Reviewerly/exHarmony huggingface dataset:
Bridge the Gap: Industrial Data Pipeline oriented Recommendation Enhancement

In a world of overloading choices, recommender system has become indispensable as personalized information filter, which makes it of great interest to both industry and academia. Recently, inspiring insights are provided by deep learning based algorithms, and bring us many state-of-the-art recommender systems [1]. However, there exists a widening gap between researchers and engineers: academics usually prefer end-to-end recommender systems, whereas engineers tend to design a recommender system as a three stage pipeline, including preprocessing original data, recalling a candidate subset, and ranking recommendation results. Such pipeline can greatly benefit system development and project management, which has been proven by many Internet companies over recent years. To bridge the gap between end-to-end and pipeline, we propose three research directions and relevant methodologies, including label propagation based methods for preprocessing, graph neural network based negative sampling strategy for recall, and graph-based model of implicit interactions for ranking. In the following, we will describe them in detail.
Partial correlation graphical LASSO

Abstract Standard likelihood penalties to learn Gaussian graphical models are based on regularizing the off‐diagonal entries of the precision matrix. Such methods, and their Bayesian counterparts, are not invariant to scalar multiplication of the variables, unless one standardizes the observed data to unit sample variances. We show that such standardization can have a strong effect on inference and introduce a new family of penalties based on partial correlations. We show that the latter, as well as the maximum likelihood, and logarithmic penalties are scale invariant. We illustrate the use of one such penalty, the partial correlation graphical LASSO, which sets an penalty on partial correlations. The associated optimization problem is no longer convex, but is conditionally convex. We show via simulated examples and in two real datasets that, besides being scale invariant, there can be important gains in terms of inference.
Studiu privind activitățile de prevenire a tulburărilor de limbaj în grădinițe

The present research starts from the idea of preventing language disorders from an early age, given the results of previous research on the effectiveness of early interventions. Following a preliminary documentation that consisted in the study of Romanian legislation, specialized literature, speech therapy aids, speech therapy software, sites with medical profile and special pedagogy, it was found an insufficient approach to prevention and we wanted to investigate what activities speech therapy teachers consider to be included in the concept of prevention. We also sought to find out to what extent speech therapists take into account the prevention part and what activities they carry out for this purpose. The results were limited and we see them from at least two perspectives: the small group of participants that led to limited data and the absence or quasi-absence of prevention in speech therapy, this being too little described in the literature and outlined as a methodology in school documents. The most common options chosen by language therapists for prevention were to carry out speech therapy activities in prevention groups under the age of 5 and to carry out information activities for educators and parents and only then screening activities
SLRP: Improved heatmap generation via selective layer‐wise relevance propagation

Deep learning has been recently applied to various areas of artificial intelligence, where it has displayed excellent performance. However, many deep-learning models are a black box, which makes it difficult to interpret the models and understand the predictions. Explainability is crucial for critical real-world systems (in the fields such as defense, aerospace, and security). To solve this problem, the concept of explainable artificial intelligence has emerged. For image classification, various approaches have been proposed to visually explain the model's prediction. A typical approach is layer-wise relevance propagation, which generates a heatmap, where each pixel value represents the contributions to the model's predictions. However, even advanced versions of layer-wise relevance propagation (such as contrastive layer-wise relevance propagation and softmax-gradient layer-wise relevance propagation) have some limitations. Here, selective layer-wise relevance propagation, which generates a clearer heatmap than the existing methods by combining relevance-based methods and gradient-based methods is proposed. To evaluate the proposed method and verify its effectiveness, we conduct comparative experiments. Qualitative and quantitative results show that selective layer-wise relevance propagation produces less noisy, class-discriminative, and object-preserving results. The proposed method can be used to improve the explainability of deep-learning models in image classification.
Peramalan Beban Listrik Jangka Pendek Dengan MenggunakanMetode Regresi Linier

Short-term electricity load forecasting is a very important factor in the planning and operation of electric power systems. The purpose of electricity load forecasting is that demand and supply of electricity can be balanced. The pattern of consumption of electrical loads is different each time interval at the Harapan Indah Gas Insulated Switchgear (GIS) substation. The selection of the GIS Harapan Indah substation is based on the fact that it is a new substation operating since September 2017. It makes the electrical load variance problem not homogeneous. There are many methods used to produce accurate and precise electrical load forecasting. With the hope that the current load is channeled right and according to consumer needs. In this study discusses short-term electricity load forecasting with Linear Regression and Energy Coefficients methods wherein the two methods are compared to find the percentage value of a small error and the level of accuracy is very good. The calculation in this study uses historical electric load data at PT PLN (Persero) Greater Jakarta Distribution Unit in March 2019 on GIS Harapan Indah. The results showed that the comparison of average errors from Monday to Sunday value of 7.92% with an accuracy value of 92.08% for Linear Regression and 10.46% with an accuracy value of 89.54% for the Energy Coefficient
Safety Performance of Inverted Pendulum Type Personal Mobility Vehicle in Emergency Braking

