Reviewerly/exHarmony huggingface dataset:
Automatic Chinese Spelling Checking and Correction Based on Character-Based Pre-trained Contextual Representations

Automatic Chinese spelling checking and correction (CSC) is currently a challenging task especially when the sentence is complex in semantics and expressions. Meanwhile, a CSC model normally requires a huge amount of training corpus which is usually unavailable. To capture the semantic information of sentences, this paper proposes an approach (named as DPL-Corr) based on character-based pre-trained contextual representations, which helps to significantly improve the performance of CSC. In DPL-Corr, the module of spelling checking is a sequence-labeling model enhanced by deep contextual semantics analysis, and the module of spelling correction is a masked language model integrated with multilayer filtering to obtain the final corrections. Based on experiments on SIGHAN 2015 dataset, DPL-Corr achieves a significantly better performance of CSC than conventional models.
Execution and analysis of classic neural network algorithms when they are implemented in embedded systems

Many algorithms related to neural networks are used in a large number of applications, most of them implemented on computational equipment that have great processing and storage capacities, however, new communication schemes such as the Internet of Things, need that neural algorithms can be executed from small electronic devices, devices that do not have large storage or processing capacities, but they can function as intelligent control centres for the different "things" connected to the Internet. Currently, there are various electronic devices that allow generating low-cost intelligent technology projects that permit interaction within the Internet of things, such as the Arduino UNO, Tiva-C, and BeagleBone development boards. In this project, we present the analysis of the Perceptron, ADALINE and Hopfield neural network algorithms, when they are executed within the three mentioned development boards, in order to define the best tool to be utilized when using such neural schemes and few data are processed. Economic cost, temporary response and technical capabilities of electronic devices have been evaluated.
MonoNet: Towards Interpretable Models by Learning Monotonic Features.

Being able to interpret, or explain, the predictions made by a machine learning model is of fundamental importance. This is especially true when there is interest in deploying data-driven models to make high-stakes decisions, e.g. in healthcare. While recent years have seen an increasing interest in interpretable machine learning research, this field is currently lacking an agreed-upon definition of interpretability, and some researchers have called for a more active conversation towards a rigorous approach to interpretability. Joining this conversation, we claim in this paper that the difficulty of interpreting a complex model stems from the existing interactions among features. We argue that by enforcing monotonicity between features and outputs, we are able to reason about the effect of a single feature on an output independently from other features, and consequently better understand the model. We show how to structurally introduce this constraint in deep learning models by adding new simple layers. We validate our model on benchmark datasets, and compare our results with previously proposed interpretable models.
Discrete configuration spaces of squares and hexagons

A fixed fraction of a large box is occupied by tiny spheres; how difficult is it for those spheres to move around in the space available in the box, and how does that difficulty depend on the chosen density? We consider a discrete version of this problem, a generalization of the familiar fifteen-piece sliding puzzle on the 4 by 4 square grid. On larger grids with more pieces and more holes, asymptotically how fast can we move the puzzle into the solved state? We consider these questions for both sliding squares and sliding hexagons, to model very densely packed disks.
Three-dimensional waypoint following for fixed-wing unmanned aerial vehicles in obstacle-filled environments

The paper computes optimal paths for fixed-wing unmanned aerial vehicles with bounded turn radii to follow a series of waypoints with specified directions in a three-dimensional obstacle-filled environment. In the existing literature, it was proved that the optimal path is of circular turn–straight line–circular turn (CSC) type for two consecutive waypoint configurations, when the points are sufficiently far apart and there is no obstacle in the field. The maximum of all minimum turn radii corresponding to all possible two-dimensional circular maneuvers was used for both the initial and final turns to develop the CSC-type paths. But, this paper considers the minimum turn radii for initial and final turns, corresponding to the maneuvering planes and which produces shorter CSC-type paths. In an obstacle-filled environment the shortest path may collide with obstacles, so a strategy is proposed to switch to the next best path that does not collide with obstacles. Using this technique, a series of waypoints is followed in the presence of obstacles of different types, for example, cylindrical, hemispherical, and spherical in shapes with different sizes. Finally, simulation results are presented to show the efficiency of the algorithm for obstacle avoidance. The computation time listed here indicates the potentiality of this algorithm for implementation in real time.
FSC-CapsNet: Fractionally-Strided Convolutional Capsule Network for complex data

