Reviewerly/exHarmony huggingface dataset:
Adoption of Mobile Applications to Improve the Reading Habits of Rural Readers in Southern Africa's Secondary Schools

Advances in digital technologies especially mobile applications, are dramatically altering social and educational conditions. This also applies to the reading habits of the rural child readers. Reading has been considered a cornerstone of education development and social transformation. It has been argued that advancements in digitalization and the enhancement of digital development could be beneficial for rural readers. However, digital inequalities persist and affect rural child readers. This paper investigated how mobile applications could be adopted to improve the reading habits of rural readers in southern African secondary schools. This qualitative study employed a multiple-case study design in five rural secondary schools in Beitbridge District, Zimbabwe, and five rural secondary schools in Vhembe district, Limpopo, South Africa. Focus group interview with the learners was adopted as a data collection tool. The findings revealed that some learners in Beitbridge schools did not even have mobile phones and reliable internet. The findings of the study also revealed that some rural learners in Limpopo are using different types of social media applications, including WhatsApp and Facebook. The findings showed that some learners spent five or so hours on these social media applications for entertainment purposes only and this has a negative effect on their reading. The results of the study show that some learners do not even know other mobile applications which enhance reading for instance pocket readers, e-books, Scribd, and FB readers. The study concludes that if rural learners in southern Africa are guided and trained well, they would adopt mobile applications to improve their reading habits. Future studies could be done to encourage the learners to write their own stories in their local languages and these could be published via mobile applications.
Components of digital assistants in higher education environments

In a current research project at a German university, a digital study assistant based on conversational AI is being developed. In this research project, four main application areas of the digital assistant are planned: communicator, for answering questions, conversation, and mentoring; a planner, for performing time and task management and course planning; a motivator, for actively managing learning success with gamification elements; and an analyzer, for processing necessary information about the student's study progress. In order to identify different application examples and possibilities for these four areas of the digital study assistant, the aim of this paper is to conduct a literature review. In addition to application examples and possibilities, implementation goals and the improvement of study results through such applications were researched.
Image enhancement method based on exposure fusion for UAV aerial photography

针对无人机航拍图像光照不均匀及自然雾导致影像质量退化问题, 提出了一种无人机航拍图像增强算法。利用改进的低照度图像增强算法均衡亮度对比度; 为了解决均衡后图像过增强问题, 提出了联合去雾及曝光融合的色彩矫正增强方法; 为了保留增强图像的边缘纹理信息, 设计了一种效果更佳的细节增强算法, 处理后统计直方图更为平滑, 可在一定程度上抑制部分噪声, 细节纹理信息更强。实验结果表明, 所提的航拍图像增强算法, 能够有效解决因光照不均或自然雾引起的影像退化现象, 提高了无人机航拍图像的质量, 主客观图像质量评价指标优于现有绝大多数主流算法, 性能更佳。
A feature extraction method for small sample data based on optimal ensemble random forest

High dimensional small sample data is the difficulty of data mining. When using the traditional random forest algorithm for feature selection, it is to have the poor stability and low accuracy of feature importance ranking caused by over fitting of classification results. Aiming at the difficulties of random forest in the dimensionality reduction of small sample data, a feature extraction algorithm ote-gwrffs is proposed based on small sample data. Firstly, the algorithm expands the samples based on the generated countermeasure network Gan to avoid the over fitting phenomenon of traditional random forest in the small sample classification. Then, on the basis of data expansion, the optimal tree set algorithm based on weight is adopted to reduce the impact of data distribution error on feature extraction accuracy and improve the overall stability of decision tree set. Finally, the weighted average of the weight and feature importance measure of a single decision tree is used to obtain the feature importance ranking, which solves the problem of low accuracy and poor stability in the feature selection process of small sample data. Through the UCI data set, the present algorithm is compared with the traditional random forest algorithm and the weight based random forest algorithm. The ote-gwrffs algorithm has higher stability and accuracy for processing high-dimensional and small sample data.
Incorporating Prior Type Information for Few-Shot Knowledge Graph Completion

