Reviewerly/exHarmony huggingface dataset:
New Lights on Mineralogical and Geochemical Characteristics of Cretaceous Rocks, Gabal Qabaliat Area, South West Sinai, Egypt

The Cretaceous sedimentary succession at Gabal El-Qabaliat range in age from Aptian-Albian to Maastrichtian.Lithostratigraphically, the succession is subdivided into Malha, Raha, Abu Qada, Wata, Matulla and Sudr formations.The mineralogy of bulk samples of Malha Formation reveals one association (quartz, kaolinite, anhydrite, muscovite and calcite followed by dolomite).The Raha Formation reveals the presence of two cycles of environmental conditions.(calcite then quartz, kaolinite and gypsum; quartz, halite, hematite, muscovite, kaolinite and gypsum).Abu Qada Formation has two cycles of environmental conditions (quartz, halite, montmorillonite, kaolinite, muscovite and calcite; quartz, anhydrite, glauconite and hematite) but Wata Formation has one cycle (calcite, dolomite and quartz).Matulla Formation has four associations (calcite, dolomite, anhydrite, hematite, quartz; calcite, dolomite, quartz, goethite, hematite and gypsum; quartz, goethite, gypsum, calcite, hematite, glauconite, kaolinite; quartz, montmorillonite, halite, anhydrite, muscovite and calcite).The clay mineralogy of Raha Formation reveals one clay association (montmorillonite, saponite and kaolinite), but Abu Qada Formation reveals the presence of two cycles of environmental conditions (lower one: kaolinite, chlorite, saponite, illite and goethite; second: montmorillonite, saponite, chlorite=kaolinite, goethite).Matulla Formation has two clay associations [montmorillonite with monor illite, saponite > kaolinite (lower cycle; middle part of this formation), montmorillonite= saponite (2 nd cycle)].Form the geochemical studies, the variation in each major oxides and trace elements either in clastic or non-clastic rocks discussed revealing the kind of clay mineral associations and other minor components present.Two groups were identified based on CaO and MgO contents; 1 st group has > 54% CaO and low MgO including Raha, Matulla and Sudr limestones while the other group including the moredolomitic facies of Abu Qada.The dolomitic facies of Abu Qada exhibit high CaO compared with the Wata and Matulla formations.Four groups were identified based on SiO 2 and CaO contents, the 1 st group has <1% SiO 2 and > 54% CaO including Sudr, Matulla and Raha limestones; the 2 nd one has 1-2.2%SiO 2 and 47-54% CaO including Abu Qada samples; the 3 rd one has ~3% SiO 2 and 35% CaO as in Wata dolomitic limestone and the 4 th one has 27% SiO 2 and 37% CaO including dolomitic limestone of Matulla Formation.Ba and Sr contents of limestone facies reflect a separation between the different types, where Ba contents of dolomitic facies are more than their contents in limestone facies of Abu Qada, Wata, and Matulla.The Sr contents have the opposite trend where Sr content of limestone are more than that of dolomitic facies (Raha and Matulla).
Risk Mitigation Strategies for Cloud Data Security in IT and ITES Sector during COVID-19

Digital Transformation of all the realms of life became obligatory after the spread of COVID-19. IT, Education, work, commerce, shopping, sales, industry - transformed into digital mode. The IT (Information Technology) and ITES (IT Enabled services) had to undergo a tremendous transformation in the operational aspects due to the rapid implementation of various protective measures as a consequence of the pandemic. ‘WFH (Work from Home)’ became a new normal. These factors enhance the significance of cloud data security. This paper performs a qualitative analysis of 3 major components of Cloud a) Iaas-Infrastructure as a Service ,b) PaaS- Platform as a Service and c) SaaS-Software as a Service and evaluate in detail the risk mitigation strategies that could be implemented with respect to cloud data security. Meeting the confidentiality, trust and reliability aspects necessary for multinational clients, obligation of completion of tasks by the deadlines, maintaining the confidentiality and sensitivity of data, preventing the inadvertent malicious intrusions - all add up to the additional significant risks and challenges, as the probability for the occurrence of these events in the cloud have increased exponentially due to the large-scale applications of cloud during the pandemic. In future, many IT companies have decided to migrate entirely to cloud and operate mainly using cloud services, as the operational expenses are drastically reduced and that contributes to a gap in the existing research areas, considering the volume, confidentiality and criticality of the data handled. The implementation of efficient and effective risk mitigation strategies become crucial considering the long-term implementation of a cloud operating model. The objective of this paper is to qualitatively understand with the aid of secondary data, the risk mitigation strategies that could be proactively implemented in IT and ITES sector for cloud data security. This paper concentrates mostly on the identification of risk mitigation strategies implemented at present, evaluation of those strategies and the identification of gaps in the current strategies. In this paper, the existing strategies and gaps in the system are analysed, and an initial proposal of advanced risk management strategies for cloud data security in IT and ITES Sector is provided.
How Fine-Tuning Allows for Effective Meta-Learning

