Reviewerly/exHarmony huggingface dataset:
SATPin: Axiom Pinpointing for Lightweight Description Logics Through Incremental SAT

One approach to axiom pinpointing (AP) in description logics is its reduction to the enumeration of minimal unsatisfiable subformulas, allowing for the deployment of highly optimized methods from SAT solving. Exploiting the properties of AP, we further optimize incremental SAT solving, resulting in speedups of several orders of magnitude: through persistent incremental solving the solver state is updated lazily when adding clauses or assumptions. This adaptation consistently improves the runtime of the tool by an average factor of 3.8, and a maximum of 38. SATPin, our system, was tested over large biomedical ontologies and performed competitively.
Norm-Based Curriculum Learning for Neural Machine Translation

A neural machine translation (NMT) system is expensive to train, especially with high-resource settings. As the NMT architectures become deeper and wider, this issue gets worse and worse. In this paper, we aim to improve the efficiency of training an NMT by introducing a novel norm-based curriculum learning method. We use the norm (aka length or module) of a word embedding as a measure of 1) the difficulty of the sentence, 2) the competence of the model, and 3) the weight of the sentence. The norm-based sentence difficulty takes the advantages of both linguistically motivated and model-based sentence difficulties. It is easy to determine and contains learning-dependent features. The norm-based model competence makes NMT learn the curriculum in a fully automated way, while the norm-based sentence weight further enhances the learning of the vector representation of the NMT. Experimental results for the WMT'14 English-German and WMT'17 Chinese-English translation tasks demonstrate that the proposed method outperforms strong baselines in terms of BLEU score (+1.17/+1.56) and training speedup (2.22x/3.33x).
Two-level fusion big data compression and reconstruction framework combining second-generation wavelet and lossless compression

Abstract In view of the characteristics of big data, fuzziness, and real time of data acquisition and transmission in the fuzzy information system faced by aircraft health management, to reduce the load of airborne data processing and transmission system under the condition of limited airborne computing resources and strong time constraints, the data collected by the airborne system are first compressed, and the amount of data are reduced before transmission and reconstructed after transmission. In view of the situation that the compression ratio of primary data compression is too small and the compression time is too long for large-scale fuzzy systems to meet the transmission requirements of the system, this paper combines the advantages of lossy compression method which consumes less time and lossless compression method which has higher compression ratio, and innovatively proposes a two-level data compression and reconstruction framework combining lossy compression and lossless compression. The optimization analysis is carried out. Taking a real aero-engine health sample as an example, the validity, scientificity, and robustness of the proposed framework are verified by comparing with data compression and reconstruction algorithm based on redundant sparse representation and compressed sensing.
An efficient data masking for securing medical data using DNA encoding and chaotic system

Data security is utmost important for ubiquitous computing of medical/diagnostic data or images. Along with must consider preserving privacy of patients. Recently, deoxyribose nucleic acid (DNA) sequences and chaotic sequence are jointly used for building efficient data masking model. However, the state-of-art model are not robust against noise and cropping attack (CA). Since in existing model most digits of each pixel are not altered. This work present efficient data masking (EDM) method using chaos and DNA based encryption method for securing health care data. For overcoming research challenges effective bit scrambling method is required. Firstly, this work present an efficient bit scrambling using logistic sine map and pseudorandom sequence using chaotic system. Then, DNA substitution is performed among them to resist against differential attack (DA), statistical attack (SA) and CA. Experiment are conducted on standard considering diverse images. The outcome achieved shows proposed model efficient when compared to existing models.
Deep neural network for joint light field deblurring and super-resolution

The recent works on the light field (LF) image enhancement are focused on specific tasks such as motion deblurring and super-resolution. State-of-the-art methods are limited with the specific case of 3-degree-of-freedom (3-DOF) camera motion (for motion deblurring) and straight-forward high-resolution neural network (for super-resolution (SR)). In this work, we proposed a framework that utilizes the deep neural net to solve LF spatial super- resolution and deblurring under 6-DOF camera motion. The neural network is designed with end-to-end fashion and trained in multiple stages to perform robust super-resolution and deblurring. Our neural network achieves superior results in terms of quantitative and qualitative performance compared to the recent state-of-the-art LF deblurring and SR algorithms.
BERT Based Multilingual Machine Comprehension in English and Hindi

