Reviewerly/exHarmony huggingface dataset:
Monotonic Multihead Attention

Simultaneous machine translation models start generating a target sequence before they have encoded or read the source sequence. Recent approaches for this task either apply a fixed policy on a state-of-the art Transformer model, or a learnable monotonic attention on a weaker recurrent neural network-based structure. In this paper, we propose a new attention mechanism, Monotonic Multihead Attention (MMA), which extends the monotonic attention mechanism to multihead attention. We also introduce two novel and interpretable approaches for latency control that are specifically designed for multiple attentions heads. We apply MMA to the simultaneous machine translation task and demonstrate better latency-quality tradeoffs compared to MILk, the previous state-of-the-art approach. We also analyze how the latency controls affect the attention span and we motivate the introduction of our model by analyzing the effect of the number of decoder layers and heads on quality and latency.
Convolutional Neural Networks (CNN)

Convolutional neural networks (CNN) are a specific type of neural network systems that are particularly suited for computer vision problems such as image recognition. In such tasks, the dataset is represented as a 2-D grid of pixels. See Figure 35-1.
Co-attention and Aggregation Based Chinese Recognizing Textual Entailment Model

Recognizing Textual Entailment is a fundamental task of natural language processing, and its purpose is to recognize the inferential relationship between two sentences. With the development of deep learning and construction of relevant corpus, great progress has been made in English Textual Entailment. However, the progress in Chinese Textual Entailment is relatively rare because of the lack of large-scale annotated corpus. The Seventeenth China National Conference on Computational Linguistics (CCL 2018) first released a Chinese textual entailment dataset that including 100,000 sentence pairs, which provides support for application of deep learning model. Inspired by attention models on English, we proposed a Chinese recognizing textual entailment model based on co-attention and aggregation. This model uses co-attention to calculate the feature of relationship between two sentences, and aggregates this feature with another feature obtained from sentences. Our model achieved 93.5% accuracy on CCL2018 textual entailment dataset, which is higher than the first place in previous evaluations. Experimental results showed that recognition of contradiction relations is difficult, but our model outperforms other benchmark models. What’s more, our model can be applied to Chinese document based question answer (DBQA). The accuracy of the experiment results on the dataset of NLPCC2016 is 72.3%.
Possibilities of using virtual reality as a means for therapy from fear of spiders

The subject of this article is to find out whether virtual reality can affect phobia of spiders. For this pre-research were performed 20 experiments on a small sample of subjects. Two approaches were used to measure during the experiments. The values of results are represented by this post.
A Closer Look at Data Bias in Neural Extractive Summarization Models

In this paper, we take stock of the current state of summarization datasets and explore how different factors of datasets influence the generalization behaviour of neural extractive summarization models. Specifically, we first propose several properties of datasets, which matter for the generalization of summarization models. Then we build the connection between priors residing in datasets and model designs, analyzing how different properties of datasets influence the choices of model structure design and training methods. Finally, by taking a typical dataset as an example, we rethink the process of the model design based on the experience of the above analysis. We demonstrate that when we have a deep understanding of the characteristics of datasets, a simple approach can bring significant improvements to the existing state-of-the-art model.A
Towards Diverse Paraphrase Generation Using Multi-Class Wasserstein GAN

Paraphrase generation is an important and challenging natural language processing (NLP) task. In this work, we propose a deep generative model to generate paraphrase with diversity. Our model is based on an encoder-decoder architecture. An additional transcoder is used to convert a sentence into its paraphrasing latent code. The transcoder takes an explicit pattern embedding variable as condition, so diverse paraphrase can be generated by sampling on the pattern embedding variable. We use a Wasserstein GAN to align the distributions of the real and generated paraphrase samples. We propose a multi-class extension to the Wasserstein GAN, which allows our generative model to learn from both positive and negative samples. The generated paraphrase distribution is forced to get closer to the positive real distribution, and be pushed away from the negative distribution in Wasserstein distance. We test our model in two datasets with both automatic metrics and human evaluation. Results show that our model can generate fluent and reliable paraphrase samples that outperform the state-of-art results, while also provides reasonable variability and diversity.
Virtual reality – level of immersion within the crime investigation

The 3D reconstruction and visualization of crime scenarios, such as criminal assaults, traffic accidents or homicides are defining a new approach with potential for increasing effectiveness within the criminal investigation process. Modern 3D capturing and processing methods are utilized to create an authentic and immersive virtual environment. Nonetheless, an immersion to virtual environment enables different levels of view. Virtual reality immersion views which are proposed and described. These views are specific by reasons of its role within the criminal investigation as well as its technical severity. Moreover, the possible methods for the design of proposed virtual views are analyzed and the next research steps are exposed.
Gender Classification using Fisherface and Support Vector Machine on Face Image

