Reviewerly/exHarmony huggingface dataset:
Learning to Censor by Noisy Sampling

Point clouds are an increasingly ubiquitous input modality and the raw signal can be efficiently processed with recent progress in deep learning. This signal may, often inadvertently, capture sensitive information that can leak semantic and geometric properties of the scene which the data owner does not want to share. The goal of this work is to protect sensitive information when learning from point clouds; by censoring the sensitive information before the point cloud is released for downstream tasks. Specifically, we focus on preserving utility for perception tasks while mitigating attribute leakage attacks. The key motivating insight is to leverage the localized saliency of perception tasks on point clouds to provide good privacy-utility trade-offs. We realise this through a mechanism called Censoring by Noisy Sampling (CBNS), which is composed of two modules: i) Invariant Sampling: a differentiable point-cloud sampler which learns to remove points invariant to utility and ii) Noise Distorter: which learns to distort sampled points to decouple the sensitive information from utility, and mitigate privacy leakage. We validate the effectiveness of CBNS through extensive comparisons with state-of-the-art baselines and sensitivity analyses of key design choices. Results show that CBNS achieves superior privacy-utility trade-offs on multiple datasets. The code for our work can be found https://github.com/java-abhinav07/CBNS-ECCV2022 .
Visualización de Datos Aplicada en el Análisis del Impacto de la Educación a Distancia a Nivel Preescolar Durante la Pandemia por COVID-19

The present project was based on the realization of a descriptive model for preschool teachers and managers, in order to know the results obtained during the confinement as a result of COVID 19, which was characterized by the use of a virtual environment. As a means to carry out the interaction, experiences, strategies and activities necessary to develop school skills, the study makes use of data visualization techniques, following the Soukup and Davidson methodology itself, which developed a pilot project and had a set of data, in which the evaluations carried out during the 2019-2022 period for fourteen public preschool institutions in the state of Guerrero were reflected and divided into six areas to be analyzed, including language and communication, thought mathematics, exploration of the environment, social-emotional, arts and physical education.
Pengembangan Interactive Module of Disgestive System Berbasis Multimedia Autoplay

This study aims to develop a multimedia autoplay based module of disgestive. The module is the smallest unit of teaching and learning programs that students learn individually or or learners to themselves. This development research method refers to the ADDIE model which has five stages, namely: 1) Analysis (analysis of students' needs, so that the resulting output is in the form of identification of needs and task analysis based on needs), 2) Design (planning or designing), 3) Developmentation (making the design a reality), 4) Implementation (concrete steps to implement the learning system that is being developed, 5) Evaluate (giving values ​​to the learning program. The stages used in the development of this module are limited to 3 stages, namely analysis, design, and development. The percentage of the results of the validation of the first material expert was 89.47% with very valid criteria and the percentage of the results of the validation of the second material expert was greater than the first by 90.78%, with very valid criteria as well. And for the results of the first validation media expert got a percentage of 85.71% with very feasible criteria and the results of the second validation by the media expert were greater than the first validation of 97.36% with very feasible criteria, so it can be concluded that the development of the digestive system module in The validity level of human based on multimedia autoplay is seen that it is very feasible and can be used in learning.
Implementation of Genetic Algorithm in Determining Class Schedules Based on User Needs

Education is an important thing in life. In practice, education consists of teachers and students. A teacher teaches a subject to students. Students are taught by several or more teachers. There is a provision in which a class of students can only receive one subject at a time. Teachers can also only teach one subject at a time. This requires a system that is used to regulate so that these provisions can be fulfilled without ignoring other provisions. The system used to manage these problems is a subject scheduling system. This system regulates the class placement of students with the subjects they study. In manual implementation, the process is very inefficient in terms of time, human resources and thought power. The method that can be used to solve this problem is the genetic algorithm. A genetic algorithm is a heuristic method or procedure inspired by the natural selection process. Genetic algorithms are generally used to produce high-quality solutions to problems in the form of optimization and search by relying on biologically inspired operators such as mutation, crossbreeding, and selection
A Reparameterization-Based Textual Adversarial Attack Approach with Multi-Objective Geometric Loss

