Reviewerly/exHarmony huggingface dataset:
The COVID-19 and mental health. The impact that the isolation caused by Covid-19 has on the levels of anxiety in a group of elderly people institutionalized at nursing home

The Coronavirus-19-induced pandemic implied the adoption of several measures in an attempt to contain the spread of the virus, thus social isolation and quarantine are highlighted. In addition to the pandemic itself, such measures may contribute to disturb the mental health of the elderly, and this context the emergence/worsening of anxiety and depression are emphasised. Hence, as an attempt to understand whether the pandemic had any effect on the anxiety levels of institutionalised elderly people in a nursing home, we determined as the objective of this study: To understand the impact of the pandemic caused by Covid-19 on the anxiety levels of institutionalised elderly people in the Freixial do Campo Nursing Home. In order to perform the study, thirteen elders were selected, based on the clinical process evaluation; however an element of this sample refused to participate in the study. For data collection, a General Data Questionnaire and the Geriatric Anxiety Inventory were applied and, after applying these instruments, the following question was posed: Is any of the aspects that you have responded positively in the Geriatric Anxiety Inventory related to the Covid-19 pandemic? We conclude that the pandemic effectively increases anxiety in the elderly, a fact that is more associated with the fear of the family being infected or becoming severely ill because of the disease than due to the isolation or quarantine.
How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation

Having recognized gender bias as a major issue affecting current translation technologies, researchers have primarily attempted to mitigate it by working on the data front. However, whether algorithmic aspects concur to exacerbate unwanted outputs remains so far under-investigated. In this work, we bring the analysis on gender bias in automatic translation onto a seemingly neutral yet critical component: word segmentation. Can segmenting methods influence the ability to translate gender? Do certain segmentation approaches penalize the representation of feminine linguistic markings? We address these questions by comparing 5 existing segmentation strategies on the target side of speech translation systems. Our results on two language pairs (English-Italian/French) show that state-of-the-art sub-word splitting (BPE) comes at the cost of higher gender bias. In light of this finding, we propose a combined approach that preserves BPE overall translation quality, while leveraging the higher ability of character-based segmentation to properly translate gender.
Analysis of efficiency interventions on electricity consumption at retail stores

In this thesis we want to present the steps I followed during myinternship at Energia&Progetti CO2Save to build an algorithm to evaluate if and how much some efficiency intervention reduced the energy consumption in the retail stores of a client.In the first section of the thesis we present the company I worked for, the problem we needed to solve and the dataset we had to model and build our algorithm. In the second chapter we explore many different approaches, from the simple linear regression model, to the AutoRegressive model, passing by some Machine Learning algorithms as the k-Nearest Neighbors and the Decision Tree; for each one of these methods, we presented a bit of theory and the results we got, highlighting the pros and cons of their application. All the method seen in chapter 2 had been studied, in different courses, during the Master Degree in Data Science, so in the third section we present a model that hasn’t been covered during any course I followed: the State-Space model; this class of models is very wide and flexible and it proved to be a good choice for our problem. In section three, therefore, we see some of the theory behind this method and the results that we managed to reach. Finally, in the conclusion, we summarize what we have seen before and we try and figure what could be the following steps to improve our results and make our algorithm suitable for even larger tasks.
Augmented creativity

This work explores a new process of creativity generation under the guide of Jordanous's Four PPPPerspectives (2016) and speculates intertwined relationships among multi-contributors in computational creativity. This work can also be seen as an experimental multispecies storytelling on creativity.
Build your own network

When nobody would build a phone network for people in a rural Mexican village, they decided to go it alone.
Bhavna - Sentiment Analysis using Video Colour Magnification

Facial micro-expressions are visible only for a short amount of time so they are not visible to the naked eye, to make this possible we are combining the advancements in video magnification algorithms, which reveal subtle temporal fluctuations in video frames that are difficult to see with the naked eye, and state of the art facial micro-expression recognition neural network models, to enhance the accuracy of facial emotion detection for sentiment analysis. The objective of this project is to detect any minute changes in the video and detect human expressions accurately. Traditional models are sensitive to the intensity of the facial movements captured, and fail to detect the subtle expressions. In this paper, we propose to increase the precision of microexpression recognition to perceive facial movement information of a video frame. The efficiency of our proposed system is based on the CASME & CASME II dataset.
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for  Speed-Accuracy Optimization

