Reviewerly/exHarmony huggingface dataset:
Enhancing Natural Language Inference Using New and Expanded Training Data Sets and New Learning Models

Natural Language Inference (NLI) plays an important role in many natural language processing tasks such as question answering. However, existing NLI modules that are trained on existing NLI datasets have several drawbacks. For example, they do not capture the notion of entity and role well and often end up making mistakes such as “Peter signed a deal” can be inferred from “John signed a deal”. As part of this work, we have developed two datasets that help mitigate such issues and make the systems better at understanding the notion of “entities” and “roles”. After training the existing models on the new dataset we observe that the existing models do not perform well on one of the new benchmark. We then propose a modification to the “word-to-word” attention function which has been uniformly reused across several popular NLI architectures. The resulting models perform as well as their unmodified counterparts on the existing benchmarks and perform significantly well on the new benchmarks that emphasize “roles” and “entities”.
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning

We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogeneous learning tasks, which correspond to different generative processes, often rely on data with a shared graph structure. Accordingly, our model combines a graph convolutional network (GCN) with multiple variational autoencoders, thus embedding the nodes of the graph (i.e., samples for the tasks) in a uniform manner, while specializing their organization and usage to different tasks. With a focus on healthcare applications (tasks), including clinical topic modeling, procedure recommendation and admission-type prediction, we demonstrate that our method successfully leverages information across different tasks, boosting performance in all tasks and outperforming existing state-of-the-art approaches.
Virtual reality and 21st century education

Virtual Reality (RV), is one of the technological tools of the 21st century applied in the field of education and has increased significantly the use of it, to achieve the expected learning in students, the present This article aimed to demonstrate that VR as an innovative tool in 21st-century education contributes significantly to the teaching-learning process of students and teachers. It has worked based on a descriptive methodology-documentary, applying the analysis and interpretation of content, was limited to the review of articles published in digital journals, located in different databases, among the results there is a challenge that education should Assume and actively incorporate virtual reality, such as the technology that is a fundamental part of the daily life of the new generations, as an educational paradigm focused on the student, where it is intended to be autonomous and independent in the generation of learning.
Democratic librarianship: the role of the medical library in promoting democracy and social justice

Evidence suggests that Erich Meyerhoff was one of the first practitioners of democratic librarianship throughout his long and productive life. This essay defines democratic librarianship in the context of democratic ideals and social justice and posits actions that the profession should be taking to thrive and lead in a multicultural environment, including being a place for active engagement, crucial conversations, and debate. Democratic librarianship is broader than social justice but incorporates social justice ideals in promoting a socially just and democratic society. Libraries…are essential to the functioning of a democratic society;…and libraries are the great tools of scholarship, the great repositories of culture, and the great symbols of the freedom of the mind. [1]-Franklin D. Roosevelt.
APPLICATION OF CONVOLUTIONAL NEURAL NETWORK FOR HISTOPATHOLOGICAL ANALYSIS

Ab s t r a c t.Among all types of cancer, breast cancer is the most common.In 2017 breast cancer was the fourth rate for death reasons in Ukraine.The paper is devoted to the automatization of histopathological analysis, which can improve the process of cancer stage diagnosis.The purpose of the paper is to research the ability to use convolutional neural networks for classifying biopsy images for cancer diagnosis.The tasks of research are: analyzing cancer statistics in Europe and Ukraine; analyzing usage of Machine Learning in cancer prognosis and diagnosis tasks; preprocessing of BreCaHAD dataset images; developing a convolutional neural network and analyzing results; the building of heatmap.The object of the research is the process of detecting tumors in microscopic biopsy images using Convolutional Neural Network.The subject of the research is the process of classifying healthy and cancerous cells using deep learning neural networks.The scientific novelty of the research is using ConvNet trained on the BreCaHAD dataset for histopathological analysis.The theory of deep learning neural networks and mathematical statistics methods are used.In result it is obtained that the classification accuracy for a convolutional neural network on the test data is 0.935, ConvNet was effectively used for heatmap building.K e ywor d s : deep
Robust Local Descriptor for Color Object Recognition