Representation learning has been widely studied in the context of meta-learning, enabling rapid learning of new tasks through shared representations. Recent works such as MAML have explored using fine-tuning-based metrics, which measure the ease by which fine-tuning can achieve good performance, as proxies for obtaining representations. We present a theoretical framework for analyzing representations derived from a MAML-like algorithm, assuming the available tasks use approximately the same underlying representation. We then provide risk bounds on the best predictor found by fine-tuning via gradient descent, demonstrating that the algorithm can provably leverage the shared structure. The upper bound applies to general function classes, which we demonstrate by instantiating the guarantees of our framework in the logistic regression and neural network settings. In contrast, we establish the existence of settings where any algorithm, using a trained with no consideration for task-specific fine-tuning, performs as well as a learner with no access to source tasks in the worst case. This separation result underscores the benefit of fine-tuning-based methods, such as MAML, over methods with frozen representation objectives in few-shot learning.
An individual-centered approach to visualize people’s opinions and demographic informationc

The noble way to substantiate decisions that affect many people is to ask these people for their opinions. For governments that run whole countries, this means asking all citizens for their views to consider their situations and needs.Organizations such as Africa's Voices Foundation, who want to facilitate communication between decision-makers and citizens of a country, have difficulty mediating between these groups. To enable understanding, statements need to be summarized and visualized. Accomplishing these goals in a way that does justice to the citizens' voices and situations proves challenging. Standard charts do not help this cause as they fail to create empathy for the people behind their graphical abstractions. Furthermore, these charts do not create trust in the data they are representing as there is no way to see or navigate back to the underlying code and the original data. To fulfill these functions, visualizations would highly benefit from interactions to explore the displayed data, which standard charts often only limitedly provide.To help improve the understanding of people's voices, we developed and categorized 80 ideas for new visualizations, new interactions, and better connections between different charts, which we present in this report. From those ideas, we implemented 10 prototypes and two systems that integrate different visualizations. We show that this integration allows consistent appearance and behavior of visualizations. The visualizations all share the same main concept: representing each individual with a single dot. To realize this idea, we discuss technologies that efficiently allow the rendering of a large number of these dots. With these visualizations, direct interactions with representations of individuals are achievable by clicking on them or by dragging a selection around them. This direct interaction is only possible with a bidirectional connection from the visualization to the data it displays. We discuss different strategies for bidirectional mappings and the trade-offs involved. Having unified behavior across visualizations enhances exploration. For our prototypes, that includes grouping, filtering, highlighting, and coloring of dots. Our prototyping work was enabled by the development environment Lively4. We explain which parts of Lively4 facilitated our prototyping process. Finally, we evaluate our approach to domain problems and our developed visualization concepts.Our work provides inspiration and a starting point for visualization development in this domain. Our visualizations can improve communication between citizens and their government and motivate empathetic decisions. Our approach, combining low-level entities to create visualizations, provides value to an explorative and empathetic workflow. We show that the design space for visualizing this kind of data has a lot of potential and that it is possible to combine qualitative and quantitative approaches to data analysis.
On Locality of Local Explanation Models

Shapley values provide model agnostic feature attributions for model outcome at a particular instance by simulating feature absence under a global population distribution. The use of a global population can lead to potentially misleading results when local model behaviour is of interest. Hence we consider the formulation of neighbourhood reference distributions that improve the local interpretability of Shapley values. By doing so, we find that the Nadaraya-Watson estimator, a well-studied kernel regressor, can be expressed as a self-normalised importance sampling estimator. Empirically, we observe that Neighbourhood Shapley values identify meaningful sparse feature relevance attributions that provide insight into local model behaviour, complimenting conventional Shapley analysis. They also increase on-manifold explainability and robustness to the construction of adversarial classifiers.
The hidden curriculum

Nowadays, in schools, students seem to gain much knowledge on the topics that are not included in the official statements of the course guides. Learning that contains information, beliefs, and the ways of behaving in the society relates to a hidden curriculum of schooling which is contrasted with the formal one. It is determined as the lesson conducted informally and usually unintentionally in a school system. The hidden curriculum helps not only to shape the individual but the system of the society in general. This paper consists of several sections and each of it examines a particular topic concerning the unwritten curriculum in a school system. These sections create a full picture of the importance of hidden curriculum learning and research its impact and relation to the social and moral school education. Finally, the main purpose of this study is to explore the peculiarities of the hidden curriculum phenomenon and investigate all the aspects of this subject.Key Words: Hidden curriculum, teaching, knowledge, instruction, education.
A DEIXIS ANALYSIS OF DONALD TRUMP PRESS CONFERENCE CORONAVIRUS TASK FORCE

