Reviewerly/exHarmony huggingface dataset:
Analyzing voter behavior on social media during the 2020 US presidential election campaign

Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. In this context, the present manuscript provides a precise view of the 2020 US presidential election by jointly applying topic discovery, opinion mining, and emotion analysis techniques on social media data. In particular, we exploited a clustering-based technique for extracting the main discussion topics and monitoring their weekly impact on social media conversation. Afterward, we leveraged a neural-based opinion mining technique for determining the political orientation of social media users by analyzing the posts they published. In this way, we were able to determine in the weeks preceding the Election Day which candidate or party public opinion is most in favor of. We also investigated the temporal dynamics of the online discussions, by studying how users' publishing behavior is related to their political alignment. Finally, we combined sentiment analysis and text mining techniques to discover the relationship between the user polarity and sentiment expressed referring to the different candidates, thus modeling political support of social media users from an emotional viewpoint.
A Blockchain-Enabled Distributed Advanced Metering Infrastructure Secure Communication (BC-AMI)

The world is facing an urgent need to provide secure communication and data access control in advanced metering infrastructure (AMI) because conventional cryptographic key management and authentication protocols are at stake. The cryptography schemes entirely rely on trusted third parties (TTPs), leading to a single point of failure and increasing network overhead. In response to this inefficiency and security compromise, this study proposes a blockchain-enabled distributed AMI secure communication scheme. In the proposed work, smart contract (SC), an integrated part of the blockchain, is programmed to substitute traditional TTP-based transaction systems, which operate in a distributed, immutable, and trustworthy manner. In this paper, we implemented practical Byzantine fault tolerance (PBFT) consensus algorithm and Hyperledger Fabric (HLF) blockchain platform to ensure Byzantine fault tolerance in the blockchain transaction. Performance analysis shows that the proposed BC-AMI scheme has the advantage of incurring the least amount of communication and time costs compared with similar studies while ensuring security against some common cyber-attacks.
The effect of Foreign direct investment and financial development on economic growth: Evidence from global income countries

Prior researchers have explored the role of FDI and economic growth or financial development and economic growth in a particular sample of countries or region while no collective studies on the effect of FDI, banks and stock market on economic growth in region or income-based groups have been conducted yet. Using a balanced panel data set of the globe of 193 upper middle income (UMI), lower middle income (LMI) and high income (HMI) countries for the period of 1998 to 2018, the study ever the first time explore the role of FDI, banks and stock markets financial development on economic growth by employing static methods and Dynamic approaches which contributes to the scarce literature on the collective and across income-based groups of countries. All model findings for the global panel indicates that FDI affect economic growth significantly and positively in the global panel, lower middle income (LMI) and upper middle income (UMI) countries where it’s not true for high income (HI) countries. Banking sector development also affect economic growth significantly but negatively in the global panel, high income and upper middle-income countries while not significant for the lower middle income (LMI) countries. Stock market development also affects economic growth significantly but negatively in the global panel. Furthermore, the result concludes that FDI have a larger effect on economic growth than does banks or stock market financial development. This study suggest high income countries regards improving FDI attraction, lower middle income (LMI) countries in regards improve banking sector and collectively suggest to improve major driver and functioning of banking sector and stock markets to spur economic growth. This study is beneficial for the government channels and financial sector of the study countries to make further decision.
‘Ted Cruz cucks again’

This article provides a multimodal semiotic analysis of the word cuck as used in online white supremacist spaces. A fundamental belief of the white supremacist ‘alt-right’ movement is an anti-Semitic narrative that positions Jewish people as a ‘global elite’ that seeks to oppress and eliminate white populations. Central to this belief is that Jewish people actively manipulate populations of colour, Black people in particular, to overtake white populations in a process known as ‘white genocide’ or ‘the great replacement’. Based on a digital ethnography of alt-right communities on Voat, Twitter and Reddit, this article demonstrates how the memeified word ‘cuck’, a pejorative term for ‘weak’ men on the US political right wing, draws from and reproduces this white nationalist conspiracy theory through allusions to interracial cuckold pornography. While disguised as innocuous, expressions like ‘cuck’ provide insight into how the alt-right weaponises misogynist and racist humour in its radicalisation efforts.
Design Considerations for Immersive Virtual Reality Applications for Older Adults: A Scoping Review

