Reviewerly/exHarmony huggingface dataset:
Internet Connectivity Among Indigenous and Tribal Communities in North America - A Focus on Social and Educational Outcomes

Broadband access is an important part of enhancing rural community development, improving the general quality of life. Recent telecommunications stimulus projects in the U.S. and Canada were intended to increase availability of broadband through funding infrastructure investments, largely in rural and remote regions. However, there are various small, remote, and rural communities, who remain unconnected. Connectivity is especially important for indigenous and tribal communities to access opportunities for various public services as they are generally located in remote areas. In 2016, the FCC reported that 41% of U.S. citizens living on tribal lands, and 68% of those in the rural areas of tribal lands, lacked access to broadband, and that the lack of service hindered tribal nations from building their internal structures for self-governance, economic opportunity, education, public safety, and cultural preservation. It is significant to study the indigenous and tribal communities to bridge the digital divide and bring transformative change to these communities. Our findings show some geographic, economic, technological, and generational challenges for connectivity, some unique uses of connectivity for cultural practices, and its impact on educational outcomes in these communities. Since the inception of Southern California Tribal Network, high school graduation has become radically more attainable in these communities due to additional resources made available via the Internet. Connected North promoted interactive Internet learning for students in remote regions to combat endemic local social issues such as a low graduation rate of approximately 20%, and the highest global per capita suicide rate among youth. The contribution of this study is three-fold. First, we identify the geographic, economic, technological, and political challenges for reliable connectivity among indigenous and tribal communities. Second, we show how some tribes and advocacy groups are handling the connectivity themselves, such as operating community networks and remote educational services. Third, we provide an overview of potential impacts of broadband, primarily at the social and educational levels among indigenous and tribal communities. This study suggests approaches for more sustainable connectivity facilities to remote Indigenous communities and reinforces the need for community-driven initiatives to ensure connectivity is conducted in a culturally and technically appropriate manner.
APLIKASI TEKNOLOGI 3D PRINTER DALAM RANGKA PENGEMBANGAN PEMBUATAN KOMPONEN MINIATUR DI KARANG TARUNA DUSUN BENDUNGAN

Replica is an imitation of an object in a size that is greatly reduced by the level of precision and detail that resembles the original object. One of the replica commonly made is a replica of transportation, especially buses, and one of the producers is Karang Taruna Dusun Bendungan , which is located in Karangmojo Subdistrict , Gunung Kidul District . Making replica so far has been done manually by relying on the cutter as a forming tool. In making manuals there are several obstacles faced, namely difficulties when making molds which include cowl molds, seat molds, AC cover molds and other parts. In addition, making miniature manually has disadvantages such as not being precise in shape and size with the original object and if there is a small change in a particular part, then it must be made from scratch again. Based on these constraints the team provided a solution by making a 3D printer machine that could be used to support bus replica production. Making a 3D printer machine with dimensions of 48 x 46 x 44 cm got a positive response. I n addition, the conducted training were enthusistically participated by the members. Utilization of 3D printer machines greatly helps the production process and increases the efficiency of time by 76% at the stage of making master prints.
Quantum Features of Atom–Field Systems in the Framework of Deformed Fields

We propose a new kind of Schrödinger cat state introduced as a superposition of spin coherent states in the framework of noncommutative spaces. We analyze the nonclassical features for these noncommutative deformed states in terms of the main physical parameters. The physical importance of deformed states is that they provide a convenient description of a large set of laser systems. As an application, we develop the Jaynes–Cummings model by considering the interaction among atoms and cat state fields associated to deformed spin algebras. In this context, we show the dynamical behavior of the nonlocal correlation and nonclassical properties in these quantum systems.
Securing Smart Homes using a Behavior Analysis based Authentication Approach

This paper presents TRICA, a security framework for smart homes. When using controlling apps (e.g., smartphone app), TRICA makes sure that only legitimate users are allowed to control their Internet of Things (IoT) devices. Leveraging User Behavior Analytics (UBA) and Anomaly Detection (AD) techniques, TRI CA collects and processes the historical cyber and physical activities of the user in addition to the historical states of the smart home system to build a One Class Support Vector Machines (OCSVM) model. This model is then used as a baseline from which anomalous commands (i.e., outliers) should be detected and rejected, while normal commands (i.e., targets) should be considered as legitimate and allowed to be executed. Experiments conducted on adapted real-world data properly show the feasibility of such user behavior-based authentication approach. TRICA exhibits low false accept and false reject rates ensuring both security and user convenience, respectively.
Monte Carlo Tree Search player for Mai- Star and Balance Evaluation

