Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Disentangled Representation for Goal-Conditioned Reinforcement Learning

Goal-conditioned reinforcement learning is a crucial yet challenging algorithm which enables agents to achieve multiple user-specified goals when learning a set of skills in a dynamic environment. However, it typically requires millions of the environmental interactions explored by agents, which is sample-inefficient. In the letter, we propose a skill learning framework DR-GRL that aims to improve the sample efficiency and policy generalization by combining the Disentangled Representation learning and Goal-conditioned visual Reinforcement Learning. In a weakly supervised manner, we propose a Spatial Transform AutoEncoder (STAE) to learn an interpretable and controllable representation in which different parts correspond to different object attributes (shape, color, position). Due to the high controllability of the representations, STAE can simply recombine and recode the representations to generate unseen goals for agents to practice themselves. The manifold structure of the learned representation maintains consistency with the physical position, which is beneficial for reward calculation. We empirically demonstrate that DR-GRL significantly outperforms the previous methods in sample efficiency and policy generalization. In addition, DR-GRL is also easy to expand to the real robot.
ArgRewrite V.2: an annotated argumentative revisions corpus

Analyzing how humans revise their writings is an interesting research question, not only from an educational perspective but also in terms of artificial intelligence. Better understanding of this process could facilitate many NLP applications, from intelligent tutoring systems to supportive and collaborative writing environments. Developing these applications, however, requires revision corpora, which are not widely available. In this work, we present ArgRewrite V.2, a corpus of annotated argumentative revisions, collected from two cycles of revisions to argumentative essays about self-driving cars. Annotations are provided at different levels of purpose granularity (coarse and fine) and scope (sentential and subsentential). In addition, the corpus includes the revision goal given to each writer, essay scores, annotation verification, pre- and post-study surveys collected from participants as meta-data. The variety of revision unit scope and purpose granularity levels in ArgRewrite, along with the inclusion of new types of meta-data, can make it a useful resource for research and applications that involve revision analysis. We demonstrate some potential applications of ArgRewrite V.2 in the development of automatic revision purpose predictors, as a training source and benchmark.
Fast quantum state transfer and entanglement for cavity-coupled many qubits via dark pathways

Quantum state transfer (QST) and entangled state generation (ESG) are important building blocks for modern quantum information processing. To achieve these tasks, convention wisdom is to consult the quantum adiabatic evolution, which is time-consuming, and thus is of low fidelity. Here, using the shortcut to adiabaticity technique, we propose a general method to realize high-fidelity fast QST and ESG in a cavity-coupled many qubits system via its dark pathways, which can be further designed for high-fidelity quantum tasks with different optimization purpose. Specifically, with a proper dark pathway, QST and ESG between any two qubits can be achieved without decoupling the others, which simplifies experimental demonstrations. Meanwhile, ESG among all qubits can also be realized in a single step. In addition, our scheme can be implemented in many quantum systems, and we illustrate its implementation on superconducting quantum circuits. Therefore, we propose a powerful strategy for selective quantum manipulation, which is promising in cavity coupled quantum systems and could find many convenient applications in quantum information processing.
Sentiment Analysis of COVID-19 Tweets by Machine Learning and Deep Learning Classifiers

Sentiment analysis helps in deciding the emotions of a person that they like to express as neutral, negative, or positive. Many algorithms of machine learning and deep learning exist for analyzing sentiments of people posted on various social media platforms. Twitter, one of the social media platforms, is becoming very helpful to understand the behavior of society at large as people conveyed their views during the COVID-19 pandemic by offering details that have been organized as tweets. This paper aims to study the performance of various classification algorithms that demands an input value and recognizes to which output category it belongs. Six machine learning algorithms, two ensemble algorithms, and four deep learning algorithms have been considered for this work. COVID-19 twitter dataset comprising of two lakh records is reviewed. Accuracy, confusion matrix, recall matrix, precision matrix, recall, precision, and F1-score parameters have been taken as performance measures.
CCGL: Contrastive Cascade Graph Learning

Supervised learning, while prevalent for information cascade modeling, often requires abundant labeled data in training, and the trained model is not easy to generalize across tasks and datasets. It often learns task-specific representations, which can easily result in overfitting for downstream tasks. Recently, self-supervised learning is designed to alleviate these two fundamental issues in linguistic and visual tasks. However, its direct applicability for information cascade modeling, especially graph cascade related tasks, remains underexplored. In this work, we present Contrastive Cascade Graph Learning (CCGL), a novel framework for information cascade graph learning in a contrastive, self-supervised, and task-agnostic way. In particular, CCGL first designs an effective data augmentation strategy to capture variation and uncertainty by simulating the information diffusion in graphs. Second, it learns a generic model for graph cascade tasks via self-supervised contrastive pre-training using both unlabeled and labeled data. Third, CCGL learns a task-specific cascade model via fine-tuning using labeled data. Finally, to make the model transferable across datasets and cascade applications, CCGL further enhances the model via distillation using a teacher-student architecture. We demonstrate that CCGL significantly outperforms its supervised and semi-supervised counterparts for several downstream tasks.
An Optimized E-Lecture Video Retrieval based on Machine Learning Classification

