Reviewerly/exHarmony huggingface dataset:
Keyword Indexing And Searching Tool (KIST): A Tool to Assist the Forensics Analysis of WhatsApp Chat

Digital forensics is a field that concerned with finding and presenting evidence sourced from digital devices, such as computers and mobile phones. Most of the forensic analysis software is proprietary, and eventually, specialized analysis software is developed in both the private and public sectors. This paper presents an alternative of forensic analysis tools for digital forensics, which specifically to analyze evidence through keyword indexing and searching. Keyword Indexing and Searching Tool (KIST) is proposed to analyze evidence of interest from WhatsApp chat text files using keyword searching techniques and based on incident types. The tool was developed by adopting the Prototyping model as its methodology. KIST includes modules such as add, edit, remove, display the indexed files, and to add WhatsApp chat text files. Subsequently, the tool is tested using functionality testing and user testing. Functionality testing shows all key functions are working as intended, while users testing indicates the majority of respondents are agree that the tool is able to index and search keyword and display forensic analysis results.
Improved autonomous error correction using variable dissipation in small logical qubit architectures

Abstract Coherence times for superconducting qubits have greatly improved over time. Moreover, small logical qubit architectures using engineered dissipation have shown great promise for further improvements in the coherence of a logical qubit manifold comprised of few physical qubits. Nevertheless, optimal working parameters for small logical qubits are generally not well understood. This work presents several approaches to finding preferential parameter configurations by looking at three different cases of increasing complexity. We begin by looking at state stabilization of a single qubit using dissipation via coupling to a lossy object. We look at the limiting factors in this approach to error correction, and how we address those by numerically optimizing the parametric coupling strength with the lossy object having an effective time-varying dissipation rate—we call this a pulse-reset cycle. We then translate this approach to more efficient state stabilization to an abstracted three-qubit flip code, and end by looking at the very small logical qubit (VSLQ). By using these techniques, we can further increase logical state lifetimes for different architectures. We show significant advantages in using a pulse-reset cycle over numerically optimized, fixed parameter spaces.
Self-Body Recognition through a Mirror: Easing Spatial-Consistency Requirements for Rubber Hand Illusion

Considering that humans recognize mirror images as copies of the real world despite misinterpreting optical reflections, spatial disagreement may be accepted in rubber hand illusion (RHI) settings when a mirror is used to show a fake hand. The present study performed two experiments to reveal how self-body recognition of a fake hand via a mirror affects RHI. First, we tested whether illusory ownership of a fake hand seen in a mirror could be induced in our experimental environment (screening experiment). Subjective evaluations using an RHI questionnaire demonstrated that embodiment of the rubber hand was evoked in the presence or absence of a mirror. We then examined whether using a mirror image for RHI allows disagreement in orientation (45 ∘ ) between the rubber and actual hands (main experiment). The participants experienced RHI even when the actual and rubber hands were incongruent in terms of orientation. These findings suggest that using a mirror masks subtle spatial incongruency or degrades the contribution of visual cues for spatial recognition and facilitates multisensory integration for bodily illusions.
Sistem Pakar Diagnosa Penyakit Tanaman Bawang Merah Menggunakan Metode Certainty Factor

The lack of knowledge of farmers and the unequal counseling about onion diseasefrom experts is a strong reason for the difficulty of overcoming or immediately treateddiseases of onions, for this requires early diagnosis of disease onion plants. Thisresearch uses the Certainty Factor method. This method uses the certainty of an experton the symptoms of each disease. By determining the value of MB (Measure ofBelieve) as the level of confidence in the hypothesis and MD (Measure of Disbelieve)the level of distrust of the hypothesis. After using the Certainty Factor formula, thevalue of each disease will be generated from the new symptoms owned by using thehighest value of each disease, so that is the result of disease diagnosis in shallots. Thisstudy uses as many as 35 data as testing and from these data obtained an accuracyvalue of 85.71%
Perkembangan Bahasa pada Anak Speechdelay

Language development in early childhood is very important for the continuity of his life as a social creature. Incoming language skills have an aspect of early childhood development. This article seeks to discuss the development of speech-based children's language in theory by analyzing documented cases of published research results. The research method used is a library research with content analysis for the discussion technique. Source of data taken from secondary data from research results in thesis or online journals, then combined with several existing theories. Based on the results of the discussion obtained data that children with speech delay will experience language delays both in terms of articulation, sound, fluency and completeness of the preparation of the letters. Many factors as a trigger that is external and internal. Most of the cases that have external factors in the form of parenting are the dominant factors in children experiencing speech delay. Mild speechdelay children can recover with appropriate meode stimulation and selection, while children with medium and large speechdays need the help of experts (therapists) and medical personnel
Super-Resolution Reconstruction Method for Single Space Object Image based on Optimized Convolution Neural Network

