Reviewerly/exHarmony huggingface dataset:
PVNet: A novel semantic segmentation model for extracting high-quality photovoltaic panels in large-scale systems from high-resolution remote sensing imagery

Timely extraction of high-quality photovoltaic (PV) panels from high-resolution remote sensing imagery can contribute to a comprehensive understanding of energy production, and supports global carbon neutrality and Sustainable Development Goals (SDGs). Existing studies for extracting PV panels only focus on small-scale rooftop PV systems but not on large-scale PV systems composed of single or several PV panels or arrays. Furthermore, the currently available public datasets related to large-scale PV systems are limited by the coarse resolution of the imagery used or annotation scale and do not provide highly detailed footprints for PV panels or attributes such as the location, quantity, or area of these panels. To fill this gap, a novel semantic segmentation model (PVNet) for extracting high-quality PV panels from the densely distributed and regularly shaped PV panels in large-scale PV systems is proposed. PVNet consists of two modules, a Coarse Prediction Module (CPM) and a Fine Optimization Module (FOM). The CPM extracts complete regions of individual PV panels by fusing low-level local features with high-level global features, while the FOM optimizes the output of CPM by residual refinement to match extracted boundaries to ground truth. High-quality details from region to boundary of PV panels are obtained through joint supervision of CPM and FOM results. PVNet was trained on a newly annotated PV Panel Dataset and tested under four scenario conditions in China where the large-scale PV industry is growing rapidly. Qualitative and quantitative results show that PVNet can achieve the highest accuracy for PV panel extraction with F1socre higher than 0.88 and IoU higher than 0.79. Area-wide PV panel mapping and comparisons with existing PV footprint datasets demonstrate that PVNet is a feasible solution for obtaining high-quality geo-spatial databases of large-scale PV systems.
An Analysis of Affective Factors that Hamper Students’ Participation in English Language Classroom

Affective is a combination of emotions, feelings, moods, and attitudes that can affect a person from the inside. This research aims to analyse the most dominant aspect of affective factors that hamper students’ participation in English language classrooms and the causes in terms of individual affective factors such as anxiety, inhibition, introversion/extroversion, self-esteem, motivation, learning style, and relational affective factors such as empathy, cross-cultural processes and classroom transactions. This research was conducted on the tenth-grade students at MA DDI Lil-Banat Parepare. The samples of this research were 25 students selected through snowballing sampling technique. This research used a qualitative descriptive to examine the phenomenon under study critically. The instruments used in this research were observations and semi-structured interviews. This research indicates that the dominant aspect of affective factors that hamper students’ participation in English language classrooms is self-esteem. The psychological condition of low self-esteem mostly happens to the students, which occurs to 48% of the sample students. Based on the results of students’ interviews, it is found that low self-esteem was caused by pessimistic about their abilities, lack of self-confidence, and a sense of inferiority.
Pathologist pupil dilation reflects experience level and difficulty in diagnosing medical images

Purpose: Digital whole slide imaging allows pathologists to view slides on a computer screen instead of under a microscope. Digital viewing allows for real-time monitoring of pathologists' search behavior and neurophysiological responses during the diagnostic process. One particular neurophysiological measure, pupil diameter, could provide a basis for evaluating clinical competence during training or developing tools that support the diagnostic process. Prior research shows that pupil diameter is sensitive to cognitive load and arousal, and it switches between exploration and exploitation of a visual image. Different categories of lesions in pathology pose different levels of challenge, as indicated by diagnostic disagreement among pathologists. If pupil diameter is sensitive to the perceived difficulty in diagnosing biopsies, eye-tracking could potentially be used to identify biopsies that may benefit from a second opinion. Approach: We measured case onset baseline-corrected (phasic) and uncorrected (tonic) pupil diameter in 90 pathologists who each viewed and diagnosed 14 digital breast biopsy cases that cover the diagnostic spectrum from benign to invasive breast cancer. Pupil data were extracted from the beginning of viewing and interpreting of each individual case. After removing 122 trials ( <10% ) with poor eye-tracking quality, 1138 trials remained. We used multiple linear regression with robust standard error estimates to account for dependent observations within pathologists. Results: We found a positive association between the magnitude of phasic dilation and subject-centered difficulty ratings and between the magnitude of tonic dilation and untransformed difficulty ratings. When controlling for case diagnostic category, only the tonic-difficulty relationship persisted. Conclusions: Results suggest that tonic pupil dilation may indicate overall arousal differences between pathologists as they interpret biopsy cases and could signal a need for additional training, experience, or automated decision aids. Phasic dilation is sensitive to characteristics of biopsies that tend to elicit higher difficulty ratings and could indicate a need for a second opinion.
Enhancement of the Quantum Coherence in a Hierarchical Environment System by Weak Measurement and Weak Measurement Reversal