Few-shot knowledge graph completion aims to infer unknown triple facts with only a small number of reference triples. Existing methods have shown a strong capability on this problem by combining knowledge representation learning and meta learning. They ignore prior knowledge in the few-shot scenario, while prior knowledge can boost useful information to handle the challenges brought by limited referenced instances. To address the above issue, we propose a few-shot knowledge graph completion model PiTI-Fs, with entity type information as prior knowledge in a two-module learning framework. In the prior knowledge learning module, we propose to extract a metagraph for capturing prior type information by entity clustering where entities in the same cluster are considered to have the same attribute. We pre-train the metagraph to learn the prior knowledge features and fuse them into the embeddings of entities. In the meta learning module, we introduce a transformer-based relation learner to model the interactions within reference entity pairs and implement an optimization-based meta learning paradigm to train our model. Our method outperforms most of baseline models for the few-shot knowledge graph completion task. The experimental results demonstrate the effectiveness of the proposed modules.
KEP-Rec: A Knowledge Enhanced User-Item Relation Prediction Model for Personalized Recommendation

For more accurate, diversified and interpretable personalized recommendation, the joint consideration of user-item interaction information and side information in knowledge graph has become a research hotspot. Traditional models based on collaborative filtering usually have cold start and sparse problems. The existing recommendation model based on knowledge graph can enrich the representation of users and items by using graph structure information from the knowledge graph, and make it more interpretable. Although the efforts have achieved a certain performance improvement, they consider all entities in knowledge graph globally for all users, and the aggregation strategy is single. In this paper, we propose KEP-Rec, a Knowledge Enhanced User-Item Relation Prediction Model for Personalized Recommendation. For a given target user and candidate item, KEP-Rec represents the user and item with enhanced information by knowledge graph for predicting the interacted probability between them and further personalized recommendation. In detail, KEP-Rec takes into account the changes in preferences of specific users and the differences in user perception of relations. Based on the idea of collaborative filtering, KEP-Rec selects an extended entity set of the items relevant with target user and candidate item as the initial set to propagate in knowledge graph. Moreover, KEP-Rec sets an item-aware attention mechanism to consider the interaction of candidate items with different weights given by target user's historical preferences to realize the diverse representation of the user preferences. In the propagation process alone knowledge graph, the relation embedding is considered for target user to achieve personalization. Empirical results on three real datasets of music, books, and movies show that KEP-Rec significantly outperforms state-of-the-art methods.
Shallow Diffusion Motion Model for Talking Face Generation from Speech

Talking face generation is synthesizing a lip synchronized talking face video by inputting an arbitrary face image and audio clips. People naturally conduct spontaneous head motions to enhance their speeches while giving talks. Head motion generation from the speech is inherently difficult due to the nondeterministic mapping from speech to head motions. Most existing works map speech to motion in a deterministic way by conditioning certain styles, leading to sub-optimal results. In this paper, we decompose the speech motion into two complementary parts: pose modes and rhythmic dynamics. Accordingly, we introduce a shallow diffusion motion model (SDM) by equipping a two-stream architecture, i.e., a pose mode branch for primary posture generation, and a rhythmic motion branch for rhythmic dynamics synthesis. On one hand, diverse pose modes are generated by conditional sampling in a latent space, guided by speech semantics. On the other hand, rhythmic dynamics are synced with the speech prosody. Extensive experiments demonstrate the superior performance against several baselines, in terms of fidelity, similarity, and syncing with speech.
Switching Machine Improvisation Models by Latent Transfer Entropy Criteria

Machine improvisation is the ability of musical generative systems to interact with either another music agent or a human improviser. This is a challenging task, as it is not trivial to define a quantitative measure that evaluates the creativity of the musical agent. It is also not feasible to create huge paired corpora of agents interacting with each other to train a critic system. In this paper we consider the problem of controlling machine improvisation by switching between several pre-trained models by finding the best match to an external control signal. We introduce a measure SymTE that searches for the best transfer entropy between representations of the generated and control signals over multiple generative models.
An examination into pre-Service English-as-a-Foreign-Language teachers’ Self-Perceived technological-Pedagogical-Content-knowledge

A literature review indicates that no research has been conducted to date to examine pre-service English as a foreign language (EFL) teachers’ self-perceived technological pedagogical content knowledge (TPACK). Therefore, this study explores pre-service EFL teachers’ (PSEFLTs) self-perceived TPACK, to understand whether it changes in line with their year of study and their views on the probable contribution of the pre-service English teacher education program to their TPACK. The findings obtained from the analysis of the data collected from a 36-item EFL TPACK questionnaire, conducted by performing descriptive statistics, one-way ANOVA and a post hoc analysis, revealed that the level of PSEFLTs’ technological knowledge was the highest, while their content knowledge was the lowest. In addition, there was a statistically significant difference in 28 items between the PSEFLTs’ TPACK, resulting from the responses of the second-year students. The content analysis of the qualitative data gathered from telephone interviews demonstrated that even though the interviewees overwhelmingly regarded their TPACK level as high, they suggested that the curriculum for this program could be enriched by adding a course to it on the use of technology in EFL teaching.
Social Recommendation Algorithm Based on Self-Supervised Hypergraph Attention

