Reviewerly/exHarmony huggingface dataset:
Stylistic Dominants Of Internet Communication

The article is devoted to the study of the stylistic space of the modern Internet and the identification of stylistic dominants in it.The relevance of the research is due to both extralinguistic factors (the digital revolution and the increasing influence of the Internet among other media channels) and intralinguistic factors (a special form of language existence in new communicative conditions).The novelty lies in the mediastilistic approach to the study of the language and style of the Internet, in which a specific stylistic space is allocated in the Internet space, structured by stylistic dominants.The Renaissance of audio in modern media culture and the emphasis on the conversational nature of written Internet communication expands the share of oral communication in the Internet.The language of the Internet has a special, oralwritten form of expression.But with the development of Internet communication, the share of verbality (oral communication) increases, as the Internet becomes the main platform for interpersonal and intergroup communication.The speed of information transfer and the focus on colloquial and informal communication provokes the development of compressed language forms.Compression and verbality contribute to the development of multimedia signs that contain emotions, paralinguistics, and visuals.Multimediality by means of various compressed signs (emojis, emoticons, etc.), provides a wide range of opportunities to bring online communication closer to offline communication, as it conveys emotionality, gestures, and prosodics of written speech.Verbality, compression and multimediality in close relationship with creativity form the specifics of the stylistic space of Internet communication.
Sharper bounds for online learning of smooth functions of a single variable

We investigate the generalization of the mistake-bound model to continuous real-valued single variable functions. Let $\mathcal{F}_q$ be the class of absolutely continuous functions $f: [0, 1] \rightarrow \mathbb{R}$ with $||f'||_q \le 1$, and define $opt_p(\mathcal{F}_q)$ as the best possible bound on the worst-case sum of the $p^{th}$ powers of the absolute prediction errors over any number of trials. Kimber and Long (Theoretical Computer Science, 1995) proved for $q \ge 2$ that $opt_p(\mathcal{F}_q) = 1$ when $p \ge 2$ and $opt_p(\mathcal{F}_q) = \infty$ when $p = 1$. For $1 &lt; p &lt; 2$ with $p = 1+ε$, the only known bound was $opt_p(\mathcal{F}_{q}) = O(ε^{-1})$ from the same paper. We show for all $ε\in (0, 1)$ and $q \ge 2$ that $opt_{1+ε}(\mathcal{F}_q) = Θ(ε^{-\frac{1}{2}})$, where the constants in the bound do not depend on $q$. We also show that $opt_{1+ε}(\mathcal{F}_{\infty}) = Θ(ε^{-\frac{1}{2}})$.
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations

Intent detection is a key component of modern goal-oriented dialog systems that accomplish a user task by predicting the intent of users' text input. There are three primary challenges in designing robust and accurate intent detection models. First, typical intent detection models require a large amount of labeled data to achieve high accuracy. Unfortunately, in practical scenarios it is more common to find small, unbalanced, and noisy datasets. Secondly, even with large training data, the intent detection models can see a different distribution of test data when being deployed in the real world, leading to poor accuracy. Finally, a practical intent detection model must be computationally efficient in both training and single query inference so that it can be used continuously and re-trained frequently. We benchmark intent detection methods on a variety of datasets. Our results show that Watson Assistant's intent detection model outperforms other commercial solutions and is comparable to large pretrained language models while requiring only a fraction of computational resources and training data. Watson Assistant demonstrates a higher degree of robustness when the training and test distributions differ.
Cognitive modeling as a means of assessment and formation of systemic thinking

The article is devoted to the development of systems thinking of students. Systems thinking nowadays becomes crucial for specialists in the field of environmental management. However, the peculiarities of modern education since school are not conducive to the widespread formation of system thinking skills. The authors propose to use cognitive modeling to develop systems thinking in students, as well as to assess the degree of formation of these skills.
Konservasi Temuan Makam Kuno Keramat dan Perkembangan Islam di Medan

&lt;p class="06IsiAbstrak"&gt;the purpose of this research is to find out how the distribution of the existence of ancient tombs in the city of Medan, the form of management of ancient tombs in the city of Medan based on the Law of Cultural Heritage No. 11 of 2010, and the contribution of the findings of ancient tombs to the development of Islam in the Village Martubung Medan. The method used is transect walk. This method is carried out as a strategy to map the existence of cultural heritage from the existence of complexes of ancient tombs. His findings that the identification of ancient tombs in the city of Medan, especially in Martubung village, namely the Tomb of Datuk Hasan, Tomb of Datuk Payung, Tomb of Datuk Tongah, Datuk Dadih and Tomb of Datuk Hitam. To maintain the cultural heritage, the government collaborates with local communities to conduct socialization and conservation activities that include physical protection, transfer, excavation and recording, maintenance. The discovery of five ancient Islamic tomb complexes in Martubung Village shows the historical events of the entry and development of Islam in North Sumatra and cultural changes through the process of human adaptation to the environment.&lt;/p&gt;
Status of Digital Literacy among Elementary and Secondary Students in Punjab

