Reviewerly/exHarmony huggingface dataset:
EMOCOV: Machine learning for emotion detection, analysis and visualization using COVID-19 tweets

The adversarial impact of the Covid-19 pandemic has created a health crisis globally all over the world. This unprecedented crisis forced people to lockdown and changed almost every aspect of the regular activities of the people. Thus, the pandemic is also impacting everyone physically, mentally, and economically, and it, therefore, is paramount to analyze and understand emotional responses during the crisis affecting mental health. Negative emotional responses at fine-grained labels like anger and fear during the crisis might also lead to irreversible socio-economic damages. In this work, we develop a neural network model and train it using manually labeled data to detect various emotions at fine-grained labels in the Covid-19 tweets automatically. We present a manually labeled tweets dataset on COVID-19 emotional responses along with regular tweets data. We created a custom Q&A roBERTa model to extract phrases from the tweets that are primarily responsible for the corresponding emotions. None of the existing datasets and work currently provide the selected words or phrases denoting the reason for the corresponding emotions. Our classification model outperforms other systems and achieves a Jaccard score of 0.6475 with an accuracy of 0.8951. The custom RoBERTa Q&A model outperforms other models by achieving a Jaccard score of 0.7865. Further, we present a historical emotion analysis using COVID-19 tweets over the USA including each state level analysis.
A State of Art Survey for Intelligent Energy Monitoring Systems

In this study, the significance and necessities of surveillance systems have been investigated in several areas - both in the use of neural networks, street lighting systems, factories, and laboratories - for the monitoring systems, especially concerning the design of artificial intelligence programs. The importance of these initiatives and how they can affect any sector and industry reach an essential point from here. Here we reach an important point. An algorithm and an extraordinary approach have been used in every field to develop an intelligent programmer. Something has been mentioned here: the ability to access these intelligent programs in all areas of life. We concentrate on a variety of fields of use and design of monitoring systems in this review article.
Federated-Learning-Empowered Collaborative Data Sharing for Vehicular Edge Networks

The Internet of Vehicles connects all vehicles and shares dynamic vehicular data via wireless communications to effectively control vehicles and improve traffic efficiency. However, due to vehicular movement, vehicular data sharing based on conventional cloud computing can hardly realize real-time and dynamic updates. To address these challenges, artificial intelligence (AI)-empowered mobile/multi-access edge computing (MEC) has been regarded as a promising technique for intelligently supporting various vehicular services and applications in proximity of vehicles. In this article, we investigate the issue of collaborative data sharing in vehicular edge networks (VENs) with the deployment of AI-empowered MEC servers. Furthermore, we present a specific mode for collaborative data sharing. Then we propose a novel collaborative data sharing scheme with deep Q-network and federated learning to ensure efficient and secure data sharing in the VEN. Evaluation results demonstrate the effectiveness of the proposed scheme on reducing latency of vehicular data sharing. Finally, we discuss several open issues and future challenges of the AI-empowered VEN.
The Evolution of the Manosphere across the Web

We present a large-scale characterization of the Manosphere, a conglomerate of Web-based misogynist movements focused on men's issues, which has prospered online. Analyzing 28.8M posts from 6 forums and 51 subreddits, we paint a comprehensive picture of its evolution across the Web, showing the links between its different communities over the years. We find that milder and older communities, such as Pick Up Artists and Men's Rights Activists, are giving way to more extreme ones like Incels and Men Going Their Own Way, with a substantial migration of active users. Moreover, our analysis suggests that these newer communities are more toxic and misogynistic than the older ones.
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models

Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning.In this work, we show that finetuning LMs in the few-shot setting can considerably reduce the need for prompt engineering.In fact, one can use null prompts, prompts that contain neither task-specific templates nor training examples, and achieve competitive accuracy to manually-tuned prompts across a wide range of tasks.While finetuning LMs does introduce new parameters for each downstream task, we show that this memory overhead can be substantially reduced-finetuning only the bias terms can achieve comparable or better accuracy than standard finetuning while only updating 0.1% of the parameters.All in all, we recommend finetuning LMs for few-shot learning as it is more accurate, has relatively stable performance across different prompts, and can be made nearly as efficient as using frozen LMs.
ChronoR: Rotation Based Temporal Knowledge Graph Embedding

Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static graphs. In this paper, we study the challenging problem of inference over temporal knowledge graphs. In particular, the task of temporal link prediction. In general, this is a difficult task due to data non-stationarity, data heterogeneity, and its complex temporal dependencies. We propose Chronological Rotation embedding (ChronoR), a novel model for learning representations for entities, relations, and time. Learning dense representations is frequently used as an efficient and versatile method to perform reasoning on knowledge graphs. The proposed model learns a k-dimensional rotation transformation parametrized by relation and time, such that after each fact's head entity is transformed using the rotation, it falls near its corresponding tail entity. By using high dimensional rotation as its transformation operator, ChronoR captures rich interaction between the temporal and multi-relational characteristics of a Temporal Knowledge Graph. Experimentally, we show that ChronoR is able to outperform many of the state-of-the-art methods on the benchmark datasets for temporal knowledge graph link prediction.
Silver: Silent VOLE and Oblivious Transfer from Hardness of Decoding Structured LDPC Codes

We put forth new protocols for oblivious transfer extension and vector OLE, called Silver, for SILent Vole and oblivious transfER. Silver offers extremely high performances: generating 10 million random OTs on one core of a standard laptop requires only 300 ms of computation and 122 KB of communication. This represents 37% less computation and $$\sim $$ 1300 $$\times $$ less communication than the standard IKNP protocol, as well as $$\sim $$ 4 $$\times $$ less computation and $$\sim $$ 14 $$\times $$ less communication than the recent protocol of Yang et al. (CCS 2020). Silver is silent: after a one-time cheap interaction, two parties can store small seeds, from which they can later locally generate a large number of OTs while remaining offline. Neither IKNP nor Yang et al. enjoys this feature; compared to the best known silent OT extension protocol of Boyle et al. (CCS 2019), upon which we build up, Silver has 19 $$\times $$ less computation, and the same communication. Due to its attractive efficiency features, Silver yields major efficiency improvements in numerous MPC protocols. Our approach is a radical departure from the standard paradigm for building MPC protocols, in that we do not attempt to base our constructions on a well-studied assumption. Rather, we follow an approach closer in spirit to the standard paradigm in the design of symmetric primitives: we identify a set of fundamental structural properties that allow us to withstand all known attacks, and put forth a candidate design, guided by our analysis. We also rely on extensive experimentations to analyze our candidate and experimentally validate their properties. In essence, our approach boils down to constructing new families of linear codes with (plausibly) high minimum distance and extremely low encoding time. While further analysis is of course welcomed in order to gain total confidence in the security of Silver, we hope and believe that initiating this approach to the design of MPC primitives will pave the way to new secure primitives with extremely attractive efficiency features.
Evolutionary Game Analysis of Blockchain Technology Preventing Supply Chain Financial Risks

Because of the risks existing in supply chain finance, taking accounts receivable factoring business as the research object, this paper uses the evolutionary game method to analyzes the factors affecting the decision-making of the participants in supply chain finance, constructs an evolutionary game model between small and medium-sized enterprises and financial institutions, and analyzes the mechanism of blockchain to solve the financial risks of the supply chain by comparing the changes of evolutionary stability strategies before and after the introduction of blockchain technology. This paper aims to reduce financing risks by analyzing the mechanism of blockchain technology in supply chain finance. It is found that, firstly, blockchain technology can reduce the credit risk of financial institutions and solve financing problem. Credit risk plays a decisive role in whether financial institutions accept financing business decisions. Blockchain technology can reduce the operational risk of financial institutions and improve the business income of financial institutions. Secondly, the strict regulatory environment formed by blockchain technology makes the default behavior of small and medium-sized enterprises and core enterprises in a high-risk state at all times. No matter the profit distribution proportion that small and medium-sized enterprises can obtain through collusion, they will not choose to default, which effectively solves the paradox that small and medium-sized enterprises cannot obtain loans from financial institutions despite the increased probability of compliance. Then, the evolutionary game between financial institutions and small and medium-sized enterprises is balanced in that financial institutions accept business applications, small and medium-sized enterprises abide by the contract, and the convergence effect is better. Therefore, blockchain technology not only reduces the financing risk of financial institutions but also helps to solve the financing problems of small and medium-sized enterprises.
Attention-Based CNN and Bi-LSTM Model Based on TF-IDF and GloVe Word Embedding for Sentiment Analysis