Social network information has been widely applied to traditional recommendations that have received significant attention in recent years. Most existing social recommendation models tend to use pairwise relationships to explore potential user preferences, but overlook the complexity of real-life interactions between users and the fact that user relationships may be higher order. These approaches also ignore the dynamic nature of friend influence, which leads the models to treat different friend influences equally in different ways. To address this, we propose a social recommendation algorithm that incorporates graph embedding and higher-order mutual information maximization based on the consideration of social consistency. Specifically, we use the graph attention model to build higher-order information among users for deeper mining of their behavioral patterns on the one hand; while on the other hand, it models user embedding based on the principle of social consistency to finally achieve finer-grained inference of user interests. In addition, to alleviate the problem of losing its own hierarchical information after fusing different levels of hypergraphs, we use self-supervised learning to construct auxiliary branches that fully enhance the rich information in the hypergraph. Experimental results conducted on two publicly available datasets show that the proposed model outperforms state-of-the-art methods.
Bitcoin Forecasting Performance Measurement: A Comparative Study of Econometric, Machine Learning and Artificial Intelligence-Based Models

Bitcoin is a type of Cryptocurrency that relies on Blockchain technology and its growing popularity is leading to its acceptance as an alternative investment. However, the future value of Bitcoin is difficult to predict due to its significant volatility and speculative behavior. Considering this, the key objective of this research is to assess Bitcoins’ explosive behavior during 2013–2022 including the most volatile COVID-19 pandemic and Russia–Ukraine war period and to forecast its price by comparing the predictive abilities offive different econometric, machine learning and artificial Intelligence methods namely, ARIMA, Decision Tree, Random Forest, SVM, and Artificial Intelligence Long Short-Term Memory Network (AI-LSTM). The precision of such methodologies has been assessed using root mean square error (RMSE) and mean average per cent error (MAPE) values. The findings confirmed that the AI-LSTM model performs better than other forecast models in predicting Bitcoins’ opening price on the following working day. Therefore, Bitcoin traders, policymakers, and financial institutions can use the model effectively to better forecast the next day’s opening price.
RAGOSRA: Rough attributed goal oriented software requirements analysis method

Goal oriented software requirements analysis method is used for the analysis of elicited functional goals (FGs) and non-functional goals (NFGs) of a system in which goals are decomposed and refined into sub-goals until requirements from the sub-goals are identified. Based on the critical analysis, we found that most of the attention of goal-oriented methods is on the crisp and fuzzy logic during the analysis of the software goals or requirements. In these methods’ prior information about the type of membership function is required; and the selection of membership function depends on the subjective justification. As a result, it lacks objectivity and may affect the ranking values of the goals or requirements during the analysis. Therefore, this paper presents a rough attributed goal-oriented software requirements analysis (RAGOSRA) method in which rough preference matrix has been used to capture the opinions of different stakeholders. The results of the RAGOSRA method are compared by considering the following criteria, i.e., goal types, goal links, types of data used in the analysis, stakeholder perceptions and time complexity with some fuzzy based methods. Based on the time complexity analysis, it is found that RAGOSRA method requires only 24 operations for the selection of goals for the dataset having 3 NFGs and 4 FGs of an institute examination system. On the other hand, FAGOSRA method, fuzzy TOPSIS method, and fuzzy AHP method requires 36, 166, and 240 operations respectively.
Kinect Body Sensor Technology-Based Quantitative Assessment Method for Basketball Teaching

Emphasizing the process and neglecting the end is the core idea of the research and implementation of college physical education learning and assessment, while the performance is the main form of evaluation results. This paper takes the quantitative assessment of basketball teaching as an example and proposes a new Kinect body sensor technology-based quantitative assessment method for basketball teaching. Specifically, for basketball technology recognition and assessment tasks, the Kinect body sensor is first used to collect volunteer's 3D skeleton motion data, then feeding the collected skeleton sequence to the vision transformer network to model the long-distance dependency. And based on this, the skeleton motion recognition network and skeleton motion assessment network are developed. The experimental results show that the proposed networks can well recognize and quantitatively assess the standard and non-standard basketball skill motions.
Dialogue Logic Aware and Key Utterance Decoupling Model for Multi-Party Dialogue Reading Comprehension