Immersive virtual reality (iVR) has gained considerable attention recently with increasing affordability and accessibility of the hardware. iVR applications for older adults present tremendous potential for diverse interventions and innovations. The iVR literature, however, provides a limited understanding of guiding design considerations and evaluations pertaining to user experience (UX). To address this gap, we present a state-of-the-art scoping review of literature on iVR applications developed for older adults over 65 years. We performed a search in ACM Digital Library, IEEE Xplore, Scopus, and PubMed (1 January 2010–15 December 2019) and found 36 out of 3874 papers met the inclusion criteria. We identified 10 distinct sets of design considerations that guided target users and physical configuration, hardware use, and software design. Most studies carried episodic UX where only 2 captured anticipated UX and 7 measured longitudinal experiences. We discuss the interplay between our findings and future directions to design effective, safe, and engaging iVR applications for older adults.
Noise tolerant drift detection method for data stream mining

Drift detection methods identify changes in data streams. Such changes are called concept drifts. Existing drift detection methods often assume that the input is a noise-free data stream. However, in real world applications, for example, data streams generating from internet of things are normally contaminated with noise. (noise, i.e. class noise and/or attribute noise). In this paper, we propose a Noise Tolerant Drift Detection Method (NTDDM), which is based on two-step detection and validation function to detect drifts, and filters out the false drifts caused by the noise. The NTDDM is compared with six well-known drift detection methods and tested on four benchmarks having different levels. Three performance indicators are proposed to determine whether the drift detection is made within a reasonable time, and the length of time to the known drift starting point. The comparative studies demonstrate that NTDDM outperforms the existing methods, over these performance indicators. Our proposed method has achieved a statistically significant improvement on drift detection compared to the methods in experiment. The proposed NTDDM makes it possible to efficiently and effectively detect drift in a noisy data stream.
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning

Recent work has shown that NLP tasks such as Relation Extraction (RE) can be recasted as Textual Entailment tasks using verbalizations, with strong performance in zero-shot and fewshot settings thanks to pre-trained entailment models.The fact that relations in current RE datasets are easily verbalized casts doubts on whether entailment would be effective in more complex tasks.In this work we show that entailment is also effective in Event Argument Extraction (EAE), reducing the need of manual annotation to 50% and 20% in ACE and WikiEvents respectively, while achieving the same performance as with full training.More importantly, we show that recasting EAE as entailment alleviates the dependency on schemas, which has been a roadblock for transferring annotations between domains.Thanks to the entailment, the multi-source transfer between ACE and WikiEvents further reduces annotation down to 10% and 5% (respectively) of the full training without transfer.Our analysis shows that the key to good results is the use of several entailment datasets to pre-train the entailment model.Similar to previous approaches, our method requires a small amount of effort for manual verbalization: only less than 15 minutes per event argument type is needed, and comparable results can be achieved with users with different level of expertise.
GenIE: Generative Information Extraction

Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets that are consistent with a predefined set of entities and relations from a knowledge base schema.Most existing works are pipelines prone to error accumulation, and all approaches are only applicable to unrealistically small numbers of entities and relations.We introduce GenIE (generative information extraction), the first end-toend autoregressive formulation of closed information extraction.GenIE naturally exploits the language knowledge from the pre-trained transformer by autoregressively generating relations and entities in textual form.Thanks to a new bi-level constrained generation strategy, only triplets consistent with the predefined knowledge base schema are produced.Our experiments show that GenIE is state-of-the-art on closed information extraction, generalizes from fewer training data points than baselines, and scales to a previously unmanageable number of entities and relations.With this work, closed information extraction becomes practical in realistic scenarios, providing new opportunities for downstream tasks.Finally, this work paves the way towards a unified end-to-end approach to the core tasks of information extraction.
Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Fine-tuning pretrained contextual word embedding models to supervised downstream tasks has become commonplace in natural language processing. This process, however, is often brittle: even with the same hyperparameter values, distinct random seeds can lead to substantially different results. To better understand this phenomenon, we experiment with four datasets from the GLUE benchmark, fine-tuning BERT hundreds of times on each while varying only the random seeds. We find substantial performance increases compared to previously reported results, and we quantify how the performance of the best-found model varies as a function of the number of fine-tuning trials. Further, we examine two factors influenced by the choice of random seed: weight initialization and training data order. We find that both contribute comparably to the variance of out-of-sample performance, and that some weight initializations perform well across all tasks explored. On small datasets, we observe that many fine-tuning trials diverge part of the way through training, and we offer best practices for practitioners to stop training less promising runs early. We publicly release all of our experimental data, including training and validation scores for 2,100 trials, to encourage further analysis of training dynamics during fine-tuning.
Public opinion monitoring through collective semantic analysis of tweets