To date, a large number of platforms have been launched into outer space. More and more attention has been paid to space object images. However, it is difficult to obtain high-quality spatial images. One of the most important reasons is that the sensor technology is not good enough. Inspired by the typical super-resolution reconstruction method based on deep CNN, a super-resolution reconstruction method of single space target image based on deep CNN is proposed. This method uses Nesterov acceleration gradient method to train the network. The experimental results show that the method is feasible in the reconstruction of high-resolution spatial target image.
NAIST’s Machine Translation Systems for IWSLT 2020 Conversational Speech Translation Task

This paper describes NAIST's NMT system submitted to the IWSLT 2020 conversational speech translation task.We focus on the translation disfluent speech transcripts that include ASR errors and non-grammatical utterances.We tried a domain adaptation method by transferring the styles of out-of-domain data (United Nations Parallel Corpus) to be like in-domain data (Fisher transcripts).Our system results showed that the NMT model with domain adaptation outperformed a baseline.In addition, slight improvement by the style transfer was observed.
Petrographic structures and Hardy – Weinberg equilibrium

The article is devoted to the most narrative side of modern petrography – the definition, classification and nomenclature of petrographic structures. We suggest a mathematical formalism using the theory of quadratic forms (with a promising extension to algebraic forms of the third and fourth orders) and statistics of binary (ternary and quaternary, respectively) intergranular contacts in a polymineralic rock. It allows constructing a complete classification of petrographic structures with boundaries corresponding to Hardy – Weinberg equilibria. The algebraic expression of the petrographic structure is the canonical diagonal form of the symmetric probability matrix of binary intergranular contacts in the rock. Each petrographic structure is uniquely associated with a structural indicatrix – the central quadratic surface in n-dimensional space, where n is the number of minerals composing the rock. Structural indicatrix is an analogue of the conoscopic figure used for optical recognition of minerals. We show that the continuity of changes in the organization of rocks (i.e., the probabilities of various intergranular contacts) does not contradict a dramatic change in the structure of the rocks, neighboring within the classification. This solved the problem, which seemed insoluble to A.Harker and E.S.Fedorov. The technique was used to describe the granite structures of the Salminsky pluton (Karelia) and the Akzhailau massif (Kazakhstan) and is potentially applicable for the monotonous strata differentiation, section correlation, or wherever an unambiguous, reproducible determination of petrographic structures is needed. An important promising task of the method is to extract rocks' genetic information from the obtained data.
Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition

In general, the labels used in sequence labeling consist of different types of elements.For example, IOB-format entity labels, such as B-Person and I-Person, can be decomposed into span (B and I) and type information (Person).However, while most sequence labeling models do not consider such label components, the shared components across labels, such as Person, can be beneficial for label prediction.In this work, we propose to integrate label component information as embeddings into models.Through experiments on English and Japanese fine-grained named entity recognition, we demonstrate that the proposed method improves performance, especially for instances with low-frequency labels.
Linear Neural Parsing and Hybrid Enhancement for Enhanced Universal Dependencies

To accomplish the shared task on dependency parsing we explore the use of a linear transition-based neural dependency parser as well as a combination of three of them by means of a linear tree combination algorithm.We train separate models for each language on the shared task data.We compare our base parser with two biaffine parsers and also present an ensemble combination of all five parsers, which achieves an average UAS 1.88 point lower than the top official submission.For producing the enhanced dependencies, we exploit a hybrid approach, coupling an algorithmic graph transformation of the dependency tree with predictions made by a multitask machine learning model.
A robust reputation system using online reviews?