The paper presents Artificial Intelligence (AI) players developed for the game Mai-Star's characters. The Monte Carlo Tree Search algorithm simulates player characters, geishas. Mai-Star is a card game for three to six players. Each player can have multiple cards at one moment. Almost every card has an associated effect, depending on the rules adopted. The game has Geisha that allow players to make some special actions called abilities. The AI player uses the Single Observer Information Set Monte Carlo Tree Search (SO-ISMCTS) algorithm. We developed a Mai-Star computer game with an interface to interact with, for human competitive testing. We also implemented a random AI player. Using statistical evaluations of the generated AI players, we find that the game is unbalanced for any one-versus-one match-up. It further suggested that one of the geishas named Oboro has features that give her an advantage over other geishas for winning. Oboro was re-played against the other Geisha after decreasing her abilities (a nerf), which confirms how amazing her powerset was as Oboro's winning rate decreased from ninety-four percent to a still astonishing seventy-one percent.
Genens: An AutoML System for Ensemble Optimization Based on Developmental Genetic Programming

We propose an AutoML system for pipeline optimization based on developmental genetic programming - genens. It is built atop of scikit-learn pipelines, and it focuses on both hyperparameter and architecture optimization. Compared to existing systems, it enables to optimize more complex ensembles, while exploring simpler models at the same time. The system has been evaluated on selected benchmark datasets from the AutoML benchmark, producing competitive results.
Thoughts on Research Imperatives in Data Fusion

Concepts and principles of Data Fusion gained some specificity as far back as in the mid-1980’s, and the field has been maturing now for some decades, with societies, journals and expansive research thrusts in academia, industry and government. Nevertheless, there are a number of major deficiencies in capability that remain to be addressed and have prevented the field from realizing some of its major potential contributions to a wide range of application domains. Among such shortfalls are: cross-level synergies (meaning fusion model Levels; see the references cited below), robust methods for association of disparate/non-commensurate data, advances in capability to associate and fuse hard and soft data, realization of adaptable, multi-algorithm systems, and well-defined, efficient and effective entry points for human involvement and exploitation, among others. This paper reviews the state of the art in these named areas and is a statement about the need for framing research imperatives for potentially major advances in the field.
Human Dimensions in the Sound Universe

Human dimensions in the sound universe can be recognized in the interaction of two complex spheres of relations. Nevertheless, as the author have argued in detail on another occasion, specific features are determined at the level of phonemes and appear in sound symbolism mostly in a combined state, where their symbolic value is always contextual. In Japanese culture the people find certain conventionalized ways of representing nature, typically expressed in words like kikinashi, a stylized way of interpreting natural sounds, especially bird and insect voices; and mitate, a conventional way of considering something fictitious, mimetic or artificial as real, authentic or natural. As for musical instruments, the effort has been made to exclude noises and to determine precisely the pitch of sounds. A highly artificial use of sound symbolism in musical composition is found in the work of some modern Western composers, such as Arnold Schonberg, Luciano Berio and Charles Amirkhanian.
Armonia sinfonia omologia. Dall’eccezionalità della consonanza all'armonia dei contrari passando per l'emancipazione dalla dissonanza: von Helmholtz, Platone, Eraclito

The idea of resonance is analyzed through its mode of operation: the necessary agreement between resonant and resonator and the consonance that derives from it. The concept of exceptionality of the consonance, due to Hermann von Helmholtz, is examined, and it is brought back to the tradition of ancient philos ophy, to Plato, who indicates in the One the destiny over the dissonance, in opposition to Heraclitus, who affirms the harmony of opposites.
Tsinghua University Neural Machine Translation Systems for CCMT 2020

This paper describes the neural machine translation system of Tsinghua University for the bilingual translation task of CCMT 2020. We participated in the Chinese $$\leftrightarrow $$ English translation tasks. Our systems are based on Transformer architectures and we verified that deepening the encoder can achieve better results. All models are trained in a distributed way. We employed several data augmentation methods, including knowledge distillation, back-translation, and domain adaptation, which are all shown to be effective to improve translation quality. Distinguishing original text from translationese can lead to better results when performing domain adaptation. We found model ensemble and transductive ensemble learning can further improve the translation performance over the individual model. In both Chinese $$\rightarrow $$ English and English $$\rightarrow $$ Chinese translation tasks, our systems achieved the highest case-sensitive BLEU score among all submissions.
SpaceCRAFT VR: an Event-Driven, Modular Simulation Platform with Fully-Asynchronous Physics