Recently, a novel neural network called CapsNet has attracted the attention of many researchers. It is a great attempt to overcome the drawback of convolutional neural networks (CNNs) and achieves state-of-the-art performance on some simple datasets like MNIST. However, this network architecture is built specifically for MNIST and gets a poor performance on more complex datasets like CIFAR-10. To address this problem, aiming at complex data, we propose a new CapsNet architecture called Fractionally-Strided Convolutional Capsule Network (FSC-CapsNet). We modify both network structures of the encoder and decoder of CapsNet. For the purpose of extracting better features, we increase the number of convolutional layers before capsule layer in the encoder and improve the reconstruction performance by adopting two fractionally-strided convolutional layers in the decoder. In addition, no pooling layers are used in our architecture. To assess the performance of our proposed network on complex data, we conduct experiments with a single model without using any ensembled methods and data augmentation techniques on five real-world datasets, which are of higher dimensionality and larger size than MNIST. The experimental results demonstrate that our proposed method achieves better performance and improves the reconstruction performance compared with the normal CapsNet.
Joint Learning of Named Entity Recognition and Dependency Parsing using Separate Datasets

Joint learning of different NLP-related tasks is an emerging research eld in Machine Learning. Yet, most of the recent models proposed on joint learning require a dataset that is annotated jointly for all the tasks involved. Such datasets are available only for frequently used languages. In this paper, we propose a novel BiLSTM CRF based joint learning model for dependency parsing and named entity recognition tasks, which has not been employed before for Turkish to the best of our knowledge. This enables joint learning of various tasks for languages that have limited amount of annotated datasets. Our model, tested on a frequently used NER dataset for Turkish, has comparable results with the state-of-the-art systems. We also show that our proposed model out performs the joint learning model which uses a single dataset.
SMTCoq: Mixing Automatic and Interactive Proof Technologies

SMTCoq is a plugin for the Coq interactive theorem prover to work in conjunction with automated theorem provers based on Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT), in an efficient and expressive way. As such, it allows one to formally establish, in a proof assistant, mathematical results relying on large combinatorial properties that require automatic Boolean reasoning. To this end, the huge SAT proofs coming from such problems can be safely checked in Coq and combined with standard mathematical Coq developments in a generic and modular way, for instance to obtain a formal proof of the Erdős Discrepancy Conjecture. To achieve this objective with the same degree of safety as Coq itself, SMTCoq communicates with SAT and SMT solvers that, in addition to a yes/no answer, can output traces of their internal proof search. The heart of SMTCoq is thus a certified, efficient and modular checker for such traces expressed in a format that can encompass most aspects of SMT reasoning. Preprocessors—that need not be certified—for proof traces coming from the state-of-the-art SMT solvers CVC4 and veriT and SAT solvers zChaff and Glucose are implemented. Coq can thus work in conjunction with widely used provers. From a proof assistant perspective, SMTCoq also provides a mechanism to let Coq users enjoy automation provided by external provers.
The Utilities of Prominent Learning Theories for Mobile Collaborative Learning (MCL) with Reference to WhatsApp and M-learning

Within the context of global pervasive mobile technologies and social media, education is not an exception. In particular, the social media application called “WhatsApp” was the third most popular app with about 1.5 billion active users world-wide at the end of 2018. Consequently, many students have invested significant amounts of time and resources into WhatsApp for collaborative learning. However, the problem was that the literature had not evaluated mobile collaborative learning (MCL), a core feature of WhatsApp, in relation to prominent learning theories. This lead to the following research question: What utilities do prominent learning theories have for MCL? The research objective was to review the literature on prominent learning theories and expose their utility for MCL. Thus, the study was theoretical and exploratory in nature and synthesized relevant and peer-reviewed literature that was obtained from a thorough literature search strategyThe study found that some of the prominent learning theories have better utilities for MCL than others, although all do have utilities for MCL. In particular, the learning theories of collaborative learning, communities of practice, connectivism, conversation theory and social learning theory appear to have directly relatable utilities for MCL and MCL on WhatsApp. Having a broad view of all these learning theories enables teachers to think holistically about learning and make better decisions about which learning theory or theories are most appropriate to guide teaching in any particular learning context, including MCL. As rapidly changing and pervasive technology has become a permanent part of education, it is vital that teachers are able to adapt their teaching with different learning theories to better fit the education landscape.
NERSE: Named Entity Recognition in Software Engineering as a Service

Named Entity Recognition (NER) is a computational linguistics task that seek to classify every word in a document as falling into different category. NER serves as an important component for many domain specific expert systems. Software engineering is one such domain where very minimum work has been done on identifying entities specific to domain. In this paper, we present NERSE, a tool that enables the user to identify software specific entities. It is developed with machine learning algorithms trained on software specific entity categories using Conditional Random Fields (CRF) and Bidirectional Long Short-Term Memory - Conditional Random Fields (BiLSTM-CRF). NERSE identifies 22 different categories of entities specific to software engineering domain with 0.85% and 0.95% for CRF (source code for Named Entity Recognition Model CRF is available at https://github.com/prathapreddymv/NERSE ) and BiLSTM-CRF (source code for Named Entity Recognition Model BiLSTM-CRF is available at https://github.com/prathapreddymv/NERSE ) models respectively.
Statistical Learning Ability as a Measure of Cognitive Function

