Reviewerly/exHarmony huggingface dataset:
A Meta-Learning Approach to Error Prediction

Machine Learning is one of the most trending topics nowadays. The reason is of course for being more and more present in our everyday life, even if we do not notice it. What goes even more unnoticed is the fact that every Machine Learning model needs computational power. And of course, it also needs data. But how many data are necessary to build the best Machine Learning model possible, and how many times do we need to retrain a model so that it does not become obsolete as data change? That kind of questions are the ones that can reduce unnecessary costs to a company. In this paper we propose a novel approach to predict the performance of a model given some characteristics of the data, that are called meta-features. The goal is, indeed, to only train a new model when some error metric (e.g., RMSE) is expected to decrease substantially compared with a previously trained model. This approach is best applied in scenarios of data streaming or in Big Data, as well on Interactive Machine Learning scenarios. We validate it on a real Fraud Detection case and this scenario is also briefly described.
Research on aesthetic models based on neural architecture search

Computational aesthetics, which uses computers to learn human aesthetic habits and ultimately replace humans in scoring images, has become a hot topic in recent years due to its wide application. Most of the initial research is to manually extract features and use classifiers such as support vector machines to score images. With the development of deep learning, traditional manual feature extraction methods are gradually replaced by convolutional neural networks to extract more comprehensive features. However, it is a huge challenge to artificially design an aesthetic neural network. Recently, Neural Architecture Search has upsurged to find suitable neural networks for many tasks in deep learning. In this paper, we first attempt to combine Neural Architecture Search with computational aesthetics. We design and apply a customized progressive differentiable architecture search strategy to obtain a light-weighted and efficient aesthetic baseline model. In addition, we simulate the multi-person rating mechanism by outputting the distribution of the aesthetic value of the image, replacing the previous classification scheme of judging the beauty and unbeauty of the image by the threshold value, and propose a self-weighted Earth Mover’s Distance loss to better fit human subjective scoring. Based on the baseline model, we further introduce several strategies including an attention mechanism, the dilated convolution, and adaptive pooling, to enhance the performance. Finally, we design several groups of comparative experiments to demonstrate the effectiveness of our baseline aesthetic model and the introduced improvement strategies.
Generalization Training Support System Promoting Focusing Target Changes

Generalization extracts common features from target concepts and rephrases them into an abstract concept. This operation is often used to acquire knowledge from examples that use it. For instance, when we grasp the usage situation of an English word, we generalize the words that are often used with a target English word (target concepts) to derive the word that represents the usage situation (abstract concept). However, sometimes we are not able to determine the common features and cannot derive the abstract concept, especially when the number of target concepts is large. For such cases, focusing on a subset of target concepts provides an opportunity to notice common features, since it is easier to find common features with a small number of target concepts. To establish this technique of minimizing the focus targets, this research aims to develop a generalization training support system that provides target concepts and promotes focusing on their subsets when we fail to derive an abstract concept from the given target concepts.
A Transformer-based Chinese Non-autoregressive Speech Synthesis Scheme

At present, the main research hotspot in the field of speech synthesis is still English speech synthesis, and there are few non-autoregressive Chinese speech synthesis models. During the Chinese migration process of FastSpeech2, we found that the naturalness of the synthesized audio was not good enough and there were some abnormal interruptions and incorrect pronunciation. Inspired by the training method of generative adversarial network, we use FastSpeech2 as the generator, and add a discriminator to force FastSpeech2 to generate audio more similar to the real audio. In order to realize a complete text to Mel spectrogram speech synthesis scheme, we design a text-to-phoneme converter based on corpus and rule constraints. And we conduct experiments on Baker dataset. The results show that our model achieves a better Mel Cepstral Distance than that of FastSpeech2. And our model can achieve a mean opinion score of 3.94, which is slightly better than the original model.
Mapping Cases of Violence Against Women and Children in Pekanbaru City

