Reviewerly/exHarmony huggingface dataset:
The Communicative Functions of Emojis: Evidence from Jordanian Arabic-Speaking Facebookers

Purpose. This paper examines the communicative (also known as pragmatic) functions of the most common five emojis in the Jordanian context as perceived by Jordanian Facebook users. &#x0D; Methods. The data were collected in four stages. First, the researcher shared a post on his Facebook account in which he asked his Jordanian-Arabic speaking virtual friends to report in a comment the most common emojis they use. The researcher compiled 174 comments / responses with 1716 emoji tokens. Second, the received tokens were used to identify the most common five emojis. Third, in order to identify the set of functions of each emoji, the researcher shared another post in which he asked the same previous group to report when each of them tends to press each emoji and for what purposes. Based on the received comments, a preliminary list of functions was prepared. Finally, the proposed functions were subjected to a validation process by two Jordanian-Arabic speaking linguists and three senior students from the Department of English at the University of Jordan. Most of their judgments were compatible with those of the researcher. To further validate the data, the acceptability of the identified functions were tested against the intuition of 261 Jordanian BA students at the University of Jordan. &#x0D; Results. The findings show that the five most common emojis in the Jordanian context are (1) the Face With Tears of Joy, (2) the Red Heart, (3) the Slightly Smiling Face, (4) the Face Blowing a Kiss, and (5) the Winking Face. Furthermore, emojis are not only used to show emotions, but can also act as markers of illocutionary force, as face saving devices, and as boosters of rapport. The set of emojis examined in this study can be employed to perform 19 multiple illocutionary acts including but not limited to expressive acts (happiness, admiration, etc.), directive acts (e.g, directing the addressee to stop doing something) and declarative acts (e.g., threatening). Emojis are not solely used to convey the functions envisaged by their creators. Instead, with time, emojis start to drift extensively from their semantic import by acquiring a wide spectrum of new illocutions. &#x0D; Conclusions. The study concludes that although emojis are evolving and developing at a rapid pace, becoming more diverse, pervasive and integral in our daily communications, sharing even some of the characteristics of human language such as arbitrariness, they remain a mode of communication within computer-mediated communication (CMC). At this stage, they can mainly play the role of non-verbal cues that help us understand the intended message and function as a parallel lingua franca limited in domains of CMC.
Dynamical simulation via quantum machine learning with provable generalization

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. This provides a guarantee that our algorithm is resource-efficient, both in terms of qubit and data requirements. Our numerics exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota.
SATMargin: Practical Maximal Frequent Subgraph Mining via Margin Space Sampling

Maximal Frequent Subgraph (MFS) mining asks to identify the maximal subgraph that commonly appears in a set of graphs, which has been found valuable in many applications in social science, biology, and other domains. Previous studies focused on reducing the search space of MFSs and discovered the theoretically smallest search space. Despite the success in theory, no practical algorithm can exhaustively search the space as it is huge even for small graphs with only tens of nodes and hundreds of edges. Moreover, deciding whether a subgraph is an MFS needs to solve subgraph monomorphism (SM), an NP-complete problem that introduces extra challenges. Here, we propose a practical MFS mining algorithm that targets large MFSs, named SATMargin. SATMargin adopts random walk in the search space to perform efficient search and utilizes a customized conflict learning Boolean Satisfiability (SAT) algorithm to accelerate SM queries. We design a mechanism that reuses SAT solutions to combine the random walk and the SAT solver effectively. We evaluate SATMargin over synthetic graphs and 6 real-world graph datasets. SATMargin shows superior performance to baselines in finding more and larger MFSs. We further demonstrate the effectiveness of SATMargin in a case study of RNA graphs. The identified frequent subgraph by SATMargin well matches the functional core structure of RNAs previously detected in biological experiments. Our software can be found at https://github.com/MuyiLiu2022/SATMargin-and-Baselines.
Multimodal Hate Speech Detection from Bengali Memes and Texts

