Reviewerly/exHarmony huggingface dataset:
Implementation of Genetic Algorithm in Making a Covid-19 Vaccination Schedule

At the end of 2019, the World was attacked by a very deadly virus. Starting from the city of Wuhan in China, this virus began to grow and attack the world and became a pandemic. Until now the world is still being attacked by this pandemic. Various experts in this world took turns looking for a solution to this problem. Various studies and studies were carried out to find a way out and treatment. In the middle of 2020, research began to be intensively carried out to make a covid 19 vaccine. In doing things that are quite complicated, of course we need the help of technology to do it. In this case, we can use an algorithm in scheduling the covid 19 vaccination. In this regard, this study would like to discuss the application of genetic algorithms in making the covid 19 vaccination schedule. The genetic Algorithm was chosen because it can use many variables in its application compared to the FIFO method. The FIFO method is indeed simpler, the first to register will be scheduled, but to support more variables it is more efficient to use Genetic Algorithms. The use of genetic algorithms in this study was to find solutions and apply them to the program to schedule the covid 19 vaccination. genetics in scheduling accurate and effective COVID-19 vaccinations.
Multi-scale 2D Representation Learning for weakly-supervised moment retrieval.

Video moment retrieval aims to search the moment most relevant to a given language query. However, most existing methods in this community often require temporal boundary annotations which are expensive and time-consuming to label. Hence weakly supervised methods have been put forward recently by only using coarse video-level label. Despite effectiveness, these methods usually process moment candidates independently, while ignoring a critical issue that the natural temporal dependencies between candidates in different temporal scales. To cope with this issue, we propose a Multi-scale 2D Representation Learning method for weakly supervised video moment retrieval. Specifically, we first construct a two-dimensional map for each temporal scale to capture the temporal dependencies between candidates. Two dimensions in this map indicate the start and end time points of these candidates. Then, we select top-K candidates from each scale-varied map with a learnable convolutional neural network. With a newly designed Moments Evaluation Module, we obtain the alignment scores of the selected candidates. At last, the similarity between captions and language query is served as supervision for further training the candidates' selector. Experiments on two benchmark datasets Charades-STA and ActivityNet Captions demonstrate that our approach achieves superior performance to state-of-the-art results.
L’esperienza del dolore e le ragioni della speranza. Riflessioni sulla recente evoluzione del dibattito giurisprudenziale in tema di dignità umana e fine vita

SOMMARIO: 1. Una premessa di metodo - 2. Giustizia limiti e sacralita del “fine vita” - 3. Tra indisponibilita della vita e diritto di autodeterminazione individuale. La prospettiva della Corte EDU … - 4. … e le prudenti soluzioni del diritto italiano tra leggi e prassi giurisprudenziale - 5. Considerazioni conclusive: esiste la liberta di congedarsi dal mondo?The experience of pain and the reasons for hope. Reflections on the end of life of postmodern manABSTRACT: The surprising development of biotechnology, new knowledge and unprecedented possibilities of intervention on human beings, have strongly affected the natural events of being born and dying, bringing out new rights and new instances of protection, aimed at ensuring respect for individual self-determination and human dignity in the face of the increasingly pressing intrusiveness of science and technology. Nonetheless, these possibilities offered by science may, for some, not coincide with the ideal conception of life and human dignity, since these are interventions capable of prolonging survival in often very critical conditions, not always adequate to guarantee a life worth living. At this point, the question is whether there is a way to pursue a harmonious composition between the concepts of life and human dignity, in the extreme variety of cases where there is a question of their protection, especially with regard to end-of-life issues.
A Probabilistic Framework for Knowledge Graph Data Augmentation

We present NNMFAug, a probabilistic framework to perform data augmentation for the task of knowledge graph completion to counter the problem of data scarcity, which can enhance the learning process of neural link predictors. Our method can generate potentially diverse triples with the advantage of being efficient and scalable as well as agnostic to the choice of the link prediction model and dataset used. Experiments and analysis done on popular models and benchmarks show that NNMFAug can bring notable improvements over the baselines.
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial  Training

Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by slightly perturbing the features of correctly labeled training examples. By formalizing this malicious attack as finding the worst-case training data within a specific $\infty$-Wasserstein ball, we show that minimizing adversarial risk on the perturbed data is equivalent to optimizing an upper bound of natural risk on the original data. This implies that adversarial training can serve as a principled defense against delusive attacks. Thus, the test accuracy decreased by delusive attacks can be largely recovered by adversarial training. To further understand the internal mechanism of the defense, we disclose that adversarial training can resist the delusive perturbations by preventing the learner from overly relying on non-robust features in a natural setting. Finally, we complement our theoretical findings with a set of experiments on popular benchmark datasets, which show that the defense withstands six different practical attacks. Both theoretical and empirical results vote for adversarial training when confronted with delusive adversaries.
A scalable network for simultaneous pairwise quantum key distribution via entanglement-based time-bin coding

We present simultaneous quantum key distribution (QKD) between any two pairs of participants in a 4-party star-shaped optical fiber network without a trusted node. Our QKD system is the first to extend an entanglement-based time-bin coding protocol to multiple pairs of participants. We demonstrate key exchange over distances of more than 75 km and show that our network can be readily extended to at least 34 participants by using wavelength-division multiplexing in the optical C-band and with small modifications up to 102 participants. Our scheme is compatible to multiplexing approaches that were implemented earlier with polarization-based QKD and can thereby be extended to a fully connected network. In contrast to polarization-based QKD, the all-fiber system we present is insensitive to polarization instabilities in the fiber link. This enables key distribution networks with low quantum bit error rates and stable secure key rates using deployed fibers even under challenging environmental conditions.
PEMBANGUNAN APLIKASI HASIL BRIEFING PADA THE HILLS HOTEL BATAM

Operational activities in hotels are the most important thing in the hospitality business because they are closely related to service. Errors and deficiencies on the operational side can directly affect service to guests. The delivery of operational activities between departments at the time of the briefing must be clear so that there is no miscommunication between departments. Submission of the report on the results of the briefing via email that difficulties were found and even were not conveyed properly. The fact of this finding is because some employees only open emails via computers because the report results are in the form of .xlsx files and there are still many incoming emails that result in unanswered information on the results of the briefing. The research method used is observation, interviews, and literature study. The software method used in software development is to use the waterfall model. This application development uses Visual Studio Code and uses MySQL as a database management system. The final result of this research is to resolve the findings of ongoing problems by building a reporting application and reminder from the results of mobile-based briefings that can be used anywhere and anytime. Reporting all archives that are neatly organized can improve operational activities at The Hills Hotel Batam without any miscommunication between department heads.
Chapter 11 Applications of conventional machine learning and deep learning for automation of diagnosis: case study

Computerized diagnostic methods are commonly used in recent years as initial screening methods for the detection of diseases. Machine learning (ML) and deep learning (DL) are extensively used in the medical field for the investigation of various computer-aided diagnostic applications. Remarkable progress has been made toward the development of such systems in various branches of the medical field by many researchers using the ML approach. In the present work, we compare and contrast the suitability of ML and DL for two different kinds of applications. Suitability of ML and DL methods experimented separately for categorization of white blood cells into the five normal types and abnormal types, and classification of uterine cervix images into cancerous and normal. Both the techniques performed equally well in the case of peripheral blood smear images due to visibly clear features of white blood cells. In the case of uterine cervix images, DL performed better compared to that of ML. This is because of features in the images are not differentiable. From this study, we conclude that conventional ML is useful when features are visible, availability of dataset and resource is a constraint. DL is useful when feature engineering is a difficult task and the availability of the resource is not a constraint.
Neural Networks with Scikit-Learn, Keras, and H2O

This chapter executes and assesses nonlinear neural networks to address binary classification using a diverse set of comprehensive Python frameworks (i.e., Scikit-Learn, Keras, and H2O).
An EEG Analysis on the Perception of the Consumers Regarding Video-Commercials from the Automotive Industry

