Reviewerly/exHarmony huggingface dataset:
Modelling of the distribution of Te, As, Bi, Sb, Sn (TABS) and Se in the Merensky Reef and the implication for PGE collection mechanisms in magmatic sulfide deposits

Two main models have been proposed to explain the collection of platinum-group elements (PGE) from silicate liquid by an immiscible sulfide liquid. In the first model, the distribution of PGE is chemically controlled by their very high partition coefficients into an immiscible sulfide liquid. Alternatively, it has been proposed that PGE self-organize as nanometer size clusters in the sulfide liquid, which could be captured by an immiscible sulfide liquid. The main difference between both models is that in the cluster model the distribution of PGE would be physically controlled by the surface properties of nanometer-sized particles. Consequently, the partition coefficients of PGE, and other cluster-forming elements, would not affect their distribution in the cluster model. A way of testing which mechanism is more appropriate is to model the expected distribution of chalcophile elements with different partition coefficients in a sulfide deposit, and then compare with measured values. If the cluster model is valid, the distribution of the elements should not be controlled by their partition coefficients. We have measured the concentrations of Te, As, Bi, Sb (TABS), Se and PGE in the parental magmas from which the Bushveld Complex have formed (Marginal Zone), and used the results to model the composition of the Merensky Reef. The components of the reef have been modelled as cumulate sulfide liquid, silicate liquid and cumulate (silicate minerals and oxides). The cumulate sulfide liquid was calculated using the zone refining equation and taking into account the different partition coefficients of PGE, Se and TABS between silicate and sulfide liquids, and an N factor of 20,000. The modeled concentrations closely resemble the measured values obtained for a section across the Merensky Reef at the Impala mine. The modeling also reveals that the distributions of PGE, Se, Te and Bi in the reef are essentially controlled by the presence of sulfide minerals, whereas As and Sb distributions are controlled by both sulfide minerals and melt component. This is because PGE, Se, Te and Bi are moderately to highly chalcophile elements, whereas As and Sb are only slightly chalcophile elements. Therefore, our results support the model of distribution of PGE, Se and TABS controlled by their different chalcophile nature, and do not favor their distribution to be physically controlled as suggested by the cluster model.
Time series activity classification using gated recurrent units

&lt;span&gt;The population of elderly is growing and is projected to outnumber the youth in the future. Many researches on elderly assisted living technology were carried out. One of the focus areas is activity monitoring of the elderly. AReM dataset is a time series activity recognition dataset for seven different types of activities, which are bending 1, bending 2, cycling, lying, sitting, standing and walking. In the original paper, the author used a many-to-many Recurrent Neural Network for activity recognition. Here, we introduced a time series classification method where Gated Recurrent Units with many-to-one architecture were used for activity classification. The experimental results obtained showed an excellent accuracy of 97.14%.&lt;/span&gt;
Knowledge economy and digital bureaucracy. What do experts discuss?

The problems discussed at the sessions of the Expert Board for Knowledge Economy Management and the Expert Board for Debureaucratization in Education and Science of the State Duma Committee for Education and Science are reviewed. New challenges of swollen digital bureaucracy, distance work modes and need for verified expertize call for shaping appropriate national policy, are discussed. The focus is made on efficiency of administration in education and science at the stage of digital transformation.
Blockchain Technology Trends in G20 Countries: The Case of Saudi Arabia

This study aims to explore the usage trends of blockchain technology in the G20 countries, with a particular focus on Saudi Arabia. As an emerging technology, blockchain is already being widely used in a number of industries including in the financial and supply chain sectors. For the purpose of this research, a questionnaire was developed to explore the adoption trends of blockchain technology in Saudi organizations. Saudi Arabia was chosen to be the case study for the research topic, as it is one of the largest oil producers in the world and holds the presidency of the G20 in 2020. The results of the study showed that some organizations in Saudi do not currently intend to implement blockchain technology and there is a lack of knowledge in some industries about this technology. Some of the obstacles preventing blockchain technology adoption were also identified.
custom: Does Foreign Aid Influence Corruption? New Evidence in East Africa Community Member Countries

The key objective in this paper is to examine the foreign aid impact on corruption in East Africa Community Member countries. The paper employed Pooled Ordinary Least Square (Pooled OLS), Random Effect (RE) and Fixed Effect (FE) approach to find out the external aid effects on corruption. The study used panel data covering from the year from 1996 to 2017 for 5 EAC member nations. The study findings revealed that foreign aid flow has reduced impact on the level of corruption in EAC nations. Based on the findings which found that aid flows reduce the level of corruption in EAC member countries, it is suggested that cooperation between donor countries should be strengthen when allocate aid and through attaching conditions among other things which help to improve governance and institutional framework. In addition, the allocation of aid to the poor countries should be based on selective approach in such way that major conditions for a nation to meet the requirements for aid should be institutional quality reforms.
African Economic Development