Deixis is the study of deictic or indexical articulations in language exchanges, prefaces, and monolog like you, presently, today. Deixis can be named as a unique sort of linguistic property, managing increasingly natural syntactic classifications of individual, place, time. The purpose of this research are to describe the types and the most dominant of the deictic words used in the Donald Trump Press Conference Coronavirus Task Force. The researchers designed this research as descriptive qualitative research. The data was gathered from a speech of donald trump press conference and using theory Yule (1996). The result showed that there are three types of deixis, they are Personal Deixis, Time Deixis and Place Deixis. This research found that there are three types of deixis in press conference, they are Personal Deixis 23 occurances, Time Deixis 8 occurance, Place Deixis 1 occurance. Based on this analysis it can be concluded that personal deixis is the most dominant types of deixis used than the other types. Keywords :  Deixis, Donald Trump, Coronavirus
Potential Hydroelectric Power Plant for a Remote Area Utilizing Subwatershed Lawe-Simpali

The fulfilment of electricity needs in Aceh Province has not been evenly distributed so that the potential of available natural resources continues to be explored and destroyed to be utilized. One of the high available natural potentials in Aceh province is watershed and subwatershed. This study aims to study the potential use of Lawe-simpali subwatershed as a hydroelectric power plant to meet the electrical energy needs of remote communities around the Lawe-simpali Subwatershed, Aceh Province, Indonesia. The method used in this research is a literature study and field survey. Four types of methods for estimating rainfall frequency distribution were tested on climatological data obtained from around the Lawe-simpali subwatershed area. A Der Weduwen method is used to determine water discharge with a return period of up to 100 years. The results show that the Log Pearson Type III rainfall estimation method is more suitable for use in the Subwatershed Lawe-simpali area with a coefficient of inclination of 0.9. The estimated water discharge that can be generated from this Subwatershed for a biennial return period is 1571.85 m3/s. The discharge has the potential to be able to generate electricity by 26.2 kW for planning of 2 m head and 131.1 kW for planning head of 10 m. This shows that the Lawe-simpali subwatershed in Aceh Province has great potential to become micro and mini hydroelectric power so that it can meet the electrical energy needs of remote communities around it.
Translating Headers of Tabular Data: A Pilot Study of Schema Translation

Schema translation is the task of automatically translating headers of tabular data from one language to another.High-quality schema translation plays an important role in crosslingual table searching, understanding and analysis.Despite its importance, schema translation is not well studied in the community, and state-of-the-art neural machine translation models cannot work well on this task because of two intrinsic differences between plain text and tabular data: morphological difference and context difference.To facilitate the research study, we construct the first parallel dataset for schema translation, which consists of 3,158 tables with 11,979 headers written in 6 different languages, including English, Chinese, French, German, Spanish, and Japanese.Also, we propose the first schema translation model called CAST, which is a header-to-header neural machine translation model augmented with schema context.Specifically, we model a target header and its context as a directed graph to represent their entity types and relations.Then CAST encodes the graph with a relational-aware transformer and uses another transformer to decode the header in the target language.Experiments on our dataset demonstrate that CAST significantly outperforms state-of-the-art neural machine translation models.
A Unified Encoding of Structures in Transition Systems.

Transition systems usually contain various dynamic structures (e.g., stacks, buffers). An ideal transition-based model should encode these structures completely and efficiently. Previous works relying on templates or neural network structures either only encode partial structure information or suffer from computation efficiency. In this paper, we propose a novel attention-based encoder unifying representation of all structures in a transition system. Specifically, we separate two views of items on structures, namely structure-invariant view and structure-dependent view. With the help of parallel-friendly attention network, we are able to encoding transition states with O(1) additional complexity (with respect to basic feature extractors). Experiments on the PTB and UD show that our proposed method significantly improves the test speed and achieves the best transition-based model, and is comparable to state-of-the-art methods.
The Path to "Intelligent Chinese Class" under the New Curriculum Standard

Under the new curriculum standard, constructing an efficient Chinese classroom is an important task of Chinese teaching reform. Intelligent Chinese Class means to stand at the height of intelligence, through effective teaching methods or means, optimized teaching design, beautiful teaching language, wonderful teacher-student interaction and other teaching links.Through these links, we promote the meticulous implementation of teaching and the coordinated development of students' language literacy, ideological literacy and thinking quality.
PENDAMPINGAN INOVASI PRODUK KESET KAKI KAIN PERCA UNTUK MENINGKATKAN STRATEGI PEMASARAN MELALUI E-COMMERCE SHOPEE DI DESA JAJAG