Nowadays, every human individual interacts with video-commercials on digital devices such as TV, laptop, smartphones. The persona that watches the video-commercials may be or not able to react and perceive the subconscious emotions that are send once with the verbal message and the exposure of the product. The research explained in this article uses the cognitive neuroscience applied in marketing to see the effects of the video-commercials onto the human brain and how the attention of the consumers is influenced by certain stimuli, like sound on and off, the narrative speech of one auto brand in the native language (Romanian) of the participants involved in the experiment, and also a foreign language(English), the speed of the movie, the intensity of the light in the room. In this research was used the NeuroSpectrum 2 electroencephalograph, which provided useful information regarding the states of the human mind and the impact of the video-commercials for two types of audience: healthy brain subjects and subjects with certain neurological brain damages. The first objective of this research based on medical tools, is to see what modifications appear in the electrical activity of the brain of the participants before and after watching the commercials. The second objective is to detect certain feelings that make the commercial be likable, such as the states of happiness and calmness that the video-commercials produces to the subjects exposed to the video-commercials. The main results obtained after the experiment are that the movie induces to all the subjects a state of relaxation and meditation leading in some cases to a better, nearly normal cognitive activity for the participants with brain damages, by inducing them the state of calmness and finally leading them to sleep.
Robust causal structure learning with some hidden variables

We introduce a new method to estimate the Markov equivalence class of a directed acyclic graph (DAG) in the presence of hidden variables, in settings where the underlying DAG among the observed variables is sparse, and there are a few hidden variables that have a direct effect on many of the observed variables. Building on the so‐called low rank plus sparse framework, we suggest a two‐stage approach which first removes the effect of the hidden variables and then estimates the Markov equivalence class of the underlying DAG under the assumption that there are no remaining hidden variables. This approach is consistent in certain high dimensional regimes and performs favourably when compared with the state of the art, in terms of both graphical structure recovery and total causal effect estimation.
Text Classification for Task-based Source Code Related Questions.

There is a key demand to automatically generate code for small tasks for developers. Websites such as StackOverflow provide a simplistic way by offering solutions in small snippets which provide a complete answer to whatever task question the developer wants to code. Natural Language Processing and particularly Question-Answering Systems are very helpful in resolving and working on these tasks. In this paper, we develop a two-fold deep learning model: Seq2Seq and a binary classifier that takes in the intent (which is in natural language) and code snippets in Python. We train both the intent and the code utterances in the Seq2Seq model, where we decided to compare the effect of the hidden layer embedding from the encoder for representing the intent and similarly, using the decoder's hidden layer embeddings for the code sequence. Then we combine both these embeddings and then train a simple binary neural network classifier model for predicting if the intent is correctly answered by the predicted code sequence from the seq2seq model. We find that the hidden state layer's embeddings perform slightly better than regular standard embeddings from a constructed vocabulary. We experimented with our tests on the CoNaLa dataset in addition to the StaQC database consisting of simple task-code snippet-based pairs. We empirically establish that using additional pre-trained embeddings for code snippets in Python is less context-based in comparison to using hidden state context vectors from seq2seq models.
LibGuides: How To Guide: NoodleTools Citing & Research Management Platform: Primary, Secondary, Tertiary - What's the Difference?

Gives the definition of a primary source, and explains the differences between primary, secondary, and tertiary sources and gives examples of each
Provable Lifelong Learning of Representations

In lifelong learning, the tasks (or classes) to be learned arrive sequentially over time in arbitrary order. During training, knowledge from previous tasks can be captured and transferred to subsequent ones to improve sample efficiency. We consider the setting where all target tasks can be represented in the span of a small number of unknown linear or nonlinear features of the input data. We propose a provable lifelong learning algorithm that maintains and refines the internal feature representation. We prove that for any desired accuracy on all tasks, the dimension of the representation remains close to that of the underlying representation. The resulting sample complexity improves significantly on existing bounds. In the setting of linear features, our algorithm is provably efficient and the sample complexity for input dimension $d$, $m$ tasks with $k$ features up to error $\epsilon$ is $\tilde{O}(dk^{1.5}/\epsilon+km/\epsilon)$. We also prove a matching lower bound for any lifelong learning algorithm that uses a single task learner as a black box. Finally, we complement our analysis with an empirical study.
The Resistance Factors Affecting the Adoption of Healthcare Robots Technology in Thailand Government Hospital

