Reviewerly/exHarmony huggingface dataset:
Cryptocurrencies in an economic context

Cryptocurrencies are online protocols that serve as virtual currencies. The most famous cryptocurrency is bitcoin, which is based on a white paper published under the pseudonym “Satoshi Nakamoto” and which was launched in 2009. The cryptocurrency model has shifted financial transactions away from the use of central authorities to the described system using cryptographic protocols. Cryptocurrencies set the stage for participation of millions of international and anonymous participants, thereby resulting in a profound economic impact. Ten attributes characterize cryptocurrencies in today’s global economic environment: infancy, evolvement, volatility, inclusion, anonymity, legitimacy, energy, location, technology and no oversight. In the economic realm, these attributes suggest that countries differ in their ability to develop cryptocurrencies within the country or even to actively participate in cryptocurrency trading. Given the reliance on certain skills, technological infrastructure and government policies, there exist a variation worldwide in cryptocurrency usage across countries. The chapter also presents an overview on the key concepts discussed in this book.
A study on the direction of improvements of the KSL curriculum from the perspective of curriculum implementation factors.

This study explored in what directions the 2017 KSL curriculum shouldbe improved in order to be properly implemented in the field. To thisend, it was analyzed according to the implementation factors, directionof action, validity, clarity, and feasibility factors, and the followingimprovement measures were derived: (1) From the action directionfactor, it should be designed as a framework in which teachers canreconstruct the curriculum from the perspective of mutual adaptation.(2) In terms of validity, the contents of teaching and learning andlanguage materials should be organized according to achievementstandards. In addition, it is suggested that the 2017 KSL curriculumcould be organized so that it can be cross-referenced with theachievement standards of this curriculum by establishing therelationship between upper or other curriculum. (3) In terms of clarity,the contents of culture and academic language proficiency must bepresented in detail. In addition, it is required to specify the details ofteaching and learning methods by each school level and Korean level.(4) In terms of feasibility factors, it is suggested to present aneffective teaching and learning methods of this curriculum operated asa grade-free system, and to provide a curriculum manual.
Learning Resources Pages: Math Help: Home

This guide provides resources for students looking for learning assistance outside of class.
Coherent Hierarchical Multi-Label Classification Networks

Hierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the classes. In this paper, we propose C-HMCNN(h), a novel approach for HMC problems, which, given a network h for the underlying multi-label classification problem, exploits the hierarchy information in order to produce predictions coherent with the constraint and improve performance. We conduct an extensive experimental analysis showing the superior performance of C-HMCNN(h) when compared to state-of-the-art models.
Perception of Students Toward the Use of PowerPoint Presentations as a Teaching Tool

Nowadays, the usage of PowerPoint as a teaching tool in class rooms has become more popular due to innovative teaching pedagogy. PowerPoint improves the clarity of presentations, helps to illustrate the message, and engages the students. PowerPoint can be used to project visuals, which would otherwise be difficult to bring to the classroom. The main objective of the study is to understand the awareness of students about the use of Power-Point as a teaching tool in classrooms and to study the participation factor of students while PowerPoint is used. In general, most of the teachers prefer the teaching method of using PowerPoint because it delivers the material easily and the facilities in the PowerPoint make students more focused. The use of images, animations, videos, and sounds will attract the attention of students and is a part of innovation in teaching. Majority of students feel that PowerPoint is an effective tool for teaching. The atmosphere of the classroom becomes interesting when the teacher teaches using PowerPoint. The students preferred PowerPoint over the traditional lecture style in the teaching–learning process because they think it helps them to grasp the content easily.
Likelihood of organizations in the Los Angeles area implementing an earthquake early warning system