Protection of women and children is a mandate contained in the laws of the Republic of Indonesia to the government to protect women and children from acts of violence or discrimination. The mandate is carried out by the government through several efforts to handle and prevent it through government programs that are made every year. However, until now the municipal government of Pekanbaru through the women's empowerment and child protection office does not yet have data on mapping the protection of women and children disaggregated by sex, age, type of case, and location of cases so that many child protection programs are not properly targeted according to the level of violence and types of violence in areas exposed to cases of violence. The application of the mapping application is a solution to this problem because, with computerized data on the protection of women and children, the government will make it easier to design management and prevention programs. This application displays complete indicators including data on violence mapped in the geographic form of the city of Pekanbaru which is divided into the scope of the sub-district and given a color as an indicator of high or low cases of violence in the area, violence data is displayed disaggregated according to gender, age, types of cases and locations of incidents and in addition, this application applies a case-control design to provide recommendations to the government regarding handling and prevention programs in areas exposed to cases of violence against women and children. This application will make it easier for the government to design programs to protect women and children and to reduce the number of violence against women and children which always increases every year.
Algorithmic Finance: Algorithmic Trading across Speculative Time-Spaces

The speeds at which transactions are completed in global financial markets are accelerating and, in the process, connecting financial centers around the globe like never before. Algorithmic trading at high frequency is a form of automated trading in which machines, rather than humans, make the decision to buy or sell in spatiotemporal sequences. Insofar as they have agency of their own, their actions support the owners of the means of production. These techniques codevelop with new financial geographies. Accordingly, I examine technological change and speculative time-spaces of algorithmic strategies at stock exchanges. By analyzing algorithmic finance, I examine how—and to what extent—time, speed, location, and distance become critical for algorithmic finance by configuring time-spaces as competitive factors. The analysis interprets time-spaces of high-frequency trading strategies through the ways in which algorithmic finance constititutes what I term mobile market-informational epicenters. This article discusses the spatiotemporalities of market information and examines whether space-times of privately owned high-frequency trading infrastructures result in a juxtaposition between “public” and “private” market information across digital and physical space. It thereby responds to the questions of what role geography plays when algorithms make money in microseconds and how techno-financial time-spaces turn into competitive advantage.
Face Recognition Application Based On Improved SSD Network Model

This article first briefly introduces the face recognition system. By improving the traditional model of the SSD network, the lightweight MobileNet network model replaces the original VGG network model, and the Elu activation function replaces the Relu function in the original model to better accelerate training and improve The accuracy of classification can determine the location of the face more accurately and quickly, better change the traditional image preprocessing method, and improve the accuracy of face recognition. Finally, a comparison of common backbone networks shows that the algorithm in this experiment has a greater improvement in face recognition accuracy than other backbone networks.
Consumers’ Acceptance of a Voice Commerce Application in FMCG in Germany, U.S. and U.K.

Shopping-related voice assistant applications are on the rise, but their acceptance differs depending on the country. This paper examines customers' acceptance of a voice commerce application developed by a global fast moving consumer goods company based on a survey of online shoppers (n = 824) conducted in Germany, U.K. and the U.S. The main objective of the study is to identify which factors influence the acceptance of the voice commerce application, and whether there are differences between Germany, the U.S. and U.K. An integrated explanatory model was developed based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with the antecedents: hedonic motivation, performance expectancy, effort expectancy and social influence. The original model was expanded to include the construct of perceived risk with the dimensions privacy and functional risk. The main result is that there are differences between the three countries regarding the factors that influence the acceptance of the voice commerce application. Only two factors have a significant influence in all three countries: performance expectancy and social influence, with performance expectancy demonstrating the strongest effect. From the perceived risks, only privacy risk has a negative influence on the intention to use the voice application in Germany. This study indicates that researches on the consumers' acceptance from one country should not be applied readily to another. It is rather advisable to consider the unique circumstances of each country.
Impact of WhatsApp on the Vocabulary Acquisition of Secondary School Students in Ogbomoso, Oyo State, Nigeria

The advent of technology has brought a change in the dynamics of second language teaching and learning. WhatsApp has been a common tool used among students for communicating and chatting with friends, peers, and relatives. Its commonality may be because of its easy installation as mobile application on mobile devices. Educators are beginning to explore its potentials toward seeing how it can help students in their learning. Since it is apparently a novel tool in the education sector, its usefulness in acquiring vocabulary by secondary school students is yet to be ascertained. Therefore, this study examined the impact of WhatsApp on vocabulary acquisition of secondary school students in Ogbomoso, Oyo State, Nigeria. This study was premised on the constructivist learning theory since WhatsApp provides students the opportunity to construct their vocabulary knowledge. The sample for the study comprised 250 students from five secondary schools randomly selected in Ogbomoso, Oyo State. Data for the study were generated from the participants using self-made questionnaire. The questionnaire is structured into 5 points from Strongly Agree to Strongly Disagree. Mean and standard deviation were employed to analyse the data. Findings showed that more than half of the students believe WhatsApp is expedient in learning and acquiring vocabulary. It further revealed that the majority of the students perceive WhatsApp as an effective social network that enhances their vocabulary acquisition as it is easy to acquire new words and understand their meanings through WhatsApp. The study concluded that language teachers should devise means of adopting social media such as WhatsApp to teach their students since students are favourably disposed to learning through WhatsApp.
Incorporating EDS Graph for AMR Parsing

