Reviewerly/exHarmony huggingface dataset:
Maintaining Language Politeness Through Learning Advice in Japanese

In the world of education, the ability and sensitivity in delivering advice is needed. This is because advice is a speech that is very often used in interactions with students in building character. With regard to various important factors that must be considered in delivering speech advice, especially in learning foreign languages which may have different speech cultures. This research aims to obtain an overview how the speakers maintaining politeness in advice utterance. The maintaining politeness of advice in terms of strategy, use of modifications, in term of politeness principle, in term of cooperative principle, type of expression and variety of language used in advice utterance, so it is hoped that used as instruments in maintaining politeness in the concept of learning. This research is based on a qualitative paradigm, with content analysis techniques and inductive procedures. Sharpening the focus of the study in this research is on its pragmatic competence. Based on the research results, it can be concluded that the way to maintain politeness in speaking is to use a speech strategy, either directly or indirectly, followed by modifications in the speech. In giving advice, the most priority to pay attention to by speakers is the interests and benefits of the interlocutors, then sympathy for the interlocutors, then in giving advice, not to be arrogant, not criticizing the interlocutors and as much as possible praising the interlocutors, and to avoid disapproval of the interlocutors. Then in carrying out the conversation the speaker expresses what is needed, is relevant to the conversation, speaks the right thing, and speaks clearly. In addition to the use of these expressions and speech strategies, when delivering speeches, the ways of maintaining politeness also pay attention to the mental or psychological condition of the interlocutors.
Analisa Tingkat Usability Berdasarkan Human Computer Interaction Untuk Sistem Pemesanan Tiket Online Kereta Api

The Current technological developments make PT. KAI (Kereta Api Indonesia) as a business entity that regulates and provides rail transportation services has been transformed into providing online ticket reservation services, one of which is through the KAI Access application. But it needs to be realized that the marketplace trend has also penetrated travel deals that also provide train ticket reservations, one of which is Traveloka. This interesting phenomenon is the basis for conducting research to see whether the application owned by PT KAI has been able to compete with other applications that also provide online train ticket sales services. By being able to compete, PT KAI can show credibility in providing online ticket transactions. Both applications are tested using the Usability indicators from HCI: Learnability, Efficiency, Memorability, Errors, and Satisfaction. This study resulted in a comparison of the two applications by each HCI indicator. The discussion on each indicator it can be seen the classification of the level of usefulness from KAI Access and Traveloka
Conditional Generation of Temporally-ordered Event Sequences

Models of narrative schema knowledge have proven useful for a range of event-related tasks, but they typically do not capture the temporal relationships between events. We propose a single model that addresses both temporal ordering, sorting given events into the order they occurred, and event infilling, predicting new events which fit into an existing temporally-ordered sequence. We use a BART-based conditional generation model that can capture both temporality and common event co-occurrence, meaning it can be flexibly applied to different tasks in this space. Our model is trained as a denoising autoencoder: we take temporally-ordered event sequences, shuffle them, delete some events, and then attempt to recover the original event sequence. This task teaches the model to make inferences given incomplete knowledge about the events in an underlying scenario. On the temporal ordering task, we show that our model is able to unscramble event sequences from existing datasets without access to explicitly labeled temporal training data, outperforming both a BERT-based pairwise model and a BERT-based pointer network. On event infilling, human evaluation shows that our model is able to generate events that fit better temporally into the input events when compared to GPT-2 story completion models.
Annotating Errors and Emotions in Human-Chatbot Interactions in Italian

This paper describes a novel annotation scheme specifically designed for a customer-service context where written interactions take place between a given user and the chatbot of an Italian telecommunication company. More specifically, the scheme aims to detect and highlight two aspects: the presence of errors in the conversation on both sides (i.e. customer and chatbot) and the “emotional load” of the conversation. This can be inferred from the presence of emotions of some kind (especially negative ones) in the customer messages, and from the possible empathic responses provided by the agent. The dataset annotated according to this scheme is currently used to develop the prototype of a rule-based Natural Language Generation system aimed at improving the chatbot responses and the customer experience overall.
Perbandingan Interpretasi Analisis Data Page View dan Click Related Post Pada Website Edukasi Sebagai Implisit Feedback Dengan Hasil Interpretasi Penggunaan Teknologi EyeTracking

