Reviewerly/exHarmony huggingface dataset:
DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation

Task-oriented dialogue generation is challenging since the underlying knowledge is often dynamic and effectively incorporating knowledge into the learning process is hard.It is particularly challenging to generate both humanlike and informative responses in this setting.Recent research primarily focused on various knowledge distillation methods where the underlying relationship between the facts in a knowledge base is not effectively captured.In this paper, we go one step further and demonstrate how the structural information of a knowledge graph can improve the system's inference capabilities.Specifically, we propose DialoKG, a novel task-oriented dialogue system that effectively incorporates knowledge into a language model.Our proposed system views relational knowledge as a knowledge graph and introduces (1) a structure-aware knowledge embedding technique, and (2) a knowledge graph-weighted attention masking strategy to facilitate the system selecting relevant information during the dialogue generation.An empirical evaluation demonstrates the effectiveness of DialoKG over state-of-theart methods on several standard benchmark datasets.
EASE: Entity-Aware Contrastive Learning of Sentence Embedding

We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities.The advantage of using entity supervision is twofold: (1) entities have been shown to be a strong indicator of text semantics and thus should provide rich training signals for sentence embeddings; (2) entities are defined independently of languages and thus offer useful cross-lingual alignment supervision.We evaluate EASE against other unsupervised models both in monolingual and multilingual settings.We show that EASE exhibits competitive or better performance in English semantic textual similarity (STS) and short text clustering (STC) tasks and it significantly outperforms baseline methods in multilingual settings on a variety of tasks.Our source code, pretrained models, and newly constructed multilingual STC dataset are available at https: //github.com/studio-ousia/ease.
Pengembangan LKPD Interaktif Berbasis STEAM pada Kompetensi Pengetahuan IPS Siswa Kelas V di SD No. 3 Sibanggede

The problem that occurs is the use of LKPD that has not been maximized in the learning process. LKPD used in the learning process only comes from.The student worksheets are not interactive, so that it has an impact on the low learning outcomes for the social studies content of the fifth grade students at SD No. 3 Sibanggede. In this study, data collection was carried out using test methods in the form of objective tests and non-test methods in the form of unstructured interviews, observations and questionnaires. The techniques of qualitative descriptive analysis, quantitative descriptive analysis and inferential statistical analysis (t-test sample dependent) were used to analyze the data obtained. This study uses the ADDIE model. The results of the research obtained are the development of STEAM-based interactive student worksheet using the ADDIE model, the results of the interactive student worksheet feasibility test based on the results reviews are of subject content expert 93.75% with very good qualifications, the results review are of the learning design expert 87.50% with good qualifications, the results of the review expertof learning media are 90.00% with very good qualifications, the results of individual trials are 90.00% with very good qualifications, the results of small group trials are 91.66% with very good qualifications and the results of field trials are 88, 40% with good qualifications. Based on the results of the t- test, it is obtained that with db = n 1 = 25− 1 = 24, ttablet of significance 5% = 1,711, it can be seen that tcount &gt; ttable. So that rejected accepted. This means that interactive student worksheets are effective in improving Social Studies learning outcomes for fifth grade elementary school.
Real Time Face Mask Detection by using CNN

The ongoing Covid-19 (coronavirus) outbreak is worsening the worldwide health crisis, impacting our daily lives. Wearing a face mask is the most basic form of prevention against coronavirus and also it is considered as one of the most significant survival suggestions. Nowadays, the correctness of wearing face masks is manually monitored, and it is impossible to alert people in overcrowded areas or public locations. For this reason, machine learning frameworks such as openCV, keras, scikit-learn, and tensorflow are employed. The proposed approach intends to develop a new way to automatically detect the correctness of face mask in human face. If no facemask is detected, the proposed model will inform or alert the concerned person. To detect face, an openCV with haar-cascade classifier is employed. The Convolutional Neural Network (CNN) model is also used to detect or train the proposed dataset, which includes the images of different persons with or without face mask. This technique leverages an accuracy of about 99.1%.
Visual-spatial dimension integration in digital pathology education enhances anatomical pathology learning