Numerous machine learning (ML) and deep learning (DL)-based approaches have been proposed to utilize textual data from social media for anti-social behavior analysis like cyberbullying, fake news detection, and identification of hate speech mainly for highly-resourced languages such as English. However, despite having a lot of diversity and millions of native speakers, some languages like Bengali are under-resourced, which is due to a lack of computational resources for natural language processing (NLP). Similar to other languages, Bengali social media contents also include images along with texts (e.g., multimodal memes are posted by embedding short texts into images on Facebook). Therefore, only the textual data is not enough to judge them since images might give extra context to make a proper judgement. This paper is about hate speech detection from multimodal Bengali memes and texts. We prepared the only multimodal hate speech dataset for-a-kind of problem for Bengali, which we use to train state-of-the-art neural architectures (e.g., Bi-LSTM/Conv-LSTM with word embeddings, ConvNets + pre-trained language models, e.g., monolingual Bangla BERT, multilingual BERT-cased/uncased, and XLM-RoBERTa) to jointly analyze textual and visual information for hate speech detection. Conv-LSTM and XLM-RoBERTa models performed best for texts, yielding F1 scores of 0.78 and 0.82, respectively. As of memes, ResNet-152 and DenseNet-161 models yield F1 scores of 0.78 and 0.79, respectively. As for multimodal fusion, XLM-RoBERTa + DenseNet-161 performed the best, yielding an F1 score of 0.83. Our study suggests that text modality is most useful for hate speech detection, while memes are moderately useful.
A quantum generative model for multi-dimensional time series using Hamiltonian learning

Synthetic data generation has proven to be a promising solution for addressing data availability issues in various domains. Even more challenging is the generation of synthetic time series data, where one has to preserve temporal dynamics, i.e., the generated time series must respect the original relationships between variables across time. Recently proposed techniques such as generative adversarial networks (GANs) and quantum-GANs lack the ability to attend to the time series specific temporal correlations adequately. We propose using the inherent nature of quantum computers to simulate quantum dynamics as a technique to encode such features. We start by assuming that a given time series can be generated by a quantum process, after which we proceed to learn that quantum process using quantum machine learning. We then use the learned model to generate out-of-sample time series and show that it captures unique and complex features of the learned time series. We also study the class of time series that can be modeled using this technique. Finally, we experimentally demonstrate the proposed algorithm on an 11-qubit trapped-ion quantum machine.
Self-Supervised Learning for Recommender Systems: A Survey

In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data. Self-supervised learning (SSL), as an emerging technique for learning from unlabeled data, has attracted considerable attention as a potential solution to this issue. This survey paper presents a systematic and timely review of research efforts on self-supervised recommendation (SSR). Specifically, we propose an exclusive definition of SSR, on top of which we develop a comprehensive taxonomy to divide existing SSR methods into four categories: contrastive, generative, predictive, and hybrid. For each category, we elucidate its concept and formulation, the involved methods, as well as its pros and cons. Furthermore, to facilitate empirical comparison, we release an open-source library SELFRec (https://github.com/Coder-Yu/SELFRec), which incorporates a wide range of SSR models and benchmark datasets. Through rigorous experiments using this library, we derive and report some significant findings regarding the selection of self-supervised signals for enhancing recommendation. Finally, we shed light on the limitations in the current research and outline the future research directions.
A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity Recognition

Cross-lingual Named Entity Recognition (NER) has recently become a research hotspot because it can alleviate the data-hungry problem for low-resource languages. However, few researches have focused on the scenario where the source-language labeled data is also limited in some specific domains. A common approach for this scenario is to generate more training data through translation or generation-based data augmentation method. Unfortunately, we find that simply combining source-language data and the corresponding translation cannot fully exploit the translated data and the improvements obtained are somewhat limited. In this paper, we describe our novel dual-contrastive framework ConCNER for cross-lingual NER under the scenario of limited source-language labeled data. Specifically, based on the source-language samples and their translations, we design two contrastive objectives for cross-language NER at different grammatical levels, namely Translation Contrastive Learning (TCL) to close sentence representations between translated sentence pairs and Label Contrastive Learning (LCL) to close token representations within the same labels. Furthermore, we utilize knowledge distillation method where the NER model trained above is used as the teacher to train a student model on unlabeled target-language data to better fit the target language. We conduct extensive experiments on a wide variety of target languages, and the results demonstrate that ConCNER tends to outperform multiple baseline methods. For reproducibility, our code for this paper is available at https://github.com/GKLMIP/ConCNER.
The Impact of Latency on Navigation in a First-Person Perspective Game

Competitive first-person shooter games are played over a network, where latency can degrade player performance. To better understand latency's impact, a promising approach is to study how latency affects individual game actions, such as moving and shooting. While target selection (aiming and shooting at an opponent) is fairly well studied, navigation (moving an avatar into position) is not. This paper presents results from a 30-person user study that evaluates the impact of latency on first-person navigation using a custom "hide and seek" game that isolates avatar movement in a manner intended to be similar to movement in a first-person shooter game. Analysis of the results shows latency has pronounced effects on player performance (score and seek positioning), with subjective opinions on Quality of Experience following suit.
On the value of project productivity for early effort estimation

