Reviewerly/exHarmony huggingface dataset:
Propose a New Quality Model for M-Learning Application in Light of COVID-19

The coronavirus disease (COVID-19) prevented millions of students around the world from receiving their lessons, because of the closure of thousands of schools. The new COVID-19 global epidemic invaded the barriers of time and space. Using mobile phones in education is a new form of the distance learning system. M-learning is characterized by many characteristics, the most important of which are providing an interactive educational environment, flexibility in space and time, better adaptation to individual needs, acquisition of knowledge, interactive effectiveness, and developing self-learning skills for students. The main aim of this paper is to suggest a quality model for M-learning applications for children which contains the most common characteristics of M-learning, which must be taken into account when designing M-learning applications. Through previous studies related to the quality model for M-learning applications, we proposed two quality characteristics, technical and pedagogical. We proposed 8 subcharacteristics with their features following the structure of the IOS/IEC 912 and DeLone and McLean IS model to find the effect of technical and pedagogical factors on user satisfaction with M-learning applications for children. Results show that the proposed model can be useful and effective to ensure the development of high-quality M-learning applications.
The impact of student learning aids on deep learning and mobile platform on learning behavior

Purpose Deep learning (DL) technology is used to design a voice evaluation system to understand the impact of learning aids on DL and mobile platforms on students’ learning behavior. Design/methodology/approach DL technology is used to design a speech evaluation system. Findings The experimental results show that the speech evaluation system designed has a high accuracy rate, the highest agreement rate with manual evaluation of pronunciation is 89.5%, and the correct speech recognition rate is 96.64%. The designed voice evaluation system and the manual voice rating system have a maximum error rate of 2%. The experimental results suggest that it is necessary to further optimize the learning aids for mobile platform. The learning aids of the mobile platform need to be further optimized to promote the improvement of student learning efficiency. Originality/value The results show that the speech evaluation system designed has good practical application value, and it provides a certain reference value for the future study of learning tools on DL.
The Future Warfare with Multidomain Applications of Artificial Intelligence: Research Perspective

We live in a period when historical fiction has become current reality. With our future being automated, using AI on a daily basis will only get more convenient. Making military weapons to detect, monitor, and engage a human being with attacks may all be done in the privacy of one's own garden. There is a plethora of AI software out there that can be readily integrated into combat weapons. The automobile industry is already incorporating AI into vehicles to assess driving circumstances and give augmented reality to drivers via heads-up displays in order to assist avert accidents. Similarly, artificial intelligence will be utilized to study the battlefield and give soldiers with augmented reality information via heads-up displays and weapon control systems. Since AI is not a single technology, it has been argued that it might be used by the military in a variety of ways. Intelligence, surveillance, and reconnaissance (ISR) activities, as well as processing and interpreting sensor data and geographic imaging analysis, are all examples of AI. Artificial intelligence has the potential to reduce human involvement in conflict, whether it is employed for combat robots or data analysis. AI has the potential to profoundly alter the nature of war. The article mainly focussed on warfare technologies and applications. The main aim of this review is to understand the current applications being used in armed forces and proposed technologies of artificial intelligence.
Voice-based control system for smart hospital wards: a pilot study of patient acceptance

The smart hospital's concept of using the Internet of Things (IoT) to reduce human resources demand has become more popular in the aging society.To implement the voice smart care (VSC) system in hospital wards and explore patient acceptance via the Technology Acceptance Model (TAM).A structured questionnaire based on TAM was developed and validated as a research tool. Only the patients hospitalized in the VSC wards and who used it for more than two days were invited to fill the questionnaire. Statistical variables were analyzed using SPSS version 24.0. A total of 30 valid questionnaires were finally obtained after excluding two incomplete questionnaires. Cronbach's α values for all study constructs were above 0.84.We observed that perceived ease of use on perceived usefulness, perceived usefulness on user satisfaction and attitude toward using, and attitude toward using on behavioral intention to use had statistical significance (p < .01), respectively.We have successfully developed the VSC system in a Taiwanese academic medical center. Our study indicated that perceived usefulness was a crucial factor, which means the system function should precisely meet the patients' demands. Additionally, a clever system design is important since perceived ease of use positively affects perceived usefulness. The insight generated from this study could be beneficial to hospitals when implementing similar systems to their wards.
Differentially Private Singular Value Decomposition for Training Support Vector Machines