Literature review demonstrated a surprising lack of publications on digital e-learning pathology resources for senior medical undergraduates and interns. An interactive Digital Pathology Repository (iDPR) integrating two- and three-dimensional (2D, 3D) high-resolution anatomical pathology images with correlated digital histopathology was developed. The novel iDPR was rigorously evaluated using mixed methods to assess pathology knowledge gains (pre- and post-tests), quality impact analysis (questionnaire), user feedback (focus group discussions) and user visual behaviour (eye gaze tracking analysis of 2D/ 3D images).Exposure to iDPR appeared to improve user pathology knowledge, as observed by significantly increased test scores on topic-related quizzes (n = 69, p < 0.001). In addition, most users were highly satisfied with the key design elements of the iDPR tool. Focus group discussion revealed the iDPR was regarded as a relevant online learning resource, although some minor technical issues were also noted. Interestingly, visual behaviour trends indicated that specific diagnostic pathological lesions could be correctly identified faster in 3D images, when compared to 2D images.The iDPR offers promise and potential in pathology education for senior clinical students and interns, gauging from both qualitative and quantitative positive user feedback. With incorporation of image annotations and interactive functionality, and with further technology development, this would prove a useful tool for diagnostic pathology and telepathology. As images with added visual-spatial dimension can provide enhanced detail and aid more rapid diagnosis, future applications of the iDPR could include virtual reality or holographic images of anatomical pathology specimens.
VREFL: Verifiable and Reconnection-Efficient Federated Learning in IoT scenarios

The privacy of social IoT data needs to be ensured while it is used by Federated Learning (FL). Secure aggregation is a cryptographic primitive that can ensure the privacy of social IoT data. However, in IoT scenarios, the secrecy of secure aggregation inputs makes the aggregated results less credible and the frequent disconnection and reconnection of IoT workers will cause prohibited communication overhead. In this work, we propose the Verifiable and Reconnection-Efficient Federated Learning (VREFL) framework, which can simultaneously support secure aggregation of local updates, verification of aggregated results, and rapid reconnection of dropped workers. Specifically, the novelty can be twofold: (1) we let each worker generate its homomorphic hash proof for the verification while updating locally. These hash proofs will be bound together through the Merkle Tree (MT) for further investigation by the central server; (2) we use the Chinese Remainder Theory (CRT) to help each worker efficiently update its “obsolete mask” after reconnection. We also theoretically prove that VREFL is secure. In addition, we implement a prototype of VREFL and conduct experiments on real-world datasets with different settings. Extensive experimental results prove that our protocol is feasible and efficient.
Engagement in Protective Behaviors by Digital Exposure Notification Users During the COVID-19 Pandemic, Washington State, January–June 2021

Smartphone-based digital exposure notification (EN) tools were introduced during the COVID-19 pandemic to supplement strained case investigation and contact tracing efforts. We examined the influence of an EN tool implemented in Washington State, WA Notify, on user engagement in behaviors that protect against COVID-19 transmission.From January 25 through June 30, 2021, we administered 2 surveys to WA Notify users who received notification of a possible COVID-19 exposure. The initial survey, sent when users received a notification, focused on intent to engage in protective behaviors. The follow-up survey captured data on self-reported actual engagement in protective behaviors and contact by a public health contact tracer.Of 1507 WA Notify users who completed the initial survey, 40.1% (n = 604) reported intending to seek COVID-19 testing and 67.1% (n = 1011) intended to watch for COVID-19 symptoms. Of 407 respondents to the follow-up survey, 57.5% (n = 234) reported getting tested and 84.3% (n = 343) reported watching for COVID-19 symptoms. Approximately 84% (n = 1266) of respondents to the initial survey received a notification from WA Notify before being reached by public health contact tracers; on follow-up, 42.5% (n = 173) of respondents reported never being contacted by public health.Our findings suggest that WA Notify users may initiate protective behaviors earlier than nonusers who will not know of an exposure until notified by public health or by a known contact. Digital EN tools may be a valuable addition to existing public health outbreak investigation and response activities.
Contrastive learning from label distribution: A case study on text classification

State-of-the-art text classification models are dominated by deep neural networks, but they still struggle to the issue of poor generalization ability when using cross entropy loss for training. One of the reasons is the training samples are usually annotated with hard labels, ignoring the inter-label relationship among these labels. This results in the outputted distributions to violate the label-correlation. Although the widely used contrastive learning is able to learn highly-expressive feature representations that generalize well across the training and test sets, contrastive learning in the embedding space is difficult to model the label correlation and may still output unreasonable label distributions. In this paper, we suggest contrastive learning using label distributions and present a novel label-level contrastive learning (LLCL) paradigm, which can constrain the unreasonable label distributions from model outputs. We hypothesize that the label distributions of the instances in the same class are more similar than those from other classes. We introduce two label-level contrastive learning losses, namely supervised contrastive learning and self-supervised contrastive learning. After adding our proposed losses to the cross-entropy loss as regularizer for the training text classification model, our model obtains the average improvement of 0.74% over the strong RoBERTa-Large baseline on ten datasets. Particularly, our contrastive learning in the label space can effectively capture label correlations better than that in the embedding space, achieving an improvement of 6.2% in the top-2 accuracy on the SST-5 dataset. We also demonstrate that our proposed method is more effective, especially in the text classification tasks with a large label space or limited labeled data. Last but not least, our model does not rely on any kind of specialized architectures, data augmentation methods, or additional unsupervised data.
Less is More