The advent of internet has lead to colossal development of e-learning frameworks. The efficiency of such systems however relies on the effectiveness and fast content based retrieval approaches. This paper presents a methodology for efficient search and retrieval of lecture videos based on Machine Learning (ML) text classification algorithm. The text transcript is generated exclusively from the audio content extracted from the video lectures. This content is utilized for the summary and keyword extraction which is used for training the ML text classification model. An optimized search is achieved based on the trained ML model. The performance of the system is compared by training the system using Naive Bayes, Support Vector Machine and Logistic Regression algorithms. Performance evaluation was done by precision, recall, F-score and accuracy of the search for each of the classifiers. It is observed that the system trained on Naive Bayes classification algorithm achieved better performance both in terms of time and also with respect to relevancy of the search results.
Memes and emojis in Chinese compliments on Weibo

Abstract Noted for the ability to generate meanings across linguistic boundaries, nonverbal communication in computer-mediated communication has attracted increasing research attention. This study examines the prevalent but under-investigated phenomenon of memes and emojis employed in Chinese compliments on the social platform Weibo. Data were collected in a follower-based community on Weibo, the Topic. The results revealed that memes that did not feature the image of the complimented target outnumbered those memes with the figure of the complimented target. Memes without the image of the complimented target were employed to express compliments, either through explicit or implicit expressions, although explicit expressions of compliments were used more frequently. All of the memes served to compliment the appearance, performance, and personality of the complimented targets. Regarding emojis, there were three patterns of emojis employed in online compliments, namely, emojis initiating compliments, emojis inserted in compliments, and emojis closing compliments. Overall, emojis functioned as transitions and intensifiers which increased the force of compliments.
Realization of Fast All-Microwave Controlled-Z Gates with a Tunable Coupler

The development of high-fidelity two-qubit quantum gates is essential for digital quantum computing. Here, we propose and realize an all-microwave parametric controlled-Z (CZ) gates by coupling strength modulation in a superconducting Transmon qubit system with tunable couplers. After optimizing the design of the tunable coupler together with the control pulse numerically, we experimentally realized a 100 ns CZ gate with high fidelity of 99.38% ± 0.34% and the control error being 0.1%. We note that our CZ gates are not affected by pulse distortion and do not need pulse correction, providing a solution for the real-time pulse generation in a dynamic quantum feedback circuit. With the expectation of utilizing our all-microwave control scheme to reduce the number of control lines through frequency multiplexing in the future, our scheme draws a blueprint for the high-integrable quantum hardware design.
Experimental validation of different PV power prediction models under Beni Mellal climate, implications for the energy nexus

Analyzing the efficiency of Photovoltaic installations in real situations is a difficult task, as there are many models and software packages available. Generally, software and models cannot predict Photovoltaics output under all weather and geographic conditions. It is necessary to develop a protocol to assess the viability and profitability of potential Photovoltaic installations based on a real-world system. In this paper, analysis of the performance of a photovoltaic system is carried out and the production of 3 Photovolatic prediction models was evaluated, and their accuracy was compared to one year of actual measurements of an monocristalline silicon Photovoltaics grid-connected plant. The results show that models M1, M2, and M3 are all adequate to simulate Photovoltaic performance in the climatic conditions of the Beni Mellal region with an advantage for the M1 model.
ORGANIZING TARGETED TEAM TRAINING OF VOCATIONAL EDUCATION TEACHERS

The article casts doubt upon the appropriateness of mass multi- and transprofessionalism, including management integration into education, as well as the transition of vocational trainers and other vocational education teachers to freelance. The article offers a targeted on-site-and-distance form of organizing modern professional and pedagogical education to preserve its basic principles.
Privacy-Preserving Ensemble Infused Enhanced Deep Neural Network Framework for Edge Cloud Convergence

We propose a privacy-preserving ensemble infused enhanced Deep Neural Network (DNN) based learning framework in this paper for Internet-of-Things (IoT), edge, and cloud convergence in the context of healthcare. In the convergence, edge server is used for both storing IoT produced bioimage and hosting DNN algorithm for local model training. The cloud is used for ensembling local models. The DNN-based training process of a model with a local dataset suffers from low accuracy, which can be improved by the aforementioned convergence and Ensemble Learning. The ensemble learning allows multiple participants to outsource their local model for producing a generalized final model with high accuracy. Nevertheless, Ensemble Learning elevates the risk of leaking sensitive private data from the final model. The proposed framework presents a Differential Privacy-based privacy-preserving DNN with Transfer Learning for a local model generation to ensure minimal loss and higher efficiency at edge server. We conduct several experiments to evaluate the performance of our proposed framework.
Fair-SRS: A Fair Session-based Recommendation System

