Reviewerly/exHarmony huggingface dataset:
Interpretable Rules Using Inductive Logic Programming Explaining Machine Learning Models: Case Study of Subclinical Mastitis Detection for Dairy Cows

With the development of Internet of Things technology and the widespread use of smart devices, artificial intelligence is now being applied as a decision-making tool in a variety of fields.To make machine learning models, including deep neural network models, more interpretable, various techniques have been proposed.In this paper, a method for explaining the outputs of machine learning models using inductive logic programming is described.For an evaluation of this method, diagnostic models of bovine mastitis were trained using a dataset of dairy cows, and interpretable rules were obtained to explain the trained models.As a result, the rules obtained indicate that the trained classifiers detected mastitis cases depending on certain variations in the electrical conductivity (EC) values, and in some of these cases, the EC and lactate dehydrogenase fluctuated in different ways.The interpretable rules help people understand the outputs of machine learning models and encourage a practical introduction of the models as decision-making tools.
Education and Training in the Digital Era: A Compass for “Building Forward Better”

Knowledge is the greatest raw material. To build a secure future, you need knowledge, skills, to network and get involved in the new. Digitization with the technological and social challenges requires a rethinking and adjustment of professional qualifications at the same time. More than half of 70% of the open AI positions in companies are currently unfilled. This high number describes the dilemma and the challenges for the teaching and training of tomorrow. Are we ready for the technological challenges of tomorrow? This research found that there is significantly high potential for diverse teams, staffing the STEM fields with more female specialists and fully using specialist knowledge and personal qualifications of men and women. Last but not least, this research contributes setting up compass for building forward better by ensuring gender-independent decision-making structures in human-technology interaction and use diversity as key for reaching sustainable goals and set milestones in the digital era. The main research results consist that the foundation for diversity, inclusion and sustainable digital innovations can only be ensured by adjusted education and academic training. To achieve sustainable digital transformation with a strategy foundation, education and academic training are most essential. This research fills the gap by highlighting the importance of these two essential areas that call for immediate action: Diversity, education, and training represent the drivers for the sustainable success in the Digital Age.
Accelerating Physics Simulations with TPUs: An Inundation Modeling Example

Recent advancements in hardware accelerators such as Tensor Processing Units (TPUs) speed up computation time relative to Central Processing Units (CPUs) not only for machine learning but, as demonstrated here, also for scientific modeling and computer simulations. To study TPU hardware for distributed scientific computing, we solve partial differential equations (PDEs) for the physics simulation of fluids to model riverine floods. We demonstrate that TPUs achieve a two orders of magnitude speedup over CPUs. Running physics simulations on TPUs is publicly accessible via the Google Cloud Platform, and we release a Python interactive notebook version of the simulation.
H4D: Human 4D Modeling by Learning Neural Compositional Representation

Despite the impressive results achieved by deep learning based 3D reconstruction, the techniques of directly learning to model 4D human captures with detailed geometry have been less studied. This work presents a novel framework that can effectively learn a compact and compositional representation for dynamic human by exploiting the human body prior from the widely used SMPL parametric model. Particularly, our representation, named H4D, represents a dynamic 3D human over a temporal span with the SMPL parameters of shape and initial pose, and latent codes encoding motion and auxiliary information. A simple yet effective linear motion model is proposed to provide a rough and regularized motion estimation, followed by per-frame compensation for pose and geometry details with the residual encoded in the auxiliary code. Technically, we introduce novel GRU-based architectures to facilitate learning and improve the representation capability. Extensive experiments demonstrate our method is not only efficacy in recovering dynamic human with accurate motion and detailed geometry, but also amenable to various 4D human related tasks, including motion retargeting, motion completion and future prediction. Please check out the project page for video and code: https://boyanjiang.github.io/H4D/.
Adaptive Attentional Network for Few-Shot Relational Learning of Knowledge Graphs

Few-shot knowledge graph reasoning is a research focus in the field of knowledge graph reasoning. At present, in order to expand the application scope of knowledge graphs, a large number of researchers are devoted to the study of the multi-shot knowledge graph model. However, as far as we know, the knowledge graph contains a large number of missing relations and entities, and there are not many reference examples at the time of training. In this paper, our goal is to be able to infer the correct entity given a few training instances, or even only one training instance is available. Therefore, we propose an adaptive attentional network for few-shot relational learning of knowledge graphs, extracting knowledge based on traditional embedding methods, using the Transformer mechanism and hierarchical attention mechanism to obtain hidden attributes of entities, and then using a noise checker to filter out unreasonable candidate entities. Our model produces large performance improvements on the NELL-One dataset.
A Part-of-Speech Tagger for Yiddish