The purpose of this activity is to help improve marketing and add new product innovations to the parcain foot doormat, in hopes of achieving increased sales and improving the quality of the selling price of the pared fabric doormat. Some of the problems faced by partners are as follows: 1) lack of innovation in making fabric doormats. 2) Marketing products that still use offline sales and have not used the role of technology. Therefore, through the Real Work Lecture (KKN) program IAI Ibrahimy Genteng Banyuwangi conducted assistance activities to assist in the innovation of parched cloth doormat products and online marketing to increase sales. Mentoring activities are carried out for 4 weeks with 1 meeting per week. The stages of implementation of this assistance include: 1) observation and analysis of problems, 2) activity planning, 3) preparation of schedules of activities and materials, 4) training and mentoring, 5) evaluation stages. The results of the assistance showed that: 1) the mentoring participants have been able to make more innovative fabric doormat products, 2) the mentoring participants have been able to market their products online.
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers

As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem. We introduce MAUVE, a comparison measure for open-ended text generation, which directly compares the learnt distribution from a text generation model to the distribution of human-written text using divergence frontiers. MAUVE scales up to modern text generation models by computing information divergences in a quantized embedding space. Through an extensive empirical study on three open-ended generation tasks, we find that MAUVE identifies known properties of generated text, scales naturally with model size, and correlates with human judgments, with fewer restrictions than existing distributional evaluation metrics.
Combined Use of Local and Global Features for Classification of Breast Lesion Using DCE-MRI Images

Recently, the use of dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) technique is widely used to detect and diagnose breast cancer.This technique has shown to be very useful particularly in screening women with high risk for breast cancer, as well as assessing the potential effects of new therapy.Thus, the aim of the present study is to appraise the efficacy of combined employment of global and local features in discriminating malignant and benign lesions.A dataset of one hundred and twenty one (121) DCE-MRI investigations was assembled and used.Out of that number, fifty (50) were biopsy-proved malignant tumors and seventy-one (71) were benign.Firstly, the suspicious mass regions were automatically detected and segmented with 3D region growing algorithm.Meanwhile, Local and global features were used.Thereafter, sequential floating forward selection method (SFFS) and support vector machine classifier (SVM) were used for classification.The overall classification performance of different kind of features were evaluated via receiver operating characteristic (ROC) analysis in a 3-fold cross validation scheme.It was observed that global feature produced classification accuracy of 84.32 % followed by local feature with accuracy of 85.95 %.When the local and global features were combined, the classification accuracy increased to 94.36 %.Based on the obtained results, this study has demonstrated that the combined use of local and global features could effectively function as a better indicator in differentiating malignant and benign tumors.
Um breve estudo da literatura brasileira sobre comportamento informacional

several analysis about the set of international studies in the domain of information behavior have been carried out to evaluate the theoretical and methodological trends in the area. These assessments provide a critical analysis of the research that steer future work by scholars in the field. However, equivalent studies on Brazilian research are few and not comprehensive. The present work analyzed the national literature on information behavior to: (1) compare if the main international trends in research on information behavior are also occurring in Brazil; (2) conduct an analysis of the research in the field and guide future work. After a literature review and synthesis of the main tendencies of this area indicated in the international works, a bibliographic portfolio was compiled using the Proknow-C methodology to select 233 national studies on human information behavior, dating from 2000 to 2017.  The 233 papers selected were analyzed for the type of research, research objective and conceptual basis. Data analysis through descriptive statistics showed that Brazilian research follows most of the international trends as national studies focus more on people than on systems; use of more than one data collection procedure; there is a predominance of qualitative methods as a research approach; and the questionnaire and interview are the predominant methods of data collection. This work provides a starting point for verifying the characteristics and direction of Brazilian research on information behaviour
FROM TRADITIONAL GROWTH TO SUSTAINABLE DEVELOPMENT: 20 YEARS OF HIGHER EDUCATION IN CHINA (1999–2019)

This paper focuses on the characteristics and trends in China’s higher education development from traditional growth and expansion to sustainable development from 1999 to 2019. Sustainable development of higher education is a realistic and rational choice in China’s future. China’s higher education has experienced traditional development from extension to integration, which provides the foundation for sustainable development of China’s higher education. After 20 years of development, China’s higher education has gained remarkable achievements in scale expansion and program construction. The process of sustainable development of higher education in China shows the need to adapt to the necessities of society and the masses, which serve as the driving force of the government. In effect, the process optimises the allocation of educational resources, meets the needs of people and society, and adheres to the standards of program development and quality improvement and drawing upon the lessons from the development of higher education in other countries.  Keywords: higher education; historical change; traditional growth; sustainable development