Computer vision technology has been widely used in many applications and devices that involves biometric recognition. One of them is gender classification which has notable challenges when dealing with unique facial characteristics of human races. Not to mention the challenges from various poses of face and the lighting conditions. To perform gender classification, we resize and convert the face image into grayscale then extract its features using Fisherface. The features are reduced into 100 components using Principal Component Analysis (PCA) then classified into male and female category using linear Support Vector Machine (SVM). The test that conducted on 1014 face images from various human races resulted in 86% of accuracy using standard k-NN classifier while our proposed method shows better result with 88% of accuracy.
Towards Zero-resource Cross-lingual Entity Linking

Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia. XEL is challenging for most languages because of limited availability of requisite resources. However, much previous work on XEL has been on simulated settings that actually use significant resources (e.g. source language Wikipedia, bilingual entity maps, multilingual embeddings) that are unavailable in truly low-resource languages. In this work, we first examine the effect of these resource assumptions and quantify how much the availability of these resource affects overall quality of existing XEL systems. Next, we propose three improvements to both entity candidate generation and disambiguation that make better use of the limited data we do have in resource-scarce scenarios. With experiments on four extremely low-resource languages, we show that our model results in gains of 6-23% in end-to-end linking accuracy.
RESOURCES OF NATIONAL ELECTRONIC SUBSCRIPTION USAGE BY RAS RESEARCH INSTITUTES AND SCIENTIFIC CENTERS

The Interest of RAS Organizations to Resources of National Electronic Subscription, suggested by Ministry of Science and Higher Education and Russian Fond for Basic Research (RFBR) and this Resources Practical Using are considered. This Consideration is made, using the Example of the Access to these Resources from the Library for Natural Sciences (LNS) RAS Centralized Library Net (CLN) Libraries and served by these Net’s Libraries Scientific Organizations of RAS. Role and Positions of the Libraries in this Process is estimated. The Need of the Provision of maximally broader Access for Scientists from the Libraries (as well as Access from the Scientific Organizations directly) is approved.
Designing Mobile Technologies for Neurodiversity

Mobile applications have a great potential in making everyday environments more accessible from the cognitive point of view, allowing neurodiverse people, such as individuals with autism, dementia, or ADHD, to gain independency and find continuous support. This workshop will discuss the main technological, methodological, theoretical and design issues that researchers and practitioners are facing when designing mobile devices and services for neurodiversity, exploring novel strategies to address them. In doing so, we want to focus on the neurodiverse people's idiosyncratic needs, also exploring ways for directly involving them in the design process.
Proposal for a Security Model for a Popular Voting System Process in Latin America

In Latin America, there is a need for better security systems in different national or sectional votes, which is why this study was carried out, to show a proposal for a security model that helps to obtain better results and to this purpose has been selected information on the database for different Latin American countries, it has also been considered descriptive aspects and with a quantitative approach, to provide an objective livelihood.It has been intended to identify several security requirements that give confidence to citizens.Currently in Latin America studies continue to be carried out on technological systems for electronic voting and their real effectiveness in different countries, and then improve or implement them.Not all have come to implement it completely but in part due precisely to the demands it demands, also because of the limited information and because there are few related studies on these issues.The growth of technology and its easy access has allowed it to be used in different areas and politics is no exception, which is why different modalities of electronic voting have been implemented so that they can reduce some failures that are maintained in the traditional voting system.In conclusion, it is noted that voters need to know better about the new systems applicable to counting, it encourages individual reinsurance, which does not reveal the identity of the voter, as well as transparency, easy access information and auditability.Awareness is suggested about the use of electronic voting and the benefits it brings without also excluding negative factors because there is not totally secure and reliable digital voting system.Citizen participation must go hand in hand with political decision-making, so that processes are transparent, and results are protected.With all these criteria it can be noted that it is necessary to consider a comprehensive electronic security model that guarantees that reliability and integrity of the secret election vote, bearing in mind that the purpose is to reduce and prevent errors.
POSSIBLE PROBLEMS OF EVALUATION OF SPATIAL-TEMPORAL FEATURES OF EARTHQUAKE CATALOG REPRESENTATIVITY: CASE STUDY FOR THE KAMCHATKA CATALOG OF GEOPHYSICAL SURVEY OF RAS

