Reviewerly/exHarmony huggingface dataset:
(Retracted) Cloud and deep learning-based image analyzer

The purpose of the proposed cloud and deep learning-based image analyzer technique is to discuss various challenges with existing conventional methods of managing business images and why we should adopt a better approach. We propose a cloud-based solution, which is more secure, durable, robust, and much faster compared with conventional methods of business image management. This approach tries to rectify the issue of storing, searching, categorizing, and managing images by combining the benefits of cloud along with computer vision and image processing. We thoroughly discuss the idea along with detailed analysis of requirement gathering, feasibility study, and architecture designing. Furthermore, we discuss the implementation code, detailed comparison of different image processing algorithms, empirical analysis of this approach compared with other conventional approaches in terms of cost, accuracy of image processing algorithms, data entry time, searching time, etc. Finally, we emphasize the best approach and image processing algorithm chosen based on the practically proven results.
A novel art gesture recognition model based on two channel region-based convolution neural network for explainable human-computer interaction understanding

The application development of hot technology is both an opportunity and a challenge. The vision-based gesture recognition rate is low and real-time performance is poor, so various algorithms need to be studied to improve the accuracy and speed of recognition. In this paper, we propose a novel gesture recognition based on two channel region-based convolution neural network for explainable human-computer interaction understanding. The input gesture image is extracted through two mutually independent channels. The two channels have convolution kernel with different scales, which can extract the features of different scales in the input image, and then carry out feature fusion at the fully connection layer. Finally, it is classified by the softmax classifier. The two-channel convolutional neural network model is proposed to solve the problem of insufficient feature extraction by the convolution kernel. Experimental results of gesture recognition on public data sets NTU and VIVA show that the proposed algorithm can effectively avoid the over-fitting problem of training models, and has higher recognition accuracy and stronger robustness than traditional algorithms.
Exploring the Impact of Visualization Design on Non-Expert Interpretation of Hurricane Forecast Path

Hurricane forecast graphics have the challenging task of communicating information about spatial and temporal uncertainty. Although forecasting accuracy has improved, the popular track forecast cone or “Cone of Uncertainty” graphic, produced by the National Hurricane Center, is poorly understood by the general public. A better understanding of the forecast can potentially assist in timely decisions and life-saving actions. This study evaluates the impact of visualization design, tropical cyclone characteristics, subjective numeracy, and subjective graphicacy on visual attention to and user interpretation of hurricane forecast graphics. Forty-three non-expert participants completed forecast path estimates for nine tropical cyclones, comparing their responses when using the National Hurricane Center’s cone of uncertainty graphic and two alternative forecast visualizations. Results show that design modifications did not alter visual attention patterns or improve interpretations. Results also indicate that subjective numeracy, subjective graphicacy, and tropical cyclone characteristics, in combination, influence estimates of hurricane forecast tracks. The findings from this study inform redesign efforts of hurricane risk communication products.
Quantum teleportation and thermal entanglement of two-scattering qubit state in graphene lattices

This paper presents an analytical investigation of the teleportation and the thermal entanglement of two identical qubits in the ground state of graphene lattices. The elastic interaction between the two qubits is considered as a scattering process. The modified Hamiltonian and density matrix of the system were presented. The teleportation and the entanglement of two-qubit system are analyzed through the study of the average fidelity of teleportation [Formula: see text], the concurrence [Formula: see text] and the entanglement of formation [Formula: see text]. The results depend on the temperature [Formula: see text] and also on the parameters of the system as the [Formula: see text] parameter of the interaction potential and [Formula: see text] band parameter. The study shows that the teleportation and the entanglement decrease when the temperature [Formula: see text] increases but they become all the more important as the parameters [Formula: see text] and [Formula: see text] increase.
High-Precision Depth Map Estimation from Missing Viewpoints for 360-Degree Digital Holography

In this paper, we propose a novel model to extract highly precise depth maps from missing viewpoints, especially for generating holographic 3D content. These depth maps are essential elements for phase extraction, which is required for the synthesis of computer-generated holograms (CGHs). The proposed model, called the holographic dense depth, estimates depth maps through feature extraction, combining up-sampling. We designed and prepared a total of 9832 multi-view images with resolutions of 640 × 360. We evaluated our model by comparing the estimated depth maps with their ground truths using various metrics. We further compared the CGH patterns created from estimated depth maps with those from ground truths and reconstructed the holographic 3D image scenes from their CGHs. Both quantitative and qualitative results demonstrate the effectiveness of the proposed method.
The Proof-of-Concept of a Blockchain Solution for Construction Logistics Integrating Flows: Lessons from Sweden