Evaluating sellers in an online marketplace is an important yet nontrivial task. Many online platforms such as eBay and Amazon rely on buyer reviews to estimate the reliability of sellers on their platform. Such reviews are, however, often biased by: (1) intentional attacks from malicious users and (2) conflation between a buyer?s perception of seller performance and item satisfaction. Here, we present a novel approach to mitigating these issues by decoupling measures of seller performance and item quality, while reducing the impact of malignant reviews. An extensive simulation study shows that our proposed method can recover seller reputations with high rank correlation even under assumptions of extreme noise.
The UniMelb Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection

The paper describes the University of Melbourne's submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection.Our team submitted three systems in total, two neural and one non-neural.Our analysis of systems' performance shows positive effects of newly introduced data hallucination technique that we employed in one of neural systems, especially in low-resource scenarios.A non-neural system based on observed inflection patterns shows optimistic results even in its simple implementation (>75% accuracy for 50% of languages).With possible improvement within the same modeling principle, accuracy might grow to values above 90%.
PARTNER: Human-in-the-Loop Entity Name Understanding with Deep Learning

Entity name disambiguation is an important task for many text-based AI tasks. Entity names usually have internal semantic structures that are useful for resolving different variations of the same entity. We present, PARTNER, a deep learning-based interactive system for entity name understanding. Powered by effective active learning and weak supervision, PARTNER can learn deep learning-based models for identifying entity name structure with low human effort. PARTNER also allows the user to design complex normalization and variant generation functions without coding skills.
A Dual Privacy Preserving Algorithm in Spatial Crowdsourcing

Spatial crowdsourcing assigns location-related tasks to a group of workers (people equipped with smart devices and willing to complete the tasks), who complete the tasks according to their scope of work. Since space crowdsourcing usually requires workers’ location information to be uploaded to the crowdsourcing server, it inevitably causes the privacy disclosure of workers. At the same time, it is difficult to allocate tasks effectively in space crowdsourcing. Therefore, in order to improve the task allocation efficiency of spatial crowdsourcing in the case of large task quantity and improve the degree of privacy protection for workers, a new algorithm is proposed in this paper, which can improve the efficiency of task allocation by disturbing the location of workers and task requesters through k -anonymity. Experiments show that the algorithm can improve the efficiency of task allocation effectively, reduce the task waiting time, improve the privacy of workers and task location, and improve the efficiency of space crowdsourcing service when facing a large quantity of tasks.
The Economic Advantages of Blockchain Technology in E-Procurement

This article aims to review economic advantages of the one of the most modern cutting edge technologies, namely blockchain for eProcurement.As eProcurement (including its part -Supply chain) is a concept consisting of plenty of different processes, therefore specific advantages per such major different processes are explained.Assumptions and pre-conditions of Blockchain application are explained.Article's main conclusion is that due to the main characteristics of Blockchain technology (safety, transparency, reliability, speed and cost efficiency) payment settlement and products' shipping tracking/inventory management are eProcurement's phases that could benefit from this technology the most, among others (such as analysis -requirements' gathering, sourcing/vendors' selection and contract management cycle).
Joint Training with Semantic Role Labeling for Better Generalization in Natural Language Inference

End-to-end models trained on natural language inference (NLI) datasets show low generalization on out-of-distribution evaluation sets.The models tend to learn shallow heuristics due to dataset biases.The performance decreases dramatically on diagnostic sets measuring compositionality or robustness against simple heuristics.Existing solutions for this problem employ dataset augmentation which has the drawbacks of being applicable to only a limited set of adversaries and at worst hurting the model performance on other adversaries not included in the augmentation set.Our proposed solution is to improve sentence understanding (hence out-of-distribution generalization) with joint learning of explicit semantics.We show that a BERT based model trained jointly on English semantic role labeling (SRL) and NLI achieves significantly higher performance on external evaluation sets measuring generalization performance.
Application of Cloud Educational Technologies for Teacher Competence Development

The research deals with solving an urgent pedagogical problem â€“ finding ways for teacher competence development based on the use of innovative educational technologies that meet the modern requirements of society, pedagogical science and practice. It is established that the educational process on-line can be managed with the use of cloud technologies that influence the architecture, the services provided and the logistics of implementation of training courses. The experimental use of Google Apps Education Edition and OwnCloud was tested in distance training courses for teacher competence development with up to 10 years of practical experience. The obtained results and the dynamics of the development of the motivational, cognitive, activity and personal components of teacher competences were analysed using the methods of mathematical statistics. This study reveals that Google Apps Education Edition has powerful tools and extensive opportunities for distance learning, and is an effective means of enhancing the development of all components of teacher competence. The use of these services in the process of distance learning courses to increase teacher competence almost completely solves the problem of interaction with the teaching staff and the learning environment. This helps to establish the correspondence of the content of assignments to the studied topics and to ensure a professional focus of project assignments of teachers. This model can be used to develop and implement services that allow the organization of personal information space for teachers (on the example of blogs and Google cloud service). https://doi.org/10.26803/ijlter.19.5.18
An Intelligent Machine Learning and Self Adaptive Resource Allocation Framework for Cloud Computing Environment