This paper demonstrates Fair-SRS, a Fair Session-based Recommendation System that predicts user's next click based on their historical and current sessions. Fair-SRS provides personalized and diversified recommendations in two main steps: (1) forming user's session graph embeddings based on their long- and short-term interests, and (2) computing user's level of interest in diversity based on their recently-clicked items' similarity. In real-world scenarios, users tend to interact with more or fewer contents at different times, and providers expect to receive more exposure for their items. To achieve the objectives of both sides, the proposed Fair-SRS optimizes recommendations by making a trade-off between accuracy and personalized diversity.
Towards a Simpler Lattice Gadget Toolkit

As a building block, gadgets and associated algorithms are widely used in advanced lattice cryptosystems. The gadget algorithms for power-of-base moduli are very efficient and simple, however the current algorithms for arbitrary moduli are still complicated and practically more costly despite several efforts. Considering the necessity of arbitrary moduli, developing simpler and more practical gadget algorithms for arbitrary moduli is crucial to improving the practical performance of lattice based applications. In this work, we propose two new gadget sampling algorithms for arbitrary moduli. Our first algorithm is for gadget Gaussian sampling. It is simple and efficient. One distinguishing feature of our Gaussian sampler is that it does not need floating-point arithmetic, which makes it better compatible with constrained environments. Our second algorithm is for gadget subgaussian sampling. Compared with the existing algorithm, it is simpler, faster, and requires asymptotically less randomness. In addition, our subgaussian sampler achieves an almost equal quality for different practical parameters. Overall these two algorithms provide simpler options for gadget algorithms and enhance the practicality of the gadget toolkit.
Comparison of irradiance forecasts from operational <scp>NWP</scp> model and satellite‐based estimates over Fennoscandia

Abstract Accurate irradiance forecasts are needed for the growing solar energy industry also in Northern Europe. We have compared irradiance forecasts from an operational numerical weather prediction (NWP) model, a satellite‐based model, and persistence models. We aim to determine whether operational NWP models are suitable for forecasting irradiance at the high latitudes, and how their accuracy compares to the satellite‐based model. We have included all members and the ensemble average of the MetCoOp ensemble prediction system (MEPS), the MetCoOp‐Nowcasting (MNWC) system, the satellite‐based Solis‐Heliosat model, and two persistence models. The comparison is made as a point comparison against in situ irradiance observations in Finland and Sweden, for intra‐day forecasts with hourly and 15‐min output and the full forecast of MEPS with hourly output. In addition, we show two energy market case studies. We find the operational NWP models to be very suitable for irradiance forecasting in the area, up to the full horizon of the forecasts. Solis‐Heliosat errors grow with lead time, while the NWP model errors are largest in the beginning, settling to smaller values after the first hours. Solis‐Heliosat has more accuracy for the first 2–3 h of the forecast, after which NWP models produce better forecasts. However, during morning periods Solis‐Heliosat is found to have limited accuracy, while conversely, MNWC performs better in the morning than in the afternoon. The energy market case study highlights the same results: NWP models do well with forecasting irradiance in Fennoscandia, but the optimal selection of forecast model depends on the required forecast horizon and time.
Information Field Theory and Artificial Intelligence

Information field theory (IFT), the information theory for fields, is a mathematical framework for signal reconstruction and non-parametric inverse problems. Artificial intelligence (AI) and machine learning (ML) aim at generating intelligent systems, including such for perception, cognition, and learning. This overlaps with IFT, which is designed to address perception, reasoning, and inference tasks. Here, the relation between concepts and tools in IFT and those in AI and ML research are discussed. In the context of IFT, fields denote physical quantities that change continuously as a function of space (and time) and information theory refers to Bayesian probabilistic logic equipped with the associated entropic information measures. Reconstructing a signal with IFT is a computational problem similar to training a generative neural network (GNN) in ML. In this paper, the process of inference in IFT is reformulated in terms of GNN training. In contrast to classical neural networks, IFT based GNNs can operate without pre-training thanks to incorporating expert knowledge into their architecture. Furthermore, the cross-fertilization of variational inference methods used in IFT and ML are discussed. These discussions suggest that IFT is well suited to address many problems in AI and ML research and application.
Analyzing the first-year students’ motivation in choosing a technical university during the pandemic