Image category recognition is important to access visual information on the level of objects and scene types.In this paper, we propose a new approach for color object recognition using the powerful information provided by the color.This approach is based on the combination of Gray-Edge color constancy, hue components in HSV (hue, saturation, value) color space and cell and bin ideas used in the HOG (Histograms of Gradients) descriptors.The proposed oriented descriptor benefits of the invariance of hues against light intensity change, light intensity shift and light intensity change and shift, and solve its missing of invariance against light color change by using Gray-Edge color constancy.Moreover, the use of cells and bins in this proposed descriptor building boost its invariance the geometric and photo-metric transformation and increases the recognition rate.SVM classifiers (Support Vector Machine) which is a strong classification method known for its flexibility and its power of generalization are used for the training and recognition steps.The proposed method is evaluated on two publicly available datasets including Columbia Object Image Library and The Amsterdam Library of Object Images and obtained a recognition rate of 95.64% and 96.48% - clearly showing the exceptional performance compared to existing methods.
A Learning Based Branch and Bound for Maximum Common Subgraph Related Problems

The performance of a branch-and-bound (BnB) algorithm for maximum common subgraph (MCS) problem and its related problems, like maximum common connected subgraph (MCCS) and induced Subgraph Isomorphism (SI), crucially depends on the branching heuristic. We propose a branching heuristic inspired from reinforcement learning with a goal of reaching a tree leaf as early as possible to greatly reduce the search tree size. Experimental results show that the proposed heuristic consistently and significantly improves the current best BnB algorithm for the MCS, MCCS and SI problems. An analysis is carried out to give insight on why and how reinforcement learning is useful in the new branching heuristic.
SRM Superpixel Merging Framework for Precise Segmentation of Cervical Nucleus

Cervical nuclei contain important diagnostic characteristics useful for identifying abnormality in cervical cells. Therefore, an accurate segmentation of nuclei is the primary step in computer-aided diagnosis. However, cell overlapping, uneven staining, poor contrast, and presence of debris elements make this task challenging. A novel method is presented in this paper to detect and segment nuclei from overlapping cervical smear images. The proposed framework segments nuclei by merging superpixels generated by statistical region merging (SRM) algorithm using pairwise regional contrasts and gradient boundaries. To overcome the limitation of finding the optimal parameter value, which controls the coarseness of the segmentation, a new approach for SRM superpixel generation was introduced. Quantitative and qualitative assessment of the proposed framework is carried out using Overlapping Cervical Cytology Image Segmentation Challenge - ISBI 2014 dataset of 945 cervical images. In comparison with the state-of-the-art methods, the proposed methodology achieved superior segmentation performance in terms of Dice similarity coefficient 0.956 and pixel-based recall 0.962. Other evaluation measures such as pixel-based precision 0.930, object-based precision 0.987, and recall 0.944, also compare favorably with some recently published studies. The experimental results demonstrate that the proposed framework can precisely segment nuclei from overlapping cervical cell images, while keeping high level of precision and recall. Therefore, the developed framework may assist cytologists in computerized cervical cell analysis and help with early diagnosis of cervical cancer.
Let There Be Light: Revealing Hidden MPLS Tunnels With TNT

Internet topology discovery aims at analyzing one of the most complex distributed systems currently deployed. Usually, it relies on measurement campaigns using hop-limited probes sent with traceroute. However, this probing tool comes with several limits. In particular, some MPLS clouds might obfuscate collected traces. The resulting Internet maps, their inferred properties, and the graph models are thus incomplete and inaccurate. In this paper, we introduce TNT (Trace the Naughty Tunnels), an extension to Paris traceroute for revealing, or at least detect, all MPLS tunnels along a path. First, along with traceroute and ping probes, TNT looks for hints indicating the presence of hidden tunnels. Those hints are peculiar patterns in the resulting output, e.g., significant TTL shifts or duplicate IP addresses. Second, if those hints trigger alarms, TNT launches additional dedicated probing for possibly revealing hidden tunnels. We use GNS3 to reproduce, verify, and understand the limits and capabilities of TNT in a controlled environment. We also calibrate the thresholds at which alarms are triggered through a dedicated measurement campaign. Finally, we deploy TNT on the Archipelago platform and provide a quantified classification of MPLS configurations. All our results, including the data, the code, and the GNS3 experiments, are fully and publicly available.
Finding Action Tubes with a Sparse-to-Dense Framework