The completeness of the catalog, understood as the magnitude threshold from which earthquakes are recorded without gaps, is usually determined by the linear part of the magnitude-frequency relationship in accordance with the Gutenberg-Richter law. The article provides examples of an incorrect estimate of the completeness of the Kamchatka earthquake catalog. The author used the earthquake catalog of the Kamchatka branch of the Geophysical Survey RAS. The article shows that the value of this underestimation cannot be explained by the peculiarities of the earthquake registrations and further processing at various stages of the development of the Kamchatka network. The formal assessment of large spatial or temporal samples from the catalog gives a significant underestimation of the real representativity of the catalog. This effect is associated with spatio-temporal variations of b-value.
Multilingual Information Access (MLIA) Tools on Google and WorldCat: Bi/Multilingual University Students’ Experience and Perceptions

This article reports on the results of an exploratory user-centered study that examined how technological advancements in natural language processing (NLP) such as the availability of multilingual information access (MLIA) tools impact the information searching behavior of bi/multilingual academic users. Thirty-one bi/multilingual students participated in a controlled lab-based user experiment in which they carried out two assigned tasks each on Google and WorldCat for a total of four tasks, and then completed a post experiment questionnaire. The captures from the experiment showed 86.7% of the participants using multilingual information access tools. Further analyses of the captures also showed that participants were more likely to use MLIA tools when the instructions for the task were stated in their native language. An independent samples t-test revealed that participants spent less time on their searches when they used MLIA tools. The study revealed considerable diversity in the information searching behavior of the participants, even within the same pair of languages, and even for the same user. Diversity was noted for instance, on which tasks MLIA tools were used and in how these tools were used. User-centered designed, personalized multilingual information retrieval (PMLIR) models could hold promise for best representing the information searching behavior of bi/multilingual users.
Data Sharing at Scale: A Heuristic for Affirming Data Cultures

Addressing the most pressing contemporary social, environmental, and technological challenges will require integrating insights and sharing data across disciplines, geographies, and cultures. Strengthening international data sharing networks will not only demand advancing technical, legal, and logistical infrastructure for publishing data in open, accessible formats; it will also require recognizing, respecting, and learning to work across diverse data cultures. This essay introduces a heuristic for pursuing richer characterizations of the “data cultures” at play in international, interdisciplinary data sharing. The heuristic prompts cultural analysts to query the contexts of data sharing for a particular discipline, institution, geography, or project at seven scales – the meta, macro, meso, micro, techno, data, and nano. The essay articulates examples of the diverse cultural forces acting upon and interacting with researchers in different communities at each scale. The heuristic we introduce in this essay aims to elicit from researchers the beliefs, values, practices, incentives, and restrictions that impact how they think about and approach data sharing – not in an effort to iron out differences between disciplines, but instead to showcase and affirm the diversity of traditions and modes of analysis that have shaped how data gets collected, organized, and interpreted in diverse settings.
Robustness of the concatenated quantum error-correction protocol against noise for channels affected by fluctuation

In quantum error correction, the description of the noise channel cannot be completely accurate and fluctuation always appears in the noise channel. It is found that when fluctuation of the physical noise channel is considered, the average effective channel is dependent only on the average of the physical noise channel, and the average of the physical noise channel here plays the role of the independent error model in previous works. Now one may conclude that in the independent error model, the results in previous works are also valid for the average channel where fluctuation exists. In some typical cases, our numerical simulations in the concatenated quantum error-correction protocol with a five-qubit code, seven-qubit Steane code, and nine-qubit Shor confirm this conjecture. For a five-qubit code, the effective channels approximate to the depolarizing channel as the concatenated level increases. For the Steane code, the effective channels approximate to one Pauli channel as the concatenated level increases. For the Shor code, the effective channels approximate to either a Pauli-$X$ or Pauli-$Z$ channel in each level, and in the next concatenated level, the effective channels approximate to the other. The numerical results for these codes indicate that the degree of approximation increases as the concatenated level increases, and the fluctuation of the noise channel decays exponentially as concatenated quantum error correction is performed. On the error-correction threshold, the attenuation ratio of the standard deviation of channel fidelity has a roughly stable value. In contrast, standard deviations of off-diagonal elements of the quantum process matrix (Pauli form) decay more quickly than standard deviations of diagonal elements.
A Hypergraph Matching Labeled Multi-Bernoulli Filter for Group Targets Tracking

