Reviewerly/exHarmony huggingface dataset:
Crawling Parallel Data for Bilingual Corpus Using Hybrid Crawling Architecture

The quality of translation work mainly depends on the understanding of the words in their domain. If machine translation can accurately translate the words in a domain in different languages, it can even avoid any human communication error. To achieve this, a high-quality bilingual corpus is crucial as they are always the basis of state-of-the-art machine translation system. However, it is complicated to construct the corpus with large amount of parallel data. In this paper, a new crawling architecture, called Hybrid Crawling Architecture (HCA), will be proposed, which efficiently and effectively collects parallel data from the Web for the bilingual corpus. HCA aims at targeted websites, which contains articles in at least two different languages. As it is a mixture of Focused crawling architecture and Parallel crawling architecture, HCA takes advantages over both architectures. In intensive experiments on crawling parallel data of relevance topics, HCA significantly outperforms Focused crawling architecture and Parallel crawling architecture for 30% and 200% respectively, in terms of quantity.
WBC-AMNet: Automatic classification of WBC images using deep feature fusion network based on focalized attention mechanism

The recognition and classification of White Blood Cell (WBC) play a remarkable role in blood-related diseases (i.e., leukemia, infections) diagnosis. For the highly similar morphology of different WBC subtypes, it is too confused to classify the WBC effectively and accurately for visual observation of blood cell smears. This paper proposes a Deep Convolutional Neural Network (DCNN) with feature fusion strategies, named WBC-AMNet, for automatically classifying WBC subtypes based on focalized attention mechanism. To obtain more localized attention of CNN, the fusion features of the first and the last convolutional layer are extracted by focalized attention mechanism combining Squeeze-and-Excitation (SE) and Gather-Excite (GE) modules. The new method performs successfully in classifying monocytes, neutrophils, lymphocytes, and eosinophils on the complex background with an overall accuracy of 95.66%, better than that of general CNNs. The multi-classification accuracy of WBC-AMNet with the background segmentation is over 98% in all cases. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to visualize the attention heatmaps of different feature maps.
COVID-HateBERT: a Pre-trained Language Model for COVID-19 related Hate Speech Detection

With the dramatic growth of hate speech on social media during the COVID-19 pandemic, there is an urgent need to detect various hate speech effectively. Existing methods only achieve high performance when the training and testing data come from the same data distribution. The models trained on the traditional hateful dataset cannot fit well on COVID-19 related dataset. Meanwhile, manually annotating the hate speech dataset for supervised learning is time-consuming. Here, we propose COVID-HateBERT, a pre-trained language model to detect hate speech on English Tweets to address this problem. We collect 200M English tweets based on COVID-19 related hateful keywords and hashtags. Then, we use a classifier to extract the 1.27M potential hateful tweets to re-train BERT-base. We evaluate our COVID-HateBERT on four benchmark datasets. The COVID-HateBERT achieves a 14.8%-23.8% higher macro average F1 score on traditional hate speech detection comparing to baseline methods and a 2.6%-6.73% higher macro average F1 score on COVID-19 related hate speech detection comparing to classifiers using BERT and BERTweet, which shows that COVID-HateBERT can generalize well on different datasets.
Effects on Co-Presence of a Virtual Human: A Comparison of Display and Interaction Types

Recently, artificial intelligence (AI)-enabled virtual humans have been widely used in various fields in our everyday lives, such as for museum exhibitions and as information guides. Given the continued technological innovations in extended reality (XR), immersive display devices and interaction methods are evolving to provide a feeling of togetherness with a virtual human, termed co-presence. With regard to such technical developments, one main concern is how to improve the experience through the sense of co-presence as felt by participants. However, virtual human systems still have limited guidelines on effective methods, and there is a lack of research on how to visualize and interact with virtual humans. In this paper, we report a novel method to support a strong sense of co-presence with a virtual human, and we investigated the effects on co-presence with a comparison of display and interaction types. We conducted the experiment according to a specified scenario between the participant and the virtual human, and our experimental study showed that subjects who participated in an immersive 3D display with non-verbal interaction felt the greatest co-presence. Our results are expected to provide guidelines on how to focus on constructing AI-based interactive virtual humans.
Seeing the Bigger Picture? Ramping up Production with the Use of Augmented Reality

