Reviewerly/exHarmony huggingface dataset:
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.
Software Development Teamwork From An Information Security Perspective

The present study demonstrates how to combine the international standard ISO/IEC 27005 and the US standard NIST SP 800-30 to perform risk analyzes, through the example of a fictional software development company.Starting with a brief introduction to the company, the reader can get acquainted with the basic concepts of the DevOps approach in order to have a more accurate view of the processes taking place within the development company.Subsequently, starting from the Hungarian regulatory environment, an overview is presented of the current state of information security standards, taking into account the NATO and ENISA information security product catalogues.After that an ISO-NIST combined risk analysis technique is briefly described, the foundations of which were laid in 2017 by Putra, Fandi A., and others. A simple example of the application of the technique is also shown.
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation

Medical report generation, which aims to automatically generate a long and coherent report of a given medical image, has been receiving growing research interests. Existing approaches mainly adopt a supervised manner and heavily rely on coupled image-report pairs. However, in the medical domain, building a large-scale image-report paired dataset is both time-consuming and expensive. To relax the dependency on paired data, we propose an unsupervised model Knowledge Graph Auto-Encoder (KGAE) which accepts independent sets of images and reports in training. KGAE consists of a pre-constructed knowledge graph, a knowledge-driven encoder and a knowledge-driven decoder. The knowledge graph works as the shared latent space to bridge the visual and textual domains; The knowledge-driven encoder projects medical images and reports to the corresponding coordinates in this latent space and the knowledge-driven decoder generates a medical report given a coordinate in this space. Since the knowledge-driven encoder and decoder can be trained with independent sets of images and reports, KGAE is unsupervised. The experiments show that the unsupervised KGAE generates desirable medical reports without using any image-report training pairs. Moreover, KGAE can also work in both semi-supervised and supervised settings, and accept paired images and reports in training. By further fine-tuning with image-report pairs, KGAE consistently outperforms the current state-of-the-art models on two datasets.
Denoising of Multi Spectral Images Using Diagonal Tensor Decomposition Technique

It's difficult to efficiently and effectively filter photos from more than one channel. Color and multispectral image (MSI) denoising combine contemporary nonlocal and transform domain algorithms to group similar patches to take use of natural images' self-similarity and sparse linear approximation. It is common to use a recursive procedure with a high number of comparable patches to model group level correlation in order to improve sparsity. A patch level representation's importance cannot be overstated. Our primary focus in this study is to examine the impact and possibility of representation at the patch level using a general formulation with a block diagonal matrix. We also demonstrate that a simple transform-threshold-inverse strategy can generate extremely competitive results by training a good global patch basis and a local principal component analysis transform in the grouping dimension. Fast implementation is also created to reduce computational complexity. Its robustness, effectiveness, and efficiency have been extensively tested on simulated and actual datasets.
Semantic-Based Opinion Summarization

The amount of information available online can be overwhelming for users to digest, specially when dealing with other users' comments when making a decision about buying a product or service.In this context, opinion summarization systems are of great value, extracting important information from the texts and presenting them to the user in a more understandable manner.It is also known that the usage of semantic representations can benefit the quality of the generated summaries.This paper aims at developing opinion summarization methods based on Abstract Meaning Representation of texts in the Brazilian Portuguese language.Four different methods have been investigated, alongside some literature approaches.The results show that a Machine Learning-based method produced summaries of higher quality, outperforming other literature techniques on manually constructed semantic graphs.We also show that using parsed graphs over manually annotated ones harmed the output.Finally, an analysis of how important different types of information are for the summarization process suggests that using Sentiment Analysis features did not improve summary quality.
HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning

Taxonomies are valuable resources for many applications, but the limited coverage due to the expensive manual curation process hinders their general applicability. Prior works attempt to automatically expand existing taxonomies to improve their coverage by learning concept embeddings in Euclidean space, while taxonomies, inherently hierarchical, more naturally align with the geometric properties of a hyperbolic space. In this paper, we present HyperExpan, a taxonomy expansion algorithm that seeks to preserve the structure of a taxonomy in a more expressive hyperbolic embedding space and learn to represent concepts and their relations with a Hyperbolic Graph Neural Network (HGNN). Specifically, HyperExpan leverages position embeddings to exploit the structure of the existing taxonomies, and characterizes the concept profile information to support the inference on unseen concepts during training. Experiments show that our proposed HyperExpan outperforms baseline models with representation learning in a Euclidean feature space and achieves state-of-the-art performance on the taxonomy expansion benchmarks.
ASPECTS OF THE READINESS OF FOREIGN STUDENTS TO MASTER THE EDUCATIONAL PROGRAMS OF RUSSIAN MILITARY UNIVERSITIES