Maliciously designed adversarial samples entail severe security threats to the Pre-trained Language Models (PLMs), which are widely deployed in the natural language processing systems. Such systems would benefit from the research line on adversarial sample attacks by improving the adversarial robustness. Most existing literature focuses on achieving a high success rate of attacks in the vanilla model. They do not, however, consider the samplesâ€™ semantic consistency and fluency. Furthermore, computational complexity is another issue due to the frequent search on perturbations and queries on target models. In this study, we propose a simple yet effective adversarial attack framework by using the reparameterization methods on discreate token sequence. Particularly, a parameterized adversarial distribution based on the Gumbel distribution is optimized through perplexity regularization and embedding-based constraint, such that adversarial perturbations is obtained. In addition, we introduce the multi-objective geometric loss to address the learning imbalance issue on multiple tasks during optimization. To accommodate the pure black-box scenario, we further extend our framework by incorporating GPT-2 into our framework. Detailed experimental studies of the proposed work have been carried out using six benchmark datasets and four SOTA fine-tuned models. The results demonstrate significant reduction on the accuracy of the target model, while preserving fluency, semantic consistency and similarity under human evaluations. Finally in the hard label black-box settings, high level of performance can also be achieved by our approach with fewer queries in comparison with baseline methods.
Motion Generation Using Reinforcement Learning for Violinist Robot

This study aims at building a system capable of automatically determining the bowing and fingering parameters for an anthropomorphic violinist robot. We adopt the reinforcement leaning for determining the bow speed and the bowing-direction under the constant bow-force condition, in which a neural network is embedded as a value function. In the previous report, the learning success rate was small, which might be caused by the reward values. Thus, the reword values were changed in this report. In addition, we applied the simulation method to a short musical piece of four bars. As a result, we found that more stable leaning results were obtained by using the new rewards values, and our proposed method can be applied to the four-bar musical piece.
Penerapan Model Course Review Horay Pada Pembelajaran IPS di Kelas IV SDN Ciputat 04 Kota Tangerang Selatan

The purpose of this study was to determine the improvement of social studies learning outcomes for class IV at SDN Ciputat 04, South Tangerang City through the implementation of the course review horay learning model. This study uses the Class Action Research (CAR) method according to Kemmis and Tanggart which consists of four stages of activity in each cycle, namely: (1) planning, (2) action, (3) observation, and (4) reflection. The results showed an increase of 89% of the number of fourth grade students in the second cycle. This shows that there is an increase that exceeds the target set by the previous researchers, which is 80%. Thus it can be concluded that classroom action research on social studies subjects for class IV SDN Ciputat 04 South Tangerang City using the Course Review Horay model has succeeded in improving student learning outcomes. In addition, the implementation of the Course Review Horay model can also create meaningful learning for students.Keywords: learning model; course review horay; social studies learning; learning outcomes
SISTEMA DE DISPONIBILIZAÇÃO DE DADOS SOBRE A SAÚDE NO BRASIL

The importance of state transparency in the disclosure and availability of data is one of the main pillars for a democracy. Although there are advances that seek to encourage the practice in Brazil, the available data lack a standard organization, accessibility and interactivity in portals where data are stored, with the health area being one of the least explored. To mitigate this problem, a system that interacts with datasets in the healthcare area was developed so that they are made available to users in a visual and interactive way, with the objective of improving the understanding of the database. The motivation for creating the portal is due to the lack of visibility and adequate organization of information in the country. As a result, after the evaluation made by users, about 62.5% agreed that the visual representation helped in understanding the database and 83.3% of users agreed that the portal is important to help with data transparency.
Additional use cases for RFID tags by implementing 3D printed push-button functionalities

Passive ultra-high frequency (UHF) radio frequency identification (RFID) tags can be embedded to objects and to the surrounding environment for identification and sensing. The potential of the technology can be further increased by designing new features, such as push-button functionalities, to the tags. In this study, the additional features were created in a unique way through 3D printing, as it enables the designing and manufacturing of versatile and individually tailored products. The first prototype was created around a commercial tactile push-button, while the second prototype was fully self-designed and based on thick two-sided tape and copper tape. These kinds of structures can be used to sense the state of the battery-free push-button wirelessly from a distance. Both prototype devices were found to be fully functional when tested with a hand-held RFID reader from a 1-meter distance by 4 people. Finally, care professionals were gathered to ideate versatile future use cases for the developed push-button prototypes as an assistive technology. New possibilities for using the buttons were identified especially for persons with limited cognitive and physical capabilities. The next steps are to first design special-purpose push-button objects based on these ideas and then 3D print prototypes of these objects.
Multi-Resolution Context Aggregation Network for Single Image Rain Removal