Problem definition: Firms increasingly use augmented reality (AR) devices to improve their production ramp-up processes. These devices appear useful, yet little is known about their broader impact on worker productivity and behavior. Academic/practical relevance: Efficient production ramp-ups are particularly important when product life cycles are short. An ongoing debate among academics and practitioners pertains to how Industry 4.0, and AR devices in particular, can accelerate the ramp-up. The current study provides empirical evidence related to AR in the production ramp-up context, examines the strengths and weaknesses of AR, and tests four hypotheses, leading to a more nuanced view of AR use in the manufacturing ramp-up. Methodology: A framed field experiment in a manufacturing plant provides a test of how quickly workers can perform new tasks with and without AR support and how the use of AR affects their ability to suggest process improvements. Results: When faced with a new task, workers instructed by AR smart glasses use 43.8% less time to complete the task compared with a control group that relies on paper-based instructions. However, workers that use AR glasses consistently use 23% more time than the control group when both groups repeat the task without either AR or paper-based instructions. Task difficulty moderates this relationship; workers assigned to a more difficult task benefit the most from AR instructions. After the devices are removed, workers instructed based on paper improve their productivity faster through learning than those instructed by AR. In addition, the former group suggests better process improvements than the latter one. Managerial implications: Although these results indicate substantially higher productivity resulting from AR devices, they also support the view that, once instructed through AR devices, workers come to rely on this new technology without fully internalizing the task. This failure to internalize their task then leads workers to suggest less useful process improvements.
Combat Tanking in Education

This study examines the impact on engagement and affective outcomes of playful learning in social virtual reality (SVR) for distance and open education settings and aims to provide a practical framework for playful learning design. Three case studies in the United States and Greece were examined where playful learning was organized for e-learning. Data collection methods include questionnaires, observation of online course meetings, reviewing of course communications and chat logs records, student and instructor interviews, student coursework assessments, and reflections. Findings indicated that playful learning experiences in SVR can increase academic interest, intrinsic motivation, engagement level, satisfaction, and completion rates. Instructor genuine investment in a thoughtful, sophisticated humorous approach was vital for student buy-in. Finally, guiding principles for playful learning design in SVR are summarized in a model involving four dimensions: theme, actions, narrative, and auxiliary components (TANC).
Application and Investigation of Multimedia Design Principles in Augmented Reality Learning Environments

Digital media have changed the way educational instructions are designed. Learning environments addressing different presentation modes, sensory modalities and realities have evolved, with augmented reality (AR) as one of the latest developments in which multiple aspects of all three dimensions can be united. Multimedia learning principles can generally be applied to AR scenarios that combine physical environments and virtual elements, but their AR-specific effectiveness is unclear so far. In the current paper, we describe two studies examining AR-specific occurrences of two basic multimedia learning principles: (1) the spatial contiguity principle with visual learning material, leveraging AR-specific spatiality potentials, and (2) the coherence principle with audiovisual learning material, leveraging AR-specific contextuality potentials. Both studies use video-based implementations of AR experiences combining textual and pictorial representation modes as well as virtual and physical visuals. We examine the effects of integrated and separated visual presentations of virtual and physical elements (study one, N = 80) in addition to the effects of the omission of or the addition of matching or non-matching sounds (study two, N = 130) on cognitive load, task load and knowledge. We find only few significant effects and interesting descriptive results. We discuss the results and the implementations based on theory and make suggestions for future research.
Testing Your Question Answering Software via Asking Recursively

Question Answering (QA) is an attractive and challenging area in NLP community. There are diverse algorithms being proposed and various benchmark datasets with different topics and task formats being constructed. QA software has also been widely used in daily human life now. However, current QA software is mainly tested in a reference-based paradigm, in which the expected outputs (labels) of test cases need to be annotated with much human effort before testing. As a result, neither the just-in-time test during usage nor the extensible test on massive unlabeled real-life data is feasible, which keeps the current testing of QA software from being flexible and sufficient. In this paper, we propose a method, qaAskeR, with three novel Metamorphic Relations for testing QA software. qaAskeR does not require the annotated labels but tests QA software by checking its behaviors on multiple recursively asked questions that are related to the same knowledge. Experimental results show that qaAskeR can reveal violations at over 80% of valid cases without using any preannotated labels. Diverse answering issues, especially the limited generalization on question types across datasets, are revealed on a state-of-the-art QA algorithm.
Secure Partial Aggregation: Making Federated Learning More Robust for Industry 4.0 Applications