The article presents the results of a study of the readiness of foreign students to master the educational programs of Russian higher military educational institutions, carried out from the standpoint of a functional and personal approach. The concept of readiness is clarified, the components of readiness are highlighted and their content is described.
A Unified and Fast Interpretable Model for Predictive Analytics

Predictive analytics aims to build machine learning models to predict behavior patterns and use predictions to guide decision-making. Predictive analytics is human involved, thus the machine learning model is preferred to be interpretable. In literature, Generalized Additive Model (GAM) is a standard for interpretability. However, due to the one-to-many and many-to-one phenomena which appear commonly in real-world scenarios, existing GAMs have limitations to serve predictive analytics in terms of both accuracy and training efficiency. In this paper, we propose FXAM (Fast and eXplainable Additive Model), a unified and fast interpretable model for predictive analytics. FXAM extends GAM's modeling capability with a unified additive model for numerical, categorical, and temporal features. FXAM conducts a novel training procedure called Three-Stage Iteration (TSI). TSI corresponds to learning over numerical, categorical, and temporal features respectively. Each stage learns a local optimum by fixing the parameters of other stages. We design joint learning over categorical features and partial learning over temporal features to achieve high accuracy and training efficiency. We prove that TSI is guaranteed to converge to the global optimum. We further propose a set of optimization techniques to speed up FXAM's training algorithm to meet the needs of interactive analysis. Thorough evaluations conducted on diverse data sets verify that FXAM significantly outperforms existing GAMs in terms of training speed, and modeling categorical and temporal features. In terms of interpretability, we compare FXAM with the typical post-hoc approach XGBoost+SHAP on two real-world scenarios, which shows the superiority of FXAM's inherent interpretability for predictive analytics.
Scalable Neural Data Server: A Data Recommender for Transfer Learning

Absence of large-scale labeled data in the practitioner's target domain can be a bottleneck to applying machine learning algorithms in practice. Transfer learning is a popular strategy for leveraging additional data to improve the downstream performance, but finding the most relevant data to transfer from can be challenging. Neural Data Server (NDS), a search engine that recommends relevant data for a given downstream task, has been previously proposed to address this problem. NDS uses a mixture of experts trained on data sources to estimate similarity between each source and the downstream task. Thus, the computational cost to each user grows with the number of sources. To address these issues, we propose Scalable Neural Data Server (SNDS), a large-scale search engine that can theoretically index thousands of datasets to serve relevant ML data to end users. SNDS trains the mixture of experts on intermediary datasets during initialization, and represents both data sources and downstream tasks by their proximity to the intermediary datasets. As such, computational cost incurred by SNDS users remains fixed as new datasets are added to the server. We validate SNDS on a plethora of real world tasks and find that data recommended by SNDS improves downstream task performance over baselines. We also demonstrate the scalability of SNDS by showing its ability to select relevant data for transfer outside of the natural image setting.
Investigation Of 3D Coverage For LTE Network Using Drone

The use of mobile LTE network has grown exponentially in recent years. Therefore, it is important to constantly maintain both the performance and service quality of mobile network operation at top level to maximize customer satisfaction. The main goal of this project is to obtain regression models for path loss exponents and shadowing for radio channel between airborne UAV and cellular networks. The use of unmanned aerial vehicles (UAVs) to autonomously survey LTE network performance parameters and subsequently approximate the signal strength on ground level with regression model is developed in this research. LTE network performance parameters such as Received Signal Strength Indicator (RSSI) and Reference Signal Received Power (RSRP) are autonomously collected with the predefined fly path and analysed to approximate ground signal strength from 15m to 30m above ground. The data is then used to fit with commonly used path loss models (Log-distance Alpha-Beta, SUI/Erceg, Cost 231 Hata). The R-square value, R2, is subsequently calculated to obtain the well fit of the model to the data. The R-square value, R2, obtain is 48%, 8.41%, and 4.65% for 1m, 20m, and 30m respectively. The R-square value, R2, indicates that the model does not fit with the collected data well enough which is typically above 80% shows good fit.
NGETREN: BEDAH JURUSAN AKUNTANSI DAN KEDOKTERAN