Support vector machine (SVM) is an efficient classification method in machine learning. The traditional classification model of SVMs may pose a great threat to personal privacy, when sensitive information is included in the training datasets. Principal component analysis (PCA) can project instances into a low-dimensional subspace while capturing the variance of the matrix A as much as possible. There are two common algorithms that PCA uses to perform the principal component analysis, eigenvalue decomposition (EVD) and singular value decomposition (SVD). The main advantage of SVD compared with EVD is that it does not need to compute the matrix of covariance. This study presents a new differentially private SVD algorithm (DPSVD) to prevent the privacy leak of SVM classifiers. The DPSVD generates a set of private singular vectors that the projected instances in the singular subspace can be directly used to train SVM while not disclosing privacy of the original instances. After proving that the DPSVD satisfies differential privacy in theory, several experiments were carried out. The experimental results confirm that our method achieved higher accuracy and better stability on different real datasets, compared with other existing private PCA algorithms used to train SVM.
InDepth

Mobile Augmented Reality (AR) demands realistic rendering of virtual content that seamlessly blends into the physical environment. For this reason, AR headsets and recent smartphones are increasingly equipped with Time-of-Flight (ToF) cameras to acquire depth maps of a scene in real-time. ToF cameras are cheap and fast, however, they suffer from several issues that affect the quality of depth data, ultimately hampering their use for mobile AR. Among them, scale errors of virtual objects - appearing much bigger or smaller than what they should be - are particularly noticeable and unpleasant. This article specifically addresses these challenges by proposing InDepth, a real-time depth inpainting system based on edge computing. InDepth employs a novel deep neural network (DNN) architecture to improve the accuracy of depth maps obtained from ToF cameras. The DNN fills holes and corrects artifacts in the depth maps with high accuracy and eight times lower inference time than the state of the art. An extensive performance evaluation in real settings shows that InDepth reduces the mean absolute error by a factor of four with respect to ARCore DepthLab. Finally, a user study reveals that InDepth is effective in rendering correctly-scaled virtual objects, outperforming DepthLab.
Translation-Based Embeddings with Octonion for Knowledge Graph Completion

Knowledge representation learning achieves the automatic completion of knowledge graphs (KGs) by embedding entities into continuous low-dimensional vector space. In knowledge graph completion (KGC) tasks, the inter-dependencies and hierarchical information in KGs have gained attention. Existing methods do not well capture the latent dependencies between all components of entities and relations. To address this, we introduce the mathematical theories of octonion, a more expressive generalized form of complex number and quaternion, and propose a translation-based KGC model with octonion (TransO). TransO models entities as octonion coordinate vectors, relations as the combination of octonion component matrices and coordinate vectors, and uses specific grouping calculation rules to interact between entities and relations. In addition, since hyperbolic Poincaré space in non-Euclidean mathematics can represent hierarchical data more accurately and effectively than traditional Euclidean space, we propose a Poincaré-extended TransO model (PTransO). PTransO transforms octonion coordinate vectors into hyperbolic embeddings by exponential mapping, and integrates the Euclidean-based calculations into hyperbolic space by operations such as Möbius addition and hyperbolic distance. The experimental results of link prediction indicate that TransO outperforms other translation-based models on the WN18 benchmark, and PTransO further achieves state-of-the-art performance in low-dimensional space on the well-established WN18RR and FB15k-237 benchmarks.
Validating the Benefits of Glanceable and Context-Aware Augmented Reality for Everyday Information Access Tasks

Glanceable Augmented Reality interfaces have the potential to provide fast and efficient information access for the user. However, where to place the virtual content and how to access them depend on the user context. We designed a Context-Aware AR interface that can intelligently adapt for two different contexts: solo and social. We evaluated information access using Context-Aware AR compared to current mobile phones and non-adaptive Glanceable AR interfaces. We found that in a solo scenario, compared to a mobile phone, the Context-Aware AR interface was preferred, easier, and significantly faster; it improved the user experience; and it allowed the user to better focus on their primary task. In the social scenario, we discovered that the mobile phone was slower, more intrusive, and perceived as the most difficult. Meanwhile, Context-Aware AR was faster for responding to information needs triggered by the conversation; it was preferred and perceived as the easiest for resuming conversation after information access; and it improved the user&#x2019;s awareness of the other person&#x2019;s facial expressions.
An Ontology-Based Chatbot to Enhance Experiential Learning in a Cultural Heritage Scenario