In this era, use of technology becomes more important than ever in daily life, therefore, the significance of digital literacy has been enhanced.It is now an essential skill to survive in the 21 st century.This study is about the digital literacy skill of elementary and secondary school students.The study was delimited to basic computer skills.The sample of students comprised of 200 students out of which 99 students were female and 101 were male students.To find the status of basic computer skills, students of elementary, and secondary school students (from science and Arts stream) were purposely selected.The data was collected through an online digital literacy tool where focus was to measure basic computer skills.There were 38 items in the tool and 30 minutes were given to the students to solve it.The score was also generated online.It was found that students were having less basic computer skills which may lead to weak digital literacy skills.Most of the students even do not know the basics of computers.One of the reasons of low or poor basic computers skills was the unavailability of computers and laptops in students' homes.It is recommended to make computers compulsory for secondary classes too so that student can handle the computer gadgets and can equip themselves with digital literacy skills.
A Model Proposal for Analyzing Open-Source Information in End-User Computers

Cloud, grid, parallel, volunteer, and opportunistic computing are different forms of distributed computing and each of these concepts addresses different problems in data analysis. One of the latest virtualization technologies, containerization, provides new capabilities with a thin layer on end-user desktop computers in the field of distributed computing. From the viewpoint of management information systems, the information is the key element in decisionmaking processes, and in the age of the Internet the information that is necessary for organizations is accessible from publicly available open sources. Every organization needs to operate an open-source intelligence process to use the open-source data in different levels of management. However, the problem is that each organization needs data scientists and hardware and software resources to fulfill this requirement. Big enterprise companies or governments can make an investment to solve this problem, but small and medium-sized enterprises or small institutions may not have enough budget. A distributed microservice-based data analysis model with the technology of Docker containerization on the end-user computers is proposed.
The Surge of Cryptocurrency in the Asia Pacific Economic Countries

Crypto currencies are computerized web based traded coupons. They are part of a block-chain technology system, which has procured admiration globally. It utilizes cryptographic scrips and sophisticated marks to assess exchanges. It preserves a strategic detachment from dual outlay of the analogous coins. This notion gave the world an intuition of operations without the intervention of a regulatory body, thus bringing down the transaction cost to its minimum. There is a dare need to better understand and utilize crypto currencies to its fullest potential. The prime purpose of this paper is to explore the financial and non-financial factors that corollaries the prices of these digital currencies in the Asia Pacific Economic Countries (APEC). This paper has explored the crypto currency attractive efficacy and the macroeconomic viability with its price. The significant relationship between the various components have disclosed the prominence of the cryptocurrency in the economic growth of the countries. The study is of significant help in identifying the components that can help in stabilizing the extreme volatility associated with cryptocurrencies. This paper is of prime importance for the legislators in identifying the law-giving areas, for the improved secured future economy
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THE TECHNOLOGY OF SEMI-AUTOMATIC META-MARKUP PROGRAM AND THE PRINCIPLES OF AUTOMATING THE MORPHOLOGICAL MARKUP OF THE KAZAKH LANGUAGE NATIONAL CORPUS TEXTS

When studying the Kazakh language, it is necessary to pay great attention to the field of corpus linguistics and study its theoretical and practical aspects of the world level. Special editions of scientific journals also publish articles on General and specific issues related to the creation and operation of text corpora around the world. However, it is known that Kazakh linguistics requires special study of many issues related to corpus linguistics. It includes: the definition of corpus linguistics and its main concepts, the place of corpus linguistics in the structure of linguistics, methods, etc. Theoretical and practical aspects of the above-mentioned corpus linguistics should also be taken into account when creating a database of texts in the Kazakh language based on a computer corpus. If corpus linguistics is formed as a special section of Kazakh linguistics, it will allow many specialists in the Kazakh language to use large-scale experimental materials, find the necessary language data and make appropriate changes. All this contributes to a new look at the empirical approaches to the reliability of research in the Kazakh language and the introduction of the most important language materials in the field of science.
Reconstruction of the Ancient River Network and Prospects of the Placer Gold Mineralization on the Southern Part of the Miass Intermountain Depression (South Urals)