Compulsory education for 12 years ending in high school. After high school, students are entitled to continue their education on campus to obtain a bachelor's degree. This activity aims to provide an overview of the world of lectures today. The method used is using an online platform due to the pandemic situation. The participants of this activity are 12th graders and 30 followers of LBB Neutron Malang Instagram. LBB Neutron Malang is one of the largest tutoring institutions in Malang which includes students from elementary to high school. In this activity there were 2 resource persons from the accounting department and the medical department and 1 moderator from the LBB Neutron Malang. This activity gave students a new perspective on the world of lectures. The materials discussed included why to choose the major, how to get tips and tricks to pass the exam, what kind of profession in the future, what kind of soft skills are needed in the world of work. This activity is expected to be positive for students to choose the right major, not just a trend.&#x0D; Keywords: Major, Accounting, Campus
Towards Understanding the Condensation of Neural Networks at Initial Training

Empirical works show that for ReLU neural networks (NNs) with small initialization, input weights of hidden neurons (the input weight of a hidden neuron consists of the weight from its input layer to the hidden neuron and its bias term) condense on isolated orientations. The condensation dynamics implies that the training implicitly regularizes a NN towards one with a much smaller effective size. In this work, we illustrate the formation of the condensation in multi-layer fully connected NNs and show that the maximal number of condensed orientations in the initial training stage is twice the multiplicity of the activation function, where "multiplicity" indicates the multiple roots of activation function at origin. Our theoretical analysis confirms experiments for two cases, one is for the activation function of multiplicity one with arbitrary dimension input, which contains many common activation functions, and the other is for the layer with one-dimensional input and arbitrary multiplicity. This work makes a step towards understanding how small initialization leads NNs to condensation at the initial training stage.
Sistem Informasi Pengelolaan Perjalanan Dinas Berbasis Mobile Web di Dinas Penanaman Modal ESDM dan Transmigrasi Provinsi Gorontalo

Official travel is a trip carried out by employees in an institution/company related to official duties. Official travel occurs in each institution, for instance, at the Office of Investment, Energy and Mineral Resources, and Transmigration in Gorontalo Province. Yet, there are several problems encountered in the official travel system. The problem is that institution’s data recap regarding official travel will be lost every year. Another problem is in the process of report filling, where the report filling can only be performed by the treasurer. As a result, the treasurer has difficulties since s/he must input various reports, and it took a long time. Equally important,as long as the report has not been filled in, the employees cannot perform official travel. Based on the problem above, the proposed solution is creating an information system that can facilitate the treasurer in managing the data of employees and searching the annual reports. Keywords: Offiaical Travel, Official Travel System
Feature Set Selection for Sentiment Analysis

Abstract: With proliferation of online blogging web sites, hundreds of thousands of text posts are generated. Using this rich information facilitate educated purchasing of objects, discovering and public developments involving more than a few merchandise in the market, discovering political inclination of societies previous to a country wide election, and many others. Considering that the final decade, Sentiment evaluation has received increased attention from many researchers as a procedure for addressing subject matters, such as the a fore mentioned ones. This paper specializes in Sentiment evaluation and use of sentiment Features. In this paper we have created the feature set and given input to svm and result verified for sentiment. Keywords: Sentiment analysis, support vector machine, maximum entropy, with features, without features, artificial intelligence.
Schematic Structure and Linguistic Realizations of Virtual English Language Lectures in the Social Context of the Pandemic Covid-19

English language lectures are held virtually at the English Language Department, Faculty of Languages and Arts, Universitas Negeri Medan due to the containment of the pandemic covid-19 spread.The virtual lectures as the social context potentially affects the text of classroom interaction.Specifically, the social context affects the schematic structures and their linguistic realizations.This study is intended to analyze the virtual lectures in order to study its schematic structures and their linguistic realizations.The design of the study is a descriptive qualitative one.The data sources of are virtual English language lectures held by lecturers.The lectures are related to Integrated English language skills (listening, speaking, reading and writing), Structure, Vocabulary, and Pronunciation.The data are utterances of the lecturers and the students which are obtained by applying (participant) observation, interview and document analysis.Linguistic analysis is based on theories of systemic functional linguistics (SFL).The findings of the study indicates that the schematic structures of the virtual lectures and and their linguistic realizations are affected by the social context.Findings of the study results in both theoretical and practical implications and are expected to be applied in developing and evaluating virtual English language and in assessing the student achievements resulted from the virtual lectures.
The road to conscious machines: AI through failed ideas