Severe weather such as rain will cause serious degradation of image quality and affect the accuracy of computer vision algorithms. In order to better extract the features of multi-scale rain streaks and restore the important detailed feature information of the image, a new single image rain removal based on multi-resolution context aggregation network is proposed. Firstly, the shuffling operation is used to convert a single resolution input image into a multi-spatial resolution input, which enables the network to rapidly expand the acceptance field at the low spatial resolution and extract more refined rain streak features at high spatial resolution. The low-resolution rain streak features are aggregated from top to bottom into high spatial resolution, and the network is guided to extract multi-scale rain streak information. Secondly, the multi-resolution feature enhancement block is used to refine the details of the image at different resolutions to prevent the loss or blurring of the details in the rain-removed image. This method uses local residual densely connect blocks and squeeze-excitation network to enhance the ability and efficiency of the network to extract features. Finally, the mixed loss function constructed in this paper is used to obtain higher evaluation index values while improving the human visual perception of rain removal images. The experimental results show that proposed method achieves significant rain removal effects on the Rain100H, Rain100L, and Rain12 synthetic datasets and real rain datasets. Compared with the existing algorithms, the proposed method can achieve significant improvement in qualitative and quantitative indicators and has a high degree of detail retention.
Comment Texts Sentiment Analysis Based on Improved Bi-LSTM and Naive Bayes

With the development of the Internet, various social media have attracted many people to share their thoughts and describe their feelings. Most of these comments are emotional. In the era of big data, we can discover the hidden value through massive data. Like these comments, we can analyze them through sentiment analysis to obtain the emotions contained in people's comments. The information obtained from these analyses can help enterprises better serve people to achieve corporate development and help social media carry out online community governance. Among the methods of emotion analysis, deep learning has been rapidly developed and widely applied in recent years, such as RNN and LSTM. This paper uses bidirectional LSTM to capture bidirectional semantic dependence better. Traditional machine learning methods also have specific applications in emotion analysis. Naive Bayes, SVM, decision tree model, and other algorithm models with classification ability can be well applied to emotion analysis. Information gain represents the degree of information complexity (uncertainty) reduction under a condition, and the importance of a word in classification can be judged by information gain. In this paper, we use an improved information gain formula. We screened out important classification features through improved information gain and applied these features to feature extraction of the naive Bayes model. Compared with the naive Bayes model, the accuracy of the improved naive Bayes model has been improved to some extent. We also combine the improved information gain model. We use bidirectional LSTM by adding a dimension. Behind the last hidden state calculated by bidirectional LSTM. This dimension is the sum of the forward word improvement information gained in the statement. This Bi-LSTM model, combined with improved information gain, also improves accuracy compared with the traditional Bi-LSTM model.
SIMULAÇÃO DE DUAS TÉCNICAS DE PREDIÇÃO PARA NEGOCIAÇÃO DE CRIPTOMOEDAS

Marked by the high volatility in the price of cryptocurrencies, this market is responsible for the movement of trillions of dollars, with Bitcoin as its main exponent, a currency that in 2021 exceeded the value of 60 thousand dollars.Thus, several techniques already used in the stock markets, such as technical and fundamental analysis, became part of the buying and selling strategies of investors in this market.In addition, computational techniques in the field of Artificial Intelligence, such as Long Short Term Memory (LSTM) artificial neural networks, have gained prominence in forecasting the financial market.Given this scenario, this work aims to carry out a comparative analysis between the use of technical analysis and LSTM for the prediction of favorable moments for the purchase, sale and maintenance of cryptocurrencies.The result obtained by the model using technical indicators was not satisfactory, since it obtained a profit of only 3.84% during the period of 30 days.The LSTM model, on the other hand, achieved an average profit of 22.94% in the simulations.
Bayesian optimization for improving the cycle time of soft robotic hand system

This study proposes the methodology to optimize the motion of a manipulator that equips a pneumatic soft robotic gripper for improving the cycle time. The proposed methodology tunes the motion speed and acceleration of the manipulator, using the Bayesian optimization. The flexibility of the soft gripper could cause in its vibration that prevents the accurate positioning of the manipulator. The slow motion of the manipulator makes the vibration small, however, the cycle times become longer. Hence, this study presents the motion- optimization methodology to reduce the cycle time including the manipulator's operation and vibration-convergence times. Considering that it is difficult to analyze the dynamics of the soft grippe, the motion parameters are optimized through the actual exploration.
THEORETICAL FOUNDATIONS OF MODERNIZATION OF THE TEACHER EDUCATION SYSTEM FOR SUSTAINABLE DEVELOPMENT

The article reveals the theories of modernization, which can be used as a theoretical and methodological basis for updating the pedagogical education system, providing its humanitarian and economic vectors. The determinants of determining the strategic directions of modernization of pedagogical education for sustainable development in the unity of retrospective-reflexive, prescriptive and prospective dimensions are described.
Bridging the Gap between Application Performance Analysis and System Monitoring