The analysis used in this study uses the help of Google Analytics to understand how the user's behavior on the Calculus learning material educational website page. Are users interested in recommendation articles? The answer to this question provides insight into the user's decision process and suggests how far a click is the result of an informed decision. Based on these results, it is hoped that a strategy to generate feedback from clicks should emerge. To evaluate the extent to which feedback shows relevance, versus implicit feedback to explicit feedback collected manually. The study presented in this study differs in at least two ways from previous work assessing the reliability of implicit feedback. First, this study aims to provide detailed insight into the user decision-making process through the use of a recommendation system with an implicit feedback feature. Second, evaluate the relative preferences that come from user behavior (user behavior). This differs from previous studies which primarily assessed absolute feedback.
Development of a webmapping application for access to geospatial information on Tourism in Cameroon

Cameroon is known as Africa in miniature and therefore has great tourism potential. In view of the low level of exploitation of this wealth, the State of Cameroon has committed since 2010 to the redevelopment of sites, access roads and places of recreation. For an effective and efficient dissemination of its tourism offer, a webmapping application was implemented in 2012. However, it could not be published due to a number of shortcomings, in particular the use of a mono scale map at 1: 500,000 and the lack of interactivity with the map. In this article we propose an improved version of this application and through a software development process, a new platform has been implemented and allows on the OSM multi-scale basemap, interactive access to tourism offers on Cameroon in providing descriptive information and illustrative photos on request.
Fast Interleaved Bidirectional Sequence Generation

Independence assumptions during sequence generation can speed up inference, but parallel generation of highly inter-dependent tokens comes at a cost in quality. Instead of assuming independence between neighbouring tokens (semi-autoregressive decoding, SA), we take inspiration from bidirectional sequence generation and introduce a decoder that generates target words from the left-to-right and right-to-left directions simultaneously. We show that we can easily convert a standard architecture for unidirectional decoding into a bidirectional decoder by simply interleaving the two directions and adapting the word positions and selfattention masks. Our interleaved bidirectional decoder (IBDecoder) retains the model simplicity and training efficiency of the standard Transformer, and on five machine translation tasks and two document summarization tasks, achieves a decoding speedup of ~2x compared to autoregressive decoding with comparable quality. Notably, it outperforms left-to-right SA because the independence assumptions in IBDecoder are more felicitous. To achieve even higher speedups, we explore hybrid models where we either simultaneously predict multiple neighbouring tokens per direction, or perform multi-directional decoding by partitioning the target sequence. These methods achieve speedups to 4x–11x across different tasks at the cost of <1 BLEU or <0.5 ROUGE (on average).
Medicare with Machine Learning and Deep Learning

India is facing a scarcity of medical expertise, modern medical equipment, and hospitals. People in rural areas don’t even have access to healthcare facilities. At the same time, country is struggling to fight with deadly diseases like malaria and pneumonia that causes thousands of deaths every year. Hence, it becomes the necessity to come up with an alternate solution that can eliminate the challenges posed by lack of enough medical professionals and health infrastructure. Artificial intelligence concepts machine learning and deep learning can be applied to automate the process of disease identification and diagnosis. The paper proposes the system named “Medicare” that operates in two parts. The first module allows the user to predict the potential diseases by providing the likely symptoms. The module works on the concept of machine learning. We feed the dataset comprises of various diseases and associated symptoms pairs. Both doctors and normal people can make use of this disease predictor. Doctors can use this to verify their diagnosis results with the outcome of the system. Normal people can use this to diagnose the disease at home. The system depicts the potential diseases with their probabilities and advises them whether to consult the doctors or not. The second module allows us to test whether a medical image is positive or negative. This module works on the concept of a convolutional neural network that performs the image processing on the image to test the image. Little bit modification will allow the person to test several images at the same time, hence will save time and cost.
ABOUT FEATURES OF MUTATION APPLICATION IN A MODIFIED OPERATOR GENETIC ALGORITHM

Genetic algorithm is a method of optimization based on the concepts of natural selection and genetics. Genetic algorithms are used in software development, in artificial intelligence systems, a wide range of optimization problems and in other fields of knowledge.One of the important issues in the theory of genetic algorithms and their modified versions is the search for the best balance between performance and accuracy. The most difficult in this sense are problems where the fitness function in the search field has many local extremes and one global or several global extremes that coincide.The effectiveness of the genetic algorithm depends on various factors, such as the successful creation of the primary population. Also in the theory of genetic algorithms, recombination methods play an important role to obtain a better population of offspring. The aim of this work is to study some types of mutations using a modified genetic algorithm to find the minimum function of one variable.The article presents the results of research and analysis of the impact of some mutation procedures. Namely, the effect of mutation on the speed of achieving the solution of the problem of finding the global extremum of a function of one variable. For which a modified genetic algorithm is used, where the operators of the "generalized crossover" are stochastic matrices
Crowd Counting Method Based on Cross-Source Multi-View and Multi-Granularity Video Fusion