The economic, information and material flows in construction logistics are usually disintegrated—but blockchain could integrate them into a shared digital ledger supported by smart contracts, thus creating value for the relevant actors (e.g. contractors, suppliers). Therefore, we explore such a solution’s potential by conceptualizing, developing, implementing, and testing a relevant pilot in the Swedish context. Theoretically, we adopt sociomateriality, thus understanding blockchain as a digital facilitator of transactions, flow integration, social interactions, and trust development. Methodologically, we review the literature on blockchain for construction logistics, and report from our empirical studies in Sweden. The literature review showed that core blockchain properties can generate value for construction logistics (e.g. reduction of accounting rework)—however, there currently exist no use cases beyond concepts and pilots. Moreover, implementation can be challenging due to practical and security constraints—also reflected in our own empirical material. Regardless, the solution was conceptualized as a permissioned private proof-of-authority blockchain named BlogCHAIN, and developed into an online application based on Hyperledger Besu. Testing BLogCHAIN revealed that the practitioners’ resistance and powerplay required a simplification of our initial concept. Indeed, the flows were integrated, more decentralization and transparency were achieved, and previously time-consuming processes were facilitated—but the main contractor retained control over critical logistics segments. Our lessons-learned showed that several issues can jeopardize the adoption of blockchain, like existing power balances and unrealistic hopes of transparency and accountability beyond established business practices. The solution could be integrated with the IoT and machine learning in the future.
Bridging the Gap Between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing

A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i.e., whether certain factors (e.g., related to life events) are associated with an outcome (e.g., depression). In recent years, deep learning has become a potential alternative approach for conducting such analyses by predicting an outcome from a collection of factors and identifying the most "informative" ones driving the prediction. However, this approach has had limited impact as its findings are not linked to statistical significance of factors supporting hypotheses. In this article, we proposed a flexible and scalable approach based on the concept of permutation testing that integrates hypothesis testing into the data-driven deep learning analysis. We apply our approach to the yearly self-reported assessments of 621 adolescent participants of the National Consortium of Alcohol and Neurodevelopment in Adolescence (NCANDA) to predict negative valence, a symptom of major depressive disorder according to the NIMH Research Domain Criteria (RDoC). Our method successfully identifies categories of risk factors that further explain the symptom.
DiffTREAT: Differentiated Traffic Scheduling Based on RNN in Data Centers

Transmission schemes in data centers are supposed to accurately distinguish flow types for different scheduling. However, prior efforts failed to meet the needs at all levels in a cost-effectively way. Nor the existing schemes proved applicable to all the diverse scenarios or dynamic traffic patterns. Therefore, we proposed <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Diff</u> erentiated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tr</u> affic sch <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> duling in d <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ta cen <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ers (DiffTREAT) based the Recurrent Neural Network (RNN), aiming to simplify the transmission in the dynamic and diverse network scenarios. First, DiffTREAT utilizes deep learning methods for traffic classification and flow size prediction. Second, according to the classified results of flows, DiffTREAT adopts multilevel priority queues to ensure the preferential transmission of latency-sensitive flows while optimizing the overall average flow completion time (FCT). Third, DiffTREAT employs the network cache to increase the capacity of data center networks (DCN), which effectively fights against the traffic burst and improves the throughput of latency-insensitive flows. DiffTREAT has been tested in different topologies in the contexts of diverse network loads and real-world workloads. Experiment results showed that compared with state-of-the-art schemes, DiffTREAT yielded both the lower average flow completion time for latency-sensitive flows and the higher throughput for latency-insensitive flow.
Keke AI Competition: Solving puzzle levels in a dynamically changing mechanic space

The Keke AI Competition introduces an artificial agent competition for the game Baba is You - a Sokoban-like puzzle game where players can create rules that influence the mechanics of the game. Altering a rule can cause temporary or permanent effects for the rest of the level that could be part of the solution space. The nature of these dynamic rules and the deterministic aspect of the game creates a challenge for AI to adapt to a variety of mechanic combinations in order to solve a level. This paper describes the framework and evaluation metrics used to rank submitted agents and baseline results from sample tree search agents.
Regularized Soft Actor-Critic for Behavior Transfer Learning