Sentiment analysis (SA) detects people’s opinions from text engaging natural language processing (NLP) techniques. Recent research has shown that deep learning models, i.e., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer-based provide promising results for recognizing sentiment. Nonetheless, CNN has the advantage of extracting high-level features by using convolutional and max-pooling layers; it cannot efficiently learn a sequence of correlations. At the same time, Bidirectional RNN uses two RNN directions to improve extracting long-term dependencies. However, it cannot extract local features in parallel, and Transformer-based like Bidirectional Encoder Representations from Transformers (BERT) are the computational resources needed to fine-tune, facing an overfitting problem on small datasets. This paper proposes a novel attention-based model that utilizes CNNs with LSTM (named ACL-SA). First, it applies a preprocessor to enhance the data quality and employ term frequency-inverse document frequency (TF-IDF) feature weighting and pre-trained Glove word embedding approaches to extract meaningful information from textual data. In addition, it utilizes CNN’s max-pooling to extract contextual features and reduce feature dimensionality. Moreover, it uses an integrated bidirectional LSTM to capture long-term dependencies. Furthermore, it applies the attention mechanism at the CNN’s output layer to emphasize each word’s attention level. To avoid overfitting, the Guasiannoise and GuasianDroupout are adopted as regularization. The model’s robustness is evaluated on four English standard datasets, i.e., Sentiment140, US-airline, Sentiment140-MV, SA4A with various performance matrices, and compared efficiency with existing baseline models and approaches. The experiment results show that the proposed method significantly outperforms the state-of-the-art models.
SupermarQ: A Scalable Quantum Benchmark Suite

The emergence of quantum computers as a new computational paradigm has been accompanied by speculation concerning the scope and timeline of their anticipated revolutionary changes. While quantum computing is still in its infancy, the variety of different architectures used to implement quantum computations make it difficult to reliably measure and compare performance. This problem motivates our introduction of SupermarQ, a scalable, hardware-agnostic quantum benchmark suite which uses application-level metrics to measure performance. SupermarQ is the first attempt to systematically apply techniques from classical benchmarking methodology to the quantum domain. We define a set of feature vectors to quantify coverage, select applications from a variety of domains to ensure the suite is representative of real workloads, and collect benchmark results from the IBM, IonQ, and AQT@LBNL platforms. Looking forward, we envision that quantum benchmarking will encompass a large cross-community effort built on open source, constantly evolving benchmark suites. We introduce SupermarQ as an important step in this direction.
Transfer learning strategies for solar power forecasting under data scarcity

Abstract Accurately forecasting solar plants production is critical for balancing supply and demand and for scheduling distribution networks operation in the context of inclusive smart cities and energy communities. However, the problem becomes more demanding, when there is insufficient amount of data to adequately train forecasting models, due to plants being recently installed or because of lack of smart-meters. Transfer learning (TL) offers the capability of transferring knowledge from the source domain to different target domains to resolve related problems. This study uses the stacked Long Short-Term Memory (LSTM) model with three TL strategies to provide accurate solar plant production forecasts. TL is exploited both for weight initialization of the LSTM model and for feature extraction, using different freezing approaches. The presented TL strategies are compared to the conventional non-TL model, as well as to the smart persistence model, at forecasting the hourly production of 6 solar plants. Results indicate that TL models significantly outperform the conventional one, achieving 12.6% accuracy improvement in terms of RMSE and 16.3% in terms of forecast skill index with 1 year of training data. The gap between the two approaches becomes even bigger when fewer training data are available (especially in the case of a 3-month training set), breaking new ground in power production forecasting of newly installed solar plants and rendering TL a reliable tool in the hands of self-producers towards the ultimate goal of energy balancing and demand response management from an early stage.
Deep learning for computational cytology: A survey

Computational cytology is a critical, rapid-developing, yet challenging topic in medical image computing concerned with analyzing digitized cytology images by computer-aided technologies for cancer screening. Recently, an increasing number of deep learning (DL) approaches have made significant achievements in medical image analysis, leading to boosting publications of cytological studies. In this article, we survey more than 120 publications of DL-based cytology image analysis to investigate the advanced methods and comprehensive applications. We first introduce various deep learning schemes, including fully supervised, weakly supervised, unsupervised, and transfer learning. Then, we systematically summarize public datasets, evaluation metrics, versatile cytology image analysis applications including cell classification, slide-level cancer screening, nuclei or cell detection and segmentation. Finally, we discuss current challenges and potential research directions of computational cytology.
EXTREME PROGRAMMING VS SCRUM: A COMPARISON OF AGILE MODELS