Abstract With the rapid growth of the world's population and the rapid development of urbanization, the issue of crowd gathering safety has aroused widespread concern in society. Extensive video surveillance systems provide rich data support for dense crowd management. Video-based crowd counting and density estimation methods are the core technologies to ensure the safety of crowd gathering. Different from single-view video analysis, cross-source multi-view multi-granularity video contains more cross-information. The complementary sharing of information is of great help to solve the problems such as occlusion in the current crowd counting. Therefore, this article proposes a crowd counting method based on cross-source multi-view and multi-granularity video distributed information fusion. By establishing a distributed structure from different cameras that matches low-altitude and high-altitude views, it uses fine-grained from low-altitude images. The high-resolution local information corrects and supplements the global information from high-altitude images, so as to calculate a more accurate and global number and density of people. This method is actually applied to the landmark building of Suzhou Life City Square. The changes in the number of people and the movement situation during the evacuation are analyzed and evaluated, and good results are obtained.
Studies on Computer Intelligent Image Recognition Algorithm and Technology

With the rising level of economic development in China year by year, there have been obvious technological advances in many fields of science and technology, including the computer field. Relevant Chinese ministries attach importance to computer technology. The technology has been updating and developing all the time while researchers in the computer field are also constantly improving their researches on the intelligent technology in order to study and explore more new things. One can better be recognized with the current computer intelligent technology, even in an environment that is difficult to identify. In this article, the algorithm and application of computer image recognition technology is studied and analyzed to lay a theoretical foundation for its further development and application.
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels

ImageNet has been arguably the most popular image classification benchmark, but it is also the one with a significant level of label noise. Recent studies have shown that many samples contain multiple classes, despite being assumed to be a single-label benchmark. They have thus proposed to turn ImageNet evaluation into a multi-label task, with exhaustive multi-label annotations per image. However, they have not fixed the training set, presumably because of a formidable annotation cost. We argue that the mismatch between single-label annotations and effectively multi-label images is equally, if not more, problematic in the training setup, where random crops are applied. With the single-label annotations, a random crop of an image may contain an entirely different object from the ground truth, introducing noisy or even incorrect supervision during training. We thus re-label the ImageNet training set with multi-labels. We address the annotation cost barrier by letting a strong image classifier, trained on an extra source of data, generate the multi-labels. We utilize the pixel-wise multi-label predictions before the final pooling layer, in order to exploit the additional location-specific supervision signals. Training on the re-labeled samples results in improved model performances across the board. ResNet-50 attains the top-1 classification accuracy of 78.9% on ImageNet with our localized multi-labels, which can be further boosted to 80.2% with the CutMix regularization. We show that the models trained with localized multi-labels also outperforms the baselines on transfer learning to object detection and instance segmentation tasks, and various robustness benchmarks. The re-labeled ImageNet training set, pre-trained weights, and the source code are available at {https://github.com/naver-ai/relabel_imagenet}.
Less is Better: A cognitively inspired unsupervised model for language segmentation

Language users process utterances by segmenting them into many cognitive units, which vary in their sizes and linguistic levels. Although we can do such unitization/segmentation easily, its cognitive mechanism is still not clear. This paper proposes an unsupervised model, Less-is-Better (LiB), to simulate the human cognitive process with respect to language unitization/segmentation. LiB follows the principle of least effort and aims to build a lexicon which minimizes the number of unit tokens (alleviating the effort of analysis) and number of unit types (alleviating the effort of storage) at the same time on any given corpus. LiB’s workflow is inspired by empirical cognitive phenomena. The design makes the mechanism of LiB cognitively plausible and the computational requirement light-weight. The lexicon generated by LiB performs the best among different types of lexicons (e.g. ground-truth words) both from an information-theoretical view and a cognitive view, which suggests that the LiB lexicon may be a plausible proxy of the mental lexicon.
Automating Interoperability Testing to Improve Open Standards for the Internet