Existing imitation learning methods mainly focus on making an agent effectively mimic a demonstrated behavior, but do not address the potential contradiction between the behavior style and the objective of a task. There is a general lack of efficient methods that allow an agent to partially imitate a demonstrated behavior to varying degrees, while completing the main objective of a task. In this paper we propose a method called Regularized Soft Actor-Critic which formulates the main task and the imitation task under the Constrained Markov Decision Process framework (CMDP). The main task is defined as the maximum entropy objective used in Soft Actor-Critic (SAC) and the imitation task is defined as a constraint. We evaluate our method on continuous control tasks relevant to video games applications.
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess

In this work, we adapt a training approach inspired by the original AlphaGo system to play the imperfect information game of Reconnaissance Blind Chess. Using only the observations instead of a full description of the game state, we first train a supervised agent on publicly available game records. Next, we increase the performance of the agent through self-play with the on-policy reinforcement learning algorithm Proximal Policy optimization. We do not use any search to avoid problems caused by the partial observability of game states and only use the policy network to generate moves when playing. With this approach, we achieve an ELO of 1330 on the RBC leaderboard, which places our agent at position 27 at the time of this writing. We see that self-play significantly improves performance and that the agent plays acceptably well without search and without making assumptions about the true game state.
Playing Good-Quality Games with Weak Players by Combining Programs with Different Roles

Computer programs have become stronger than top-rated human players in several games. However, weak players may not enjoy playing against these strong programs. In this study, we propose combining two programs with different roles to create programs suitable for weak players. We use a superhuman program that generates candidate moves and evaluates how good the moves are, as well as a program that evaluates the moves’ naturalness. We implement an instance for Go, which employs a superhuman program, KataGo, and a neural network trained using human games. Experiments show that the proposed method is promising for playing good-quality games with weak players.
Network based on the synergy of knowledge and context for natural language inference

The goal of natural language inference (NLI) is to judge the logical relationship between sentence pairs, including entailment, contradiction, and neutral. At present, many researchers have shown that the introduction of external knowledge is helpful for NLI. However, the existing models are not effective enough for the utilization of knowledge information and not sufficient for the exploration of context information. In this paper, we propose a multi-branch network based on the synergy of knowledge and context. It applies two kinds of information synergy in the way that both knowledge information and context information promote performance improvement while modeling their separately in independent branches. In the context branch, we present a multi-level and dynamic assisted attention to construct sufficient interaction between sentence pairs. In the knowledge branch, we design a Knowledge-based Graph Attention Network (K-GAT) to capture the structural information of the knowledge, and the attention mechanism is used for knowledge interaction. In addition, for strengthening the relation between sentence pairs, we provide a relation branch to capture the context and knowledge relations of sentence pairs. In order to avoid introducing redundant external knowledge as much as possible, we adopt the introduction of five selected semantic dependencies based knowledge types. Experiments show that our model achieves strong competitive results on all three popular NLI datasets.
overviewR - Easily Explore Your Data in R

The package overviewR (Meyer & Hammerschmidt, 2022) helps users to get an overview of data with a particular emphasis on the extent that the distinct units of observation are covered for the entire time frame of the data set.With (large) data sets that have many different observations over a long period, it becomes increasingly difficult to identify each unique observation and its exact coverage in the data.In particular, if some observations are not included for the entire time span of the data -either because they entered later, dropped out earlier, or have gaps in between -it can become difficult to spot potential problems in the data's time and scope.As Staniak & Biecek (2019) argue, these tools are increasingly in demand.The main advantages of overviewR are threefold: 1) it allows users to quickly understand the data and the distribution of the observations over time, 2) it offers several helper functions that facilitate complex variable engineering tasks such as the merging of multiple data frames on different time formats or the generation of specific time periods, and 3) it allows to show this information both in a visual and tabular form that is convenient for academic publications.
Towards Automatic Detection of Inappropriate Content in Multi-dialectic Arabic Text