This book challenges conventional wisdoms both about economic performance and about policies for economic development in African countries. Its starting point is the striking variation in economic performance: unevenness and inequalities form a central fact. The authors highlight not only differences between African countries but also variations within countries, differences often organized around distinctions of gender, class, and ethnic identity. For example, school dropout and neonatal mortality have been reduced, particularly for some classes of women in some areas. Horticultural and agribusiness exports have grown far more rapidly in some countries than others. These variations (and many others) point to opportunities for changing performance, reducing inequalities, learning from other African policy experiences, and escaping the ties of structure and legacies of a colonial past. The book rejects teleological illusions and Eurocentric prejudice, but does pay close attention to the results of policy in more industrialized parts of the world. Seeing the contradictions of capitalism for what they are—fundamental and enduring—may help policy officials protect themselves against the misleading idea that development is likely to be a smooth, linear process, or that it would be were certain impediments removed. The authors criticize a wide range of orthodox and heterodox economists, especially for their cavalier attitude to statistical sources. Drawing on decades of research and policy experience, they combine careful use of available evidence from a range of African countries with heterodox political economy insights (mainly derived from Kalecki, Kaldor, and Hirschman) to make the policy case for specific types of public sector investment.
A Study on the Effect of Pair Check Cooperative Learning in Operating System Class

In the 4th Industrial Revolution, the competitiveness of the software industry is important, and as a solution to fundamentally secure the competitiveness of the software industry, education classes should be provided to educate high quality software personnel in educational institutions. Despite this social situation, software-related classes in universities are largely composed of competitive or individual learning structures. Cooperative learning is a learning model that can complement the problems of competitive and individual learning. Cooperative learning is more effective in improving academic achievement than individual or competitive learning. In addition, most learners have the advantage of having a more desirable self-image by having a successful experience. In this paper, we apply a pair check model, which is a type of cooperative learning, in operating system classes. In addition, the class procedure and instruction plan are designed to apply the pair check model. We analyze the test results to analyze the performance of the cooperative learning model.
Introduction to the Minitrack on Explainable Artificial Intelligence (XAI)

The use of Artificial Intelligence (AI) in the context of decision analytics and service science has received significant attention in academia and practice alike.Yet, much of the current efforts have focused on advancing underlying algorithms and not on decreasing the complexity of AI systems.AI systems are still "black boxes" that are difficult to comprehend-not only for developers, but particularly for users and decisionmakers.
Toward semantic representation of middleware services

Middleware is a middle-tier software that supports communication between two or more different applications and shared services. Managing the complexity and heterogeneity of distributed infrastructures is an important aspect of middleware. Middleware can provide a simple programing environment for the developer of a distributed application to support communication, information exchange, object management, and message sending, in addition to providing many functions for building distributed systems. Many classifications and definitions have been provided for middleware that emphasize giving middleware and its factors global appeal. In this paper, an ontology for middleware services is proposed to provide a global appeal for middleware, and to enrich the description of middleware services. Using this ontology, we can discover and classify incoming services into their appropriate forms according to the specifications and characteristics. The proposed ontology is helpful for users to find suitable services according to their own preferences.
Artificial Intelligence within the Detection of carcinoma

Breast cancer is usually found once symptoms seem. However, some ladies with carcinoma haven't any symptoms, that is why regular carcinoma screening is inspired. carcinoma screening with diagnostic procedure has been shown to enhance prognosis ANd scale back mortality by detection sickness at an earlier stage. However, several cancers area unit lost on screening diagnostic procedure. Screening for carcinoma with diagnostic procedure has been introduced in numerous countries over the last thirty years, at the start mistreatment analog screen-film-based systems and, over the last twenty years, transitioning to the employment of absolutely digital systems. With the introduction of conversion, the pc interpretation of pictures has been a theme of intense interest, leading to the introduction of computer-aided detection (CADe) and diagnosing (CADx) algorithms within the within the. though they were introduced with high expectations, the potential improvement within the clinical realm didn't take place, largely thanks to the high variety of false positive marks per analyzed image. The researchers used MammoScreen, AN AI tool by the corporate Therapixel which will be applied with diagnostic procedure to help in cancer detection. Mammo Screen is intended to spot regions suspicious for carcinoma on two-dimensional digital mammograms, assess their probability of malignancy supported a whole set of 4 views, and generate a group of image positions with a connected suspicion score.
Desain Jaringan Komputer Terintegrasi Menggunakan Arsitektur Campus LAN

