Reviewerly/exHarmony huggingface dataset:
A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections

Question answering (QA) systems for large document collections typically use pipelines that (i) retrieve possibly relevant documents, (ii) re-rank them, (iii) rank paragraphs or other snippets of the top-ranked documents, and (iv) select spans of the top-ranked snippets as exact answers.Pipelines are conceptually simple, but errors propagate from one component to the next, without later components being able to revise earlier decisions.We present an architecture for joint document and snippet ranking, the two middle stages, which leverages the intuition that relevant documents have good snippets and good snippets come from relevant documents.The architecture is general and can be used with any neural text relevance ranker.We experiment with two main instantiations of the architecture, based on POSIT-DRMM (PDRMM) and a BERT-based ranker.Experiments on biomedical data from BIOASQ show that our joint models vastly outperform the pipelines in snippet retrieval, the main goal for QA, with fewer trainable parameters, also remaining competitive in document retrieval.Furthermore, our joint PDRMM-based model is competitive with BERT-based models, despite using orders of magnitude fewer parameters.These claims are also supported by human evaluation on two test batches of BIOASQ.To test our key findings on another dataset, we modified the Natural Questions dataset so that it can also be used for document and snippet retrieval.Our joint PDRMM-based model again outperforms the corresponding pipeline in snippet retrieval on the modified Natural Questions dataset, even though it performs worse than the pipeline in document retrieval.We make our code and the modified Natural Questions dataset publicly available.
Students' Grammatical Errors in Essay Writing: A Pedagogical Grammar Reflection

Students of English as a Foreign Language (EFL) are expected to master the fundamental of grammar so they can produce good essays. However, despite having learnt English at secondary or university level, students tend to make many grammatical errors in their writing. This study presents the grammatical errors made by college EFL students in their essays and the pedagogical implications viewed from those errors. This is a descriptive research with 30 second-year students who enrolled Essay Writing class as participants. Thirty written essays produced by the students were analyzed for the grammatical errors. The findings revealed that there were 368 grammatical errors found in the students’ essays. The most common one was in verb use (48%). Besides, the errors were also found in nouns (12%), prepositions (8%), determiners (8%), pronouns (8%), adverbials (6), adjectives (5%), and conjunctions (5%). If teachers do not assist students to comprehend the concept of parts of speech, and essential and nonessential clauses, these students will continue to make errors in their more advanced writing. The findings may have useful pedagogical implications for English language teachers, syllabus designers, and test developers. Understanding students’ difficulties and providing appropriate grammar instructions are the keys to teach grammar.
Psychological and pedagogical support of the educational process: synergetic approach

In this article, the process of linguistic faculty students teaching using innovative digital technologies is investigated in the focus of a synergetic approach. This problem is very relevant today, since the issue of preserving the quality of knowledge when implementing intensive electronic methods of mastering communication skills in a foreign language does not lose its actuality. Synergetics as the science of complex systems is now recognized as a new scientific paradigm in the study of various objects and processes. The authors focus on the features of the synergetic approach to the education process. From the point of view of synergetics, the authors consider the process of teaching foreign languages as a holistic dynamic system capable of self-organization and self-development. E-learning courses, which have become particularly popular in modern conditions, are considered as an integral part of the educational system. We are talking about self-organization of both the educational process and its structure, which is a means of optimizing students’ learning, and activating students in order to ensure a high-quality learning process. The role of the teacher and psychological and pedagogical support of students in the learning process is emphasized. The authors note that the synergetic interpretation of the psychological and pedagogical approach to teaching students the basics of language based on digital technologies contributes to the personal growth of students and the development of a holistic dynamic system.
Interpretable Sequence Classification via Discrete Optimization

Sequence classification is the task of predicting a class label given a sequence of observations. In many applications such as healthcare monitoring or intrusion detection, early classification is crucial to prompt intervention. In this work, we learn sequence classifiers that favour early classification from an evolving observation trace. While many state-of-the-art sequence classifiers are neural networks, and in particular LSTMs, our classifiers take the form of finite state automata and are learned via discrete optimization. Our automata-based classifiers are interpretable---supporting explanation, counterfactual reasoning, and human-in-the-loop modification---and have strong empirical performance. Experiments over a suite of goal recognition and behaviour classification datasets show our learned automata-based classifiers to have comparable test performance to LSTM-based classifiers, with the added advantage of being interpretable.
METHOD FOR DETERMINING THE SEMANTIC SIMILARITY OF ARBITRARY LENGTH TEXTS USING THE TRANSFORMERS MODELS