Nowadays, the various social networking applications give rise to millions of comments or tweets every day. This large amount of unstructured and noisy data is the subject of numerous studies on the behavior of people, their thoughts, their worries, their beliefs, etc. Currently, several research studies have focused on the detection of inappropriate content. But the number of works that have been interested in the Arabic language is very small, and the number of those that have considered multi-dialect Arabic content is even smaller. This is the context in which our work takes place. We will analyze the impact of the multi-dialectism on the performance of classifiers for the identification of inappropriate Arabic content. The key contribution of the paper is twofold. Firstly, we introduce an Arabic hate speech and abusive language dataset (Tun-EL) covering three dialects (Tunisian, Egyptian, and Lebanese). Secondly, we propose a new approach for inappropriate content detection. Our approach is based on a combination of AraVec and fastText word embedding as input features and a Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model. The obtained results show that the proposed model is effective in detecting hateful and abusive multi-dialect Arabic content.
An Opinion Analysis Method Based on Disambiguation to Improve a Recommendation System

Recommendation systems are widely used in almost all websites to help the customer to make a decision in the face of information overload problems. These systems provide users with personalized recommendations and help them to make the right decisions. Customer opinion analysis it is a vast area of research, especially in recommendation systems. This justifies the growing importance of opinion analysis. Among the great classifiers to perform this task the Wide Margin Separators classifier which gives encouraging results in the literature. Despite the results found in the classification, there is a problem of ambiguity in the meaning of words, which poses a problem in recommendation systems based on opinion analysis; an Arabic word has several meanings, so there is a probability of misclassification of comments that contain these ambiguous words and therefore a false recommendation. We present after a comparative study between the methods proposed in the literature a method to do the disambiguation before going through the classification phase which we have done using the SVM algorithm. Our proposed system gives its best result in terms of accuracy 97.1%.
From Unhealthy Online Conversation to Political Violence: The Case of the January 6th Events at the Capitol

While social media platforms can serve as public discussion forums of great benefit to democratic debate, they can also become a source of disinformation, bullying, hate speech, and a setting for fruitless polemics. In extreme cases, discourse originating in or propagated through social media can stoke political polarization and partisanship, with potentially devastating consequences. One of the most dramatic such instances in recent years has been the January 6 2021 incident in Washington D.C., when a group of protesters contesting the results of the 2020 US Presidential election besieged the US Capitol building, resulting in a violent confrontation that claimed the lives of several protesters and police officers. The public reacted by posting messages on social media, discussing the actions of the rioters, with several competing and incompatible narratives eventually emerging. Aiming to understand the phenomenon of uncompromising partisan political discourse under the broad concept of unhealthy online conversation, we sample 1,300,000 Twitter posts dating from January 2021 from the #Election2020 dataset. We then train several classifiers on the Unhealthy Comment Corpus (UCC) dataset using a machine learning (ML) approach, and use the best-performing model, XLNet, to label each tweet as healthy or unhealthy, furthermore using a taxonomy of 7 attributes of unhealthy conversation: hostile, antagonistic, dismissive, condescending, sarcastic, generalization, and/or unfair generalization. We model discussion topics for each attribute using latent Dirichlet allocation (LDA) and follow their evolution over time. The results we present aim to help social media stakeholders, government regulators, and the general public better understand the dynamics of unhealthy discourse occurring on social media, and its relationship to political polarization and partisanship, and their potential to pose an internal threat to democracy.
Improving Bert-Based Model for Medical Text Classification with an Optimization Algorithm

In the field of Natural Language Processing (NLP), automatic text classification is a classic topic that involves classifying textual material into predetermined categories based on its content. These models have been effectively applied to data containing a large number of dimensional features, some of which are inherently sparse. Machine learning and other statistical approaches, such as those used in medical text categorization, appear to be extremely successful for these tasks. However, much human work is still required to classify a large collection of training data. Recent research has shown the usefulness of pre-trained language models such as Bidirectional Encoder Representations from Transformers (BERT), all of which have demonstrated their ability to reduce the amount of work required for feature engineering. However, directly using the pre-trained BERT model in the classification task does not result in a statistically significant increase in performance. To improve the result of the BERT model, we propose an optimal deep learning model based on a BERT model and hyperparameter selection. The model consists of three steps: (1) processing medical text; (2) extracting medical text features using a BERT architecture; and (3) selecting hyperparameters for the Deep Learning model based on a Particle Swarm Optimization (PSO) algorithm. Finally, our approach uses a k-Nearest Neighbors algorithm (KNN) model to predict the matching response. Experiments conducted on the Hallmarks dataset have shown that the proposed method significantly increases the accuracy of the results.