The Space Collaborative Realtime Analysis and Flight Toolkit (SpaceCRAFT) VR Platform is a new simulation platform that makes use of modern game engines and recent innovations in software architecture to enable immersive, multiuser realtime interaction in Aerospace simulations. By developing a generic data structure that is capable of incorporating multiple high fidelity scientific models and external simulations, we have empowered SpaceCRAFT applications to provide instant access to design insights, visualizations, and training experiences that previously required hundreds of hours of work on custom interface code. The most powerful aspects of the design of this platform are its efficient, network-synchronized generic data structures and handling of the combination of multiple physics model outputs from disparate sources in the propagation of simulated objects. Our system architecture approach of "asynchronous everything" has led to breakthroughs in the modularity of simulations, to the point of never needing to adapt flight software to fit a simulation structure.
Deep Learning for Indonesia Standard Industrial Classification

Indonesia Standard Industrial Classification (Klasi-fikasi Baku Lapangan Usaha Indonesia or KBLI) is a classification reference developed by Statistics Indonesia (BPS) to classify any industry according to activity it carries out. Due to a large number of activities covered in KBLI, classification is usually performed by the experts using the industry activity description. Therefore, the time necessary to classify depends on the number of experts. That means as the data size grows, the process becomes much slower. One way to lessen the amount of knowledge needed is to recommend activities similar to the textual description provided. To realize that, we build a deep learning model of Gated Recurrent Unit, fastText word vectors, and label smoothing regularization (LSR) loss. We used 2016 BPS Economic Census data and BPS official KBLI guidebook as train and test data. Our results show model is able to recommend activities matching the input. Model capability to learn general traits of each activity can be seen from the similarity of the top recommendations. This study highlights the possibility of deep learning assisted classification, allowing the process to be carried out without much knowledge by leveraging existing data.
Technologies and principles of unstructured distributed data processing in the context of modern media content providing

Purpose The COVID-19 pandemic is creating serious challenges for modern society that leads to develop new information models and methods of digital monitoring not only of the spread of the virus, but also of the socio-economic environment. Materials and methods: As sources for clarifying the parameters of such models, it is advisable to choose not a limited set of predefi ned Internet sources, but unstructured media data on an unlimited set of resources, which leads to the need to build a system for complex monitoring of social phenomena. Such system can supplement and correct mathematical and information models for the spread of viruses, aimed at minimizing the damage caused by any pandemic. Results: It is proposed to create a software system that includes a Data Retrieving subsystem (for collecting and preprocessing media data) combined with a headless browser. This allows to build a system for monitoring of social phenomena, complementing mathematical and information models of the spread of viruses, aimed at minimizing the damage they cause. The feature of developed system is the using of a natural language processing framework based on the associative-ontological approach, and software implementation of the adaptive-behavioral SEIR model, as well as a subsystem for interpreting the collected data, generating metadata for identifying and correcting the model. Conclusions: The proposed system allows to make more balanced management decisions based on the analysis of the current situation in the infosphere. An additional advantage of the system is the ability to identify poorly predictable reactions of society to certain events expressed in media content.
Finding improved predictive models with Generalized Boosted Models on Hungarian Myocardial Infarction Registry

In this paper, we present new predictive modelling results achieved with Generalized Boosted Models (GBM) on the dataset of Hungarian Myocardial Infarction Registry (n = 47,391). We investigated patients hospitalized with acute myocardial infarction from two aspects, namely the 30-day and 1-year mortality. The ROC AUC values of our new models for predicting 30-day mortality were 0.847 and 0.839 (training and validation set), while for the 1-year models these were 0.828 and 0.821, respectively. These performance values represent a strong and stable learner with almost the similar predictive power as our previously published random forest models'.
U-Vote: Ubiquitous Voting Model For General Election in Global Pandemic

for democratic countries, elections are usually scheduled according to applicable law. At the end of 2019, the world was shocked by the discovery of a new corona virus variant in Wuhan province - China, which was later named Covid-19. Not only in China, all countries in the world are affected by a pandemic in which people are not allowed to touch one another and gather directly. All social, cultural and political activities cannot be carried out, including elections that are scheduled according to the constitution. E-voting technology that has been around for about 30 years can be the solution as an alternative method to carry out elections in the pandemic era. The contribution of this research is the ubiquitous voting model for general elections, changing traditional e-voting for elections in the pandemic era, so that in practice, voters do not need to go to the polling station. Elections use the existing technology in the community and are carried out wherever voters are located, so that elections will continue to take place during the pandemic.
A comparison study between artificial neural network (ANN) and Genetic algorithms (GA) and Box-Jenkins (BJ) modeling in chaotic time series prediction