We conducted the reconstruction of the ancient river network of the basins of the right tributaries of the Uy River Shartymka and Kiruda located in the southern part of the Miass intermountain depression within the Shartymskiy graben and its southern periphery. The sequence of placer formation from Cretaceous and Miocene karst-alluvial placers (Shartymsky graben) to the Eopleistocene and Neopleistocene has been established. In the valleys of the Shartymka and Kiruda rivers, areas of inherited development have been identified. This increased the reliability of estimation of the placer gold forecast resources. The most promising areas are recommended for geological exploration licensing.
Context-Specific Causal Discovery for Categorical Data Using Staged Trees.

Causal discovery algorithms aims at untangling complex causal relationships using observational data only. Here, we introduce new causal discovery algorithms based on staged tree models, which can represent complex and non-symmetric causal effects. To demonstrate the efficacy of our algorithms, we introduce a new distance, inspired by the widely used structural interventional distance, to quantify the closeness between two staged trees in terms of their corresponding causal inference statements. A simulation study highlights the efficacy of staged trees in uncovering complex, asymmetric causal relationship from data and a real-world data application illustrates their use in a practical causal analysis.
THE IMPLEMENTATION STUDY OF STORING AND REPORTING OF NARCOTICS AND PSYCHOTROPICS AT PHARMACIES IN DENPASAR CITY, BALI PROVINCE

Introduction: Storage and reports of narcotics and psychotropics in pharmacy must be carried out according to standard operating procedures which are based on legislation on maintaining safety, efficacy, and quality of the drugs. Objective: This study aimed to observe the implementation process of narcotics and psychotropic storing and reporting in accordance to the law in force in several pharmacies in Denpasar City. Methods: Five biggest pharmacies located in Denpasar City were picked by a purposive sampling method. This study used a descriptive observational method to provide a brief overview of the term and condition of narcotics and psychotropic storing and reporting that implemented in real-time in the pharmacies. Results: Overall, the study revealed that the majority of pharmacies were successfully in term of fulfilling the various aspects of narcotics and psychotropic storing and reporting, including the storage of drugs at the special cabinets, in special the implementation process of narcotics and psychotropic that cited in the Regulation of The Minister of Health of the Republic of Indonesia Number 73 of conditions according to safety and stability, FEFO and FIFO systems, manual and electronic stock cards system, separation of damaged and/or expired drug, inventory stock, and compliance and completeness of reporting using SIPNAP. Conclusion: Responsible pharmacist must always evaluate the storage and reporting of narcotics and psychotropic in the pharmacy to guarantee quality and maintain suitability of the pharmaceutical services process according to established standards.&#x0D; Keywords: forensic pharmacy, storing and reporting, narcotics and psychotropic, legislation
To Raise or Not To Raise: The Autonomous Learning Rate Question

There is a parameter ubiquitous throughout the deep learning world: learning rate. There is likewise a ubiquitous question: what should that learning rate be? The true answer to this question is often tedious and time consuming to obtain, and a great deal of arcane knowledge has accumulated in recent years over how to pick and modify learning rates to achieve optimal training performance. Moreover, the long hours spent carefully crafting the perfect learning rate can come to nothing the moment your network architecture, optimizer, dataset, or initial conditions change ever so slightly. But it need not be this way. We propose a new answer to the great learning rate question: the Autonomous Learning Rate Controller. Find it at https://github.com/fastestimator/ARC/tree/v2.0
The Use of Photovoltaic Concentrators as Applied to Tracking Solar Cooling System.(Dept.E)

At favourable conditions, the average intensity of solar power is about 0.8 KW/m2 at noon for a clear summer day in Kuwait, Assuming a photovoltaic system total efficiency to be 6%, the corresponding specific area required during this favourable atmospheric conditions will be about 20 m2/kW; which has to be increased considerably for the rest time of the day and year, unless a tracking solar system is used to limit to some extent the increase of this specific solar, area. In order to obtain a further decrease of such a specific solar area, conical concentrators are suggested to be used with the photovoltaic cells in addition to the tracking solar system.  In this paper a comparison between the experimental voltage-current characteristics of one type of photovoltaic cells with and without concentrators is presented; illustrating the design and shape of these concentrators when used with photovoltaic cells as applied to a solar cooling system in arid zones.
Neural Distributed Source Coding.