Purpose: Healthcare robot technology is an important tool and shows an extremely important role in the healthcare industry since its potentiality to improve the healthcare service. In spite of its high ability, it cannot be assessed change related to the healthcare robot. Previously research showed that the user may make a decision to accept or resist the usage of new technology depending on assessing individual changes. The limitation of healthcare robot usage is caused by the ignoring problem of anti-usability of the user. According to barrier theories, this study develops a modify resistance model to describe the factors which resist the medical staffs’ intention to use healthcare robot. Methods: The participants were selected from medical staffs (physician, nurse, and staffs) working in government hospital located in Thailand. The data were collected using questionnaire to test a research model and it was developed based on the medical staff users in real work to test a research model. The research model is analyzed by confirmatory factor analysis (CFA) with the LISREL program. A valid sample of 466 medical staffs were collected for data analysis. Result: The empirical results confirmed that the factors resisting medical staffs’ behavioral intention to use healthcare robots were technological barrier, safety barrier and time barrier. This study is discussed in several practical and theoretical implications. Conclusion: Our study illustrates the importance of user resistance in technology acceptance in general and in health technology usage studies in particular. This study identifies key of the resistance factors to resist medical staffs’ behavioral intention to use healthcare robot. Further, the study provides a useful reference for future studies in this subject field. Recommendations/future directions: There may be other resistance of healthcare robot usage that were not included in this study and can be the subject of future research. Further, there may be additional predictors of resistance, beyond technological barrier, safety barrier, time barrier, and change barrier, that should be examined in future research. And this study is confined to the healthcare robot behavior of medical staffs only in Thailand government hospital, this study cannot be used to another group.
순환학습기반 수과학 통합 유아 목공놀이 프로그램이 유아의 수학적 능력, 과학적 능력 및 창의성에 미치는 효과

Objectives: This study aims to examine the effects of a math-science integrating woodwork play program, which is based on the learning cycle, on the improvements in young children’s mathematical, scientific, and creative abilities. Methods: Participants were 56 five-year-old children from two classes in M kindergarten located in S city. Only the experimental group participated in the math-science integrating woodwork play program. Data were analyzed using SPSS 21.0. An independent-samples t -test and a paired-samples t -test were utilized for examining the homogeneity between the experimental and the comparison groups, as well as the effectiveness of the program. Results: The findings suggest statistically significant differences in the subfactor scores that of measured mathematical abilities, scientific abilities, and creativity, between the experimental group and the control group after program implementation. Furthermore, the statistically significant differences in the scores before and after the program implementation were specifically found in the experimental group, and not in the control group. Conclusion: A math-science integrating woodwork play program, based on the learning cycle, has a positive influence on improving young children’s mathematical, scientific, and creative abilities. The results of this study can contribute to the dissemination of the math-science integrating woodwork play program. Finally, the significance of this study relates to its discovery of the value in woodwork play in terms of math-science integration and creativity development.
What Should We Do to Publish Journal Articles in the Future?

The last chapter of the book focuses on the major future trends of journal article publication. It discusses good initial responses from students, current challenges for authors, as in the three cases of Ann, Zachary, and Pat, and four core concepts related to future journal publication, that is, open science, open access, transparency standards, and early career researchers. It ends with several practical suggestions: be aware of the current transition from paper-based scientific communication to digital-based scientific communication and from subscription-based journal publication to open-access-based journal publication, be knowledgeable about the impacts of these transitions, and be competent in new skills for open science in general and open access journal publication in particular.
The baseball card as a repurposed data-rich source for tabletop game simulation

Languishing in attics and basements worldwide, baseball cards fulfill their roles as statistical historical documents, representing little more than memories to their owners of a time gone by. However, baseball cards need not be limited to historical reflection and memories alone—for each baseball card is in essence a unique ‘player historical database’; a snapshot in time whose data and mathematical potential can be exploited within the realm of game-simulation. The current project seeks to rejuvenate and repurpose the baseball card, and via statistical resampling analyses, validate its worth as a data-rich game piece that can be utilized to generate accurate tabletop baseball simulation results, when combined with modern dice-roll strategies and innovative game design.
AutoMTL: A Programming Framework for Automated Multi-Task Learning

Multi-task learning (MTL) jointly learns a set of tasks. It is a promising approach to reduce the training and inference time and storage costs while improving prediction accuracy and generalization performance for many computer vision tasks. However, a major barrier preventing the widespread adoption of MTL is the lack of systematic support for developing compact multi-task models given a set of tasks. In this paper, we aim to remove the barrier by developing the first programming framework AutoMTL that automates MTL model development. AutoMTL takes as inputs an arbitrary backbone convolutional neural network and a set of tasks to learn, then automatically produce a multi-task model that achieves high accuracy and has small memory footprint simultaneously. As a programming framework, AutoMTL could facilitate the development of MTL-enabled computer vision applications and even further improve task performance. Code of AutoMTL will be available at https://github.com/zhanglijun95/AutoMTL