We describe the construction and evaluation of a part-of-speech tagger for Yiddish. This is the first step in a larger project of automatically assigning part-of-speech tags and syntactic structure to Yiddish text for purposes of linguistic research. We combine two resources for the current work - an 80K-word subset of the Penn Parsed Corpus of Historical Yiddish (PPCHY) and 650 million words of OCR'd Yiddish text from the Yiddish Book Center (YBC). Yiddish orthography in the YBC corpus has many spelling inconsistencies, and we present some evidence that even simple non-contextualized embeddings trained on YBC are able to capture the relationships among spelling variants without the need to first "standardize" the corpus. We also use YBC for continued pretraining of contexualized embeddings, which are then integrated into a tagger model trained and evaluated on the PPCHY. We evaluate the tagger performance on a 10-fold cross-validation split, showing that the use of the YBC text for the contextualized embeddings improves tagger performance. We conclude by discussing some next steps, including the need for additional annotated training and test data.
Modeling Review History for Reviewer Recommendation:A Hypergraph Approach

Modern code review is a critical and indispensable practice in a pull-request development paradigm that prevails in Open Source Software (OSS) development. Finding a suitable reviewer in projects with massive participants thus becomes an increasingly challenging task. Many reviewer recommendation approaches (recommenders) have been developed to support this task which apply a similar strategy, i.e. modeling the review history first then followed by predicting/recommending a reviewer based on the model. Apparently, the better the model reflects the reality in review history, the higher recommender's performance we may expect. However, one typical scenario in a pull-request development paradigm, i.e. one Pull-Request (PR) (such as a revision or addition submitted by a contributor) may have multiple reviewers and they may impact each other through publicly posted comments, has not been modeled well in existing recommenders. We adopted the hypergraph technique to model this high-order relationship (i.e. one PR with multiple reviewers herein) and developed a new recommender, namely HGRec, which is evaluated by 12 OSS projects with more than 87K PRs, 680K comments in terms of accuracy and recommendation distribution. The results indicate that HGRec outperforms the state-of-the-art recommenders on recommendation accuracy. Besides, among the top three accurate recommenders, HGRec is more likely to recommend a diversity of reviewers, which can help to relieve the core reviewers' workload congestion issue. Moreover, since HGRec is based on hypergraph, which is a natural and interpretable representation to model review history, it is easy to accommodate more types of entities and realistic relationships in modern code review scenarios. As the first attempt, this study reveals the potentials of hypergraph on advancing the pragmatic solutions for code reviewer recommendation.
A Call for a Paradigm Shift in the Information Discipline

This is an appeal for the collective reconsideration of the paradigm that be currently executed in the information discipline, because it is not a suitable paradigm for the information discipline but the one suitable for physical disciplines. The unsuitable paradigm has created a situation that recalls the old saying, &ldquo;putting A&rsquo;s hat on B&rsquo;s head&rdquo;. The misuse of the paradigm has been the unique cause of all the problems related to information studies; therefore, this should indeed be changed as soon and as swiftly as possible, if we want the information discipline to enter the highest stage of its development.
AUDIO TO SIGN LANGUAGE CONVERTER

There is a communication gap between people who are not able to understand the thoughts of those people having these problems so with the help of AI this application artificially which will be able to help those peoples who have no sense of understanding the thoughts of others by their movement or motion. We will add some signs of English alphabets as predefined expressions. This application we made is for deaf people having hearing problems with them.
Penerapan Algoritma Knuth-Morris-Pratt dalam Mendeteksi Tingkat Kemiripan Judul Skripsi Berbasis Web

In terms of finding the problem topic for thesis titles, students often have difficulty in determining thesis titles, in addition, there may be similarities between students. Manually checking the title requires more effort, for it takes a system to detect the level of similarity in the thesis title. To design and build a thesis title-level detection system using the Knuth-Morris-Pratt algorithm. To find out the percentage of similarities in the title, the Knuth-Morris-Pratt algorithm at the time of matching the string is Enter query of the word to be searched, matching the word arrangement pattern used as an example at the beginning of the text. From left to right, this algorithm will match character per character pattern with characters in the corresponding text, the algorithm then shifts the wording pattern until the word arrangement pattern used as an example is at the end of the text. Based on the analysis, design and implementation of the similarity level detection system of two or more thesis titles using the Knuth Morris Pratt algorithm it can be concluded that: The detection system has been successfully created using the Knuth Morris Pratt algorithm (KMP). It has been obtained the results of analysis by the process of string matching between strings with patterns where the system postulates the results of analysis in the form of the number of word similarities and percentages
SQL Injection