The purpose of this study is to investigate the safety of inverted pendulum type personal mobility vehicle. Experiments are conducted with subjects under various conditions to investigate safety during emergency braking.
Online Bayesian approach for estimation and control of neural system

We propose a data-driven method for estimating and controling neural systems. The model parameters and latent variables are estimated online simultaneously based on stochastic EM algorithm and sequential Monte Carlo even when model parameters are unknown. Moreover, the control signal is determined from estimated values based on model predictive control. We verified the effectiveness of the proposed method using simulation data.
How Do Private Digital Currencies Affect Government Policy?

We provide a systematic classification and evaluation of the different types of digital currencies. We express skepticism regarding centralized digital currencies and focus our economic analysis on private digital currencies. We specifically highlight the potential for private digital currencies to improve welfare within an emerging market with a selfish government. In that setting, we demonstrate that a private digital currency not only improves citizen welfare but also encourages local investment and enhances government welfare. The fact that a private digital currency enhances government welfare implies a permissive regulatory policy which enables citizens to realize the previously referenced welfare gains.
Insightful Dimensionality Reduction with Very Low Rank Variable Subsets

Dimensionality reduction techniques can be employed to produce robust, cost-effective predictive models, and to enhance interpretability in exploratory data analysis. However, the models produced by many of these methods are formulated in terms of abstract factors or are too high-dimensional to facilitate insight and fit within low computational budgets.
The Study of Approaches of Content Based Image Retrieval

In a content-based image retrieval system (CBIR), the most issue is to extract the image features that effectively represent the image contents as more information. Such an extraction needs a detailed analysis of retrieval performance of image options and in order to increase the retrieval efficiency of an image, there are various algorithms represented in a content based image retrieval process and such algorithms are explained here and this paper attempts to provide a very effective survey of the recent technical approaches in the current world of image retrieval. The recent publications are included in this literature survey covering all the aspects of the research in the content based image retrieval, including the images that has low image feature extractions and also tells the effective categorization of the image retrieval.
Modulation Scheme Estimation of Multiple Signals Using Machine Learning

This paper proposes a plural modulation scheme estimation method for using machine learning. The estimation accuracy of the method is numerically verified. As the results, it is shown that the combination of modulation scheme in mixed signals can be estimated by proposed method.
Lessons learnt from the application of a participatory modelling approach in the framework of a river restoration project: case of the Gave de Pau River, Hautes-Pyrénées, France

Considering the diversity of criteria and stakes, the uncertain and stochastic nature of the physical phenomena and the multi-scale aspects to be taken into account, a river restoration project can be viewed as a complex problem. Many river managers and scientific researchers have been investigating the subject as river restoration projects deal with significant safety, environmental and economic issues. A project manager can hardly be an expert in all the disciplines to consider to come up with the optimal solution that satisfies all the involved dimensions. The integration of both local stakeholders with field experience and technicians and academics with scientific knowledge can hence benefit river restoration projects. The aim of this paper is to present an approach that considers the integration of various stakeholders engaged in river restoration issues to gather their knowledge and define the solutions that offer decent compromise considering all the dimensions involved. To this end, a group of stakeholders identified according to specific selection criteria were engaged in a modeling approach based on Bayesian Networks (BNs). BNs are increasingly being used as tools for decision-making in river management due to their natural ability to adjust to complex multi-criteria systems with multiple interactions. A participatory approach based on BNs that led to the elaboration of causal graphs is introduced in this paper. This study considers the combination of both physical knowledge associated to river systems and the relation constraints to river users and managers. The work is conducted in the framework of the restoration project of the "Lac des Gaves", an artificial lake that undergone years of sediment extractions over the past century and important flood events that highlighted several numbers of impairments.
A Search for Optimal Feature in Political Sentiment Analysis