The paper considers the results of a method development for determining the semantic similarity of arbitrary length texts based on their vector representations. These vector representations are obtained via multilingual Transformers model usage, and direct problem of determining semantic similarity of arbitrary length texts is considered as the text sequence pairs classification problem using Transformers model. Comparative analysis of the most optimal Transformers model for solving such class of problems was performed. Considered in this case main stages of the method are: Transformers model fine-tuning stage in the framework of pretrained model second problem (sentence prediction), also selection and implementation stage of the summarizing method for text sequence more than 512 (1024) tokens long to solve the problem of determining the semantic similarity for arbitrary length texts.
A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction

In this paper, we propose a novel edge-editing approach to extract relation information from a document.We treat the relations in a document as a relation graph among entities in this approach.The relation graph is iteratively constructed by editing edges of an initial graph, which might be a graph extracted by another system or an empty graph.The way to edit edges is to classify them in a close-first manner using the document and temporallyconstructed graph information; each edge is represented with a document context information by a pretrained transformer model and a graph context information by a graph convolutional neural network model.We evaluate our approach on the task to extract material synthesis procedures from materials science texts.The experimental results show the effectiveness of our approach in editing the graphs initialized by our in-house rule-based system and empty graphs. 1
RANCANG BANGUN APLIKASI MODUL PEMBELAJARAN TEKNOLOGI WAN BERBASIS ANDROID

Module as medium of learning is one of the ingredients of learning that can be used by students in independent and effective. The learning module application is made with an Android-based platform. WAN technology is one of the subjects for class XI TKJ. This application method was developed using a prototyping model approach with the java programming language using the Android Studio IDE. One of the functions of an android application for students and teachers is the creation of applications related to student learning in which there are learning materials and a collection of practice questions that can be done by students. Android applications that can be developed are e-module applications or learning modules. The results of this thesis is to produce a learning application as a means for learning WAN technology subjects in which there are several menus such as syllabus, lesson plans, modules, video tutorials, options, search and about. The presence of this application is expected to improve students' understanding abilities in learning WAN technology and contribute to learning. Keywords: Android, module, WAN technology, smartphone.
Finding Needles in Heterogeneous Haystacks

Due to intense competition and lack of real estate on the front page of large e-commerce platforms, sellers are sometimes motivated to garner non-genuine signals (clicks, add-to-carts, purchases) on their products, to make them appear more appealing to customers. This hurts customers' trust on the platform, and also hurts genuine sellers who sell their items without looking to game the system. While it is important to find the sellers and the buyers who are colluding to garner these non-genuine signals, doing so is highly nontrivial. Firstly, the set of bad actors in the system is a very small fraction of all the buyers/sellers on the platform. Secondly, bad actors ``hide" with the good ones, making them hard to detect. In this paper, we develop CONGCN, a context aware heterogeneous graph convolutional network to detect bad actors on a large heterogeneous graph. While our method is motivated by abuse detection in e-commerce, the method is applicable to other areas such as computational biology and finance, where large heterogeneous graphs are pervasive, and the amount of labeled data is very limited. We train CONGCN via novel sampling methods, and context aware message passing in a semi-supervised fashion to predict dishonest buyers and sellers in e-commerce. Extensive experiments show that our method is effective, beating several baselines; generalizable to an inductive setting and highly scalable
An analysis of the effect of stop words on the performance of the matrix comparator for entity resolution

Abstract: This paper investigates the effect of removing stop words on the performance of the matrix comparator for linking unstandardized entity references. Experiments on annotated synthetic cust...
Development of Android Chemistry Comics as an Independent Learning Application to Improve Student Learning Outcomes

Teachers often use printed book media in learning. Rarely do they want to develop and vary learning media with the aim of making the learning process more interesting. As a result, the lack of student interest in learning results in low student learning outcomes. This study was aimed to develop android chemistry comics (chemondro) on the material for the reaction rate of class XI IPA SMA N 1 Garoga. The method used is the ADDIE R D model, namely analysis, design, development, implementation, and evaluation. The analysis stage was carried out by analyzing the needs in the form of student characteristics and identifying learning media. The design stage included flowchart design and chemondro. The design results were then developed, then the application was given to the validator for a feasibility test. The implementation stage was carried out in class XI SMA N 1 Garoga. Furthermore, student learning outcomes were measured at the evaluation stage using tests. The results of this study found that chemondro-based chemical comics could be developed with the 3.6 eligibility criteria very well. The learning outcomes were found to be 81.91 in the experimental class compared to 71.76 in the control class. This study concluded that chemondro-were very suitable for use in chemistry learning, especially on the material of reaction rates and student learning outcomes were achieved with good criteria.
An Effective Quantum Genetic Algorithm Based on Drama Resource Mining Using Wireless Sensing Technology