Resource allocation is one of the major concern in cloud computing model.When several problems exists in rendering a useful resource allocator.In this research , a self adaptive resource allocation frame work based on machine learning is proposed for modelling and analysing the problem of multi-dimensional cloud resource.This novel self-adaptive resource allocation architecture consists of three stages, QoS prediction model, Improved Bat Algorithm (IBA) and Energy Efficient Model (EEM).The first one, the QoS prediction model, which depends on the same scale of system's past events data, can attain a comparable accuracy with regard to QoS prediction.Secondly, an Energy Efficient Model, which is based on Modified Clonal Selection Algorithm (MCSA) is introduced for minimizing the energy depletion.Thirdly, a runtime decision-making algorithm that depends on improved bat algorithm can rapidly decide on a suitable function for resource allocation
Toward Natural Language Generation by Humans

Natural language generation (NLG) has been featured in at most a handful of shipped games and interactive stories. This is certainly due to it being a very specialized practice, but another contributing factor is that the state of the art today, in terms of content quality, is simply inadequate. The major benefits of NLG are its alleviation of authorial burden and the capability it gives to a system of generating state-bespoke content, but we believe we can have these benefits without actually employing a full NLG pipeline. In this paper, we present the preliminary design of Expressionist, an in-development mixed-initiative authoring tool that instantiates an authoring scheme residing somewhere between conventional NLG and conventional human content authoring. In this scheme, a human author plays the part of an NLG module in that she starts from a set of deep representations constructed for the game or story domain and proceeds to specify dialogic content that may express those representations. Rather than authoring static dialogue, the author defines a probabilistic context-free grammar that yields templated dialogue. This allows a human author to still harness a computer's generativity, but in a capacity in which it can be trusted: operating over probabilities and treelike control structures. Additional features of Expressionist's design include arbitrary markup and realtime feedback showing currently valid derivations.
A Tripartite Plan-Based Model of Narrative for Narrative Discourse Generation

This paper presents a tripartite model of narrative in which three different kinds of actions are modeled: story actions, discourse actions, and narration actions. We argue that this model can support particularities of narrative discourse where typical discourse models do not (e.g., ambiguity/change of beliefs), and accounts for the difference in expressivity of the media used to convey the narrative, as opposed to bipartite models.
Towards Minimal Supervision BERT-Based Grammar Error Correction (Student Abstract)

Current grammatical error correction (GEC) models typically consider the task as sequence generation, which requires large amounts of annotated data and limit the applications in data-limited settings. We try to incorporate contextual information from pre-trained language model to leverage annotation and benefit multilingual scenarios. Results show strong potential of Bidirectional Encoder Representations from Transformers (BERT) in grammatical error correction task.
Planning Graphs for Efficient Generation of Desirable Narrative Trajectories

A goal of Experience Managers (EM) is to guide users through a space of narrative trajectories, or story branches, in an Interactive Narrative (IN). When a user performs an action that deviates from the intended trajectory, the EM uses a mediation strategy called accommodation to transition the user to a new desirable trajectory. However, generating the trajectory options then selecting the appropriate one is computationally expensive and at odds with the low-latency needs of an IN. We define three desirable properties (exemplar trajectories, narrative-theoretic comparison, and efficiency) that general solutions would possess and demonstrate how our plan-based Intention Dependency Graph addresses them.
Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition

The primary limitation of North Korean to English translation is the lack of a parallel corpus; therefore, high translation accuracy cannot be achieved.To address this problem, we propose a zero-shot approach using South Korean data, which are remarkably similar to North Korean data.We train a neural machine translation model after tokenizing a South Korean text at the character level and decomposing characters into phonemes.We demonstrate that our method can effectively learn North Korean to English translation and improve the BLEU scores by +1.01 points in comparison with the baseline.
Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks

Skip-Gram is a simple, but effective, model to learn a word embedding mapping by estimating a conditional probability distribution for each word of the dictionary.In the context of Information Geometry, these distributions form a Riemannian statistical manifold, where word embeddings are interpreted as vectors in the tangent bundle of the manifold.In this paper we show how the choice of the geometry on the manifold allows impacts on the performances both on intrinsic and extrinsic tasks, in function of a deformation parameter alpha.