Multilingual Machine Comprehension (MMC) is a Question-Answering (QA) sub-task that involves quoting the answer for a question from a given snippet, where the question and the snippet can be in different languages. Recently released multilingual variant of BERT (m-BERT), pre-trained with 104 languages, has performed well in both zero-shot and fine-tuned settings for multilingual tasks; however, it has not been used for English-Hindi MMC yet. We, therefore, present in this article, our experiments with m-BERT for MMC in zero-shot, mono-lingual (e.g. Hindi Question-Hindi Snippet) and cross-lingual (e.g. English QuestionHindi Snippet) fine-tune setups. These model variants are evaluated on all possible multilingual settings and results are compared against the current state-of-the-art sequential QA system for these languages. Experiments show that m-BERT, with fine-tuning, improves performance on all evaluation settings across both the datasets used by the prior model, therefore establishing m-BERT based MMC as the new state-of-the-art for English and Hindi. We also publish our results on an extended version of the recently released XQuAD dataset, which we propose to use as the evaluation benchmark for future research.
Transform Domain Influenced Variable Bit - Variable Pixel Medical Image Watermarking Scheme

Image watermarking plays an important role in the field of medical image security and also provides a safe pathway for transfer of medical information through public networks such as internet. In this work, variable pixel and variable bit -medical image watermarking scheme has been proposed to protect the Electronic Patient Record (EPR). The proposed algorithm uses a Linear Feedback Shift Registers (LFSR) to generate pseudo random sequences for randomly selecting the image pixels of 16 bit Digital Imaging and Communications in Medicine (DICOM) image and modified logistic map has been utilized for selecting the bits to achieve adaptive Least Significant Bit (LSB) embedding. Integer Wavelet Transform (IWT) has been applied on original image to decompose it to its sub - bands where EPR has been embedded. The algorithm comprises four phases namely, pixel separation, random pixel selection, variable bit selection and LSB embedding. Proposed watermarking scheme has been achieved a near zero Mean Square Error (MSE) and histogram analysis confirms the imperceptibility of the algorithm. The algorithm requires only 0.235542 S to perform LSB embedding on 512 × 512 DICOM images. Peak Signal to Noise Ratio (PSNR), Normalized Cross Correlation (NCC) and Structural Similarity Index (SSIM) analyses also have been performed to evaluate the efficiency of the watermarking scheme.
Assessment of Student Work and the Organization of Individual Learning Paths in Electronic Smart-Learning Systems

The popularity of electronic smart-learning systems leads to their implementation in various fields of education, and this requires the systems themselves to have extensive functionality for working with students. One of the features of using smart-learning systems in educational institutions is the need to evaluate student activities online. Another feature is an individual approach to students, the provision of which becomes more difficult both due to the remote learning form and because of the large number of students. This document contains the examples of the implementation of this functionality using the LMS Moodle electronic smart-learning system.
Sensitive Data Identification in Structured Data through GenNER Model based on Text Generation and NER

A Lot of documents in many organizations from companies to governments are shared on on-premise storage or clouds. And some of those documents may contain sensitive information such as names, social security numbers, addresses and so on. Especially a large amount of sensitive information written in Korean have been leaked nowadays. It can be severe problems to not only individuals but also many organizations. Therefore, for information protection, data loss prevention (DLP) has been needed. DLP systems based on pattern matching were popular in the past. But they have a difficulty handling new type of sensitive data whenever they come. To handle this problem, sensitive data identification with NER is proposed as a useful method of DLP system. By using NER, we can classify the words in a document into categories which consist of name, location and so on. These categories are considered as sensitive information. This approach shows good performance identifying information in unstructured data(e.g. sentences) which have contextual information whereas it has a weakness identifying sensitive information in structured data (e.g. personal names in cells of the table). Actually, a large amount of sensitive information is organized in structured data and the form of structured data varies depending on the document. Furthermore, it also has difficulties identifying data written in Korean because of its characteristics. We proposed a primary preventive measure of DLP by identifying sensitive data in tables of Korean documents combining text generation and NER models regardless of the form of tables and masking them as to share documents without disclosing sensitive information.
Kinect-Based Learning Games for Dyslexic Children

Dyslexia is a developmental disorder of reading and writing that generally found in children from 7 to 8 years old.Learning difficulty in reading fluency and difficulty in understanding normally or above average are the characteristics of children with developmental disorders.This study aims to build a game that can be easily utilized to introduce letter types and several techniques of reading with the correct spelling attached to pictures of each spelling.This educational game is Kinect-based so it is interesting and interactive for users and can help children with dyslexia in the process of learning reading.The research method used is the SDLC (System Development Life Cycle).The study also studies students' ability to recognize numbers.Based on interviews with teachers, students did not recognize all forms of numbers.The result of this study is the design of an educational game that is expected to help to improve the fluency of reading in dyslexic children.
Agricultural Model for Allocation of Crops Using Pollination Intelligence Method