Performance analysis has a long history in the high-performance computing community. On the one hand, the traditional application analysis focuses on scalable yet detailed instrumentation of parallel execution. On the other hand, per-node or cluster-wide monitoring solutions are used in data center operation. However, performance anomalies resulting from the interaction between applications, background processes and the operating system, are difficult to analyze with tools that reveal only part of the issue. In this paper, we present a novel approach that covers all aspects of individual nodes. We extend a monitoring tool to combine call stack sampling, process monitoring, and syscall recording into a symbiotic view of the application execution, background activity, and the operating system.
“Serving a Common Cause…”: “Literary Heritage” in Memoirs and Correspondence of I.S. Silberstein and S.A. Makashin (Literary Heritage Library. Iss. 8)

The book, offered to the readers’ attention, coincides with the 90th anniversary of the legendary scientific series “Literary Heritage” founded in 1931. For the first time the memoirs of the series founders, literary and art critic, collector Ilya Samoilovich Silberstein (1905–1988) and his closest colleague, permanent co-editor, initiator and compiler of many volumes, literary historian Sergei Aleksandrovich Makashin (1906–1989), as well as their correspondence (1932–1958), are published. In oral memoirs recorded in the 1980s by the Department of Oral History of the Lomonosov Moscow State University Scientific Library, it is told about the birth of the series and its fate in the most difficult conditions of the Soviet era. The documentary chronicle of the editorial team activities is recreated in all details by correspondence, moreover, from a perspective in which no other document would allow to look at it, namely from the inside, that is, as seen by the main participants in the events. The diary entries of L.K. Chukovskaya and N.A. Roskina, reflecting the scientific life of the “Literary Heritage”, are also included in the book.
Analisis Kebutuhan Pengembangan Bahan Ajar Mikrobiologi Berbasis Flipbook Maker di IKIP Budi Utomo Malang 2020

This study aims to determine the need for microbiology teaching materials based on the Flipbook Maker at IKIP Budi Utomo Malang. The development of this teaching material uses a model (ADDIE), this development model includes Analysis, Design, Development, Implementation and Evaluation. The method used is using questionnaires for students and for expert lecturers. Data were analyzed using descriptive analysis. The results showed that: 1) the number of book collections used was limited, the student learning process was bored and lacking understanding. 2) the need for Flipbook Maker teaching materials as additional references in lectures. 3) students assess the aspects of content, appearance and programming very well, making it easier for students to provide literature sources so that it can facilitate students in the process of learning activities. The conclusion in this research is that it is necessary to develop microbiology teaching materials based on the Flipbook Maker for microbiology courses.
Intelligent Load Scheduling in Cognitive Buildings: A Use Case

In the last few years, many appliances are spreading into our houses and are daily used. Such equipment significantly improves the quality of life of people, but their use, when not well regulated, can bring a needless increment in the electricity bill. Such an increment could be mitigated by using intelligent scheduling policies that guide the users toward correct exploitation of electric devices so optimizing their use while, at the same time, saving energy, money, and time. This chapter proposes a case study in which a cognitive scheduling approach is used. Such a case study, implemented in the context of the COGITO project, is devoted to automatically scheduling electric loads in houses according to user preferences, self-produced energy, and variable energy costs.
Probabilistic PCGML Approaches

In the previous chapter we introduced our first category of PCGML techniques, namely, constraint-based PCGML. Constraint-based PCGML approaches rely on data to build a model representing the structures and relationships that are allowed and not allowed between a set of variables. But what if we want to instead capture how likely or common a relationship is and not just whether it occurs? In these cases, we can start leveraging probabilistic PCGML approaches. Probabilistic PCGML approaches model a domain by capturing how likely different relationships and patterns are from the provided data. In the following sections we give more concrete descriptions of what this means. But conceptually, if we think about flipping a coin, a constraint-based model would say it is possible for the coin to land on either heads or tails. A probabilistic model would tell us that a fair coin will land on heads $$50\%$$ of the time, and tails the other $$50\%$$ . In the next section, we revisit generating a platformer level, this time with a probabilistic approach, and use this to introduce different types of probabilities. Afterwards, we discuss some important concepts for probabilistic approaches. We then introduce clustering and its ties to PCGML. Throughout the chapter as we introduce a new probabilistic approach, we discuss prior work that leveraged that approach. By the end of the chapter you should have an overview of what probabilistic PCGML has been used for in the past, and hopefully some ideas for how you could use it!