Meaning Representation) and EDS (Elementary Dependency Structures) are two popular meaning representations in NLP/NLU.AMR is more abstract and conceptual, while EDS is more low level, closer to the lexical structures of the given sentences.It is thus not surprising that EDS parsing is easier than AMR parsing.In this work, we consider using information from EDS parsing to help improve the performance of AMR parsing.We adopt a transition-based parser and propose to add EDS graphs as additional semantic features using a graph encoder composed of LSTM layer and GCN layer.Our experimental results show that the additional information from EDS parsing indeed gives a boost to the performance of the base AMR parser used in our experiments.
Research on Improved Sentence Similarity Calculation Method Based on Word2Vec and Synonym Table in Interactive Machine Translation

Sentence similarity calculation is a key technology in the field of natural processing and plays an important role in the field of interactive machine translation. This paper combines the important role of prior knowledge in interactive machine translation, and considers that algorithms based on Jaccard algorithm and Wrod2Vec have certain defects in interactive machine translation. Aiming at the phenomenon of a large number of synonyms in the translation field, this paper proposes an improved Word2Vec sentence similarity algorithm based on the synonym table. The algorithm improves the accuracy of sentence similarity calculation by constructing a synonym table and fusing word vectors. The experimental results show that compared with the conventional method, the precision and F-Measure the algorithm in calculating the sentence similarity is higher.
Research and Practice of Design Process (Knowledge) Reuse in the Data State Environment of Nuclear Power Design Company

With the business expansion, Nuclear Power Design Company has established a design portal based on the whole product line and integrated design platforms and tools. This indicates that nuclear power is changing from “document-centered design and production mode “to” data-centered digital design and production mode”. Meanwhile, the mass production of nuclear power has created an urgent application demand for a new form of archive management, information mining and design knowledge reuse in the digital environment. By analyzing the spatial evolution of archive object space in the nuclear power data state environment and using the concept of “three states” of archives in academic circles, this paper proposes the management idea of establishing the nuclear power archive object space, and reconstructs the nuclear power design process (knowledge) reuse model with “design activity” as the core and WBS (work break structure) coding structure as the framework.
Translation Norms, Translation Behavior, and Continuous Vector Space Models

Several models of the bilingual mind have been suggested (e.g., DFM, Multilink), which are inspired by connectionist and artificial neural network models. Those models aim at explaining and predicting translation latencies of human translators based on cross-linguistic similarities of word properties. To foster researching these models, bilingualism studies have developed translation norms which enumerate factors (such as concreteness or ambiguity) assumed to be responsible for delays in word recognition and word translation production. While neural machine translation (NMT) systems are currently revolutionizing the translation industry, the compatibility of those identified processes and assumed representations in bilingualism studies have not (often) been compared with measures and representations that can be traced in NMT systems. In this chapter, we map translation norms into word embeddings (i.e., vector representations of words used in NMT systems) and compare model predictions—as gathered from similarity measures of continuous word embeddings—with reported human behavioral latencies. We also investigate to what extent the findings from single-word translation experiments can be carried over to translations in context. We re-assess predictions of DFM and Multilink in the light of the findings.
Requirements Engineering in Agile Software Startups - Insights from Multiple Case Studies