Abstract The quantum coherence of a two‐qubit system in which each qubit is coupled to its own hierarchical environment is studied. The effect of the hierarchical environment is explored in order to improve the coherence effectively. It is discovered that the dynamics of coherence can be manipulated by the number N of the second‐layer cavities (), the coupling strengths Ω 0 , Ω, κ, respectively for the qubit‐cavity ( m 0 ), nearest‐neighbor, and m 0 ‐ cavities. It depends also on the decay rate Γ of second‐layer cavities. Furthermore, an effective scheme for enhancing coherence is proposed by using weak measurement and weak measurement reversal. The explicit conditions of the optimal measurement strengths to improve the coherence comparatively are derived. It is seen that the final stable value of the coherence is independent of the number N of second‐layer cavities but is related to the weak measurement strength m when the coupling strength between two nearest‐neighbor cavities is taken into account.
Challenges of the Divine Word College of San Jose Mangyan Education Center (MEC) during the new normal

The purpose of this study is to identify the challenges of the Mangyan Education Center during the new normal in order to come up with a sustainable program and to provide the government with the opportunity to assist the Mangyan Education Center.The motivation to pursue this study is rooted from the observation and learnings of the researcher about the management of the center.Descriptive research design was used in this study, with the aim of summarizing the informational content of the challenges encountered by the MEC of San Jose, Occidental Mindoro.The general evaluation was performed to 104 respondents who are connected to Mangyan Education Center.In gathering the necessary data, the researcher-made survey questionnaire serves as the main instrument of this study, patterned after the conceptual framework and its relation to the problem of the study.The study discovered that scholars were aware that the center's main goal is to help each student become a person who is firmly rooted in his or her historical past, open to ongoing societal changes, and actively embracing and promoting the richness of their cultural identity.The study findings indicate that the scholars are committed to living their lives in accordance with MEC's ideals in order to grow personally and help their community.In terms of management operation, the respondents to this study reported no problems.The directors' consultative, democratic, and transparent approach to the center encourages the center to keep up the Mangyan Education Center well managed.
IMPLEMENTATION OF GRAPH COLORING IN UMMUL MUKMININ HIGH SCHOOL STUDENT'S DORMITORY USING WELCH-POWELL ALGORITHM

In Graph Theory, the concept of vertex coloring is an interesting topic because it can be implemented in various fields in everyday life. One of them is the distribution of dorm rooms at a school in Makassar. The placement of dorm rooms is made so that no students from the same class or region are in the same room. The data of region and class will be represented in an adjacency matrix with 137 rows and columns. Furthermore, the coloring will be solved by using the Welch-Powell algorithm. The coloring results obtained were 50 colors. That means, the rooms needed to place 137 students so that no one comes from the same region, and classes are 50 rooms with a maximum capacity of 4 people in each room.
A New Instrument Monitoring Method Based on Few-Shot Learning

As an important part of the industrialization process, fully automated instrument monitoring and identification are experiencing an increasingly wide range of applications in industrial production, autonomous driving, and medical experimentation. However, digital instruments usually have multi-digit features, meaning that the numeric information on the screen is usually a multi-digit number greater than 10. Therefore, the accuracy of recognition with traditional algorithms such as threshold segmentation and template matching is low, and thus instrument monitoring still relies heavily on human labor at present. However, manual monitoring is costly and not suitable for risky experimental environments such as those involving radiation and contamination. The development of deep neural networks has opened up new possibilities for fully automated instrument monitoring; however, neural networks generally require large training datasets, costly data collection, and annotation. To solve the above problems, this paper proposes a new instrument monitoring method based on few-shot learning (FLIMM). FLIMM improves the average accuracy (ACC) of the model to 99% with only 16 original images via effective data augmentation method. Meanwhile, due to the controllability of simulated image generation, FLIMM can automatically generate annotation information for simulated numbers, which greatly reduces the cost of data collection and annotation.
Fusing Attribute Character Embeddings with Truncated Negative Sampling for Entity Alignment