This research used the data from Twitter on presidential elections in USA 2016 to understand which features better suits in predicting Election results. We compare between four features such uni-grams, bi-grams, tri-grams, and opinion words by using the data mining techniques such as Random Forest, Naïve Bayes, and Artificial Neural Network. For the analysis, we have used a Data set from Kaggle consisting of 6445 individual records. Then applied many preprocessing techniques (such as cleaning, data stemming, data normalization etc.) on the said data set to expose the well-shaped data set. For extracting proper features, we have done factor analysis. Finally, we have tested our method using the dataset and found the uni-gram showing the better accuracy of 81%. This research was implemented in R.
Design challenges of developing a blockchain-enabled smart home

This paper discusses design challenges identified during the development of blockchain-enabled capabilities of a smart home. The challenges are analyzed and classified, and a literature review is conducted to systematically aggregate possible solutions and the best practices proposed by scholars and business.
USE OF CYBER HATE IN THE ELECTORAL CAMPAIGNS BY THE MAINSTREAM POLITICAL PARTIES OF PAKISTAN

Purpose of the study: The purpose of the study is to examine the use of cyber hate by the Pakistan’s mainstream political parties. The issue of poll rigging in Pakistan’s General Elections 2013 is examined through discourse analysis of the related tweets. The study also aims at comprehending the extent to which cyber ethics were violated during the digital electoral campaigns.&#x0D; Methodology: Discourse Analysis of the tweets generated from the official Twitter handles of PTI and PMLN leaders was conducted to examine the use of cyber hate by the Pakistan’s mainstream political parties. Violation of cyber ethics was explored through the qualitative interviews of 8 purposively selected social media managers of PMLN, PPP, and PTI.&#x0D; Main Findings: The findings indicated that party leadership/politicians used the elements of cyber hate which included abusive language, provocation, and character assassination against their opponents during the digital electoral campaign in general and regarding the poll rigging issue of Pakistan’s General Elections 2013 in specific. Resultantly the tweets using strong adjectives and metaphors on the political opponents were more frequently re-tweeted and attracted more favorites.&#x0D; Applications of this study: The study can be helpful in various cross-disciplinary areas that focus on the examination of the usage and impact of social media and cyberspace as a medium for hate speech dissemination. The study can significantly contribute to areas related to cyber ethics, digital electoral campaigning, freedom of expression, and political opinion building.&#x0D; Novelty/Originality of this study: The study’s originality lies in its attempt to unfold the foundations of digital electoral campaigning in Pakistan and how cyberhate was used as a pivotal tool for advancing the political narratives in a fragile democratic society.
Sentiment Analysis for Mongolian Tweets with RNN

As information flow in the electronic online environment is increasing, the need for automated processing is increasing. Every minute, 456,000 tweets were written on Twitter. The data in the Mongolian language is already joined in that stream and created their own space since a long time ago. However, in the Mongolian language, there is a lack of research, language resources, and an implemented system by now. In this work, we tried to classify the document level sentiment polarity of Mongolian tweets based on the RNN method of deep learning. The model was tested at the 62,917 SemEval English data, the highest F1 score of 64.7.
Student Voice Identification Method

This paper deals with the problem of student identification by the teacher in the conditions of distance learning. The purpose of this research is to find a way to automatically identify a student by voice using neural networks for sound processing. The research includes two stages: neural network selection and training with Russian dataset, creating an application for teacher.
Virtual Reality Artist in Residence Program: Exploring Outreach and Learning for Architecture Students with Innovative Technology

Conference Presentation, 43rd Annual Association of Architecture School Librarians Conference
Text-To-Speech Synthesis Using Transfer Learning

We portray a neural organization based framework for text-to-speech (TTS) combination that can create discourse sound in the voice of various speakers, including those concealed during preparation. Our framework comprises of three autonomously prepared parts: (1) a speaker encoder network; (2) a grouping to-succession union organization based on Tacotron 2; (3) an auto-backward Wave Net-based vocoder network. We illustrate that the proposed model can move the information on speaker fluctuation learned by the discriminatively-prepared speaker encoder to the multi speaker TTS task, and can incorporate normal discourse from speakers concealed during preparation. We measure the significance of preparing the speaker encoder on a huge and different speaker set to acquire the best speculation execution. At last, we show that haphazardly inspected speaker embeddings can be utilized to integrate discourse in the voice of novel speakers divergent from those utilized in preparing, showing that the model has taken in a top-notch speaker portrayal.
Control of a Smart Walker for Training Using Interaction-Energy and Personalized Parameters