Multi-party dialogue machine reading comprehension (MRC) brings an unprecedented challenge due to the multiple speakers and the complex discourse linkages among speaker-aware utterances. The majority of current methods only consider the textual aspects of dialogue situations, and pay little attention to crucial speaker-aware cues. This prevents a model from capturing the speaker’s intention and important discourse information for questions in a complex discourse relationship, leading to the model giving wrong answers. In this paper, we construct a dialogue logic graph module by the relational graph convolutional network (R-GCN) to structure the dialogue information, and design a speaker prediction task to enhance the ability to capture discourse logic. Additionally, we construct a key utterance information decoupling module that focuses on the key discourse information flow involve questions, and filters out noise information. Extensive experiments FriendsQA and Molweni show that our approach outperforms competitive baselines and current state-of-the-art models, especially when dealing with more rounds of dialogue and questions involving people, events and time.
Real-Scene Reflection Removal With RAW-RGB Image Pairs

Most brands of modern consumer digital cameras nowadays are able to provide RAW-RGB image pairs conveniently, even in the automatic mode. RAW images store pixel intensities linearly related to the radiance, which could be beneficial for the image reflection removal (IRR) task. However, existing IRR solutions, usually directly restoring the background in the non-linear RGB domain, severely overlook the valuable information conveyed by readily-available RAW images. Such a negligence may limit the performance of IRR methods on real-scene images. To mitigate this deficiency, we propose a Cascaded RAW and RGB Restoration Network (CR3Net) by leveraging both the RGB images and their paired RAW versions. Specifically, we firstly separate background and reflection layers in the linear RAW domain, and then restore the two layers in the non-linear RGB format by converting RAW features into the RGB domain. A novel RAW-to-RGB module (RRM) is devised to upsample these features and mimic pointwise mappings in the camera image signal processor (ISP). In addition, we collect the first real-world dataset that contains paired RAW and RGB images for IRR. Compared with state-of-the-art approaches, our method achieves a significant performance gain of about 2.07dB in PSNR, 0.028 in SSIM, and 0.0123 in LPIPS tested on the captured dataset. The source code and dataset are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NamecantbeNULL/RAW_RGB_RR</uri> .
Enhancing Backlight and Spotlight Images by the Retinex-Inspired Bilateral Filter SuPeR-B

Backlight and spotlight images are pictures where the light sources generate very bright and very dark regions. The enhancement of such images has been poorly investigated and is particularly hard because it has to brighten the dark regions without over-enhance the bright ones. The solutions proposed till now generally perform multiple enhancements or segment the input image in dark and bright regions and enhance these latter with different functions. In both the cases, results are merged in a new image, that often must be smoothed to remove artifacts along the edges. This work describes SuPeR-B, a novel Retinex inspired image enhancer improving the quality of backligt and spotlight images without needing for multi-scale analysis, segmentation and smoothing. According to Retinex theory, SuPeR-B re-works the image channels separately and rescales the intensity of each pixel by a weighted average of intensities sampled from regular sub-windows. Since the rescaling factor depends both on spatial and intensity features, SuPeR-B acts like a bilateral filter. The experiments, carried out on public challenging data, demonstrate that SuPeR-B effectively improves the quality of backlight and spotlight images and also outperforms other state-of-the-art algorithms.
An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

Breast cancer is one of the leading cancers among women.It has the second-highest mortality rate in women after lung cancer.Timely detection, especially in the early stages, can help increase survival rates.However, manual diagnosis of breast cancer is a tedious and time-consuming process, and the accuracy of detection is reliant on the quality of the images and the radiologist's experience.However, computer-aided medical diagnosis has recently shown promising results, leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages.The research presented in this paper is focused on the multi-class classification of breast cancer.The deep transfer learning approach has been utilized to train the deep learning models, and a pre-processing technique has been used to improve the quality of the ultrasound dataset.The proposed technique utilizes two deep learning models, Mobile-NetV2 and DenseNet201, for the composition of the deep ensemble model.Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results.Subsequently, entropy-based feature selection is used.Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%, while the sensitivity and F1 score were 96.87% and 96.76%, respectively.The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.
Risk, reputation and responsibility: Cybersecurity and centralized data in United States civilian federal agencies

• The Foundations of Evidence in Policymaking Act of 2018 includes little support for necessary cybersecurity infrastructure. • U.S. federal interagency cybersecurity is too fragmented to effectively secure a centralized data initiative at scale. • Nearly half of federal agencies report lacking the infrastructure to maintain an effective cybersecurity protocol.. • A reduction in cybersecurity incidents is attributed to a 2016 reporting policy change that excludes certain events. • Infrastructure to support a standardized cybersecurity policy is a missed opportunity in the Evidence in Policymaking Act.