Reviewerly/exHarmony huggingface dataset:
Does Deep Learning Require Image Registration for Early Prediction of Alzheimer’s Disease? A Comparative Study Using ADNI Database

Image registration is the process of using a reference image to map the input images to match the corresponding images based on certain features. It has the ability to assist the physicians in the diagnosis and following up on the patient’s condition. One of the main challenges of the registration is that it takes a huge time to be computationally efficient, accurate, and robust as it can be framed as an optimization problem. In this paper, we introduce a comparative study to investigate the influence of the registration step exclusion from the preprocessing pipeline and study the counter effect of the augmentation on the model performance. We achieved the goal of the study through three experiments using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Finally, a T-statistical test is applied to validate our hypothesis with a p-value of 0.027, in which case the null hypothesis should be rejected. Our proposed approach of using augmentation without any registration outperforms the other experiments for the AD vs CN task with an AUC score of 94.62%.
An Energy &amp; Cost Efficient Task Consolidation Algorithm for Cloud Computing Systems

The power consumption of untapped resources, especially during a cloud background, represents a significant sum of the specific power use. By its nature, a resource allotment approach that takes into account the use of resources would direct to better power efficiency; this, in clouds, expands even additional, and with virtualization techniques often jobs are easily combined. Job consolidation is an effective way to expand the use of resources and sequentially reduce power consumption. Current studies have determined that server power utilization extends linearly with processor resources. This hopeful fact highlights the importance of the involvement of standardization to reduce energy utilization. However, merging tasks can also cause freedom from resources that will remain idle as the attraction continues. There are some remarkable efforts to decrease idle energy draw, usually by putting computer resources into some kind of power-saving/sleep mode. Throughout this article, we represent 2 power-conscious task reinforcement approaches to maximize resource use and explicitly consider both passive and active power consumption. Our inferences map each job to the resource at which the power consumption to perform the job is implicitly or explicitly reduced without degrading the performance of that task. Supporting our investigational outcome, our inference methods reveal the most promising power-saving potential.
Image Processing and Deep Neural Networks for Face Mask Detection

The proper use of a mask is crucial for lowering COVID 19 and transmission. According to the research, transmission is completely decreased when the mask is used appropriately. Factors like sunlight and several items can affect how appropriatel y applied face masks are classified and detected. Cotton masks, sponge masks, scarves, and other options greatly lessen the effect of personal protection in such circumstances. The research suggests a novel modified formula for classifying masks into three categories—a proper mask, a no mask, and an erroneous mask—using deep learning and machine learning. First, we provide a brand-new face mask classification and detection algorithm that combines deep learning, the viola Jones method, and Efficient-Yolov3 Wearing a mask, not wearing a mask, or wearing the wrong mask are the three options. On the dataset with or without mask pictures, the suggested system outperforms and is more accurate when compared to existing techniques. The results of experiments and analysis are also based on the classification knowledge set. In comparison to the present methodology's categorization accuracy of 84%, the anticipated formula boosted it to 97%.
Automating Scorecard and Commentary Based on Umpire Gesture Recognition

Cricket is the most played sport in the Indian subcontinent. While the sport enjoys abundant financial and human resources at the highest levels, the lower levels lack these resources which creates a resource parity between players playing at various levels. This paper aims to develop a system capable of detecting the various umpire signals from images and then automating the scorecard accordingly. The system uses a camera to capture the image of the umpire then a processor-based system is used to identify the signal and update the scorecard. In the detection process, the image is pre-processed and SIFT descriptor is applied to extract image features. The dimensions of the features is reduced with k-means and PCA. Finally, the classification of signals is done using 3 classifiers that are KNN, Decision tree and Random Forest. The model is trained with 6000 images of 6 classes which include Six, Four, Out, No ball, wide ball and a no action class. The random forest classifier gives the best accuracy at 81 percent. A novel algorithm is used for updating the scorecard. Audio commentary is provided through predefined templates.
Understanding and reflecting on teacher development of private colleges and universities - A case of Guangdong University of Science and Technology