Author(s): Riopelle, Deborah | Advisor(s): Glik, Deborah | Abstract: The probability that a strong earthquake will strike Southern California in the near future is high. Although the San Andreas Fault is the fault probably most familiar to people living in in the area, in reality four faults (San Andreas, San Jacinto, Elsinore, and Imperial) produce about half of the significant earthquakes in the region. Additionally, countless other known and unknown faults contribute to earthquake risk in the area. For decades, scientists and disaster experts have been working on developing an earthquake early warning system for Southern California that would provide approximately 10-90 seconds of warning that strong shaking is heading to the area, giving individuals and organizations the opportunity to take protective action before the shaking arrives. This dissertation study explores the potential uses of earthquake early warning by organizations and assesses the potential for implementation of an early warning system by organizations in the four sectors studied. This study used a mix method approach. For the quantitative component of the study we conducted telephone interviews with 192 representatives of organizations in four institutional sectors which included education, emergency services, health care, and utilities and transportation. The qualitative component consisted of semi-structured interviews with three key informants from each of the same sectors. Findings from each component were then compared and combined to triangulate results as well as provide a fuller picture of potential uses, barriers to, and supportive factors for implementation of earthquake early warning by organizations in Southern California. In general, support for implementing the earthquake early warning system by the organizations in the study was mixed. The best predictors of organizations being highly likely to implement earthquake early warning included low perceived financial barriers to implementation, high perceived risk from earthquakes, and belief that even 10 seconds of warning could reduce injuries in a major earthquake. Many key informants expressed interested in the system, but also enumerated many perceived barriers to its implementation at their organization. Earthquake early warning has come a long way since the data were collected for this study, particularly in terms of communications technology and our ability to disseminate alerts to many individuals very quickly. However, many of the barriers identified in this study have not been addressed. Additionally, the new technology, messages, and mechanisms for warning individuals have not been evaluated. More research is needed if earthquake early warning is to be useful and feasible for use by organizations and businesses in Southern California.
Methods for improving the system of assessing pupils' knowledge based on the qualitological approach

The article is devoted to the problem of qualitological education of the future teacher. It shows the special importance of this type of vocational education in the modern sociocultural situation. The structure and content of the teacher's qualitological competence are considered, a strategy for its formation in the conditions of qualitological education is proposed. The effectiveness of the implementation of the program is ensured by the use of competence-oriented technologies, such as: case technology, dialogue technologies, portfolio technology, rating technology and others.
Prediction of DNA Hepatitis C Virus based on Recurrent Neural Network-Back Propagation Through Time (RNN-BPTT)

Hepatitis C Virus (HCV) is one virus that has a high mutation rate in the world. To predict the mutation can be used fundamental analysis and technical analysis. Fundamental analysis relies on external factors such as attributes attached to the primer and the isolate. While technical analysis learns the movement of the mutation itself by relying on graphs and mathematical formulas. This study combines fundamental analysis and technical analysis in predicting HCV mutations. Application of Recurrent Neural Network (RNN) method as a form of technical analysis and fundamental analysis is applied in the form of including some fundamental factor data as training datasets. RNN is a neural network that has a feedback connection to the neuron itself, or a previous neuron. RNN is able to reactivate actual data values in the past to be re-entered with actual data values at the moment. This study used Elman network architecture with Back Propagation Through Time learning algorithm (BPTT) and used Linear normalization. The problem when predicting HCV mutations is how to determine the best learning rate value. Therefore the fundamental approach will also be incorporated into the neural network. On the backward process, to calculate the value of weight correction is to multiply the value of learning rate with hidden neuron value. We propose that each neuron has an adaptive learning rate according to the condition of the neuron. Where each input neuron from this study is the result of the HCV primer normalized in preliminary research. Each primer has attributes that can be developed as decision support. Test results show the smallest error of the prediction process by RMSE are 0.0163000, with accuracy prediction value are 99%.
DIECI Lezioni di Italiano (A1)

DIECI Lezioni di Italiano (A2) Author: Euridice Orlandino, Ciro Massimo Naddeo Alma Edizioni New Italian Textbook with exercises, tests, real-life examples, and digital content. (Level A2) Free pick-up at our Midtown location *Due to major USPS delays, Speakitaly NYC is not responsible for any lost or damaged packages once they are shipped and the tracking number is provided.
Methodical tools for forming key competences on the subject “Spreadsheets” at school

This paper focuses on the key role of IT training in digital competence. It is about methodological aspects of studying spreadsheets on the topic “Table information processing” in secondary schools, the learning objectives and the specifics of the tasks they provide, the sustainability of the skills and the necessary competencies for their application in solving different problems. The results of a large-scale study involving 232 high school students and 94 university students have been presented, which show the durability of students’ knowledge and skills in solving problems and tasks with the help of spreadsheets in different spheres of public life. The aim of the authors is to show and analyze the results of the research.
Explaining Machine Learning Models of Emotion Using the BIRAFFE Dataset