Distributed source coding is the task of encoding an input in the absence of correlated side information that is only available to the decoder. Remarkably, Slepian and Wolf showed in 1973 that an encoder that has no access to the correlated side information can asymptotically achieve the same compression rate as when the side information is available at both the encoder and the decoder. While there is significant prior work on this topic in information theory, practical distributed source coding has been limited to synthetic datasets and specific correlation structures. Here we present a general framework for lossy distributed source coding that is agnostic to the correlation structure and can scale to high dimensions. Rather than relying on hand-crafted source-modeling, our method utilizes a powerful conditional deep generative model to learn the distributed encoder and decoder. We evaluate our method on realistic high-dimensional datasets and show substantial improvements in distributed compression performance.
Operating Tor Relays at Universities: Experiences and Considerations

In today's digital society, the Tor network has become an indispensable tool for individuals to protect their privacy on the Internet. Operated by volunteers, relay servers constitute the core component of Tor and are used to geographically escape surveillance. It is therefore essential to have a large, yet diverse set of relays. In this work, we analyze the contribution of educational institutions to the Tor network and report on our experience of operating exit relays at a university. Taking Germany as an example (but arguing that the global situation is similar), we carry out a quantitative study and find that universities contribute negligible amounts of relays and bandwidth. Since many universities all over the world have excellent conditions that render them perfect places to host Tor (exit) relays, we encourage other interested people and institutions to join. To this end, we discuss and resolve common concerns and provide lessons learned.
On-the-fly Rectification for Robust Large-Vocabulary Topic Inference

Across many data domains, co-occurrence statistics about the joint appearance of objects are powerfully informative. By transforming unsupervised learning problems into decompositions of co-occurrence statistics, spectral algorithms provide transparent and efficient algorithms for posterior inference such as latent topic analysis and community detection. As object vocabularies grow, however, it becomes rapidly more expensive to store and run inference algorithms on co-occurrence statistics. Rectifying co-occurrence, the key process to uphold model assumptions, becomes increasingly more vital in the presence of rare terms, but current techniques cannot scale to large vocabularies. We propose novel methods that simultaneously compress and rectify co-occurrence statistics, scaling gracefully with the size of vocabulary and the dimension of latent space. We also present new algorithms learning latent variables from the compressed statistics, and verify that our methods perform comparably to previous approaches on both textual and non-textual data.
Issues in Internet Regulation in Nigeria: The need to Promulgate a Befitting Legislation

One of the most profound problems in modern Nigeria is the challenge of regulating the internet. As at the end of March 2019, the total number of internet users in Nigeria reached 111.6 million conveniently placing the country in the 7th place among the countries of the world with the highest number of internet users. There is no gainsaying that the internet has contributed immensely to the development of the country in all spheres but also the multidimensional atrocities that have been carried out via the internet cannot be gainsaid. The internet is an integral part of the structure of modern society. It is for this reason that it is no longer only the real space that should be regulated, the cyberspace has to be made subject to definite guiding principles. The paper briefly describes the history of internet regulation in Nigeria and incidences of internet abuse. Furthermore, the subject of internet censorship is discussed putting into consideration certain jurisdictions of the world. The paper also seeks to make a succinct review of the controversial Protection from internet falsehood and manipulation bill which is presently a subject of the legislative process at the senate arm of the National Assembly. The writer concludes by proffering some pragmatic recommendations which are believed to sustain the requisite capacity to ameliorate the present situation.
HetMAML: Task-Heterogeneous Model-Agnostic Meta-Learning for Few-Shot Learning Across Modalities

Existing gradient-based meta-learning approaches to few-shot learning assume that all tasks have the same input feature space. However, in the real world scenarios, there are many cases that the input structures of tasks can be different, that is, different tasks may vary in the number of input modalities or data types. Existing meta-learners cannot handle the heterogeneous task distribution (HTD) as there is not only global meta-knowledge shared across tasks but also type-specific knowledge that distinguishes each type of tasks. To deal with task heterogeneity and promote fast within-task adaptions for each type of tasks, in this paper, we propose HetMAML, a task-heterogeneous model-agnostic meta-learning framework, which can capture both the type-specific and globally shared knowledge and can achieve the balance between knowledge customization and generalization. Specifically, we design a multi-channel backbone module that encodes the input of each type of tasks into the same length sequence of modality-specific embeddings. Then, we propose a task-aware iterative feature aggregation network which can automatically take into account the context of task-specific input structures and adaptively project the heterogeneous input spaces to the same lower-dimensional embedding space of concepts. Our experiments on six task-heterogeneous datasets demonstrate that HetMAML successfully leverages type-specific and globally shared meta-parameters for heterogeneous tasks and achieves fast within-task adaptions for each type of tasks.