Private colleges and universities have become an important part of higher education system in China, but most of the full-time teachers in private colleges and universities are graduates of master’s degree. They are young, willing to study, and have new ideas and the spirit of a pathbreaker. What is lacking is that they do not master the laws of education and scientific research methods. Moreover, they are short of the practice of teaching and training of scientific research. In this paper, we proposed the countermeasures of teacher development that the government, university and teachers should do in Guangdong University of Science and Technology.
The construction of curriculum learning pattern of master of education: theoretical foundation and practical framework

The implementation and operation of the “Curriculum for Education” is an important medium and carrier for nurturing the in-depth learning literacy of postgraduates, especially the implementation and operation of the principle courses provide an important platform and guarantee for cultivating the principle thinking of M.Ed. Combined with the analysis of research data, this paper finds that (1) the learning pattern of M.Ed. courses has the characteristics and practical characteristics of openness, initiative and self-adaptability. (2) The practical framework for constructing the learning pattern of the M.Ed. programme consists of two modules that lay out progressive learning tasks that point to “academic competence in the curriculum” and “practical competence in teaching”. (3) Guiding M.Ed. students to participate in course evaluation and promoting the formation of M.Ed. learning experiences are the core mechanisms of M.Ed. course learning patterns in practice.
Reflection on contemporary bilingual education in Chinese higher education with historical perspective

This paper reflects on the contemporary bilingual education in Chinese higher education, referring to the innovative bilingual teaching practices starting from the late Qing Dynasty. With the purposes of voicing for China and learning western sciences, bilingual programs were adopted in the first few original higher education institutes. These experiences leave us deliberation and speculation on the recurrent imbalance of native and foreign languages and cultures, and on future direction to research into bilingual education in universities to enhance the cultivation of internationalized talents and the internationalization of the country.
Research on university teaching in Metaverse era from the perspective of interactive ritual chain

According to the development of Metaverse, its outstanding features and the logical relationship of Metaverse empowerment education, this paper uses the interactive ritual chain theory to explore that the arrival of Metaverse will bring new changes to link in the ritual chain of new educational symbols, “presence” in teaching, boundaries and barriers, ways of sharing emotional experience, etc. Therefore, this study mainly discusses the chain interactive logic of college teaching situation setting from the perspective of Metaverse, and then provides some ideas of teaching changes in university under the Metaverse era.
SPAC: Scalable Pattern Approximate Counting in Graph Mining

Pattern counting is a crucial task in graph pattern mining. Accurate counting is not affordable as the datasets grow larger and larger, and approximate counting is getting popular to provide an estimated answer quickly. However, current approximate counting approaches are still time-consuming and not scalable for extra-large graphs. This paper proposes SPAC, a fast and flexible pattern approximate counting method, based on the observation that pattern number distribution to degrees also follows power-law as the vertices, the common feature in graph datasets. By leveraging the distribution, SPAC can efficiently choose a small number of degrees as samples, fit the coefficients, and then calculate the pattern frequency directly. To provide flexibility for different use-cases, SPAC supports both accurate and approximate counting in the sampling phase. Moreover, edge weighting and interpolation techniques are adopted to emphasize the sample tail to improve fitting accuracy. The prototype of SPAC is implemented with GraphX on Spark, and is evaluated against various well-known graphs. The experimental results show that SPAC is up to 10x faster than accurate counting, keeping the same error level below 10%. Compared to existing approximate counting, SPAC is 1.4x–9x faster in general, while the error could be reduced to 20% of the current systems.
Long-Term Fairness Scheduler for Pay-as-You-Use Cache Sharing Systems