We propose a model for multiclass classification of time series to make a prediction as early and as accurate as possible. The matrix sequential probability ratio test (MSPRT) is known to be asymptotically optimal for this setting, but contains a critical assumption that hinders broad real-world applications; the MSPRT requires the underlying probability density. To address this problem, we propose to solve density ratio matrix estimation (DRME), a novel type of density ratio estimation that consists of estimating matrices of multiple density ratios with constraints and thus is more challenging than the conventional density ratio estimation. We propose a log-sum-exp-type loss function (LSEL) for solving DRME and prove the following: (i) the LSEL provides the true density ratio matrix as the sample size of the training set increases (consistency); (ii) it assigns larger gradients to harder classes (hard class weighting effect); and (iii) it provides discriminative scores even on class-imbalanced datasets (guess-aversion). Our overall architecture for early classification, MSPRT-TANDEM, statistically significantly outperforms baseline models on four datasets including action recognition, especially in the early stage of sequential observations. Our code and datasets are publicly available at: https://github.com/TaikiMiyagawa/MSPRT-TANDEM.
Rational-Instrumental Action of Local People in Facing Flood

The purpose of this study is to explore the rational-instrumental action in facing flood in Segeri villageIndonesia. It is based on the premise that every society or community in facing floods certainly have selfstrategywith its consideration and its orientation. This research uses in-depth interviews to farmers infacing floods. Results of interviews that mapped are based on the indicators of 'rational-instrumental’action. This study found that the farmers as local people in Segeri village in facing flood as rationalinstrumentalaction, such as: making embankment in front of the house, elevating the pole house,preparing multi-function bamboo rest place, preparing the big rubber tires. All of those actions have‘rational-instrumental’ aspects inside.
High-resolution weather simulation for sub-Saharan Africa on the World Community Grid

&amp;lt;p&amp;gt;Rainfall in Africa is difficult to estimate accurately due to the large spatial variability. Most of the monsoon rainfall is generated by convective rainstorms that can be very localized, sometimes covering less than 100 km2. The goal of the African Rainfall Project is to run the Weather and Research Forecast (WRF) model for sub-Saharan Africa at a convection-permitting resolution in order to better represent such rainfall events. The resolution will be 1km, which is finer than most studies over Africa, which typically use resolutions of 3km or more. Running WRF for such a large area at such a high resolution is computationally expensive, which is where IBM&amp;amp;#8217;s World Community Grid comes in. The World Community Grid (WCG) is part of the Social Corporate Responsibility of IBM that crowdsources unused computing power from volunteers devices and donates it to scientific projects.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The simulation was adapted to the WCG by dividing the simulation of one year over sub-Saharan Africa in many smaller simulations of 48h over 52 by 52 km domains. These simulations are small enough to be calculated on a single computer of a volunteer at the required resolution. In total, 35609 overlapping domains are covering the whole of sub-Saharan Africa. During the post-processing phase, the smaller simulations are merged back together to obtain one consistent simulation over the whole continent.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our main focus is rainfall, as this is the variable with the highest socio-economic impact in Africa. However, the outputs of the simulations include other variables such as the 2m-temperature, the 10m-wind speed and direction. These variables are outputted every 15min. At the end of this project, we will have over 3 billion files for a total of 0.5 PB. The data will be reorganized so that the different variables can be stored, searched and retrieved efficiently. After the reorganization, the data will be made publicly available.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The first validation step will be to examine the impact of dividing sub-Saharan Africa into many smaller domains. This will be done by comparing the simulation from this project to one large simulation. This simulation is obtained by running WRF at a 1km resolution on a large domain (500km by 1000km) for a shorter period, using Cartesius, the Dutch national computer. The second validation step will be to compare the simulations with satellite data and with in-situ measurements from the TAHMO network (www.tahmo.org).&amp;lt;/p&amp;gt;
Exploring Possible Solutions to Curb Internet Shutdowns in India

Through this issue brief, I begin to look at an introductory policy framework to curb Internet Shutdowns in India - the world's largest democracy which ironically, imposes the highest number of such communication blackouts. The goal is to rid the policy framework off of arbitrary, blanket shutdowns - however given capability and capacity constraints, a gradual approach will have to be taken. Solutions offered are under five heads namely, Curbing Misinformation, Legal Reforms, Transparency, Capacity Building and using a Risk-based approach for imposing shutdowns in extreme cases.
CiRA: A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts.