While interoperability testing has been one of the hallmarks of open standards and Internet protocol development for more than three decades, the process itself has been limited by manual and inconsistent testing.
TRAINING SHORT STORY WRITING FOR MASS MEDIA IN THE MIDDLE SCHOOL OF BUDI AGUNG

Literacy cannot be separated from the world of education. Literacy refers to the ability to solve problems and achieve life goals by using text as the primary medium, orally, and in writing. Literacy activities are part of the character education process through reading and writing skills. This means that character education can be obtained through reading and writing, for example, short stories. By reading and writing short stories, it is hoped that Indonesian students will be nurtured to become religious, integrity, nationalist, independent, and have cooperation. Besides, short stories that have been written can be sent to the mass media and become one of the coffers of income for students. On this basis, this community service proposal is aimed at developing a culture of literacy among students as well as building an entrepreneurial spirit by sending short stories to mass media
THE ROLE OF BANKS AND SECURITIES MARKETS IN THE POST-TRANSITION ECONOMIES OF EASTERN EUROPE

Economic theory predicts that the development of the financial sector should have a positive impact on the overall economic development. Research has predominantly confirmed this expectation, with the remark that at earlier stages of economic development this impact should be higher, while a disproportionate banking sector has detrimental effect on growth through its impact on attracting highly skilled workforce, increased presence of moral hazard and the associated banking crises. This issue has been studied only occasionally in the case of the former socialist economies of Central and Eastern Europe and the former USSR. This paper represents an attempt to analyze the impact of the banking sector and securities markets development on the economic growth of these countries. A sample of 22 countries is assembled, using data from 1995 to 2018 and a panel regression and a GMM technique are used to derive conclusions on the researched topic. The analysis has shown that the banking sector has played a positive role in the economic growth throughout the analyzed period, while the role of the stock market is not significant. This is in line with the previous studies which have confirmed that the positive role of the securities markets should be expected only at higher levels of economic development. Also, the impact of the overall financial sector is deemed to be positive.
Informative Bayesian Neural Network Priors for Weak Signals

Encoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals. Two types of domain knowledge are commonly available in scientific applications: 1. feature sparsity (fraction of features deemed relevant); 2. signal-to-noise ratio, quantified, for instance, as the proportion of variance explained (PVE). We show how to encode both types of domain knowledge into the widely used Gaussian scale mixture priors with Automatic Relevance Determination. Specifically, we propose a new joint prior over the local (i.e., feature-specific) scale parameters that encodes knowledge about feature sparsity, and a Stein gradient optimization to tune the hyperparameters in such a way that the distribution induced on the model's PVE matches the prior distribution. We show empirically that the new prior improves prediction accuracy, compared to existing neural network priors, on several publicly available datasets and in a genetics application where signals are weak and sparse, often outperforming even computationally intensive cross-validation for hyperparameter tuning.
Implementation Of Vigenere Cryptography Algorithm In Lhokseumawe State Polytechnic Storage System

Cloud Storage is a technology used to store data online. However, this technology also raises issues in terms of security. Therefore, the author wants to create a cloud storage system using cryptographic security to secure data. With cryptographic techniques that regulate cloud storage, users are without worry about borrowing or borrowing data. The cryptographic method applied is the vigenere cipher algorithm which is a type of classical cryptography. Cloud Storage is implemented on public and private networks to conduct data encryption testing. Based on tests conducted, implementation on private networks is better with an average time of 3.84 seconds for encryption and 253.6 milliseconds for decryption. While on public networks with an average time of 45.3 seconds for encryption and 392 milliseconds for decryption. To measure the effectiveness of cryptography, it is done by avalanche affect method which produces a value of 1.4% to 7% with an average of 3.92%.
VisualSparta: Sparse Transformer Fragment-level Matching for Large-scale Text-to-Image Search

Text-to-image retrieval is an essential task in multi-modal information retrieval, i.e. retrieving relevant images from a large and unlabelled image dataset given textual queries. In this paper, we propose VisualSparta, a novel text-to-image retrieval model that shows substantial improvement over existing models on both accuracy and efficiency. We show that VisualSparta is capable of outperforming all previous scalable methods in MSCOCO and Flickr30K. It also shows substantial retrieving speed advantages, i.e. for an index with 1 million images, VisualSparta gets over 391x speed up compared to standard vector search. Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index. To the best of our knowledge, VisualSparta is the first transformer-based text-to-image retrieval model that can achieve real-time searching for very large dataset, with significant accuracy improvement compared to previous state-of-the-art methods.