Development of models for emotion detection is often based on the use of machine learning. However, it poses practical challenges, due to the limited understanding of modeling of emotions, as well as the problems regarding measurements of bodily signals. In this paper we report on our recent work on improving such models, by the use of explainable AI methods. We are using the BIRAFFE data set we created previously during our own experiment in affective computing.
Risk Assessment on Cloud Computing for The Learning System in The Education Environment

Purpose-Cloud computing as a service facility in online learning systems provides good solutions for educational institutions. Reasons for educational institutions to use cloud computing as a means of learning include cloud service providers providing sizable hardware and software resources, so educational institutions do not have to pay a large amount of money to buy hardware and software as an investment. But as long as this online learning system runs there will definitely be risks, for example performance problems from internet network connections. This study aims to discuss this problem. Methodology/approach In this study, firstly identifying dangerous or harmless activities to the activities carried out in cloud computing, then determining the impact, aspects, likelihood and severity of risks, and finally determining the level of risk from these activities using a risk matrix. Finding- By using a risk register in the learning system, risk analysis and evaluation can show the Incident, Cause, impact, risk type, risk category, aspect, likelihood, severity, and risk level for each dangerous or non-hazardous activity. Originality/value - The tools used to measure the level of risk for dangerous or non-hazardous activities for the learning system can be used for more extensive research aimed at measuring institutional-level risks in the education sector.
Energy Saving in Data Centers Through Traffic and Application Scheduling

Author(s): Zhou, Liang | Advisor(s): Bhuyan, Laxmi N. | Abstract: Energy saving in data centers is challenging due to workload fluctuation and strict application latency constraints. In this dissertation, we aim to develop a suite of practical techniques to improve the energy efficiency of data centers, ensuring that they are effective, scalable and responsive. We have studied this research topic from two perspectives: server and network load consolidation and scheduling of latency-sensitive applications. All our techniques have been experimentally shown to produce more energy savings compared to state-of-the-art techniques.It is known that servers consume maximum energy in a data center. We propose Goldilocks, a resource provisioning system for optimizing both power and task completion time by allocating tasks to servers in groups. Tasks hosted in containers are grouped together by running a graph partitioning algorithm that considers the frequency of communication between the containers. The groups are allocated to a subset of the servers near to each other and all idle servers are turned off to save power. As a next step, we make the data center network energy proportional by developing a distributed traffic consolidation scheme named DREAM. DREAM splits a TCP flow across multiple paths by adapting the path selection probability for sending a flowlet. The unused switches and links are disabled to save power.Several techniques have been developed in the literature to save energy on servers through Dynamic Voltage and Frequency Scaling (DVFS) and sleep-based schemes. We have developed EPRONS to minimize the overall data center's power consumption by trading-off network slack in favor of providing additional slack by using DVFS. We design a two-step DVFS scheme to save more power for latency-sensitive applications. Since accurate estimation of the latency is difficult, we extend the two-step DVFS with a neural network based predictor to judiciously boost the CPU frequency at the right time to catch-up to the deadline. Finally, in addition to optimizations at the Index Serving Node (ISN), we design a coordinated time budget assignment framework between the aggregator and ISNs in a search engine to improve search quality and latency.
Note on Generalized Cuckoo Hashing with a Stash.

Cuckoo hashing is a common hashing technique, guaranteeing constant-time lookups in the worst case. Adding a stash was proposed by Kirsch, Mitzenmacher, and Wieder at SICOMP 2010, as a way to reduce the probability of rehash. It has since become a standard technique in areas such as cryptography, where a superpolynomially low probability of rehash is often required. Another extension of cuckoo hashing is to allow multiple items per bucket, improving the load factor. That extension was also analyzed by Kirsch et al. in the presence of a stash. The purpose of this note is to repair a bug in that analysis. Letting $d$ be the number of items per bucket, and $s$ be the stash size, the original claim was that the probability that a valid cuckoo assignment fails to exist is $O(n^{(1-d)(s+1)})$. We point to an error in the argument, and show that it is $\Theta(n^{-d-s})$.
Culture-Aware Dialogue Management for Conversational Assistants