Herff, S. A., Nur, A., Lee, J., Lee, T., &amp;amp; Agres, K. (2019). Statistical Learning Ability as a Measure of Cognitive Function. In A. K. Goel, C. M. Seifert, &amp;amp; C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society, Montreal, QB: Cognitive Science Society. Statistical Learning (SL), the ability to extract probabilistic information from the environment, is a subject of much debate. It appears intuitive that such a profound mechanism of learning should carry predictive power towards general cognitive ability. Yet, previous attempts have struggled to link SL ability to measures of general cognitive function, suffering from low correlations and mediocre test-retest reliability. Here, we deploy a new continuous auditory SL task that achieves high test-retest reliability (~ r = .8) and shows that SL ability does significantly correlate with general cognitive function (up to r =. 56). Results are discussed in light of i) the theoretical implications of the high test-retest reliability of our novel SL task, ii) SL ability as a marker of general cognitive function, and iii) future methodological considerations. -This manuscript has been peer-reviewed and accepted for publication in poster-from at the 41st Annual Meeting of the Cognitive Science Society in Montreal, Canada at the Palais des Congrès de Montréal on Wednesday July 24th – Saturday July 27th, 2019 -
THE INFLUENCE OF THE QUALITY ACCOUNTING INFORMATION SYSTEM TO THE QUALITY OF ACCOUNTING INFORMATION - EVIDENCE IN INDONESIA

Managers as stakeholders use accounting strategic management in implementing their business strategy. The essence of strategic management accounting is to create satisfaction for its customers which is known as the concept of value chain analysis. Currently the implementation of strategic management accounting has used information technology known as Accounting Information systems. Accounting Information systems produce Accounting Information that managers use in making strategic decisions in any company.&#x0D; This study used descriptive and verificative methods. Respondents in this study were 60 functional managers SOEs in Bandung Indonesia. Quality data tested by Validity and Reliability test. Statistical tests use structural equation model-PLS. Results of this study is Accounting Information Systems have a significant effect on the of accounting information on SOEs in Bandung City -Indonesia&#x0D; &#x0D; Keywords : Quality of Accounting Information Systems, Quality of Accounting Information, State Owner Enterprises &#x0D; &#x0D;
C.45 Algorithm for Classification of Causes of Landslides

Abstract— Natural disasters are disasters caused by natural events and cannot be avoided including earthquakes, tsunamis, volcanic eruptions, floods, hurricanes, droughts, and landslides. One of the natural disasters that often occurs in Indonesia is a landslide disaster. One of the regencies in West Java Province that had experienced a landslide was a Purwakarta district area. Landslide is one type of mass or rock mass movement, or a mixture of both, down or out of the slope due to the disruption of the stability of the soil or rocks making up the slope. With a data mining approach that uses the decision tree method or C4.5 Algorithm, a classification model will be made where the model functions as a classification of the causes of landslides in Purwakarta district.
Lexical entrainment reflects a stable individual trait: Implications for individual differences in language processing.

Language use is intrinsically variable, such that the words we use vary widely across speakers and communicative situations. For instance, we can call the same entity refrigerator or fridge. However, attempts to understand individual differences in how we process language have made surprisingly little progress, perhaps because most psycholinguistic instruments are better-suited to experimental comparisons than differential analyses. In particular, investigations of individual differences require instruments that have high test-retest reliability, such that they consistently distinguish between individuals across measurement sessions. Here, we established the reliability of an instrument measuring lexical entrainment, or the tendency to use a name that a partner has used before (e.g., using refrigerator after a partner used refrigerator), which is a key phenomenon for the psycholinguistics of dialogue. Online participants completed two sessions of a picture matching-and-naming task, using different pictures and different (scripted) partners in each session. Entrainment was measured as the proportion of trials on which participants followed their partner in using a low-frequency name, and we assessed reliability by comparing entrainment scores across sessions. The estimated reliability was substantial, both when sessions were separated by minutes and when sessions were a week apart. These results suggest that our instrument is well-suited for differential analyses, opening new avenues for understanding language variability. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Experimental Analysis of Artificial Neural Networks Performance for Physical Activity Recognition Using Belt and Wristband Devices

Physical activity (PA) is widely recognized as one of the important elements of personal healthy life. To date, as the development of wearable sensing technologies, it is possible to utilize wearable devices and machine learning algorithms to efficiently and accurately monitor PA types, intensity and its associated human pattern for many health applications. But there is a trade-off between less-attachment of wearable devices and achievement of high accuracy in existing PA recognition studies. This paper attempts to investigate possible utilisation of Artificial Neural Networks (ANN) achieving high recognition accuracy of PA using less-attachments of wearable devices. Following a four-steps designed experimental protocol, we collect the real activities dataset with only belt and wristband devices from 10 healthy subjects at home and gym environment. The parameters of typical PA recognition with ANN including time window sizes, features and activation functions are evaluated under 24 different subjects of activities. The experimental results indicate that ANN dealing with belt and wristband data can achieve satisfactory PA recognition results in dynamic and sedentary activities but suffers from transitional activities in both environments.
Cultural Mobile Games: Designing for ‘Many’