The development of Information and Communication Technology (ICT) to access and provide information quickly and accurately becomes very important for an organization (commercial, tertiary, government, or individual). With the rapid development of ICT, it is necessary to consider the dangers and disadvantages of misuse in local network services or in internet-based applications. Campus LAN (Local Area Network) architecture was chosen as the standard used in computer network design. The network architecture approach in this study is a enterprise architecture approach that is used for organizational logic and planning throughout the organization and aims to determine how information technology will support business processes. This method begins by identifying the infrastructure and network security needs, as well as how the information technology system supports the process of activities for all parts of education, administration and other services.
Multi-task deep representation learning method for electronic health records

Electronic health records (EHRs) data plays an important role in the development of healthcare undertaking. There are many challenges in mining EHRs, such as temporality, irregularity, sparsity, bias, etc. Thus effective feature extraction and representation are key steps before any further applications. In this paper, we propose a multi-task deep representation learning method (MTDRL) with the objective of extracting the valuable clinical information from raw data and learning an effective and interpretable patient representation. Firstly, MTDRL utilizes Bidirectional Gated Recurrent Unit (BiGRU) as an encoder to learn the hidden state vectors which are used as inputs for the following networks. This encoding part can be seen as a shared network for all tasks. Secondly, patient's in-hospital mortality prediction and sequence reconstruction are simultaneously conducted based on the encoding network. Specifically, an attention mechanism and a fully-connected layer are incorporated in prediction task and BiGRU is implemented in the other task to reconstruct the visit sequences. Finally, we apply MTDRL to real EHRs data and the experimental results demonstrate that MTDRL is capable of learning more effective patient representation and has a significant improvement in the performance of patient's in-hospital mortality prediction. Meanwhile, the prediction results can be effectively interpreted with the attention mechanism and provide a clinically meaningful references.
Nominative Case Unmarking in Korean

This paper investigates Nominative Case drop/ellipsis or unmarking in Korean. Kwon and Zribi-Hert (2008) and H. Lee (2020) claim that Nominative Case unmarking occurs in direct perception situations where identification of the agent role of an event participant (represented by a Nom Case-less subject NP) can be grounded in the here and now of utterance time. They go on to provide an information structure-theoretic account of the interpretive contrast between Nom Case-less and Nom Case-marked subject NPs in terms of f(ocus)-structure. They propose that Nom Case-less subject NPs are restructured as the matrix focus of the neutral thetic f-structure. This paper explores a new avenue, arguing that Nominative Case unmarking follows not from the information structure of the whole sentence in question, but essentially from the syntactic structure and interpretational gamut of Korean determiner-less bare common NPs where Nom Case is not marked. More specifically, it is proposed below that in Korean, contextually salient entities tend to be grammatically encoded by Nom Case-less bare NPs, thus the Nom Case unmarking option being attributed to the syntax and semantics of bare common NPs in general in this language.
Youtube Channel Development on Education: Virtual Learning Solutions during the Covid

Covid-19 pandemic makes all activities that involve many people must be limited. Likewise, with lecture activities, it must be diverted online. The availability of supporting facilities and infrastructure is the main problem. This study aims to provide a solution by developing a YouTube Channel that contains learning material in applied statistics courses. Development is carried out using the ADDIE model which consists of the Analysis, Design, Development, Implementation, and Evaluation stages. Video creation and editing using Camtasia. After the learning media is developed, it is uploaded to the YouTube Channel and integrated into the Google Classroom. Based on the results of evaluations conducted by student users, it is known that learning media through the YouTube Channel that was developed is very useful, easy to use, and has a relatively very good quality. Clear evidence of media utilization can be seen from the results of the YouTube Channel analysis which shows a significant development of the viewer.
On the use of Ontology Design Pattern for Legal Knowledge Base Engineering

In this position paper we present some advantages, that the use of Ontology Design Pattern (ODP) can have for Legal Knowledge Base Engineering and also possibilities, why the widespread use of ODP in the legal domain is still lacking. Ontologies and the knowledge bases based thereon are seldom reusable and interoperable. This results in most ontologies being built from scratch, which in turn increases the cost of ontology engineering. This can be remedied by the increased use of ODP in ontology engineering. ODP are modelling solutions to solve recurrent ontology development problems. The increased use of ODP would increase the reusability and interoperability of the resulting ontologies and would therefore decrease their engineering cost. Unfortunately, only a few ODP exist for the legal domain, since their development can be difficult and their added value has not been convincingly demonstrated yet. The second notion is due to the fact, that too few ODP are publicly available and ready to use. To standardize and accelerate the development of legal ODP, we aim to develop a methodology for creating ODP, tailored to the specific needs of the legal domain. In this paper we will present the basic structure of this methodology, which we will further developed in future work.<br/>
Static Schwinger-Level Electric Field Nonlinearities and Their Significance to Photons and Photon Entanglement