Big data, due to its promotion for industrial intelligence, has become the cornerstone of the Industry 4.0 era. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Federated learning</i> , proposed by Google, can effectively integrate data from different devices and different domains to train models under the premise of privacy preservation. Unfortunately, this new training paradigm faces security risks both on the client side and server side. This article proposes a new federated learning scheme to defend from client-side malicious uploads (e.g., backdoor attacks). In addition, we use cryptography techniques to prevent server-side privacy attacks (e.g., membership inference). The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">secure partial aggregation</i> protocol we designed improves the privacy and robustness of federated learning. The experiments show that models can achieve high accuracy of over 90% with a proper upload proportion, while the accuracy of the backdoor attack decreased from 99.5% to 0% with the best result. Meanwhile, we prove that our protocol can disable privacy attacks.
The Effect of Wearing a Face Mask on Face Image Quality

Due to the COVID-19 situation, face masks have become a main part of our daily life. Wearing mouth-and-nose protection has been made a mandate in many public places, to prevent the spread of the COVID-19 virus. However, face masks affect the performance of face recognition, since a large area of the face is covered. The effect of wearing a face mask on the different components of the face recognition system in a collaborative environment is a problem that is still to be fully studied. This work studies, for the first time, the effect of wearing a face mask on face image quality by utilising state-of-the-art face image quality assessment methods of different natures. This aims at providing better understanding on the effect of face masks on the operation of face recognition as a whole system. In addition, we further studied the effect of simulated masks on face image utility in comparison to real face masks. We discuss the correlation between the mask effect on face image quality and that on the face verification performance by automatic systems and human experts, indicating a consistent trend between both factors. The evaluation is conducted on the database containing (1) no-masked faces, (2) real face masks, and (3) simulated face masks, by synthetically generating digital facial masks on no-masked faces. Finally, a visual interpretation of the face areas contributing to the quality score of a selected set of quality assessment methods is provided to give a deeper insight into the difference of network decisions in masked and non-masked faces, among other variations.
Three improvements to the top-down solver

Abstract The local solver TD is a generic fixpoint engine which explores a given system of equations on demand. It has been successfully applied to the interprocedural analysis of procedural languages. The solver TD gains efficiency by detecting dependencies between unknowns on the fly. This algorithm has been recently extended to deal with widening and narrowing as well. In particular, it has been equipped with an automatic detection of widening and narrowing points. That version, however, is only guaranteed to terminate under two conditions: only finitely many unknowns are encountered, and all right-hand sides are monotonic . While the first condition is unavoidable, the second limits the applicability of the solver. Another limitation is that the solver maintains the current abstract values of all encountered unknowns instead of a minimal set sufficient for performing the iteration. By consuming unnecessarily much space, interprocedural analyses may not succeed on seemingly small programs. In the present paper, we therefore extend the top-down solver TD in three ways. First, we indicate how the restriction to monotonic right-hand sides can be lifted without compromising termination. We then show how the solver can be tuned to store abstract values only when their preservation is inevitable. Finally, we also show how the solver can be extended to side-effecting equation systems. Right-hand sides of these may not only provide values for the corresponding left-hand side unknowns but at the same time produce contributions to other unknowns. This practical extension has successfully been used for a seamless combination of context-sensitive analyses (e.g., of local states) with flow-insensitive analyses (e.g., of globals).
A Multi-Strategy Seeker Optimization Algorithm for Optimization Constrained Engineering Problems

This paper proposes a multi-strategy seeker optimization algorithm (MSSOA) for optimization constrained engineering problems.In this paper, three strategies were adopted to improve the poor searching capability of the seeker optimization algorithm (SOA).The first strategy was triple black hole system capture to solve the local optima issue.The second and third strategies were the multi-dimensional random interference and the precocious interference to balance the exploration and exploitation processes.These three strategies are proposed to improve respectively the SOA algorithm, and compared with the three strategies to improve together the SOA algorithm for optimizing 15 benchmark functions; the way these three strategies work together is called multi-strategy; and the efficiency of the multi-strategy is illustrated the numerical optimizing results and the convergence curves, population's positions with iterations and the search history of the benchmark functions.The proposed multi-strategy method achieved better performance in optimizing of the benchmark functions compared to other six optimization methods.The numerical and experimental results analysis were observed with respect to the optimal solution curve, the convergence curve of the fitness function, the ANOVA tests, the calculation complexity of the algorithm, the running time of the algorithm routine, the exploration and exploitation capability, the Wilcoxon's rank-sum test, the performance profile of algorithm.The results showed that the proposed multi-strategy method was efficient in the benchmark functions.The proposed multi-strategy method also achieved better performance in optimizing of the engineering problems and provided better solutions compared to other six optimization methods.
Deepfake forensics analysis: An explainable hierarchical ensemble of weakly supervised models

