the complexity of versatile, industrial-strength SMT solvers and show how we leverage it to produce detailed proofs, including for components previously unsupported by any solver. The architecture allows proofs to be produced modularly, lazily, and with numerous safeguards for correctness. This architecture has been implemented in the state-of-the-art SMT solver cvc5. We evaluate its proofs for SMT-LIB benchmarks and show that the new architecture produces better coverage than previous approaches, has acceptable performance overhead, and supports detailed proofs for most solving components.
Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image

Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person's chances of survival, an early diagnosis is a necessary prerequisite. In this regard, the existing methods of tumour detection, such as CT scans, are most commonly used to recognize infected regions. Despite this, there are certain obstacles presented by CT imaging, so this paper proposes a novel model which is a correlation-based model designed for analysis of lung cancer. When registering pictures of thoracic and abdominal organs with slider motion, the total variation regularization term may correct the border discontinuous displacement field, but it cannot maintain the local characteristics of the image and loses the registration accuracy. The thin-plate spline energy operator and the total variation operator are spatially weighted via the spatial position weight of the pixel points to construct an adaptive thin-plate spline total variation regular term for lung image CT single-mode registration and CT/PET dual-mode registration. The regular term is then combined with the CRMI similarity measure and the L-BFGS optimization approach to create a nonrigid registration procedure. The proposed method assures the smoothness of interior of the picture while ensuring the discontinuous motion of the border and has greater registration accuracy, according to the experimental findings on the DIR-Lab 4D-CT public dataset and the CT/PET clinical dataset.