The high popularity of Twitter renders it an excellent tool for political research, while opinion mining through semantic analysis of individual tweets has proven valuable. However, exploiting relevant scientific advances for collective analysis of Twitter messages in order to quantify general public opinion has not been explored. This paper presents such a novel, automated public opinion monitoring mechanism, consisting of a semantic descriptor that relies on Natural Language Processing algorithms. A four-dimensional descriptor is first extracted for each tweet independently, quantifying text polarity, offensiveness, bias and figurativeness. Subsequently, it is summarized across multiple tweets, according to a desired aggregation strategy and aggregation target. This can then be exploited in various ways, such as training machine learning models for forecasting day-by-day public opinion predictions. The proposed mechanism is applied to the 2016/2020 US Presidential Elections tweet datasets and the resulting succinct public opinion descriptions are explored as a case study.
An Improved Aquila Optimizer Based on Search Control Factor and Mutations

The Aquila Optimizer (AO) algorithm is a meta-heuristic algorithm with excellent performance, although it may be insufficient or tend to fall into local optima as as the complexity of real-world optimization problems increases. To overcome the shortcomings of AO, we propose an improved Aquila Optimizer algorithm (IAO) which improves the original AO algorithm via three strategies. First, in order to improve the optimization process, we introduce a search control factor (SCF) in which the absolute value decreasing as the iteration progresses, improving the hunting strategies of AO. Second, the random opposition-based learning (ROBL) strategy is added to enhance the algorithm’s exploitation ability. Finally, the Gaussian mutation (GM) strategy is applied to improve the exploration phase. To evaluate the optimization performance, the IAO was estimated on 23 benchmark and CEC2019 test functions. Finally, four real-world engineering problems were used. From the experimental results in comparison with AO and well-known algorithms, the superiority of our proposed IAO is validated.
Quality-Aware Decoding for Neural Machine Translation

Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search.In this paper, we bring together these two lines of research and propose quality-aware decoding for NMT, by leveraging recent breakthroughs in reference-free and reference-based MT evaluation through various inference methods like N -best reranking and minimum Bayes risk decoding.We perform an extensive comparison of various possible candidate generation and ranking methods across four datasets and two model classes and find that quality-aware decoding consistently outperforms MAP-based decoding according both to state-of-the-art automatic metrics (COMET and BLEURT) and to human assessments.Our code is available at https://github.com/deep-spin/qaware-decode.
Microsoft recommenders

The purpose of this work is to highlight the content of the Microsoft Recommenders repository and show how it can be used to reduce the time involved in developing recommender systems. The open source repository provides python utilities to simplify common recommender-related data science work as well as example Jupyter notebooks that demonstrate use of the algorithms and tools under various environments.
Automatic Speech Recognition Services

Nowadays, speech is becoming a more common, if not standard, interface to technology. This can be seen in the trend of technology changes over the years. Increasingly, voice is used to control programs, appliances and personal devices within homes, cars, workplaces, and public spaces through smartphones and home assistant devices using Amazon's Alexa, Google's Assistant and Apple's Siri, and other proliferating technologies. However, most speech interfaces are not accessible for Deaf and Hard-of-Hearing (DHH) people. In this paper, performances of current Automatic Speech Recognition (ASR) with voices of DHH speakers are evaluated. ASR has improved over the years, and is able to reach Word Error Rates (WER) as low as 5-6% [1][2][3], with the help of cloud-computing and machine learning algorithms that take in custom vocabulary models. In this paper, a custom vocabulary model is used, and the significance of the improvement is evaluated when using DHH speech.
Causal Structure Learning: A Combinatorial Perspective

In this review, we discuss approaches for learning causal structure from data, also called causal discovery. In particular, we focus on approaches for learning directed acyclic graphs and various generalizations which allow for some variables to be unobserved in the available data. We devote special attention to two fundamental combinatorial aspects of causal structure learning. First, we discuss the structure of the search space over causal graphs. Second, we discuss the structure of equivalence classes over causal graphs, i.e., sets of graphs which represent what can be learned from observational data alone, and how these equivalence classes can be refined by adding interventional data.
Semi-Supervised Domain Adaptation for Multi-Label Classification on Nonintrusive Load Monitoring