The cultural background has a great influence on the people’s behaviour and perception. With the aim of designing a culturally sensitive conversational assistant, we have investigated whether culture-specific parameters may be trained by use of a supervised learning approach. We have used a dialogue management framework based on the concept of probabilistic rules and a multicultural data set to generate a culture-aware dialogue manager which allows communication in accordance with the user’s cultural idiosyncrasies. Hence, the system response to a user action varies depending on the user’s culture. Our data set contains 258 spoken dialogues from four different European cultures: German, Polish, Spanish and Turkish. For our evaluation, we have trained a culture-specific dialogue domain for each culture. Afterwards, we have compared the probability distributions of the parameters which are responsible for the selection of the next system action. The evaluation results show that culture-specific parameters have been trained and thus represent cultural patterns in the dialogue management decision process.
Handout as the Practical Learning Tool

This learning device development was based on the observation at Public Vocational High School 3 Singaraja where the problems that occurred were 1) students only waited for teachers’ explanation, and 2) no applicative material that made the learning process difficult. The subject of this research and development was Basic Electricity and Electronic teacher from the class of X-TITL 1 in Public Vocational High School 3 Singaraja and its 35 students. The product testing was performed in two stages: 1) limited test, and 2) implementation test. The data instruments were validation and observation sheets, where the measurement aspect from the data collection was learning device validity and learning device practicality. The handout validation results obtained 80.6% score, the observation sheet of learning device implementation got 87.5% score, and the observation sheet of teacher’s activity obtained 92.6% score. The practical test of learning device was based on the analysis and observer assessment on the observation sheet of a learning device and teacher’s activity. The total percentage of learning activity was 80.4% (70% < PR < 85%), or a good category. The total percentage of teacher activity was 80.8% (70% < PG < 85%) or an active category. Thus, from two observations above, it could be stated that the learning device met the practicality criteria. Keywords: handout, valid, practical
Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction

Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data. While the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography.These irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small.In our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.
LANNS: A Web-Scale Approximate Nearest Neighbor Lookup System.

Nearest neighbor search (NNS) has a wide range of applications in information retrieval, computer vision, machine learning, databases, and other areas. Existing state-of-the-art algorithm for nearest neighbor search, Hierarchical Navigable Small World Networks(HNSW), is unable to scale to large datasets of 100M records in high dimensions. In this paper, we propose LANNS, an end-to-end platform for Approximate Nearest Neighbor Search, which scales for web-scale datasets. Library for Large Scale Approximate Nearest Neighbor Search (LANNS) is deployed in multiple production systems for identifying topK ($100 \leq topK \leq 200$) approximate nearest neighbors with a latency of a few milliseconds per query, high throughput of 2.5k Queries Per Second (QPS) on a single node, on large ($\sim$180M data points) high dimensional (50-2048 dimensional) datasets.
Proxy Network for Few Shot Learning

The use of a few examples for each class to train a predictive model that can be generalized to novel classes is a crucial and valuable research direction in artificial intelligence. This work addresses this problem by proposing a few-shot learning (FSL) algorithm called proxy network under the architecture of meta-learning. Metric-learning based approaches assume that the data points within the same class should be close, whereas the data points in the different classes should be separated as far as possible in the embedding space. We conclude that the success of metric-learning based approaches lies in the data embedding, the representative of each class, and the distance metric. In this work, we propose a simple but effective end-to-end model that directly learns proxies for class representative and distance metric from data simultaneously. We conduct experiments on CUB and mini-ImageNet datasets in 1-shot-5-way and 5-shot-5-way scenarios, and the experimental results demonstrate the superiority of our proposed method over state-of-the-art methods. Besides, we provide a detailed analysis of our proposed method.
LibGuides: Open Access: Home

Information and resources about open access (OA), including OA specific tools and types of OA.
Wild Birthplaces of Behavioral Media

Interactive digital technology provides an unprecedented new way to communicate through rich behaviors that sense and react to the world. Our most complex designs still pale in comparison to the richness of the behaviors of the simplest living creatures. We do not yet know the key techniques and aesthetics involved in arranging these configurations of creatures, environments, and computers to be able to truly harness this new behavioral medium. Just like living creatures, the behaviors of our technological devices are shaped by the context in which they develop. We need to expose our technology to a vast array of new situations, experiences, and contexts in order to mature programmable technology beyond its status as a simple tool into a ripe new medium. Naturalists studying animal behavior conduct their work in uncontrolled, wilderness field sites because it is only there that you can observe the full range of a creature’s behaviors that have evolved to fit the specific milieu. Likewise, contemporary Human–Computer Interaction researchers are conducting studies “in the wild” where technologies are experienced in situ. This chapter explores using different sites of scientific field work (expeditions, field courses, and field stations) as fertile areas for developing interactive media.