SQL Injection (SQLi) could also be a kind of an injection attack that makes it possible to execute malicious SQL statements. These statements control a database server behind an internet application. Attackers can use SQL Injection vulnerabilities to bypass application security measures. They can go around authentication and authorization of an internet page or web application and retrieve the content of the entire SQL database. They can also use SQL Injection to feature, modify, and delete records within the database.
Learning Context-based Embeddings for Knowledge Graph Completion

Abstract Purpose Due to the incompleteness nature of knowledge graphs (KGs), the task of predicting missing links between entities becomes important. Many previous approaches are static, this posed a notable problem that all meanings of a polysemous entity share one embedding vector. This study aims to propose a polysemous embedding approach, named KG embedding under relational contexts (ContE for short), for missing link prediction. Design/methodology/approach ContE models and infers different relationship patterns by considering the context of the relationship, which is implicit in the local neighborhood of the relationship. The forward and backward impacts of the relationship in ContE are mapped to two different embedding vectors, which represent the contextual information of the relationship. Then, according to the position of the entity, the entity's polysemous representation is obtained by adding its static embedding vector to the corresponding context vector of the relationship. Findings ContE is a fully expressive, that is, given any ground truth over the triples, there are embedding assignments to entities and relations that can precisely separate the true triples from false ones. ContE is capable of modeling four connectivity patterns such as symmetry, antisymmetry, inversion and composition. Research limitations ContE needs to do a grid search to find best parameters to get best performance in practice, which is a time-consuming task. Sometimes, it requires longer entity vectors to get better performance than some other models. Practical implications ContE is a bilinear model, which is a quite simple model that could be applied to large-scale KGs. By considering contexts of relations, ContE can distinguish the exact meaning of an entity in different triples so that when performing compositional reasoning, it is capable to infer the connectivity patterns of relations and achieves good performance on link prediction tasks. Originality/value ContE considers the contexts of entities in terms of their positions in triples and the relationships they link to. It decomposes a relation vector into two vectors, namely, forward impact vector and backward impact vector in order to capture the relational contexts. ContE has the same low computational complexity as TransE. Therefore, it provides a new approach for contextualized knowledge graph embedding.
Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion

Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent, they have typically evolved as standalone sub-systems whereby CM solutions are usually designed for a fixed ASV system. The work reported in this paper aims to gauge the improvements in reliability that can be gained from their closer integration. Results derived using the popular ASVspoof2019 dataset indicate that the equal error rate (EER) of a state-of-the-art ASV system degrades from 1.63% to 23.83% when the evaluation protocol is extended with spoofed trials.%subjected to spoofing attacks. However, even the straightforward integration of ASV and CM systems in the form of score-sum and deep neural network-based fusion strategies reduce the EER to 1.71% and 6.37%, respectively. The new Spoofing-Aware Speaker Verification (SASV) challenge has been formed to encourage greater attention to the integration of ASV and CM systems as well as to provide a means to benchmark different solutions.
STAU: A SpatioTemporal-Aware Unit for Video Prediction and Beyond

Video prediction aims to predict future frames by modeling the complex spatiotemporal dynamics in videos. However, most of the existing methods only model the temporal information and the spatial information for videos in an independent manner but haven't fully explored the correlations between both terms. In this paper, we propose a SpatioTemporal-Aware Unit (STAU) for video prediction and beyond by exploring the significant spatiotemporal correlations in videos. On the one hand, the motion-aware attention weights are learned from the spatial states to help aggregate the temporal states in the temporal domain. On the other hand, the appearance-aware attention weights are learned from the temporal states to help aggregate the spatial states in the spatial domain. In this way, the temporal information and the spatial information can be greatly aware of each other in both domains, during which, the spatiotemporal receptive field can also be greatly broadened for more reliable spatiotemporal modeling. Experiments are not only conducted on traditional video prediction tasks but also other tasks beyond video prediction, including the early action recognition and object detection tasks. Experimental results show that our STAU can outperform other methods on all tasks in terms of performance and computation efficiency.
Structured and Unstructured Teams for Research Software Development at The Netherlands eScience Center

This paper presents the types of teams that are currently in place at the Netherlands eScience Center. We categorize our teams into four different types: Project Teams, Collectives, Agile Teams and Scrum Teams. We provide a brief description of each team type and share stories from teams themselves to reflect on the strengths and shortcomings of each model. From our observation, we conclude that the type of team that is most suitable for each project depends on the specific needs of that project. More importantly, different types of teams are suitable for the different types of people working at the Netherlands eScience Center.
IDSP activities of scientific libraries in the era of digitalization