An agricultural model for allocation of crops is considered in this work using Pollination Intelligence Method. The model was constructed to solve farmer’s decision making in allocating crops to a piece of land using market price, known yield of crops, cost incurred during planting, and the total amount of land available. A new class of metaheuristic method called Flower Pollinated Algorithm is also presented in this work to solve the designed model. An improved version of the Flower Pollinated Algorithm called Pollination Intelligence Algorithm using an iterative scheme to override the switch parameter in Flower Pollinated Algorithm is also presented and used in solving the designed model. A case study of a farmer in Ife, Osun State, Nigeria, was used to implement the model, and the results obtained suggested that instead of allocating crops to land randomly based on farmer’s intuition, cost of planting, yield of crops, and market price were factors that must be considered by farmers for optimal profit before planting crops.
DZip: Improved General-Purpose Lossless Compression Based on Novel Neural Network Modeling

We consider lossless compression based on statistical data modeling followed by prediction-based encoding, where an accurate statistical model for the input data leads to substantial improvements in compression. We propose DZip, a general-purpose compressor for sequential data that exploits the well-known modeling capabilities of neural networks (NNs) for prediction, followed by arithmetic coding. DZip uses a novel hybrid architecture based on adaptive and semi-adaptive training. Unlike most NN based compressors, DZip does not require additional training data and is not restricted to specific data types, only needing the alphabet size of the input data. The proposed compressor outperforms general-purpose compressors such as Gzip (on average 26% reduction) on a variety of real datasets, achieves near-optimal compression on synthetic datasets, and performs close to specialized compressors for large sequence lengths, without any human input. The main limitation of DZip in its current implementation is the encoding/decoding time, which limits its practicality. Nevertheless, the results showcase the potential of developing improved general-purpose compressors based on neural networks and hybrid modeling.
Data Cleaning and Data Visualization Systems for Learning Analytics

Learning analytics focuses on collecting, cleaning, processing, visualization, and analyzing teaching and learning-related data from a great variety of academic sources. This paper presents the up-to-date findings and outcomes of the research, design, and development projects at the InterLabs Research Institute at Bradley University (USA) that are focused on the analysis and testing of effective systems to clean and visualize student academic performance data for learning analytics.
Pedestrians Egocentric Vision: Individual and Collective Analysis

Egocentric videos offer an ecological approach to study human gaze behaviour. We were interested in understanding what people look at while performing the natural task of navigating in urban environments. Is there a collective pattern among all participants or are there substantial individual differences? To this end, we recorded egocentric video and gaze data from forty-three pedestrians. Here, we present this dataset designed to benchmark future research. The content of these videos was examined with respect to the depth and category of attended objects. We observe noticeable individual differences in both factors. Following these criteria, individual gaze patterns form a number of clusters. The unique signature of each set remains to be explored, whether it is based on low-level visual features or high-level cognitive characteristics.
DEVELOPMENT OF STUDENTS' SELF-EFFICACY IN THE PROCESS OF ADAPTATION TRAINING

The article analyzes the results of theoretical and empirical studies of self-efficacy as a factor of successful professionalization. The experience of the development of adaptive training for first-year students based on the development of self-efficacy is described. The theoretical and organizational foundations of the training are substantiated. The theoretical model of the development of self-efficacy in the process of adaptation training is described. Self-efficacy is seen as a system-forming factor in the adaptation of first-year students and their primary professionalization. The development of self-efficacy in the process of adaptation training is based on the analysis of real life situations, the specific organization of group work and feedback. The result of value interaction in the process of reflection by students of their own and others' experience is the amplification and partial homogenization of group and individual value space, which in turn is a valuable basis for the adaptation of first-year students. The psychological content of training is the process of value interaction. The exchange of valuable experience in the professional sphere leads to the broadening of the ideas of each participant about the possible range of development of professional abilities, and consequently to the increase of the potential of development of the value sphere and professional motivation of each student.
The use of Autonomous weapons from the perspective of the principles of international humanitarian law

The work focuses on a number of principles of international humanitarian law that must be met by both weapons and the way they are used: distinction, military necessity and proportionality, the prohibition of causing unnecessary suffering, and humanity. The paper provides recommendations and assessments for each of the principles under consideration.&#x0D; The author justifies the need not to prohibit Autonomous weapons, but to develop rules for their use and human participation in their functioning. A significant challenge to the development of such rules is the opacity of Autonomous weapons systems, if we consider them as a complex intelligent computer system.
2kenize: Tying Subword Sequences for Chinese Script Conversion

Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP.Despite this, current approaches have insufficient performance because they do not take into account that a simplified Chinese character can correspond to multiple traditional characters.Here, we propose a model that can disambiguate between mappings and convert between the two scripts.The model is based on subword segmentation, two language models, as well as a method for mapping between subword sequences.We further construct benchmark datasets for topic classification and script conversion.Our proposed method outperforms previous Chinese Character conversion approaches by 6 points in accuracy.These results are further confirmed in a downstream application, where 2kenize is used to convert pretraining dataset for topic classification.An error analysis reveals that our method's particular strengths are in dealing with code mixing and named entities.The code and dataset is available at https:
Adaptive Gradient Coding

This paper focuses on mitigating the impact of stragglers in distributed learning system. Unlike the existing results designed for a fixed number of stragglers, we developed a new scheme called Adaptive Gradient Coding(AGC) with flexible tolerance of various number of stragglers. Our scheme gives an optimal tradeoff between computation load, straggler tolerance and communication cost. In particular, it allows to minimize the communication cost according to the real-time number of stragglers in the practical environments. Implementations on Amazon EC2 clusters using Python with mpi4py package verify the flexibility in several situations.
Synthetic data generation with probabilistic Bayesian Networks

Abstract Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct probabilistic networks from the large heterogeneous biological datasets that reflect the underlying networks of biological relationships. Currently, a variety of strategies exist for evaluating BN methodology performance, ranging from utilizing artificial benchmark datasets and models, to specialized biological benchmark datasets, to simulation studies that generate synthetic data from predefined network models. The latter is arguably the most comprehensive approach; however, existing implementations are typically limited by their reliance on the SEM (structural equation modeling) framework, which includes many explicit and implicit assumptions that may be unrealistic in a typical biological data analysis scenario. In this study, we develop an alternative, purely probabilistic, simulation framework that more appropriately fits with real biological data and biological network models. In conjunction, we also expand on our current understanding of the theoretical notions of causality and dependence / conditional independence in BNs and the Markov Blankets within.
RFI Novelty Detection using Machine Learning Techniques

In order to ensure that the Dominion Radio Astrophysical Observatory (DRAO) continues to be a great asset to the Canadian astronomical community we must work to actively protect the RF cleanliness of the site. One aspect of this much larger effort is the site monitor project. This is currently realized by an omnidirectional monitoring station mounted on the roof of the main building.A pitfall of previous RFI monitoring projects on site has been the volume of data produced, combined with the time limitations of personnel. Occupancy plots have been produced, but this tool has very limited value for day-to-day maintenance of the site. Simply, no eyes have been available to look at all of the data.Our aim is to deal with the data first: to build a rich description of the RF scene at the site in order to automatically separate “normal” events from “novel” events. To do this we use features extracted from both the spectrogram and the complex baseband waveform. This includes center frequency, bandwidth, received power, transmission duration, time of day, high-order cumulants, and more. We use unsupervised learning techniques to cluster events in this multidimensional space into hierarchical groups. The clustering results allow us to study populations of events and their relationships, rather than individual or small sets of events as in a spectrogram. This feature space also allows us to relate waveforms with similar modulations across frequency, and to reveal temporal patterns. Work is ongoing to bring this analysis into a realtime observing state, in order to provide up-to-date notifications about novel RF events occurring at the DRAO site.
When Predictive Models Collide

No trend is more visible in medicine than integrating machine learning and artificial intelligence (AI) into clinical care.Terms like "random forests" and "convolutional neural networks" that were previously the domain of computer science PhDs are now part of the vocabulary of both C-suite administrators and frontline clinicians.As the high-performance storage and computing power that underlie these models become a commodity, both the supply and demand for predictive models based on machine learning will only increase.Advances in genomics 1 and patient-generated health data will enrich predictive models, but will also increase model complexity even as they increase precision.How can clinicians and modelers prevent the messy accidents that could happen when these models collide? GENERALIZING PREDICTIVE MODELS TO COMPLEX PATIENTS][8] Yet, although models may perform well for a single disease or clinical situation, the circumstances of the laboratory where they are developed are rarely replicated in real-world clinical settings.Advanced cognitive computing solutions have been shown to have variable performance in the wild 9-12 and may require additional patient characteristics (especially genetic data) to perform optimally in certain diseases.