In general, estimating software effort using a Use Case Point (UCP) size requires the use of productivity as a second prediction factor. However, there are three drawbacks to this approach: (1) there is no clear procedure for predicting productivity in the early stages, (2) the use of fixed or limited productivity ratios does not allow research to reflect the realities of the software industry, and (3) productivity from historical data is often challenging. The new UCP datasets now available allow us to perform further empirical investigations of the productivity variable in order to estimate the UCP effort. Accordingly, four different prediction models based on productivity were used. The results showed that learning productivity from historical data is more efficient than using classical approaches that rely on default or limited productivity values. In addition, predicting productivity from historical environmental factors is not often accurate. From here we conclude that productivity is an effective factor for estimating the software effort based on the UCP in the presence and absence of previous historical data. Moreover, productivity measurement should be flexible and adjustable when historical data is available.
CapOnImage: Context-driven Dense-Captioning on Image

Existing image captioning systems are dedicated to generating narrative captions for images, which are spatially detached from the image in presentation. However, texts can also be used as decorations on the image to highlight the key points and increase the attractiveness of images. In this work, we introduce a new task called captioning on image (CapOnImage), which aims to generate dense captions at different locations of the image based on contextual information. To fully exploit the surrounding visual context to generate the most suitable caption for each location, we propose a multi-modal pre-training model with multi-level pre-training tasks that progressively learn the correspondence between texts and image locations from easy to difficult. Since the model may generate redundant captions for nearby locations, we further enhance the location embedding with neighbor locations as context. For this new task, we also introduce a large-scale benchmark called CapOnImage2M, which contains 2.1 million product images, each with an average of 4.8 spatially localized captions. Compared with other image captioning model variants, our model achieves the best results in both captioning accuracy and diversity aspects. We will make code and datasets public to facilitate future research.
Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting

Self-augmentation has received increasing research interest recently to improve named entity recognition (NER) performance in lowresource scenarios.Token substitution and mixup are two feasible heterogeneous selfaugmentation techniques for NER that can achieve effective performance with certain specialized efforts.Noticeably, self-augmentation may introduce potentially noisy augmented data.Prior research has mainly resorted to heuristic rule-based constraints to reduce the noise for specific self-augmentation methods individually.In this paper, we revisit these two typical self-augmentation methods for NER, and propose a unified meta-reweighting strategy for them to achieve a natural integration.Our method is easily extensible, imposing little effort on a specific self-augmentation method.Experiments on different Chinese and English NER benchmarks show that our token substitution and mixup method, as well as their integration, can achieve effective performance improvement.Based on the meta-reweighting mechanism, we can enhance the advantages of the self-augmentation techniques without much extra effort.
Understanding User Perceptions, Collaborative Experience and User Engagement in Different Human-AI Interaction Designs for Co-Creative Systems

Human-AI co-creativity involves humans and AI collaborating on a shared creative product as partners. In a creative collaboration, communication is an essential component among collaborators. In many existing co-creative systems users can communicate with the AI, usually using buttons or sliders. Typically, the AI in co-creative systems cannot communicate back to humans, limiting their potential to be perceived as partners rather than just a tool. This paper presents a study with 38 participants to explore the impact of two interaction designs, with and without AI-to-human communication, on user engagement, collaborative experience and user perception of a co-creative AI. The study involves user interaction with two prototypes of a co-creative system that contributes sketches as design inspirations during a design task. The results show improved collaborative experience and user engagement with the system incorporating AI-to-human communication. Users perceive co-creative AI as more reliable, personal, and intelligent when the AI communicates to users. The findings can be used to design effective co-creative systems, and the insights can be transferred to other fields involving human-AI interaction and collaboration.
Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?