Currently, pay-as-you-go cache systems have been widely available as storage services in cloud computing, and users usually purchase long-term services to obtain higher discounts. However, users’ caching needs are not only constantly changing over time, but also affected by workload characteristics, making it difficult to always guarantee high efficiency of cache resource usage. Cache sharing is an effective way to improve cache usage efficiency. In order to incentivize users to share resources, it is necessary to ensure long-term fairness among users. However, the traditional resource allocation strategy only guarantees instantaneous fairness and is not thus suitable for pay-as-you-go cache systems. This paper proposes a long-term cache fairness allocation policy, named as FairCache, with several desired properties. First, FairCache encourages users to buy and share cache resources through group purchasing, which not only allows users to get more resources than when they buy them individually, but also encourages them to lend free resources or resources occupied by low-frequency data to others to get more revenue in the future. Second, FairCache satisfies pay-as-you-go fairness, ensuring that users’ revenue is proportional to the cost paid in a long term. Furthermore, FairCache satisfies truthfulness property, which ensures that no one can get more resources by lying. Finally, FairCache satisfies pareto efficiency property, ensuring that as long as there are tasks in progress, the system will maximize resource utilization. We implement FairCache in Alluxio, and the experimental results show that FairCache can guarantee long-term cache fairness while maximizing the efficiency of system resource usage.
About some features of application of digital technologies in philological education at the present stage

The article discusses the Governmental Program Digital Kazakhstan, its tasks and requirements for the digitalization in field of education. The active position of the Almaty University of Power engineering and Telecommunication named after Gumarbek Daukeev regarding educational digitalization is also described in the article. A comparison of different interpretations of the term digitalization showed that the lack of a single definition is explained by the fact that the terminology of digitalization is only in its beginning stage. Since the use of digital technologies is fraught with certain difficulties, not all representatives of the teaching staff have high digital literacy. This was the reason for the emergence of the popular theory of Mark Prensky, who singled out the generations of digital natives (youth) and digital immigrants (educators). There is an age-old conflict between these generations, which is expressed in the fact that the latter speak with an accent and at the same time want to be teachers of the former. However, research by scientists has shown that the superiority of digital natives over digital immigrants is somewhat exaggerated. Digital literacy does not depend on age, but on the breadth of thinking and courage in using digital technologies, which the digital natives themselves are far from equally proficient in. Despite this optimistic conclusion, digital immigrants should constantly work on their digital literacy, increase the level of digital competence. In this regard, the authors of the article believe that in order to overcome the complexes, the teacher should master the skills of an ambitious teacher who is always persistent and interested in mastering various innovations, including digital ones. As for the level of digitalization in the educational space of Kazakhstan, it is at the stage of formation and development. Scientists believe that in order to accelerate digitalization, high educational institutions of Kazakhstan should take a number of measures, which are also described in the article.
How Do Artificial Neural Networks Classify Musical Triads? A Case Study in Eluding Bonini's Paradox

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units-units that use different activation functions-use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses.
Improvement of Korea Meteorological Administration Solar Energy Resources Map Using Fine-Scale Terrain Data

Abstract Real-time solar energy resources mapping is crucial for the development and management of solar power facilities. This study analyzes the effects of the digital elevation model (DEM) resolution on the accuracy of the surface insolation (insolation hereafter) calculated by the Korea Meteorological Administration solar energy mapping system, KMAP-Solar, using two DEMs of different resolutions, 1.5 km and 100 m. It is found that KMAP-Solar yields smaller land-mean insolation with the fine-scale DEM than the coarse-scale DEM. The fine-scale DEM reduces biases by as much as 32 Wm − 2 for all observation sites, especially those in complex terrain and that the insolation error reduction is correlated with the difference in sky view factor (SVF) between the coarse- and fine-scale DEM. Both the coarse- and fine-scale DEMs generate the insolation-elevation and insolation-SVF relationship which is characterized by positive (negative) correlation between the insolation and the terrain altitude (SVF). However, the coarse-scale DEM substantially underestimates these relationships compared to the fine-scale DEM, mainly because the coarse-scale DEM underrepresents large terrain slopes and/or small SVFs, most seriously in high-altitude regions. The fine-scale DEM generates a more realistic insolation distribution than the coarse-scale DEM by incorporating a wider range of key terrain parameters involved in determining insolation. Improvements of insolation calculations in KMAP-Solar using a fine-scale DEM, especially in the areas of complex terrain, is of a practical value for Korea because the operational solar resources map from KMAP-Solar supports solar energy research, solar power plant installations, and real-time prediction and management of solar power within the power grid.
Towards federated learning: An overview of methods and applications