Wireless sensor technology has penetrated various domains of today’s life and plays a vital role in the advanced technology. Numerous researchers have combined this outstanding technology with other fields such as resource mining, industry, healthcare, automobile system, gaming industry, and dramas. However, in traditional resource mining, long mining time leads to incomplete mining results along with low accuracy. In order to improve the effect of resource mining, we have proposed an effective Quantum Generic Algorithm (QGA) based on drama resource mining by using wireless sensor technology. In our proposed scheme, we have combined the RFID technology of wireless sensor with wireless network protocol stack for the purpose of collecting drama resources on the network platform. We have classified the drama resources on the network platform by using QGA based on the results of resources collection. Additionally, we have mined the semantic association features of frequent patterns of the drama resources on the platform and combined with the fuzzy attribute feature detection method. The experimental results show that this method is superior to the traditional methods in terms of resource mining time, mining results’ comprehensiveness, and mining results’ accuracy, which shows that this method has practical application value.
Non-Deterministic Conformant Planning Using a Counterexample-Guided Incremental Compilation to Classical Planning

We address the problem of non-deterministic conformant planning, i.e., finding a plan in a non-deterministic context where the environment is not observable. Our approach uses an unsound but complete reduction from non-deterministic conformant planning to classical planning to find a candidate plan; the validity of this plan is then verified by a SAT solver; if the plan is invalid, the reduction is revised to guarantee that the invalid plan will not be valid in the classical planning problem. This procedure is executed until a valid plan is found, or it is shown that there is no plan. Experiments show that this approach provides a nice trade-off between fast but unsound, and complete but slow approaches.
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning

Although recent multi-task learning methods have shown to be effective in improving the generalization of deep neural networks, they should be used with caution for safety-critical applications, such as clinical risk prediction. This is because even if they achieve improved task-average performance, they may still yield degraded performance on individual tasks, which may be critical (e.g., prediction of mortality risk). Existing asymmetric multi-task learning methods tackle this negative transfer problem by performing knowledge transfer from tasks with low loss to tasks with high loss. However, using loss as a measure of reliability is risky since low loss could result from overfitting. In the case of time-series prediction tasks, knowledge learned for one task (e.g., predicting the sepsis onset) at a specific timestep may be useful for learning another task (e.g., prediction of mortality) at a later timestep, but lack of loss at each timestep makes it difficult to measure the reliability at each timestep. To capture such dynamically changing asymmetric relationships between tasks in time-series data, we propose a novel temporal asymmetric multi-task learning model that performs knowledge transfer from certain tasks/timesteps to relevant uncertain tasks, based on the feature-level uncertainty. We validate our model on multiple clinical risk prediction tasks against various deep learning models for time-series prediction, which our model significantly outperforms without any sign of negative transfer. Further qualitative analysis of learned knowledge graphs by clinicians shows that they are helpful in analyzing the predictions of the model.
The novel Social Spider Optimization Algorithm: Overview, Modifications, and Applications

The continues in real-world problems increasing complexity motivated computer scientists and researchers to search for more-efficient problem-solving strategies. Generally natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization problems because of their ability to adjust to variety of conditions. This paper present a swarm based algorithm that is based on the cooperative behaviors between social spider, it called Social Spider Optimization (SSO) algorithm. In SSO, search agents characterize a set of spiders which together move according to a biological behavior in colony. During the past years after SSO introduction, many modifications has improved the performance of the algorithm and has been applied in several fields. In this paper, the improvements, and applications of the SSO are reviewed.
IS GOVERNANCE EFFECTIVE IN ECONOMIC GROWTH? EVIDENCE FROM MSCI COUNTRIES

The main objective of the research is to study the relationship between governance and economic development in emerging economies. We used MSCI countries as a sample due to their increasing importance. Our research is based on the World Bank data for Worldwide Governance Indicators (WGI) of observed countries from 2002 to 2018. The Principal Component Analysis (PCA) and Generalized Method of Moments (GMM) were applied to identify the relationship between the GDP index as a dependent variable and the WGI as an independent variable and other control variables. The experimental results showed that there was no significant relationship between governance and economic development. On the basis of these findings, it is emphasized that the countries in the sample should give more importance to governance.
Natural language processing: A literature survey