Temporal consistency is the key challenge of video depth estimation. Previous works are based on additional optical flow or camera poses, which is time-consuming. By contrast, we derive consistency with less information. Since videos inherently exist with heavy temporal redundancy, a missing frame could be recovered from neighboring ones. Inspired by this, we propose the frame masking network (FMNet), a spatial-temporal transformer network predicting the depth of masked frames based on their neighboring frames. By reconstructing masked temporal features, the FMNet can learn intrinsic inter-frame correlations, which leads to consistency. Compared with prior arts, experimental results demonstrate that our approach achieves comparable spatial accuracy and higher temporal consistency without any additional information. Our work provides a new perspective on consistent video depth estimation. Our official project page is https://github.com/RaymondWang987/FMNet.
Intelligent User Interfaces and Their Evaluation: A Systematic Mapping Study

Intelligent user interfaces (IUI) are driven by the goal of improvement in human–computer interaction (HCI), mainly improving user interfaces’ user experience (UX) or usability with the help of artificial intelligence. The main goal of this study is to find, assess, and synthesize existing state-of-the-art work in the field of IUI with an additional focus on the evaluation of IUI. This study analyzed 211 studies published in the field between 2012 and 2022. Studies are most frequently tied to HCI and SE domains. Definitions of IUI were observed, showing that adaptation, representation, and intelligence are key characteristics associated with IUIs, whereas adaptation, reasoning, and representation are the most commonly used verbs in their description. Evaluation of IUI is mainly conducted with experiments and questionnaires, though usability and UX are not considered together in evaluations. Most evaluations (81% of studies) reported partial or complete improvement in usability or UX. A shortage of evaluation tools, methods, and metrics, tailored for IUI, is noticed. Most often, empirical data collection methods and data sources in IUI evaluation studies are experiment, prototype development, and questionnaire.
Multimodality for Reliable Single Image Based Face Morphing Attack Detection

Face morphing attacks have demonstrated a high vulnerability on human observers and commercial off-the-shelf Face Recognition Systems (FRS), especially in the border control scenario. Therefore, detecting face morphing attacks is paramount to achieving a reliable and secure border control operation. This work presents a novel framework for the Single image-based Morphing Attack Detection (S-MAD) based on the multimodal regions such as eyes, nose, and mouth. Each of these regions is processed using colour scale-space representation on which two different types of features are extracted using Binarised Statistical Image Features (BSIF) and Local Binary Features (LBP) techniques. These features are then fed to the classifiers such as Probabilistic Collaborative Representation Classifier (P-CRC) and Spectral Regression Kernel Discriminant Analysis (SRKDA). Their decisions are combined at score level to make the final decision. Extensive experiments are carried out on three different face morphing datasets to benchmark the performance of the proposed method with the existing methods. Further, the proposed method is benchmarked on the Bologna Online Evaluation Platform (BOEP). Obtained results demonstrate the improved performance of the proposed method over existing state-of-the-art methods.
NormalAttack: Curvature-Aware Shape Deformation along Normals for Imperceptible Point Cloud Attack

Many efforts have been made on developing adversarial attack methods on point clouds. However, without fully considering the geometric property of point clouds, existing methods tend to produce clearly visible outliers. In this paper, we propose a novel NormalAttack framework towards imperceptible adversarial attacks on point clouds. First, we enforce the perturbation to be concentrated along normals to deform the underlying surface of 3D point clouds, such that tiny perturbation can make the shape deformed for better attack performance. Second, we guide the perturbation to be located more on regions with larger curvature, such that better imperceptibility is achieved. Extensive experiments on three representative networks, e.g., PointNet++, DGCNN, and PointConv, validate the effectiveness of NormalAttack and its superiority to state-of-the-art methods.
The Application of Virtual Reality Technology in Sports Psychology: Theory, Practice, and Prospect