Requirements often specify the expected system behavior by using causal relations (e.g., If A, then B). Automatically extracting these relations supports, among others, two prominent RE use cases: automatic test case derivation and dependency detection between requirements. However, existing tools fail to extract causality from natural language with reasonable performance. In this paper, we present our tool CiRA (Causality detection in Requirements Artifacts), which represents a first step towards automatic causality extraction from requirements. We evaluate CiRA on a publicly available data set of 61 acceptance criteria (causal: 32; non-causal: 29) describing the functionality of the German Corona-Warn-App. We achieve a macro F_1 score of 83%, which corroborates the feasibility of our approach.
Comparative Analysis of the Accuracy of Methods for Visualizing the Structure of a Text Collection

Visualization of multidimensional data is the most important stage of data research. Often, decisions on the further stages of the study are made from the flat view of the data based on rough proportions. High visibility and persuasiveness of representation on the plane of multidimensional vectors with the preservation of distances is used in models of distributive semantics (Word2Vec, GloVe, NaVec) successfully. On the other hand, the inaccuracy of the two-dimensional projection can lead to time being spent searching for non-existent multidimensional structures. The author set the task to evaluate the accuracy of dimensionality reduction methods with the following limitations: multi-dimensionality arises as a result of vector representation of text documents, dimensionality reduction is aimed at visualization on the plane. In numerous methods of dimension reduction, there is no separate class of approaches specifically for visualization. To measure the accuracy, an approach was chosen using marked-up data and quantifying the preservation of the markup while reducing the dimension. The author investigated 12 methods of reducing the dimension on two labeled data sets in Russian and English. Using the Silhouette Coefficient metric, the most accurate visualization method for text data was determined as UMAP with the Hellinger distance as the metric.
Comparison of Body Language in Cross-cultural Communication

Body language plays an important role in intercultural communication. However, there are more obstacles caused by the differences of language and culture. In intercultural communication, the most effective way to deal with the obstacles is to master the meanings and characteristics of body language. This thesis makes a comparison of characteristics and applications of body language in different cultures by comparison method, acquiring the approaches of reducing barriers in intercultural communication. Its aim is to help people communicate effectively in cross-cultural communication and avoid misunderstandings.
Analisis Perbandingan Dampak Kejadian Bencana Hidrometeorologi dan Geologi di Indonesia Dilihat Dari Jumlah Korban (Studi : Data Kejadian Bencana Indonesia 2018)

Year 2018 is the year of disaster in Indonesia. Three major disaster occurred in close intervals. The earthquake of West Nusa Tenggara, The earthquake triggered tsunami and liquefaction in Central Sulawesi and The Tsunami of Sundanese Straits have given huge impacts on casualties and damages. Around 2,572 disasters in 2018 caused 4,814 people killed and missing, 21 thousand people injured and 300 houses damaged. Even though hydro-meteorological disaster is the most frequent disaster i.e. 96.8%, geological disaster is the biggest contributor for the impacts of casualties and damages in 2018. Efforts of mitigation and preparedness become steps to reduce the impact on disaster that occurs. The involvement of community leaders in disaster management activities that can eventually affect their attitudes and behavior during a disaster.
Study Concept Drift in 150-year English Literature.

The meaning of a concept or a word changes over time. Such concept drift reects the change of the social consensus as well. Studying concept drift over time is valuable for researchers who are interested in language or culture evolution. Recent word embedding technologies inspire us to automatically detect concept drift in large-scale corpora. However, comparing embeddings generated from different corpora is a complex task. In this paper, we propose to use a simple approach for detecting concept drift based on the change in word contexts from different time periods and apply it to subsequent time periods so that the detailed drift could be detected and visualised. We dive into certain words to track how the meaning of a word changes gradually over a long time span with relevant historical events which demonstrates the effect of our method.
Neural Networks Technology for Evaluating Solar Energy Resources in Tashkent

In terms of economy, electricity is a commodity capable of being bought, sold and traded. Electricity is difficult to store, and it has to be available on demand. Consequently, unlike other products, it’s impossible, under normal operating conditions, to keep it in stock, to ration it, or to have customers queue for it. The solar energy generating system, whether grid-connected or stand-alone, is most commonly used in places when possible to install solar equipment (roofs, pollutes areas, closed rubbish dumps, rural and suburban areas). It is based on converting solar radiation (i.e., photons that are sent from the sun) to produce electricity. The PV system has a lot of ways of applications. For example, in developing countries, PV is used for basic life needs, such as heating and cooking, while in developed countries, the system is used to supply electricity for homes and grids. Due to its importance in the solar energy field, global solar radiation data (GSR) forecasting has become more popular to facilitate solar system installation. Solar radiation prediction and forecasting carry out considering global weather solar radiation data. Ambient temperature and relative humidity data are the most commonly used parameters to predict solar radiation and special techniques used in this study are artificial neural networks.
Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search