For past couple of years agile software methods have been quite popular among the researchers. Agile models are known as light weight in contrast with conventional software development methodologies, due to their casual, versatile and adaptable style. Agile frameworks became heartily accepted by the software society in view of their concentration towards timely software conveyance, product quality and user satisfaction. For the fulfillment of requirements and needs of different software projects multiple agile frameworks are present to choose from. Out of these models Extreme Programming and Scrum are the most recognizable and generally utilized frameworks. This research contributes by investigating these two frameworks thoroughly. This paper conducts a comprehensive comparison between Scrum and Extreme programming to track down their commonalities, dissimilarities and investigate those attributes which complement each other.
Stochastic Safest and Shortest Path Problems

Optimal solutions to Stochastic Shortest Path Problems (SSPs) usually require that there exists at least one policy that reaches the goal with probability 1 from the initial state. This condition is very strong and prevents from solving many interesting problems, for instance where all possible policies reach some dead-end states with a positive probability. We introduce a more general and richer dual optimization criterion, which minimizes the average (undiscounted) cost of only paths leading to the goal among all policies that maximize the probability to reach the goal. We present policy update equations in the form of dynamic programming for this new dual criterion, which are different from the standard Bellman equations, but produce the same solution if there exists one policy leading to the goal with probability 1 from the initial state. We demonstrate that our equations converge in infinite horizon without any condition on the structure of the problem or on its policies, which actually extends the class of SSPs that can be solved. We experimentally show that our dual criterion provides well-founded solutions to SSPs that can not be solved by the standard criterion, and that using a discount factor with the latter certainly provides solution policies but which are not optimal considering our well-founded criterion.
Measuring Nonclassicality of Bosonic Field Quantum States via Operator Ordering Sensitivity

We introduce a new distance-based measure for the nonclassicality of the states of a bosonic field, which outperforms the existing such measures in several ways. We define for that purpose the operator ordering sensitivity of the state which evaluates the sensitivity to operator ordering of the Renyi entropy of its quasi-probabilities and which measures the oscillations in its Wigner function. Through a sharp control on the operator ordering sensitivity of classical states we obtain a precise geometric image of their location in the density matrix space allowing us to introduce a distance-based measure of nonclassicality. We analyse the link between this nonclassicality measure and a recently introduced quantum macroscopicity measure, showing how the two notions are distinct.
Personal Data Management Systems: The security and functionality standpoint

Riding the wave of smart disclosure initiatives and new privacy-protection regulations, the Personal Cloud paradigm is emerging through a myriad of solutions offered to users to let them gather and manage their whole digital life. On the bright side, this opens the way to novel value-added services when crossing multiple sources of data of a given person or crossing the data of multiple people. Yet this paradigm shift towards user empowerment raises fundamental questions with regards to the appropriateness of the functionalities and the data management and protection techniques which are offered by existing solutions to laymen users. These questions must be answered in order to limit the risk of seeing such solutions adopted only by a handful of users and thus leaving the Personal Cloud paradigm to become no more than one of the latest missed attempts to achieve a better regulation of the management of personal data. To this end, we review, compare and analyze personal cloud alternatives in terms of the functionalities they provide and the threat models they target. From this analysis, we derive a general set of functionality and security requirements that any Personal Data Management System (PDMS) should consider. We then identify the challenges of implementing such a PDMS and propose a preliminary design for an extensive and secure PDMS reference architecture satisfying the considered requirements. Finally, we discuss several important research challenges remaining to be addressed to achieve a mature PDMS ecosystem.
Two semi-quantum secure direct communication protocols based on Bell states

Quantum secure direct communication allows one participant to transmit secret messages to another directly without generating a shared secret key first. In most of the existing schemes, quantum secure direct communication can be achieved only when the two participants have full quantum ability. In this paper, we propose two semi-quantum secure direct communication protocols to allow restricted semi-quantum or “classical” users to participate in quantum communication. A semi-quantum user is restricted to measure, prepare, reorder and reflect quantum qubits only in the classical basis [Formula: see text]. Both protocols rely on quantum Alice to randomly prepare Bell states, perform Bell basis measurements and publish the initial Bell states, but the semi-quantum Bob only needs to measure the qubits in classical basis to obtain secret information without quantum memory. Security and qubit efficiency analysis have been given in this paper. The analysis results show that the two protocols can avoid some eavesdropping attacks and their qubit efficiency is higher than some current related quantum or semi-quantum protocols.