Abstract Federated learning (FL) is a collaborative, decentralized privacy‐preserving method to attach the challenges of storing data and data privacy. Artificial intelligence, machine learning, smart devices, and deep learning have strongly marked the last years. Two challenges arose in data science as a result. First, the regulation protected the data by creating the General Data Protection Regulation, in which organizations are not allowed to keep or transfer data without the owner's authorization. Another challenge is the large volume of data generated in the era of big data, and keeping that data in one only server becomes increasingly tricky. Therefore, the data is allocated into different locations or generated by devices, creating the need to build models or perform calculations without transferring data to a single location. The new term FL emerged as a sub‐area of machine learning that aims to solve the challenge of making distributed models with privacy considerations. This survey starts by describing relevant concepts, definitions, and methods, followed by an in‐depth investigation of federated model evaluation. Finally, we discuss three promising applications for further research: anomaly detection, distributed data streams, and graph representation. This article is categorized under: Technologies &gt; Machine Learning Technologies &gt; Artificial Intelligence
A general family of Plotkin-optimal two-weight codes over $$\mathbb {Z}_4$$

We obtained all possible parameters of Plotkin-optimal two-Lee weight projective codes over $$\mathbb {Z}_4,$$ together with their weight distributions. We show the existence of codes with these parameters as well as their weight distributions by constructing an infinite family of two-weight codes. Previously known codes constructed by Shi et al. (Des Codes Cryptogr 88(12): 2493–2505, 2020) can be derived as a special case of our results. We also prove that the Gray image of any Plotkin-optimal two-Lee weight projective codes over $$\mathbb {Z}_4$$ has the same parameters and weight distribution as some two-weight binary projective codes of type SU1 in the sense of Calderbank and Kantor (Bull Lond Math Soc 18: 97–122, 1986).
A Framework for Securing Saudi Arabian Hospital Industry: Vision-2030 Perspective

Recent transformation of Saudi Arabian healthcare sector into a revenue producing one has signaled several advancements in healthcare in the country. Transforming healthcare management into Smart hospital systems is one of them. Secure hospital management systems which are <i>breach-proof</i> only can be termed as effective smart hospital systems. Given the perspective of Saudi Vision-2030, many practitioners are trying to achieve a cost-effective hospital management system by using smart ideas. In this row, the proposed framework posits the main objectives for creating smart hospital management systems that can only be acknowledged by managing the security of healthcare data and medical practices. Further, the proposed framework will also be helpful in gaining satisfactory revenue from the healthcare sector by reducing the cost and time involved in managing the smart hospital system. The framework is based on a hybrid approach of three key methods which include: employing the Internet of Medical Things (IoMT) and blockchain methodologies for maintaining the security and privacy of healthcare data and medical practices, and using big data analytics methodology for raising the funds and revenue by managing the bulk volume of healthcare data. Moreover, the framework will also be helpful for both the patients and the doctors, thus enabling the Kingdom of Saudi Arabia (KSA) to meet its goals of Vision-2030 by ensuring low cost, yet credible, healthcare services.
A statistical analysis of the catalogues and criticism of the 19th-century Paris Fine Art Salon: The emergence of titling in the French art world

Abstract The dominant institution in the French art world for the display of new art for most of the 19th century was the Paris Salon. Artistic reputations depended upon showing works at the Salon, and critics made their names through writing about the event. In this article, statistical analysis of the contents and structure of the catalogues of the Salon and of Salon criticism is utilized to understand how the practice of titling new paintings emerged and was adopted in the 19th-century French art world. My work shows how statistical methods can be used to investigate practical and conceptual change, and problematizes accounts of the emergence of titling given by earlier authors. There are also methodological lessons for those working in the digital humanities from the approach adopted in this article.