In this work, we postulate that Schwinger’s threshold for a dynamic electric field intensity to induce spatial nonlinearity is a special case and, more generally, it is the threshold field for both static and dynamic electric fields. Fields of this magnitude induce negative-energy charges to adapt positive energy attributes; within an atom, they also support interstate energy transfers and intrastate chaotic mixing of time-varying fields. Nonlinearity-induced chaos forms the basis for the probabilistic nature of photon creation. Answers to physical problems at atomic and lower scales continuously evolve because chaotic-like electron movements change their configurations on a time scale of 10 zs. Within atoms, frequency mixing that creates an optical frequency field occurs in the nonlinear region surrounding the nucleus. On a probabilistic basis, a ring of vacuum charge can be induced that forms into an equivalent waveguide, which confines the energy as it travels permanently away from the atom. The propagating relativistically augmented fields losslessly induce charges that bind and protect the energy-carrying fields. The photon charge-field ensemble is a closed system and possesses all first-order photon properties, including zero rest mass and permanent stability. For near-neighbor photons traveling at a speed approaching c , we find a small constant force between them that is dependent upon their relative spin orientations. Our model shows that the radius of a photon is ≈10 am and that photon wavelength information is coded by energy.
Deep Retrieval: An End-to-End Structure Model for Large-Scale Recommendations

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model and then use maximum inner product search (MIPS) algorithms to search top candidates, leading to potential loss of retrieval accuracy. In this paper, we present Deep Retrieval (DR), an end-to-end learnable structure model for large-scale recommendations. DR encodes all candidates into a discrete latent space. Those latent codes for the candidates are model parameters and to be learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the latent codes is performed to retrieve the top candidates. Empirically, we showed that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline.
The PERFORM Mask: A Psychophysiological sEnsoRs Mask FOr Real-Life Cognitive Monitoring

Everyday life is driven by a wide range of mental processes organized in cognitive, emotional and executive functions. The assessment of these abilities could be improved thanks to the rising of Virtual Reality (VR) technologies, that show a more ecological validity in respect to the artificial laboratory settings. Moreover, mental processes can be investigated via electrophysiological measures, due to the modulation of deep structures controlling the autonomic system, and in turn peripheral organs activity. According to scientific literature, measurements could derive from sensors over periocular area, that is the same area covered by a typical VR headset.
Corpus analysis of the collocations of the transitive verbs owaru and oeru

The transitivity of Japanese verbs is a topic which had been widely discussed in Japan even before the beginning of the Meiji period. This paper focuses on the transitivity of the verbs owaru 終わる (to end [trans./intrans.]) and oeru 終える (to end [trans.]). It encompasses the analysis of collocations of the two verbs and examines their objects in the pattern ‘N wo owaru’ and ‘N wo oeru’. The aim of this research is to shed new light on the usage of the two verbs. The analysis of collocations and verbal forms that accompany them gives a picture that shows collocations grouping into individual semantic categories as well as verbal forms that co-occur in each category.
A Composer Looks at Music, Ethics, and Aesthetics: An Interview with Edward Green

Interview with Edward Green.
Intensity of Senior High School Students using Local Language in daily life

Abstract. This study used a survey approach which aims to examine how the intensity of the use of local language in high school students.  Participants were high school students who were selected purposively by considering gender (50% boys and 50% girls) and the area of residence (50% suburban area and 50% rural area).  The research instrument used was in the form of 3 survey questions consisting of 1) how often to use local languages in schools, 2) how often to use local languages in the community, and 3) how often to use local languages in the family. The data collection process is carried out online using the google form platform.  The verified data were analyzed using descriptive statistical methods and inferential statistics using the SPSS 26 application. Finding show that 1) students in urban areas use local languages less  than students in rural area, 2) students use more local languages in the home environment than in the school environment and social environment, 3) there is no significant difference in the use of local languages between male students and female students. It can  be concluded that gender is not a factor affecting language attitudes for the context of using local languages.
La trilogia di Merežkovskij "Cristo e l'Anticristo" (traduzione di Giorgia Rimondi)

Italian translation of O trilogii D.S. Merežkovskogo Christos i Antichrist by Zara Minc.Translated by Giorgia Rimondi (Universita per Stranieri di Siena)