This paper explores the design of digitally mediated, gamified experiences in the environment of art exhibitions. We use the trajectories HCI framework for analysis and design, decomposing the overall experience in different levels. First, we develop a storytelling game that is meant to be experienced by `many' visitors, leveraging a group gameplay that fosters social interactions between the group members. Second, the gamified experience unfolds in `many' physical spaces, with multiple collections of artworks. We model alternative spatial layouts and discuss how these affect the evolving group experience. Finally, `many' stands for repeat visits: inviting people in gamified cultural experiences over and over again, with potentially new groups and in different exhibitions, we examine the impact of prior game participation. Throughout the paper, we showcase how the use of trajectories enables us to clearly reflect and investigate all the aforementioned `manies', in a combined, holistic way.
Litho-structural controls on orogenic gold deposits within the Sandstone greenstone belt, Yilgarn Craton, Western Australia: implications for exploration targeting

The 920 km2 Archean Sandstone greenstone belt lies in the central-northern part of the Southern Cross Domain of the Youanmi Terrane, Western Australia. The belt, forming an arrow-head geometry, is bounded by the Youanmi and Edale shear zones on its eastern and western margins, respectively. The belt has a total gold endowment, including historic production, resources and reserves, of approximately 62 t (2.0 Moz). Most known deposits are situated within a central-southern ultramafic domain, although this is likely a function of exploration focus. The distribution of historical exploration efforts and domaining of deposit types, based on litho-structural controls on ore formation, highlight the potential for significant undiscovered deposits, hosted within arrays of shear zones parallel and adjacent to the lithosphere-scale Youanmi and Edale shear zones.
Fuzziness Control of Fuzzimetric sets

Different researchers uses different partitioned fuzzy set shapes in a predefined Universe of discourse. Fuzzy sets like Small, Medium and High used to express the level of fuzziness within certain interval. This paper propose to fuzzify the fuzzy set itself by adjusting the fuzzy set shape using a certain mutation formula with manipulation of mutation factor(s). This proposal is dependent on a concept of Fuzzimetric Arcs of which a flexible control of fuzziness of Fuzzimetric Sets can be defined achieving the option of designing the fuzzy set as type-1 or type-2, simply by adjusting the mutation factor(s). Based on this concept, this paper review Fuzzimetric Arcs principle and highlight the method of controlling fuzziness of fuzzy sets.
CNN‐based multiple‐input multiple‐output radar image enhancement method

In this study, the convolutional neural network (CNN) is utilised to enhance the quality of radar images. First, a four-layer convolutional neural network is trained. The input of it is a complex valued two-dimensional low-resolution radar image, and the output is the radar-cross-section distribution image. After processed by the proposed network, the sidelobe and grating lobe in the radar image are suppressed, the main lobe of the target is sharpened. Comparing to the commonly used coherence factor method, the proposed method can enhance the image while maintaining the amplitude scaling relation between targets. The feasibility of the proposed method is testified by both simulated and experimental results.
Cross-Modal Attention-Guided Convolutional Network for Multi-modal Cardiac Segmentation

To leverage the correlated information between modalities to benefit the cross-modal segmentation, we propose a novel cross-modal attention-guided convolutional network for multi-modal cardiac segmentation. In particular, we first employed the cycle-consistency generative adversarial networks to complete the bidirectional image generation (i.e., MR to CT, CT to MR) to help reduce the modal-level inconsistency. Then, with the generated and original MR and CT images, a novel convolutional network is proposed where (1) two encoders learn individual features separately and (2) a common decoder learns shareable features between modalities for a final consistent segmentation. Also, we propose a cross-modal attention module between the encoders and decoder in order to leverage the correlated information between modalities. Our model can be trained in an end-to-end manner. With extensive evaluation on the unpaired CT and MR cardiac images, our method outperforms the baselines in terms of the segmentation performance.
Gender Prediction Based on Voting of CNN Models

Gender prediction accuracy increases as CNN architecture evolves. This paper proposes voting schemes to utilize the already developed CNN models to further improve gender prediction accuracy. Majority voting usually requires odd numbered models while proposed softmax based voting can utilize any number of models to improve accuracy. With experiments, it is shown that the voting of CNN models leads to further improvement of gender prediction accuracy and that softmax-based voters always show better gender prediction accuracy than majority voters though they consist of the same CNN models.