This exploratory case study was conducted with five IT startups in order to investigate how requirement engineering-related activities are performed and what is the state of maturity with the practices & tools used. Another objective was to study that how the startups managed their practices during Corona Virus (COVID19) pandemic time. The results indicate that flexibility and access to the online tools were the main strengths of the startups to cope up with the pandemic situation while fluctuating market demands affected them marginally. The startups do vary in domain, team size, practices and selection of the tools, with matured startups having more structured (but flexible) processes compared to younger startups. The young startups have the opportunity to learn from the practices of the matured startups, to adopt the learning in their working context. The previous software development experience of the startups and its founders does affect the maturity of the practices and selection of the tools. The flexibility and agility as evident in the working context of the startups helped them to turn pandemic situation into their business opportunities.
Research on Search Method of Knowledge Graph of Supply Chain Risk Based on Cascading Effect

Each enterprise in the supply chain is more susceptible to risks while obtaining more benefits through collaboration. In order to achieve the purpose of reducing cost and increasing efficiency, enterprises should fully understand the risk and choose the best control point. The risk knowledge graph based on DRC chain stores the risk chain composed of multiple risk sources, risks and consequences. In order to fully understand the risk, this study uses the above knowledge graph to design the corresponding graph search method according to the dynamic characteristics of the risk queue of temporary risk. The experimental results show that the search method in this study can match the nodes well, and can search the structure subgraph related to the matching nodes in the knowledge graph to evaluate the system risk, and provide support for selecting the best control point.
Entertaining Geophysics 7. Study of Seismic Events as Fortune -Telling on Coffee Grounds. Earthquake Prediction Is a Feeding Ground for Charlatans and Crooks?

This article represents the second work in a series of articles under the general title Funny Geophysics, the purpose of which is to popularize the earth sciences, and this abstract will be common to the works of the entire cycle. Answering the question of why we call serious science entertaining, we answer that at the moment there are no rigorous scientific disciplines such as seismology, tectonics, volcanology, etc., that explain the process of earthquakes and other catastrophic rock movements. Instead, there are pseudo-scientific fantasies of modern geophysicists, built on mythical ideas about the structure of the earth's crust and mantle. Figuratively speaking, we can safely say that the modern reasoning of geophysicists boils down to the fact that our Earth stands on three turtles and earthquakes are the result of the movement of these animals. This situation is partly due to the fact that our knowledge of the internal structure of the Earth is limited to a depth of 12 km, and what is deeper than this mark still remains a mystery to us. And what happens at a depth of hundreds of kilometers turns out to be a secret behind seven locks for us. But an even greater mystery for mankind is the question of why modern geophysics ossified at the level of the beginning of the 20th century and operates with the knowledge of that period, in every possible way ignoring the achievements of modern sciences? Tell me what's wrong? Then why all modern geophysics is based on the speculations of Mr. Reid and his mythical elastic stresses, which supposedly arise during the deformation of rocks? We will try to answer this question, and at the same time, using simple examples of catastrophic earthquakes and other seismic events, we will show the unforgivable errors of modern geophysics.
Data Schema to Formalize Education Research &amp; Development Using Natural Language Processing

Our work aims to aid in the development of an open source data schema for educational interventions by implementing natural language processing (NLP) techniques on publications within What Works Clearinghouse (WWC) and the Education Resources Information Center (ERIC). A data schema demonstrates the relationships between individual elements of interest (in this case, research in education) and collectively documents elements in a data dictionary. To facilitate the creation of this educational data schema, we first run a two-topic latent Dirichlet allocation (LDA) model on the titles and abstracts of papers that met WWC standards without reservation against those of papers that did not, separated by math and reading subdomains. We find that the distributions of allocation to these two topics suggest structural differences between WWC and non-WWC literature. We then implement Term Frequency-Inverse Document Frequency (TF-IDF) scoring to study the vocabulary within WWC titles and abstracts and determine the most relevant unigrams and bigrams currently present in WWC. Finally, we utilize an LDA model again to cluster WWC titles and abstracts into topics, or sets of words, grouped by underlying semantic similarities. We find that 11 topics are the optimal number of subtopics in WWC with an average coherence score of 0.4096 among the 39 out of 50 models that returned 11 as the optimal number of topics. Based on the TF-IDF and LDA methods presented, we can begin to identify core themes of high-quality literature that will better inform the creation of a universal data schema within education research.
Formal Verification of a Mixed-Trust Synchronization Protocol

Cyber-Physical Systems (CPS) are becoming widespread in many safety-critical real-time applications, such as autonomous driving, robotics systems, and unmanned aircraft. However, verifying these complex real-time systems remains an open challenge because traditional verification techniques are unable to verify all components.