Supervised training has led to state-of-the-art results in image and video denoising. However, its application to real data is limited since it requires large datasets of noisy-clean pairs that are difficult to obtain. For this reason, networks are often trained on realistic synthetic data. More recently, some self-supervised frameworks have been proposed for training such denoising networks directly on the noisy data without requiring ground truth. On synthetic denoising problems supervised training outperforms self-supervised approaches, however in recent years the gap has become narrower, especially for video. In this paper, we propose a study aiming to determine which is the best approach to train denoising networks for real raw videos: supervision on synthetic realistic data or self-supervision on real data. A complete study with quantitative results in case of natural videos with real motion is impossible since no dataset with clean-noisy pairs exists. We address this issue by considering three independent experiments in which we compare the two frameworks. We found that self-supervision on the real data outperforms supervision on synthetic data, and that in normal illumination conditions the drop in performance is due to the synthetic ground truth generation, not the noise model.
Improving Dialogue Generation via Proactively Querying Grounded Knowledge

Recent advances in pre-trained language models have significantly improved neural response generation. Further, an intelligent dialogue system should be able to give accurate responses that meet the needs of users. However, using appropriate knowledge has so far been proved difficult, because faced with a mass of relevant knowledge, the model not only needs to accurately retrieve the target information, but also integrate the information into dialogue response. In this paper, we propose a novel knowledge-based dialogue system which integrates the strength of a transformer-based generator and a knowledge retriever capable of proactively constructing queries for accurate information. Specifically, generator are used to give a response skeleton and knowledge retriever constructs a query based on response skeleton to obtain specific knowledge, which avoids excessive noise interference when generating responses. Experiments show that conversational systems that leverage knowledge integrator could generate more informative and human-like responses than strong baseline systems.
A Digital Asset Inheritance Model to Convey Online Persona Posthumously

The astounding growth of the Internet has generated digital asset extensively. Users are concerned about asset management so that the asset can be conveyed successfully to the descendent posthumously. Very few works have addressed designing and modeling of digital asset inheritance (DAI) from a technical design perspective. They have several inherent limitations such as incorrect death confirmation, high participation of nominee, the possibility of failure to obtain recovery key, and are based on many unreasonable assumptions, thus inefficient to design in the real life. In this paper, we have formalized the different categories of digital assets and defined the various security goals, required functionalities, and necessary entities to build an asset inheritance model. We have also proposed a new protocol named digital asset inheritance protocol (DAIP) using certificateless encryption (CLE) and identity-based system (IBS) to convey the user's online persona efficiently to the descendent after his death. DAIP allows the nominee to successfully retrieve the asset after the user's demise, even if a nominee is uninformed regarding the asset. We, then, provide rigorous security proofs of various properties using real-ideal worlds paradigm. Finally, we have implemented DAIP model using PBC and pycryptodome library. The simulation results affirm that it can be practically efficient to implement.
Maximal entanglement increase with single-photon subtraction

Entanglement is an indispensable quantum resource for quantum information technology. In continuous-variable quantum optics, photon subtraction can increase the entanglement between Gaussian states of light, but for mixed states the extent of this entanglement increase is poorly understood. In this work, we use an entanglement measure based the Rényi-2 entropy to prove that single-photon subtraction increases bipartite entanglement by no more than log 2. This value coincides with the maximal amount of bipartite entanglement that can be achieved with one photon. The upper bound is valid for all Gaussian input states, regardless of the number of modes and the purity.
Literature Review: Blockchain-Oriented Software Characteristics and New Stream for Software Process Improvement

Software process improvement has been through many discussions in the past few decades with relation to the software development alterations. Blockchain-oriented software has been getting more popular, and many recent scholars discussed the suitability of traditional software engineering for the new kind of software. However, the suitability of the current SPI for blockchain-oriented software has not been discussed yet. This paper aims to define the Blockchain-oriented Software characteristics that differentiate it from the traditional software. A literature search was performed considering the studies published up to the date on IEEE Explore, Springer, ACM, ScienceDirect, ProQuest, Web of Science, and emerald insight. A total of 103 studies were found, and only 49 relevant studies met the inclusion criteria. A thematic analysis was performed with an inductive approach to identify 46 distinctive Blockchain-oriented Software characteristics. This result shows the necessity for researchers to devise specific SPI to develop blockchain-oriented software to address its distinctive characteristics, which affect its development and projects. Finally, a new stream for SPI is recommended here to cover Blockchain-oriented Software Process Improvement.
Morphological annotation of the Slovak language in the Spacy library with the pretraining

We created a Slovak language model in the Spacy library. When creating the model, we used pretraining and training in the Spacy library. During the pretraining, we used Fasttext vectors and the text we collected. We used data from Slovak Dependency Treebank during the training. We created a model with an accuracy of 86.249%. We also examined the effect of pretraining on the model. All achieved results are described in the final part.