Once virtual reality technology (Virtual Reality, VR) came out, it has received a lot of attention; in recent years, it has been widely used in the study of psychology. Because it can improve the ecological validity of experimental research, the level of conditional control, reproducibility, and avoid the dangers of field operations, it has been introduced into the field of psychology by many researchers. Compared with traditional sports psychology research methods, virtual reality technology has the characteristics of multiperception, immersion, interaction, and imagination to get a better, more realistic feel and increase people's interest in sports. Taking the application of virtual reality technology in table tennis teaching in colleges and universities as an example, this study aims to review the application of virtual reality technology in sports psychology; summarize the theory, practice, and prospect of the application of virtual reality technology in sports psychology; and add new content to the research of sports psychology. It aims to review the principles, characteristics, and application of virtual reality technology in sports psychology; summarize the advantages and disadvantages of the application of virtual reality technology in sports and point out the problems that need to be paid attention to when using this method; explore the application of virtual reality technology in sports psychology; and add new content to the research of sports psychology.
An Assure AI Bot (AAAI bot)

Artificial Intelligence (AI) bots receive much attention and usage in industry manufacturing and even store cashier applications. Our research is to train AI bots to be software engineering assistants, specifically to detect biases and errors inside AI software applications. An example application is an AI machine learning system that sorts and classifies people according to various attributes, such as the algorithms involved in criminal sentencing, hiring, and admission practices. Biases, unfair decisions, and flaws in terms of the diversity, equity, and inclusion (DEI), in such systems could have severe consequences. As a Hispanic-Serving Institution, we are concerned about underrepresented groups and devoted an extended amount of our time to implementing “An Assure AI” (AAAI) Bot to detect biases and errors in AI applications. Our state-of-the-art AI Bot was developed based on our previous accumulated research in AI and Deep Learning (DL). The key differentiator is that we are taking a unique approach: instead of cleaning the input data, filtering it out and minimizing its biases, we trained our deep Neural Networks (NN) to detect and mitigate biases of existing AI models. The backend of our bot uses the Detection Transformer (DETR) framework, developed by Facebook, to monitor and detect the deep learning model’s internal biases.
A New Area Towards to Digitalization of Energy Systems: Enables, Challenges and Solutions

With the ever-increasing energy demand, the electric power sector and the energy industry are promoted to decentralization, decarbonization and digitalization. The digital platforms gradually become a megatrend for a clean energy system. Digitalization of the energy systems has conducted changes in various dimensions such as energy savings, sustainability and security. In this paper, the impact of digitalization on the electric energy systems, electric vehicles (EVs), sustainable development goal (SDGs) and greenhouse gas (GHG) emissions are analyzed and investigated. A systematic overview and discussions on the key drivers for digitalization and decentralization of energy systems are addressed. This paper provides significant insights for digitalization of the energy systems from a global perspective. Some enable, challenges and solutions are also handled to analyze the impact of digitalization.
Geochemical Data Mining by Integrated Multivariate Component Data Analysis: The Heilongjiang Duobaoshan Area (China) Case Study

The Heilongjiang Duobaoshan area is located at the confluence of the Great Xing’an Range and the Lesser Xing’an Range, and the area has undergone a complex magmatic and tectonic evolutionary history resulting in a complex and diverse geological background for mineralization. As a result of this geological complexity and the multi-period nature of mineralization, the geochemical data of the area are usually not satisfied with a single statistical distribution form, so traditional statistical methods cannot adequately explore and identify the distribution of deep-seated information in the geochemical data. Based on the above problems, this paper adopts a multivariate component data analysis method to process 14 mass fraction data elements, namely Ag, As, Au, Bi, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Sb, W, and Zn, in the 1:50,000 soil geochemical data from the Duobaoshan area of Heilongjiang. The spatial distribution and internal structural characteristics of raw, logarithmic transformation and isometric logarithmic ratio (ILR) transformed data were compared using exploratory data analysis (EDA); robust principal component analysis (RPCA) was applied to obtain the PC1 and PC2 principal component combinations associated with mineralization, and a spectrum–area (S–A) fractal model was further used to decompose the geochemical anomalies of the PC1 and PC2 principal component combinations as composite anomalies. The results show the following: (i) The data transformed by the isometric logarithmic ratio (ILR) eliminate the influence of the original data closure effect, and the spatial scale of the data is more uniform; the data are approximately normally distributed, based on which RPCA can be applied to better explore the correlation between elements and the pattern of co-associated combinations. (ii) The S–A method was further used to decompose the composite anomalies of the PC1 and PC2 principal component combination in the study area. The anomalous and background fields of the screened-out PC1 and PC2 principal component combinations reflect anomalous information on mineralization dominated by Au mineralization. Moreover, the anomaly and background information after extraction were in good agreement with the known Au deposits (points), and many geochemical anomalies with prospecting potential were obtained in the periphery, providing a theoretical basis and exploration focus for the next step in the searching and exploring of the study area.