The results of studying the motivational aspects of young people’s choice for admission to the Bonch-Bruevich Saint Petersburg State University of Telecommunications during the pandemic are presented based on processing the data from the first-year students’ annual testing. The characteristics of the respondents participating in the study are given. The areas covered by the research methodology are substantiated, and the stages of its implementation are disclosed, since testing was carried out in a delayed online mode. A detailed analysis of the results obtained is presented, some indicators of which are evaluated in relation to those of the pre-pandemic period. The main directions of the research are brought to light and substantiated. The identified negative aspects arising due to the conditions of distance learning that affect the respondents are revealed. The findings characterizing a freshman’s generalized socio-psychological portrait are presented. Recommendations on working with first-year students in a pandemic from a psychologist’s position are formulated to the university teaching staff and administration.
GpDB: A Graph-partition Based Storage Strategy for DAG-Blockchain in Edge-cloud IIoT

The industrial Internet-of-things (IIoT) has attracted extensive attention due to its real-time and automation characteristics. Edge computing and blockchain technologies facilitate the IIoT in terms of low latency services and data security respectively. However, with the continuous expansion of industrial data and the growth of industrial nodes, traditional blockchain technology has some critical limitations on low transaction throughput and high data storage costs. Directed Acyclic Graph (DAG)-blockchain adopts a graph structure of a single transaction as the basic unit, and it has the characteristics of asynchronous consensus. Some existing studies use DAGblockchain to replace the traditional blockchain to alleviate its low throughput problems like IOTA. However, with the rapid data generation in the IIoT environment, the topology scale of DAG-blockchain will increase sharply, which will aggravate the data storage cost of blockchain nodes. In this article, to reduce the data storage cost of edge servers, we design a Graphpartition based storage strategy for DAG-Blockchain (GpDB), equipped with a graph partition algorithm based on transaction freshness, which can partition DAG-blockchain topology in edge servers into two parts, which will be retained and removed respectively. Simulation shows that, in terms of storage cost, GpDB outperforms LDV and Layerchain by 62% and 74% respectively, and with the increasing number of transactions, GpDB has good scalability in reducing the storage cost, and better transaction throughput than IOTA.
Dual-angle rotation two-dimensional wavelet transform profilometry

The two-dimensional continuous wavelet transform (2D CWT) has been widely used in single-shot fringe projection profilometry (FPP) because of its multi-resolution characteristics and anti-noise ability. This Letter aims to develop a novel 2D wavelet transform model in FPP. Dual-angle rotation operation on a 2D wavelet is performed to improve the directional selectivity and frequency localization of the wavelet. The proposed method improves the accuracy of the phase calculation of wavelet transform profilometry (WTP) for higher accuracy reconstruction from the single-shot fringe. We present mathematical expressions, theoretical analysis, and the comparison to traditional 2D WTP. The experiments demonstrate the effectiveness of the proposed method.
Tightening monogamy and polygamy relations of unified entanglement in multipartite systems

We study monogamy and polygamy inequalities of unified entanglement in multipartite quantum systems. We first derive the monogamy inequality of unified-(q, s) entanglement for multi-qubit states under arbitrary bipartition, and then obtain the monogamy inequalities of the $$\alpha $$ th ( $$0\le \alpha \le \frac{r}{2}, r\ge \sqrt{2}$$ ) power of entanglement of formation for tripartite states and their generalizations in multi-qubit quantum states. We also generalize the polygamy inequalities of unified-(q, s) entanglement for multi-qubit states under arbitrary bipartition. Moreover, we investigate polygamy inequalities of the $$\beta $$ th ( $$\beta \ge \mathrm{max}\{1, s\}, 0\le s\le s_0, 0\le s_0\le \sqrt{2}$$ ) power of the entanglement of formation for $$2\otimes 2\otimes 2$$ and n-qubit quantum systems. Finally, using detailed examples, we show that the results are tighter than previous studies.
Simulating and Modeling the Risk of Conversational Search

In conversational search, agents can interact with users by asking clarifying questions to increase their chance of finding better results. Many recent works and shared tasks in both natural language processing and information retrieval communities have focused on identifying the need to ask clarifying questions and methodologies of generating them. These works assume that asking a clarifying question is a safe alternative to retrieving results. As existing conversational search models are far from perfect, it is possible and common that they could retrieve/generate bad clarifying questions. Asking too many clarifying questions can also drain a user’s patience when the user prefers searching efficiency over correctness. Hence, these models can backfire and harm a user’s search experience due to these risks from asking clarifying questions. In this work, we propose a simulation framework to simulate the risk of asking questions in conversational search and further revise a risk-aware conversational search model to control the risk. We show the model’s robustness and effectiveness through extensive experiments on three conversational datasets — MSDialog, Ubuntu Dialog Corpus, and Opendialkg — in which we compare it with multiple baselines. We show that the risk-control module can work with two different re-ranker models and outperform all of the baselines in most of our experiments.