Finding pairs of entities from two different knowledge graphs that reflect the same real-world object is the purpose of entity alignment for knowledge graphs. In recent years, techniques that use entity alignment for knowledge fusion have received widespread attention. In this article, we suggest a method for entity alignment using truncated negative sampling with attribute character embedding. The method mainly makes use of the relationship and attribute data in heterogeneous knowledge graphs to fulfil the entity alignment task. Firstly, the framework uses relationship mapping to unify the namespace of heterogeneous relationships. Secondly, the attribute character embeddings are generated using the attribute triples in the knowledge graph to unify the embedding space of heterogeneous entities. Then, the entity similarity between heterogeneous knowledge graphs is captured by structural embedding. Next, to learn more useful semantic information during negative sampling, the framework adopts a truncated negative sampling strategy to increase the generalizability of the model. The negative sampling procedure employs targets with high similarity to the target entity as negative sample targets. Finally, we performed comparison tests on two well-known real-world datasets, and the outcomes demonstrate that the proposed model outperforms three other representative advanced approaches, especially with an over 10% improvement in the Hits@k metric compared to the baseline method.
Study on Integrating Diagnostic Assessment into College English Reading Teaching Practice

This study examined how Udig reading assessment improved English teaching and learning at college level. With the help of the Udig feedback reports and a reading strategy questionnaire of students, teachers were able to identify the strength and weakness in students’ reading ability and deliver the follow-up remedial activities based on the targeted needs for ability development. Students learnt to understand their reading deficiency and adjusted their learning activities. Results show that students’ performance of reading ability improved and their scores of reading subskills rose after the remedial teaching practice and students’ efforts. Students’ response to the remedial instruction and learning activities was generally positive.
Empowering trusted data sharing for data analytics in a federated environment: A blockchain-based approach

As data analytics is used in business to increase profits, organizations use it to pursue their goals. Even if enterprise data could be already valuable on its own, in many cases, combining it with external data sources would boost the value of the output, making data sharing a need in data analytics. At the same time, organizations are reluctant to share data, as they are scared of disclosing critical information. This calls for solutions that are able to safeguard data holders by regulating how data can be shared to ensure the so-called data sovereignty. This paper focuses on the usage of data lakes as well-established technology across enterprises for data analytics where internal or publicly available data are considered. The goal is to extend data lakes with functionalities that, respecting the data sovereignty, enable a data lake also to be ingested with data shared by other organizations and to share data to external organizations. Notable, the purpose of this work is to face this issue by defining an architecture that, inserted in a federated environment: restricts data access and enables monitoring that the actual usage of data respects the data sovereignty expressed in the policies agreed upon by the involved parties; makes use of Blockchain technology as a means for guaranteeing the traceability of data sharing; and allows for balancing computation movement and data movement. The proposed approach has been applied to a healthcare scenario where several institutions (e.g., hospitals and clinics, research institutes, and medical universities) produce and collect clinical data in local data lakes.
“I Could Wear It All of the Time, Just Like My Wedding Ring:” Insights into Older People’s Perceptions of Smart Rings

We present findings from an examination of older adults' perceptions of and preferences for smart rings, a unique class of interactive wearables gaining traction in the consumer market. To this end, we conducted semi-structured interviews with nine older adults, aged between 61 and 69 years old, to understand their perceptions regarding several smart ring models representative of rings, ring-like, and ring-ready finger-augmentation devices. We also elicited our participants' preferences for using smart rings in their everyday life, such as for controlling various home appliances and electronic devices. Our results show an overall positive experience and openness toward adopting smart rings, which were perceived useful to assist with various activities. Based on our findings and following the aging research agenda in HCI, we propose future work opportunities for designing smart ring technology tailored to the needs and preferences of older adults.
Exploring Behavior Patterns for Next-POI Recommendation via Graph Self-Supervised Learning

Next-point-of-interest (POI) recommendation is a crucial part of location-based social applications. Existing works have attempted to learn behavior representation through a sequence model combined with spatial-temporal-interval context. However, these approaches ignore the impact of implicit behavior patterns contained in the visit trajectory on user decision making. In this paper, we propose a novel graph self-supervised behavior pattern learning model (GSBPL) for the next-POI recommendation. GSBPL applies two graph data augmentation operations to generate augmented trajectory graphs to model implicit behavior patterns. At the same time, a graph preference representation encoder (GPRE) based on geographical and social context is proposed to learn the high-order representations of trajectory graphs, and then capture implicit behavior patterns through contrastive learning. In addition, we propose a self-attention based on multi-feature embedding to learn users’ short-term dynamic preferences, and finally combine trajectory graph representation to predict the next location. The experimental results on three real-world datasets demonstrate that GSBPL outperforms the supervised learning baseline in terms of performance under the same conditions.
Response to: Discussion on “Syn-metamorphic sulfidation of the Gamsberg zinc deposit, South Africa” by Stefan Höhn, Hartwig E. Frimmel, and Westley Price