The author reviews cultural and leisure activities of scientific libraries. These activities is realized in the form of intellectual, development and educational activities (IDEA) in the era of digitalization. The author defines the range of libraries that offer services and technologies that correspond to IDEA and IDSP (Intellectual Development and Science Popularization) in the digitalization era. The author provides the leisure definition and predicts the merge of educational and leisure activities. Based on the national and foreign experience in the area, the author offers the most promising vectors for scientific libraries: virtual reality (VR) technologies; augmented reality technologies (AR), science documentaries, robotics courses, prototyping and 3D-printing, makerspaces for digital technologies, digital storytelling. She concludes that IDEA and IDSP activities have made a quantum leap as compared to cultural and leisure activities of public libraries. The factors of this transformation are discussed. The author concludes on the prospects of developing IDSP activities of scientific libraries.
Network form of realization of universities’ educational programs: has a certainty appeared?

In the article, the authors consider the latest by-law regulation of the institute of the network form of the implementation of educational programs in the higher education system of Russia. Despite the emergence of normative certainty on several issues, there are still quite ambiguous provisions. Thanks to this study, key ones have been identified and commented on. The popularity of the network form among universities is still relatively low. The authors have systematized several topical reasons for the conclusion of agreements on the network form by universities. Qualitative changes in the popularity of the online form can occur both thanks to new state programs (Priority-2030) and the renewal of development programs for leading Russian universities (for example, St. Petersburg State University). A separate issue is proposed at the sub-legal level as an approximate form of an agreement on the network form. The authors see the legal nature of such an agreement as undoubtedly administrative. But the conclusion of the agreement is accompanied by many nuances discussed in the article, and which must be kept in mind by universities. After the study, the authors highlighted priority issues for continuing to give the network form the expected normative certainty.
Varianceflow: High-Quality and Controllable Text-to-Speech using Variance Information via Normalizing Flow

There are two types of methods for non-autoregressive text-to-speech models to learn the one-to-many relationship between text and speech effectively. The first one is to use an advanced generative framework such as normalizing flow (NF). The second one is to use variance information such as pitch or energy together when generating speech. For the second type, it is also possible to control the variance factors by adjusting the variance values provided to a model. In this paper, we propose a novel model called VarianceFlow combining the advantages of the two types. By modeling the variance with NF, VarianceFlow predicts the variance information more precisely with improved speech quality. Also, the objective function of NF makes the model use the variance information and the text in a disentangled manner resulting in more precise variance control. In experiments, VarianceFlow shows superior performance over other state-of-the-art TTS models both in terms of speech quality and controllability.
Few-Shot Speaker Identification Using Depthwise Separable Convolutional Network with Channel Attention

Although few-shot learning has attracted much attention from the fields of image and audio classification, few efforts have been made on few-shot speaker identification. In the task of few-shot learning, overfitting is a tough problem mainly due to the mismatch between training and testing conditions. In this paper, we propose a few-shot speaker identification method which can alleviate the overfitting problem. In the proposed method, the model of a depthwise separable convolutional network with channel attention is trained with a prototypical loss function. Experimental datasets are extracted from three public speech corpora: Aishell-2, VoxCeleb1 and TORGO. Experimental results show that the proposed method exceeds state-of-the-art methods for few-shot speaker identification in terms of accuracy and F-score.
Analysis of Discourse Subjects in the Reports on COVID-19 From the Perspective of Other Narratives - Based on China Daily

2020 is a year of massive outbreaks of COVID-19, which has a huge impact on the lives of people around the world. In 2020, China Daily, a major mainstream English-language newspaper in China, provided detailed coverage of the outbreak facts in countries around the whole world. In these reports, both subjects of discourse from China and from abroad spoke out, expressing China to the world and conveying voices from the world to China. The reports about the COVID-19 in China Daily were taken as the research objects, and the relevant reports among them were manually identified and screened to build a corpus for discourse subject analysis. This study focuses the analysis on the discourse subjects as the Other, such as foreigners in China and voices from the international community. By analyzing the voices of the other in the text, readers can understand the image China has created in the international community and the responsibility and accountability China embodies as the largest developing country. By selecting and analyzing typical discourse subjects as the Other from the self-constructed corpus, this study aims to analyze the international image portrayed by China and to investigate the important role of different discourse subjects in news reporting.