Predicting the future behavior of chaotic time series in the last decade has become an important subject in statistical science. In this article, we are interested in the application of artificial neural networks and genetic algorithms to estimate the parameters of the chaostime series simulated (Logistics Map Template), to confirm the effectiveness of these new mechanisms, as well as the method Box ; Jenkins then compare the results obtained.
Improving Quality of Code Review Datasets – Token-Based Feature Extraction Method

Machine learning is used increasingly frequent in software engineering to automate tasks and improve the speed and quality of software products. One of the areas where machine learning starts to be used is the analysis of software code. The goal of this paper is to evaluate a new method for creating machine learning feature vectors, based on the content of a line of code. We designed a new feature extraction algorithm and evaluated it in an industrial case study. Our results show that using the new feature extraction technique improves the overall performance in terms of MCC (Matthews Correlation Coefficient) by 0.39 – from 0.31 to 0.70, while reducing the precision by 0.05. The implications of this is that we can improve overall prediction accuracy for both true positives and true negatives significantly. This increases the trust in the predictions by the practitioners and contributes to its deeper adoption in practice.
A Design of Improvement Method of Central Patch Controlled Security Platform Using Blockchain

The enterprise patch management system is highly dependent upon a central management server and typically suffers a single point of failure. This study suggests a hyperledger fabric blockchain-based distributed patch-management system and verifies its technological feasibility through prototyping. This is the first study to use blockchain technology for a patch-management system. It not only provides a higher level of security and availability but also affords the basis for further review when planning new business discovery of the industry to apply blockchain technology.
Early Detection of Pathogenic Social Media Accounts

This chapter introduces a time-decay causality metric and incorporates it into a causal community detection-based algorithm to identify PSMs within a short time frame around their activity. The proposed algorithm is applied to groups of accounts sharing similar causality features and is followed by a classification algorithm to classify accounts as PSM or not. Unlike existing techniques that take significant time to collect information such as network, cascade path, or content, our scheme relies solely on action log of users. Results on the ISIS-B dataset described previously, demonstrate effectiveness and efficiency of our approach. We achieved precision of 0.84 for detecting PSMs only based on their first 10 days of activity; the misclassified accounts were then detected 10 days later.
Approximate Dynamic Programming and Reinforcement Learning for Discrete States

Approximate dynamic programming (ADP) is the collective name for a wide array of numerical DP methods that, unlike the standard methods of Chap. 4 , do not ensure that an optimal policy will be found. In exchange for their approximate nature, ADP methods aim at easing the overwhelming curses of DP.
Many-to-Many Path Planning for Emergency Material Transportation in Dynamic Environment

The problem of emergency material transportation in dynamic environment requires to find optimal path between multiple emergency material storage nodes and distribution nodes in a changing routing environment, so as to guarantee the supply of materials within the shortest time. It corresponds to a many-to-many path planning problem in dynamic routing network. The existing static plan optimization and dynamic path optimization method are difficult to ensure the theoretical optimality of the solution in a dynamic disaster environment, and may lead to the failure of emergency material transportation. In this paper, a method of co-evolutionary path optimization is proposed and improved to resolve the many-to-many path planning problems. The ripple diffusion algorithm completes the search process in the form of a ripple diffusion relay race in a given routing environment. Furthermore, the coevolutionary path optimization method combines the ripple diffusion process with the routing environment change process. When different ripples compete with each other, the routing environment changes dynamically at the same time. Finally, the theoretical optimal solution is obtained in just a single off-line operation. The experimental results show that the coevolutionary path optimization method has advantages over the traditional method in success rate, solving time, optimality, and flexibility.
The developing need for AI and Machine Learning to optimize PV for increased adoption

One powerful capability which needs to be better utilized for energy production is the implementation of Artificial Intelligence and Machine Learning (AI/ML) to optimize the production and supply. In addition to production optimization of solar generated energy, AI/ML can be used to achieve cost reductions. We have explored the use of AI/ML to optimize the specific production process taking advantage of IoT and using an innovative Neural Net program developed by one of the authors. Further, this AI/ML can be used for the overall solar supply management, taking into account (1) the potential demand, (2) the solar/weather forecast, (3) the component status, (4) the system equipment conditions, etc. to optimize the energy production for targeted use. This ability to use AI/ML can effectively reduce the costs of energy supply by efficiently targeting the supply to the demand. As well, it can enable greater penetration of PV and DER. This paper will describe the following: (1) the key advantages of AI/ML, along with the current issues, (2) the use of micro-grids for template development, and (3) the overall methodology for implementation and extension to the broader PV DER segment. Specific data will be presented on the novel AI/ML proposed. A key attribute with this novel AI/ML is one can scale and transfer the template developed without re-customization. A second key attribute is the ability to perform Error Reduction based on the unique aspects of this novel AI/ML.