Abstract Cawood et al. (this issue) critize our hypothesis of a pre-Klondikean weathering/oxidation event having affected the Aggeneys-Gamsberg ore district. Instead, they reinforce the long-held view that the sulfide deposits of the ore district with its pronounced metal zonation, its unusually high mineralogical variability and numerous geochemical anomalies are the product of amphibolite- to granulite-facies metamorphic overprint of originally syn-sedimentary exhalative deposits. Here we gladly use the opportunity to counter all issues raised and explain further our evidence of oxidation and subsequent re-sulfidation of the original synsedimentary deposits.
Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, Ethiopia, 2022

Treatment with effective antiretroviral therapy (ART) reduces viral load as well as HIV-related morbidity and mortality in HIV-positive patients. Despite the expanded availability of antiretroviral therapy around the world, virological failure remains a serious problem for HIV-positive patients. Thus, Machine learning predictive algorithms have the potential to improve the quality of care and predict the needs of HIV patients by analyzing huge amounts of data, and enhancing prediction capabilities. This study used different machine learning classification algorithms to predict the features that cause virological failure in HIV-positive patients.An institution-based secondary data was used to conduct patients who were on antiretroviral therapy at the University of Gondar Comprehensive and Specialized Hospital from January 2020 to May 2022. Patients' data were extracted from the electronic database using a structured checklist and imported into Python version three software for data pre-processing and analysis. Then, seven supervised classification machine-learning algorithms for model development were trained. The performances of the predictive models were evaluated using accuracy, sensitivity, specificity, precision, f1-score, and AUC. Association rule mining was used to generate the best rule for the association between independent features and the target feature.Out of 5264 study participants, 1893 (35.06%) males and 3371 (64.04%) females were included. The random forest classifier (sensitivity = 1.00, precision = 0.987, f1-score = 0.993, AUC = 0.9989) outperformed in predicting virological failure among all selected classifiers. Random forest feature importance and association rules identified the top eight predictors (Male, younger age, longer duration on ART, not taking CPT, not taking TPT, secondary educational status, TDF-3TC-EFV, and low CD4 counts) of virological failure based on the importance ranking, and the CD-4 count was recognized as the most important predictor feature.The random forest classifier outperformed in predicting and identifying the relevant predictors of virological failure. The results of this study could be very helpful to health professionals in determining the optimal virological outcome.
Research on Evaluation Methods of Social Science Training in Information Education System

With the launch of China's 14th Five-Year Plan, the reform and development framework of social science education has been further clarified, and the development prospects of social science education and training enterprises are favored by the capital market due to their large market demand and the trend towards scale. In October 2021, the General Office of the CPC Central Committee and the General Office of the State Council issued the Opinions on Promoting the High-quality Development of Modern Vocational Education, promoting the overall rise of the A-share education sector. At present, vocational education is at the key stage of deepening the reform of industry and education and promoting the transformation and upgrading of talent cultivation. Vocational education and training enterprises will focus their development strategies on the capital market to achieve scale-driven development. With the rapid development of China's education industry, listing will help social science education and training companies further enhance their financing capacity and popularity, and effectively improve their industry competitiveness and brand effect. At the same time, few scholars at home and abroad have studied whether the listing methods of enterprises in the vocational education industry are effective and whether there are differences in the effects after listing. Therefore, this paper analyzes the listing methods and effects of social science education and training enterprises in order to help build and improve a good ecosystem of social science education and training industry, and will also provide some reference for the listing of vocational social science training enterprises.
Secure Adaptive Context-Aware ABE for Smart Environments

Predicting context-aware activities using machine-learning techniques is evolving to become more readily available as a major driver of the growth of IoT applications to match the needs of the future smart autonomous environments. However, with today’s increasing security risks in the emerging cloud technologies, which share massive data capabilities and impose regulation requirements on privacy, as well as the emergence of new multiuser, multiprofile, and multidevice technologies, there is a growing need for new approaches to address the new challenges of autonomous context awareness and its fine-grained security-enforcement models. The solutions proposed in this work aim to extend our previous LCA-ABE work to provide an intelligent, dynamic creation of context-aware policies, which has been achieved through deploying smart-learning techniques. It also provides data consent, automated access control, and secure end-to-end communications by leveraging attribute-based encryption (ABE). Moreover, our policy-driven orchestration model is able to achieve an efficient, real-time enforcement of authentication and authorization (AA) as well as federation services between users, service providers, and connected devices by aggregating, modelling, and reasoning context information and then updating consent accordingly in autonomous ways. Furthermore, our framework ensures that the accuracy of our algorithms is above 90% and their precision is around 85%, which is considerably high compared to the other reviewed approaches. Finally, the solution fulfills the newly imposed privacy regulations and leverages the full power of IoT smart environments.