It is quite hard to imagine a smart system like a voice assistant or a chat-bot or a recommender system without Natural Language Processing (NLP).It all starts with an initial unit that first interprets the data (audio or text) provided and then start making sense of the data and after proper processing of the data the actual steps are followed by the machine to throw some replies or get the work done.NLP does not fall under a discipline; rather it is a part of several different disciplines i.e. computer science, information engineering, Artificial Intelligence (AI), and linguistics.The concern of NLP is the interaction between a computer and human languages.NLP areas include Speech Recognition, Machine Translation, Automatic Text Summarization, Part-of-Speech Tagging, etc. Generally, NLP is used in many real time applications like smart homes, smart offices like Alexa, Cortana, Siri, and Google Assistant.The history of NLP generally started in the 1950s and has come a long way from then and improved a lot.This paper discusses the history of NLP, its evolution, its tools and techniques, and its applications in different fields.The paper also discusses the role of Machine Learning and Artificial Neural Networks (ANNs) to improve NLP.
Comparison of Convolutional Neural Network Models for Mobile Devices

In recent years mobile devices have become an important part of our daily lives and Deep Convolutional Neural Networks have been performing well in the task of image classification. Some considerations have to be made when running a Neural Network inside a mobile device such as computational complexity and storage size. In this paper, common architectures for image classification were analyzed to retrieve the values of accuracy rate, model complexity, memory usage, and inference time. Those values were compared and it was possible to show which architecture to choose from considering mobile restrictions.
Design of a Multi-Functional Wheelchair Based Fuzzy Analytical Hierarchy Process

Wheelchairs are tools for humans who experience illness, disabilities, and injuries that cause walking difficulties. There are many types of wheelchairs on the market, while wheelchairs that function to help users defecate flexibly have not been widely sold in the market. Therefore, it is necessary to develop a flexible and multifunctional wheelchair that can assist the users in carrying out their activities. This study aims to develop a design of the multifunctional wheelchair. The designing of multifunctional wheelchair based the FAHP (Fuzzy Analytical Hierarchy Process) method. The use of the FAHP was to select the optimal design and selection of the best alternatives among wheelchair’s indicators of design proposed. The results of calculation of weight values for wheelchair design were obtained a percentage of alternative 1 of 48% and alternative 2 of 52%.
The region as an object of projecting of neo-spheric existence in the depth of anthroposphere

The article deals with the problem of the modern education system perfection and at the same time seeks to reach the limit of the possible in the transformation of the educational space to meet the demand of time, its ability to provide social consciousness in the conditions of the ongoing crisis and those changes that caused the intensification of the dialogue between Man and Nature. The development with the participation of humans becomes of a more targeted nature – noospheric and educational, global one. This is a new way obliges a modern general education institution to look for better approaches to the production of personality and promote the development of modern man – the carrier of new values. The state of society's consciousness should be inclined to act in the direction of its spirituality. The correlation between the social and morphological organization of a human being should not be beyond the problem of profile education. he human world to a large extent begins in a human being itself, therefore, the objective of this article is the search for ways to change the educational system of the region for the sake of further formation and development of the democratic society of the anthropospheric era.
CMNN: Coupled Modular Neural Network

In this paper, we propose a multi-branch neural network architecture named Coupled Modular Neural Network (CMNN). A CMNN is a network consisting of β closely coupled sub-networks, where β is termed as the branching factor in this paper. We call the whole network a super-graph and each sub-network a sub-graph. Each sub-graph is a stand-alone neural network and shares a common block with other sub-graphs. To effectively leverage the super-graph we propose a simple but easy-to-implement Round-Robin-based learning algorithm. Each training iteration contains two phases. In the first phase, we choose a sub-graph in a Round-Robin fashion and train it using knowledge of the super-graph (distillation). In the second phase, we fine-tune the super-graph based on the updated sub-graphs. This algorithm produces a different copy of the super-graph at each iteration which acts as an improved teacher network for the sub-graph; and a different copy of one of the sub-graphs which functions as a new building block for the super-graph. To validate and test CMNN and the proposed algorithm, we conduct experiments on CIFAR-10, CIFAR-100, Tiny ImageNet and a private On-Road-Risk (ORR) datasets. Empirical results on all these four datasets indicate that we not only obtain a strong sub-graph network, the learning framework can also produce strong ensemble performance which substantiates the diversity introduced throughout the learning framework.