This paper addresses the association problem of tracking closely spaced targets in group or formation. In the Labeled Multi-Bernoulli Filter (LMB), the weight of a hypothesis is directly affected by the distance between prediction and measurement. This may generate false associations when dealing with the closely spaced multiple targets. Thus we consider utilizing structure information among the group or formation. Since, the relative position relation of the targets in group or formation varies slightly within a short time, the targets are considered as nodes of a topological structure. Then the position relation among the targets is modeled as a hypergraph. The hypergraph matching method is used to resolve the association matrix. At last, with the structure prior information introduced, the new joint cost matrix is re-derived to generate hypotheses, and the filtering recursion is implemented in a Gaussian mixture way. The simulation results show that the proposed algorithm can effectively deal with group targets and is superior to the LMB filter in tracking precision and accuracy.
An analysis of the semantic annotation task on the linked data cloud

Semantic annotation, the process of identifying key phrases in texts and linking them to concepts in a knowledge base, is an important basis for semantic information retrieval and the semantic web uptake. Despite the emergence of semantic annotation systems, very few comparative studies have been published on their performance. In this paper, we provide an evaluation of the performance of existing systems over three tasks: full semantic annotation, named entity recognition, and keyword detection. More specifically, the spotting capability (recognition of relevant surface forms in text) is evaluated for all three tasks, whereas the disambiguation (correctly associating an entity from Wikipedia or DBpedia to the spotted surface forms) is evaluated only for the first two tasks. We use logistic regression to identify significant performance differences. Although some of the annotators are specifically targeted at some task (NE, SA, KW), our results show that they do not necessarily obtain the best performance on those tasks. In fact, systems identified as full semantic annotators beat all other systems on all data sets. We also show that there is still much room for improvement for the identification of the most relevant entities described in a text.
Topic-aware Pointer-Generator Networks for Summarizing Spoken Conversations

Due to the lack of publicly available resources, conversation summarization has received far less attention than text summarization. As the purpose of conversations is to exchange information between at least two interlocutors, key information about a certain topic is often scattered and spanned across multiple utterances and turns from different speakers. This phenomenon is more pronounced during spoken conversations, where speech characteristics such as backchanneling and false-starts might interrupt the topical flow. Moreover, topic diffusion and (intra-utterance) topic drift are also more common in human-to-human conversations. Such linguistic characteristics of dialogue topics make sentence-level extractive summarization approaches used in spoken documents ill-suited for summarizing conversations. Pointer-generator networks have effectively demonstrated its strength at integrating extractive and abstractive capabilities through neural modeling in text summarization. To the best of our knowledge, to date no one has adopted it for summarizing conversations. In this work, we propose a topic-aware architecture to exploit the inherent hierarchical structure in conversations to further adapt the pointer-generator model. Our approach significantly outperforms competitive baselines, achieves more efficient learning outcomes, and attains more robust performance.
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks

The skip-connections used in residual networks have become a standard architecture choice in deep learning due to the increased training stability and generalization performance with this architecture, although there has been limited theoretical understanding for this improvement. In this work, we analyze overparameterized deep residual networks trained by gradient descent following random initialization, and demonstrate that (i) the class of networks learned by gradient descent constitutes a small subset of the entire neural network function class, and (ii) this subclass of networks is sufficiently large to guarantee small training error. By showing (i) we are able to demonstrate that deep residual networks trained with gradient descent have a small generalization gap between training and test error, and together with (ii) this guarantees that the test error will be small. Our optimization and generalization guarantees require overparameterization that is only logarithmic in the depth of the network, while all known generalization bounds for deep non-residual networks have overparameterization requirements that are at least polynomial in the depth. This provides an explanation for why residual networks are preferable to non-residual ones.
Readability characteristics of texts in middle school Turkish textbooks

Turkish textbooks are important tools for gaining skills and competencies identified in curriculum. Textbooks should be designed to contribute to students’ basic language skills as well as high level thinking skills in alignment with curriculum. The texts to be selected for textbooks should be prepared in alignment with the targeted skills and learning outcomes in terms of visuals and content. Textbooks kids encounter during elementary education play an important role in their upbringing as individuals with reading habits in later phases of their education. Therefore, texts to be included in textbooks should be selected carefully to reflect the best examples of text types, to be compatible with the child’s language and meaning universe, and should be understandable. In this study, the readibility levels of texts in Turkish course textbooks were analyzed. Readibility levels reveal the comprehensibleness of texts. Readability depends on the number of words in a sentence and syllables in a word. First the average word and sentence length were calculated and then the readibility scores of texts were calculated. Two formulas adapted to Turkish were used in identifying the readibility levels of texts. The findings were analyzed comparatively. The results showed that the readability levels of narrative texts are easier compared to informing texts.