Nonintrusive load monitoring (NILM) is a technology that analyzes the load consumption and usage of an appliance from the total load. NILM is becoming increasingly important because residential and commercial power consumption account for about 60% of global energy consumption. Deep neural network-based NILM studies have increased rapidly as hardware computation costs have decreased. A significant amount of labeled data is required to train deep neural networks. However, installing smart meters on each appliance of all households for data collection requires the cost of geometric series. Therefore, it is urgent to detect whether the appliance is used from the total load without installing a separate smart meter. In other words, domain adaptation research, which can interpret the huge complexity of data and generalize information from various environments, has become a major challenge for NILM. In this research, we optimize domain adaptation by employing techniques such as robust knowledge distillation based on teacher-student structure, reduced complexity of feature distribution based on gkMMD, TCN-based feature extraction, and pseudo-labeling-based domain stabilization. In the experiments, we down-sample the UK-DALE and REDD datasets as in the real environment, and then verify the proposed model in various cases and discuss the results.
Virtual Transformations in Human Learning Environment: An Extended Reality Approach

Today, learning environments (LEs) are proceeding towards virtual environments (VEs) in which a sense of reality can be presented in three dimensions (3D) and a sense of inclusion can be experienced. A virtual learning environment (VLE) can be presented in a more realistic and visual manner with the use of innovative technologies such as virtual reality (VR), mixed reality (MR), and augmented reality(AR). This paper examines how extended reality (XR) approaches and its applications are transforming virtual learning environments in the context of human education and learning. A systematic literature review from the Scopus, Web of Science, IEEE, ACM and Google Scholar databases was conducted in order to better understand XR's contributions to human education as well as learning.The potential uses of XR technology have been discussed in terms of its structure, past, present, and future directions of XR concerning education.The research conducted in this context has determined that XR is mostly being used in fields of education, learninig and simulating an emergency situation. XR technology can be applied to solve problems without causing harm to people or property, particularly in emergency scenarios and safety training. Finally, this study offer several novel approaches for addressing these challenges, as well as potential directions for future researchers seeking to specialize in using these emerging technologies for human education.
Students' computer-based workshops in mandatory classes of English for students majoring in psychology and linguistics: A comparative experimental study

This article addresses the issue of developing and using students’ workshops in English. In the article, such workshops are defined as the fullest form of peer-teaching in which one or several students (workshop organizers) guide their group-mates in performing extra-linguistic learning activities conducted in the target language. The research describes workshops as one of the most efficient ways of involuntary (subconscious) target language acquisition achieved through extra-linguistic practical (experiential) activities performed by way of communication in the language to be learned. The article reports the results of a comparative experimental study in which students of a non-linguistic major (Psychology) and students majoring in English as a foreign language on which their career option (Applied Linguistics) is based were practicing workshops in English in their mandatory classes on that language. The results of the experimental study clearly demonstrate and prove that workshop practice was quite successful in both cases not only in what concerns the involuntary (subconscious) development of learners’ target language communication skills. No less evident was the development of some of the students’ psychological qualities (emotional intelligence) important for their further studies and professional careers.
Mask and Reason

Knowledge graph (KG) embeddings have been a mainstream approach for reasoning over incomplete KGs. However, limited by their inherently shallow and static architectures, they can hardly deal with the rising focus on complex logical queries, which comprise logical operators, imputed edges, multiple source entities, and unknown intermediate entities. In this work, we present the Knowledge Graph Transformer (kgTransformer) with masked pre-training and fine-tuning strategies. We design a KG triple transformation method to enable Transformer to handle KGs, which is further strengthened by the Mixture-of-Experts (MoE) sparse activation. We then formulate the complex logical queries as masked prediction and introduce a two-stage masked pre-training strategy to improve transferability and generalizability. Extensive experiments on two benchmarks demonstrate that kgTransformer can consistently outperform both KG embedding-based baselines and advanced encoders on nine in-domain and out-of-domain reasoning tasks. Additionally, kgTransformer can reason with explainability via providing the full reasoning paths to interpret given answers.
Tree of Science with Scopus: A Shiny Application

Tree of Science (ToS) is a scientific literature search tool that produces a small, selected list of citations from a larger pool of citations. Initially developed for searches in the Web of Science, this paper shows how to use it with bibliographic data from Scopus. This new Shiny web application analyzes data from Scopus. It processes a dataset from a Scopus search and creates three reports. The first one shows a descriptive analysis, the second one presents the Tree of Science of the search, and the third one presents a clustering analysis of the three main subtopics. The application is accessible from this link: https://coreofscience.shinyapps.io/scientometrics/.