Smart walkers with admittance controller usually have limited dynamics and low maximal velocity to provide stable and safe behavior. To enable physically challenging training with smart walker control strategies enabling faster dynamics is needed. However, in certain situations, this can lead to instabilities, which further complicates the finding of suitable parameters for the envisioned training functionalities. To overcome these issues, we have introduced an interaction-energy limiter and developed a strategy to automatically determine individual user parameters for an adaptive admittance controller. The energy limiter bounds the controller and training elements to avoid uncomfortable and dangerous situations. These training elements are placed on a 2D map of a training environment. If the user passes these training elements with our smart walker - RoboTrainer - they are triggered. Therefore we call them spatial control actions. We can show that interaction-energy limiter successfully avoids instabilities and dangerous situations when spatial control actions are triggered. The evaluation with 22 users, which used Robo Trainer with and without individualized parameters, successfully demonstrate the benefits of the parameterization method. The presented method could be generally valuable for the implementation of smart walkers in everyday life since it provides a solution for dealing with users with different skills and a solution for safe interaction with smart walkers using high-dynamic control.
Adaptive threshold vein enhancement method based on illuminance estimation

Infrared vein images often contain a lot of noise due to the performance defects of the infrared imaging system, and these images have the characteristics of low contrast, low signal-to-noise ratio, and blurred target details. In order to improve this situation, an adaptive threshold enhancement algorithm based on guided filter illuminance estimation (ATEBIE) is proposed to enhance the detailed information of the vein infrared image in this paper, especially the vein pattern, which is of great significance for applications such as vein display and vein recognition. Firstly, the infrared image of the vein is converted to the LAB color space representation to obtain the L-component of the image; Secondly, the multi-scale Gaussian blurring is performed on the component, and the noise is removed at the same time to obtain the initial illuminance estimation; then the guided filtering is used for accuracy illumination estimation; Finally, according to the illuminance image, an adaptive threshold is adopted to dynamically adjust the image to achieve the purpose of vein image enhancement. The experimental results show that the proposed algorithm not only suppresses the image noise very well, but also effectively enhances the vein texture details, and has a good application prospect.
Tile-based Multi-source Adaptive Streaming for 360-degree Ultra-High-Definition Videos

360° UHD videos have absorbed great attention in recent years. However, as they are of significant size and usually watched from a close range, they require extremely high bandwidth for a good immersive experience, which poses a great challenge on the current single-source adaptive streaming strategies. Realizing the great potentials of tile-based video streaming and pervasive edge services, we advocate a tile-based multi-source adaptive streaming strategy for 360° UHD videos over edge networks. In order to reap its benefits, we consider a comprehensive model which captures the key components of tile-based multi-source adaptive streaming. Then we formulate a joint bitrate selection and request scheduling problem, aiming at maximizing the system utility (i.e., user QoE minus service overhead) while satisfying the service integrity and latency constraints. To solve the formulated non-linear integer programming problem efficiently, we decouple the control variables and resort to matroid theory to design an optimal master-slave algorithm. In addition, we improve our proposed algorithm with a deep learning-based bitrate selection algorithm, which can achieve a rationalization result in a short running time. Extensive datadriven simulations validate the superior performance of our proposed algorithm.
Improvements and Detailed Evaluation of Ground Obstacle Position, Size and Orientation Estimation

This paper improves and evaluates in detail a previous work of the author dealing with position, size and orientation estimation of fixed ground obstacles in aircraft sense and avoid. The improvement is the better conditioning of the system of equations by the shift of one known variable. Detailed evaluation means Monte-Carlo simulation and comparison of the disc and line-based parameter estimation methods considering also non-straight own aircraft trajectories. This is the focal point of the article as only straight trajectory results were compared until now. The mean estimation errors (in percentages) and their standard deviations with the two different methods are compared. Finally, the line-based method was better applicable for rectangular objects as expected.
Impact of Atmospheric Light on Haze Removal using Single Image

Fog or haze are the common phenomenon which creates unpleasant weather condition. This unpleasant weather condition is not favorable for outdoor photography or imaging, because image characteristics are drastically deteriorated by fog or haze. To enhance the image features, fog or haze removal is required. Fog removal is also required in different applications like recognition, tracking and navigation and consumer electronics. During the fog removal process, estimation of atmospheric light plays main role. In this paper we performed an analysis on an estimation of atmospheric light by taking 0.01%, 0.02% and 0.05% most haze-opaque pixels from foggy image and its effect on results. To perform analysis, we used dark channel prior concept with guided filter for fog or haze removal.