Deepfakes have become exponentially more common and sophisticated in recent years, so much so that forensic specialists, policy makers, and the public alike are anxious about their role in spreading disinformation. Recently, the detection and creation of such forgery became a popular research topic, leading to significant growth in publications related to the creation of deepfakes, detection methods, and datasets containing the latest deepfake creation methods. The most successful approaches in identifying and preventing deepfakes are deep learning methods that rely on convolutional neural networks as the backbone for a binary classification task. A convolutional neural network extracts the underlying patterns from the input frames. It feeds these to a binary classification fully connected network, which classifies these patterns as trustworthy or untrustworthy. We claim that this method is not ideal in a scenario in which the generation algorithms constantly evolve since the detection algorithm is not robust enough to detect comparably minor artifacts introduced by the generation algorithms. This work proposes a hierarchical explainable forensics algorithm that incorporates humans in the detection loop. We curate the data through a deep learning detection algorithm and share an explainable decision to humans alongside a set of forensic analyses on the decision region. On the detection side, we propose an attention-based explainable deepfake detection algorithm. We address this generalization issue by implementing an ensemble of standard and attention-based data-augmented detection networks. We use the attention blocks to evaluate the face regions where the model focuses its decision. We simultaneously drop and enlarge the region to push the model to base its decision on more regions of the face, while maintaining a specific focal point for its decision. In this case, we use an ensemble of models to improve the generalization. We also evaluate the models' decision using Grad-CAM explanation to focus on the attention maps. The region uncovered by the explanation layer is cropped and undergoes a series of frequency and statistical analyses that help humans decide if the frame is real or fake. We evaluate our model in one of the most challenging datasets, the DFDC, and achieve an accuracy of 92.4%. We successfully maintain this accuracy in datasets not used in the training process.
Improving Graph-Based Movie Recommender System Using Cinematic Experience

With the advent of many movie content platforms, users face a flood of content and consequent difficulties in selecting appropriate movie titles. Although much research has been conducted in developing effective recommender systems to provide personalized recommendations based on customers’ past preferences and behaviors, not much attention has been paid to leveraging users’ sentiments and emotions together. In this study, we built a new graph-based movie recommender system that utilized sentiment and emotion information along with user ratings, and evaluated its performance in comparison to well known conventional models and state-of-the-art graph-based models. The sentiment and emotion information were extracted using fine-tuned BERT. We used a Kaggle dataset created by crawling movies’ meta-data and review data from the Rotten Tomatoes website and Amazon product data. The study results show that the proposed IGMC-based models coupled with emotion and sentiment are superior over the compared models. The findings highlight the significance of using sentiment and emotion information in relation to movie recommendation.
Whether Cryptocurrency is a threat or a revolution? An analysis from ESG perspective

The authors make a fundamental initial effort to conduct a qualitative review on the digital financial revolution called 'Cryptocurrency’, mainly to provide a comprehensive discussion on whether Cryptocurrency is a threat to the Environmental, Social and Corporate Governance (ESG) investing goals? The study also aims to draw the current landscape and future landscape of Cryptocurrency in the global marketplace. The paper makes a qualitative review of the most recent and relevant articles on Cryptocurrency published using qualitative analysis. Key findings of the study reveals that Cryptocurrency is an exciting financial innovation, but it remains a financial experiment. Cryptocurrency fails the test of objectives of ESG investing. It is further concluded that the more and more production of Cryptocurrencies is not good for the environment, and it is a real threat to the ESG investment goals. The findings of the study will help the prospective and potential investors in better understanding of the Cryptocurrency as the long-term investment avenue in the global marketplace. Even though Cryptocurrency received overwhelming response in the last decade or so, the academic research on Cryptocurrency is still at the budding stage, primarily because the academic literature of financial technology is at the nascent stage. Therefore, the present study makes an honest attempt to fill this gap and provide a comprehensive analysis of Cryptocurrency in terms of ESG investment perspective.