Italy is rich in cultural attractions, many known worldwide, others more hidden and unrecognized. Cultural attractions include tangible cultural assets (works of art, archaeological excavations, and churches) and intangible ones (music, poetry, and art). Today, given the pervasive diffusion of “smart” devices, the intelligent use of modern technologies could play a crucial role in changing the habit of consulting and visiting cultural heritage mainly with traditional methodologies, making little or no use of the advantages coming from the more and more availability of digitalized resources. A realm of particular interest is “experiential learning” when applied to cultural heritage, where tourists more and more ask to be helped in discovering the richness of sites they explore. In this article, we will present an innovative chatbot-based system, called HeriBot, that supports experiential tourism. Our system has been developed and experimented with a research effort for applying ICT technologies to enhance the knowledge, valorization, and sustainable fruition of the Cultural Heritage related to the Archaeological Urban Park of Naples (PAUN—Parco Archeologico Urbano di Napoli). Our article starts exploiting the ontological approach based on a purpose ontology describing the Park Heritage. Using such an ontology, we designed a chatbot that can identify the specific characteristics and motivations of the tourist, defining language, tone, and visitable scenarios and, through the ontology, allows the visit to be transformed into a personalized educational opportunity. The system has been validated in terms of dialogue effectiveness and training efficiency by a panel of experts, and we present and discuss obtained results.
Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging

Prompting methods recently achieve impressive success in few-shot learning.These methods modify input samples with prompt sentence pieces, and decode label tokens to map samples to corresponding labels.However, such a paradigm is very inefficient for the task of slot tagging.Since slot tagging samples are multiple consecutive words in a sentence, the prompting methods have to enumerate all n-grams token spans to find all the possible slots, which greatly slows down the prediction.To tackle this, we introduce an inverse paradigm for prompting.Different from the classic prompts mapping tokens to labels, we reversely predict slot values given slot types.Such inverse prompting only requires a oneturn prediction for each slot type and greatly speeds up the prediction.Besides, we propose a novel Iterative Prediction Strategy, from which the model learns to refine predictions by considering the relations between different slot types.We find, somewhat surprisingly, the proposed method not only predicts faster but also significantly improves the effect (improve over 6.1 F1-scores on 10-shot setting) and achieves new state-of-the-art performance.
Designing for Everyday Sounds at Home with People with Dementia and their Partners

People with dementia and their caregivers aging in place have expressed the need for social, emotional, and recreational interventions at home. Listening to everyday sounds evokes memories and provides conversational cues to support social relations and elicit emotional responses for people with dementia. However, research has yet to explore how these meaningful experiences can be transferred into home settings. This paper presents the insights from our co-design study that involved three people with dementia and their partners in developing an interactive sound player for listening to everyday sounds at home. We report on the motivations of people with dementia and their caregivers to engage in meaningful sound-based activities at home and present the Tumbler as a prototype to foster initiative and agency in exploring familiar everyday sounds. We present design implications of how sound can enrich the everyday experiences of dementia by facilitating social and pleasurable moments at home.
FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

Federated learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no energy or performance impact on mobile devices, and are therefore not suitable for applications that require frequent ( online ) model updates, such as news recommenders. This article presents FLeet , the first Online FL system, acting as a middleware between the Android operating system and the machine learning application. FLeet combines the privacy of Standard FL with the precision of online learning thanks to two core components: (1) I-Prof , a new lightweight profiler that predicts and controls the impact of learning tasks on mobile devices, and (2) AdaSGD , a new adaptive learning algorithm that is resilient to delayed updates. Our extensive evaluation shows that Online FL, as implemented by FLeet , can deliver a 2.3× quality boost compared to Standard FL while only consuming 0.036% of the battery per day. I-Prof can accurately control the impact of learning tasks by improving the prediction accuracy by up to 3.6× in terms of computation time, and by up to 19× in terms of energy. AdaSGD outperforms alternative FL approaches by 18.4% in terms of convergence speed on heterogeneous data.
Conversations About Crime: Re-Enforcing and Fighting Against Platformed Racism on Reddit

With the emergence of Information and Communication Technologies (ICTs), people are being exposed to an increased volume of crime-related information, which induces fear. While the fear of crime has been explored around people's experiences with crime, little is known about how people frame their conversations about crime online. In this study, we explore how citizens talk about crime on Reddit. By collecting crime conversations from the subreddit r/baltimore, we find that, on the surface, redditors discuss topics such as comparing crime rates in Baltimore with other cities in an effort to destigmatize the depictions of Baltimore as a city rife with crime. On a deeper level, we find that through their conversations about crime, redditors are engaging in discourse frames that both re-enforce and fight against platformed racism. On the one hand, some redditors perpetuate racially coded language that is rife with anti-Black stereotypes, framing their conversations using old and new racism to cover their racism and discrimination against Black people. On the other hand, others push back against platformed racism by drawing attention to individual racism and systemic racism, and situating crime in root, societal causes. We then discuss how platformed racism operates in online conversations, and develop the concepts of weaponized identity and digital gentrification, which we argue are ways in which people, through their engagements in digital platforms, continue to perpetuate white hegemonic power structures in society. Finally, we discuss implications for how to design to support the fight against platformed racism in sociotechnical systems like Reddit. As a content note and warning, this paper discusses racist content which may be upsetting or harmful to some readers.
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding

We present a new approach to instill 4D dynamic object priors into learned 3D representations by unsupervised pre-training. We observe that dynamic movement of an object through an environment provides important cues about its objectness, and thus propose to imbue learned 3D representations with such dynamic understanding, that can then be effectively transferred to improved performance in downstream 3D semantic scene understanding tasks. We propose a new data augmentation scheme leveraging synthetic 3D shapes moving in static 3D environments, and employ contrastive learning under 3D-4D constraints that encode 4D invariances into the learned 3D representations. Experiments demonstrate that our unsupervised representation learning results in improvement in downstream 3D semantic segmentation, object detection, and instance segmentation tasks, and moreover, notably improves performance in data-scarce scenarios. Our results show that our 4D pre-training method improves downstream tasks such as object detection mAP@0.5 by 5.5%/6.5% over training from scratch on ScanNet/SUN RGB-D while involving no additional run-time overhead at test time.
Dual-Affinity Style Embedding Network for Semantic-Aligned Image Style Transfer

Image style transfer aims at synthesizing an image with the content from one image and the style from another. User studies have revealed that the semantic correspondence between style and content greatly affects subjective perception of style transfer results. While current studies have made great progress in improving the visual quality of stylized images, most methods directly transfer global style statistics without considering semantic alignment. Current semantic style transfer approaches still work in an iterative optimization fashion, which is impractically computationally expensive. Addressing these issues, we introduce a novel dual-affinity style embedding network (DaseNet) to synthesize images with style aligned at semantic region granularity. In the dual-affinity module, feature correlation and semantic correspondence between content and style images are modeled jointly for embedding local style patterns according to semantic distribution. Furthermore, the semantic-weighted style loss and the region-consistency loss are introduced to ensure semantic alignment and content preservation. With the end-to-end network architecture, DaseNet can well balance visual quality and inference efficiency for semantic style transfer. Experimental results on different scene categories have demonstrated the effectiveness of the proposed method.
3-D spatial cluster analysis of seismic sequences through density-based algorithms

SUMMARY With seismic catalogues becoming progressively larger, extracting information becomes challenging and calls upon using sophisticated statistical analysis. Data are typically clustered by machine learning algorithms to find patterns or identify regions of interest that require further exploration. Here, we investigate two density-based clustering algorithms, DBSCAN and OPTICS, for their capability to analyse the spatial distribution of seismicity and their effectiveness in discovering highly active seismic volumes of arbitrary shapes in large data sets. In particular, we study the influence of varying input parameters on the cluster solutions. By exploring the parameter space, we identify a crossover region with optimal solutions in between two phases with opposite behaviours (i.e. only clustered and only unclustered data points). Using a synthetic case with various geometric structures, we find that solutions in the crossover region consistently have the largest clusters and best represent the individual structures. For identifying strong anisotropic structures, we illustrate the usefulness of data rescaling. Applying the clustering algorithms to seismic catalogues of recent earthquake sequences (2016 Central Italy and 2016 Kumamoto) confirms that cluster solutions in the crossover region are the best candidates to identify 3-D features of tectonic structures that were activated in a seismic sequence. Finally, we propose a list of recipes that generalizes our analyses to obtain such solutions for other seismic sequences.
Research on English Teaching Reading Quality Evaluation Method Based on Cognitive Diagnostic Evaluation

In order to improve the evaluation effect of English teaching reading quality, this paper applies the cognitive diagnosis evaluation algorithm to the teaching reading quality evaluation process. Moreover, this paper compares the verification effects of Q-matrix from the perspective of nonparameterization and parameterization and considers their performance through simulation research. In addition, this paper applies the method to multilevel scoring empirical data to verify the effectiveness of the cognitive diagnostic evaluation algorithm. Finally, this paper combines cognitive diagnosis and evaluation algorithms to construct an English teaching reading quality evaluation system. The experimental research results show that the English teaching reading quality evaluation method based on cognitive diagnostic evaluation proposed in this paper can effectively improve the reading quality of English teaching.