In this paper, we present a Neural Network (NN) model based on Neural Architecture Search (NAS) and self-learning for received signal strength (RSS) map reconstruction out of sparse single-snapshot input measurements, in the case where data-augmentation by side deterministic simulations cannot be performed. The approach first finds an optimal NN architecture and simultaneously train the deduced model over some ground-truth measurements of a given (RSS) map. These ground-truth measurements along with the predictions of the model over a set of randomly chosen points are then used to train a second NN model having the same architecture. Experimental results show that signal predictions of this second model outperforms non-learning based interpolation state-of-the-art techniques and NN models with no architecture search on five large-scale maps of RSS measurements.
ISO 9126 UNTUK PENGUJIAN GAME EDUKASI MEMBUANG SAMPAH BERBASIS ANDROID

Games are applications that are familiar to people from all walks of life. Efforts to increase the efficiency of providing applications that contain educational elements require a variety of alternatives and new innovations in programming to be applied as a tool to simplify the learning process. This study aims to create a game design called the game Throw your trash by inserting educational elements about how to dispose of garbage in its place and introducing the types of waste. And the sample used is elementary school students, because of their interest in the theoretical learning process, considering that children prefer to play games. The methodology used is starting from system analysis, design and implementation stages, and testing using ISO 9126.The results of this educational game to throw garbage show that the game application based on the aspect obtained a percentage of 95% of the Functionality test and the results of the analysis of usability testing obtained a percentage of 81.89% Kata Kunci: education, games, testing, android, ISO 9126
Spam filtering using Semantic and Rule Based model via supervised learning

Exchanging information over internet becomes a mandatory requirement for everyone. Electronic mail is an important medium through which all types of information can be shared.  Despite the important role played by emails several challenges are also involved.  Email carries not only legitimate messages but several times unwanted messages also.  Spam email detection is challenging task since spammers use advanced technique through subject and email content. Emails with most fascinated words can be easily identified and classified as spam.  But the professional way in which spammers manage to send messages makes it difficult for researchers to classify. Always, Spammers concentrate on either subject or content or both to persuade users to open the email. In this paper, Rule Based Subject Analysis (RBSA) and Semantic Based Feature Selection (SBFS) techniques are integrated with the Machine Learning algorithms. Various rules are outlined to check the subject field of the emails.  Semantic based Feature selection technique is applied on the content of the email to reduce the features. RBSA and SBFS are integrated with four classifiers namely Support Vector Machine, Multinomial Naive Bayes, Gaussian Naive Bayes and Bernoulli Naive Bayes. The efficiency of the proposed techniques is tested on Enron dataset and it is observed that our proposed techniques with Support Vector Machine achieve lowest False Positive rate of 0.03.
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging.

Neural networks have shown great abilities in estimating depth from a single image. However, the inferred depth maps are well below one-megapixel resolution and often lack fine-grained details, which limits their practicality. Our method builds on our analysis on how the input resolution and the scene structure affects depth estimation performance. We demonstrate that there is a trade-off between a consistent scene structure and the high-frequency details, and merge low- and high-resolution estimations to take advantage of this duality using a simple depth merging network. We present a double estimation method that improves the whole-image depth estimation and a patch selection method that adds local details to the final result. We demonstrate that by merging estimations at different resolutions with changing context, we can generate multi-megapixel depth maps with a high level of detail using a pre-trained model.
10 years of video browser showdown.

The Video Browser Showdown (VBS) has influenced the Multimedia community already for 10 years now. More than 30 unique teams from over 21 countries participated in the VBS since 2012 already. In 2021, we are celebrating the 10th anniversary of VBS, where 17 international teams compete against each other in an unprecedented contest of fast and accurate multimedia retrieval. In this tutorial we discuss the motivation and details of the VBS contest, including its history, rules, evaluation metrics, and achievements for multimedia retrieval. We talk about the properties of specific VBS retrieval systems and their unique characteristics, as well as existing open-source tools that can be used as a starting point for participating for the first time. Participants of this tutorial get a detailed understanding of the VBS and its search systems, and see the latest developments of interactive video retrieval.
Indirect Enforcement of Artificial Intelligence 'Soft Law'

Artificial intelligence (AI) presents a major governance challenge given the complexity of its applications and the speed of its development. This paper first makes the case that "soft law" will and should play a major role in the governance of AI. Soft law consists of instruments that impose substantive expectations but are not directly enforceable by governments. This paper then summarizes the current landscape of AI soft law. Finally, its suggests a series of strategies that could make AI soft law more effective and credible.