Presented here is a review of the book by Michael Wooldridge The Road to Conscious Machines: The Story of AI. The book was published in 2021 by Pelican Publishing Company. The book presents a philosophical exploration of the AI-related quest to understand the essence of our minds, ethics, and consciousness. Nonetheless, the book is unconventional as it focuses on failures of past AI programs rather than on AI success stories. The author also indicates the possible pathways to develop new, more efficient AI paradigms. The short technical section at the end of the book offers more in-depth information on topics like Prolog, Bayesian, and deep neural networks.
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning

Unsupervised reinforcement learning aims to acquire skills without prior goal representations, where an agent automatically explores an open-ended environment to represent goals and learn the goal-conditioned policy. However, this procedure is often time-consuming, limiting the rollout in some potentially expensive target environments. The intuitive approach of training in another interaction-rich environment disrupts the reproducibility of trained skills in the target environment due to the dynamics shifts and thus inhibits direct transferring. Assuming free access to a source environment, we propose an unsupervised domain adaptation method to identify and acquire skills across dynamics. Particularly, we introduce a KL regularized objective to encourage emergence of skills, rewarding the agent for both discovering skills and aligning its behaviors respecting dynamics shifts. This suggests that both dynamics (source and target) shape the reward to facilitate the learning of adaptive skills. We also conduct empirical experiments to demonstrate that our method can effectively learn skills that can be smoothly deployed in target.
Engaging Students in the Geosciences using Effective and Versatile Climate and Data Literacy Teaching Modules

Using authentic and locally-relevant environmental data is a valuable teaching tool for providing students with opportunities for self-directed exploration of the natural world.We will present a set of instructional material that focus on climate science and data literacy, prepared by the Lawrence Hall of Science, Berkeley, CA, (https://mare.lawrencehallofscience.org/curriculum/climate-dataaclipse-activities).We will demonstrate how these materials can be used in formal (6-12 or university) and informal settings (professional development workshops for teachers and scientists, after school clubs and summer programs).Students engaging in these activities gain a deeper understanding of carbon cycling, and other phenomena related to climate change.The materials guide students through data orientation, interpretation, and synthesis as well as developing and using models and solving problems, constructing explanations, and engaging in argument based on scientific evidence.By solving problems in local contexts, the learners gain knowledge of science content, view science as relevant to their lives and future, and engage in science in socially relevant and transformative ways.Such authentic learning experiences are shown to be an effective approach to improving understanding, confidence, awareness, and interest in geoscience and STEM careers (NRC, 2007).
Cultural Understanding Using In-context Learning and Masked Language Modeling

With the rapid advancement of natural language processing (NLP) as a sub-field of artificial intelligence (AI), a number of unsupervised pre-trained language models trained on large corpus have become available (e.g. BERT and GPT-3). While these models have tremendous linguistic knowledge, a lot of other types of knowledge are embedded in them as well. We perform cross-culture analysis experiments using AI-based Masked Language Modeling (MLM) and GPT-based Generative Language Modeling (In-context learning modeling). The designed approach is to set up a cultural context in sentences with masked words (for MLM) or in a human-prompted text segment (for GPT-based NLG). Consequently, the predicted masked words or the machine generated stories will reflect measurable intercultural differences because language models are trained on different corpus in different languages, and on English corpus containing a significant amount of knowledge on foreign cultures. We show a variety of examples: geopolitical knowledge, holidays, gestures, customs, social norms, emotion schema, role schema, procedure schema, and emotion change detection based on a diplomatic speech. The deep learning neural network model encodes its knowledge in the weights of a neural network instead of as organized semantic concepts. The model can reflect biases brought in by the training data and can give us inaccurate or faulty answers. Overall, with the rapid advancement of language technology, pre-trained language models have grown more powerful, and have great potential to serve as a culturalization tool.