The task of spatial-temporal action detection has attracted increasing researchers. Existing dominant methods solve this problem by relying on short-term information and dense serial-wise detection on each individual frames or clips. Despite their effectiveness, these methods showed inadequate use of long-term information and are prone to inefficiency. In this paper, we propose for the first time, an efficient framework that generates action tube proposals from video streams with a single forward pass in a sparse-to-dense manner. There are two key characteristics in this framework: (1) Both long-term and short-term sampled information are explicitly utilized in our spatio-temporal network, (2) A new dynamic feature sampling module (DTS) is designed to effectively approximate the tube output while keeping the system tractable. We evaluate the efficacy of our model on the UCF101-24, JHMDB-21 and UCFSports benchmark datasets, achieving promising results that are competitive to state-of-the-art methods. The proposed sparse-to-dense strategy rendered our framework about 7.6 times more efficient than the nearest competitor.
Hashing Based Answer Selection

Answer selection is an important subtask of question answering (QA), in which deep models usually achieve better performance than non-deep models. Most deep models adopt question-answer interaction mechanisms, such as attention, to get vector representations for answers. When these interaction based deep models are deployed for online prediction, the representations of all answers need to be recalculated for each question. This procedure is time-consuming for deep models with complex encoders like BERT which usually have better accuracy than simple encoders. One possible solution is to store the matrix representation (encoder output) of each answer in memory to avoid recalculation. But this will bring large memory cost. In this paper, we propose a novel method, called hashing based answer selection (HAS), to tackle this problem. HAS adopts a hashing strategy to learn a binary matrix representation for each answer, which can dramatically reduce the memory cost for storing the matrix representations of answers. Hence, HAS can adopt complex encoders like BERT in the model, but the online prediction of HAS is still fast with a low memory cost. Experimental results on three popular answer selection datasets show that HAS can outperform existing models to achieve state-of-the-art performance.
Course Analysis of Library and Information Science in Korea

Curriculums play a key role in implementing the educational goals and directions of a university. Universities regularly update their curriculums to actively respond to internal and external environmental changes. Korean universities face challenges, including a steady decline in enrollment and demand-oriented convergence education. Because of the nature of convergence science, library and information science should respond more actively to new challenges. This study analyzes the current status of library and information science courses according to subjects and universities. Data was collected from the websites of each university, and various methods such as text analysis, frequency analysis, and network analysis were applied to investigate current status of LIS in Korea.
Serpentinized Peridotite-Hosted Uranium Mineralization (U–Cr–Ni–Mo–REE–Fe–Mg) in Kudada–Turamdih Area:A New Environment of Metallogeny in Singhbhum Shear Zone, India

Recent exploration efforts inKudada-Turamdih area has brought to light, for the first time, serpentinizedperidotite-hosted uranium mineralization (up to 0.188% U 3 O 8 ) of polymetallic nature (U-Cr-Ni-Mo-REE-Fe-Mg) in the domain of Singhbhum Shear Zone (SSZ).The peridotite has been emplaced into the Iron Ore Group (IOG) and represents late phase activity in the IOG.Exploratory drilling has established substantial lateral (600 m) and downdip (1000 m) continuity of the mineralization.Subhedral to anhedral disseminated uraninite grains (10-600 μm) with cell dimensions of 5.4498 to 5.4650 Å suggest crystallization in meso to hypo-thermal range (300-500°C).Magnetite, chromite, molybdenite, cobaltite, nickeline, vaesite, cerussite, pyrite and chalcopyrite have also been identified in the REE enriched (Av.1457 ppm) uraniferous peridotite.Presence of MgO (18-28%), Cr (295-3165 ppm), Ni (222-9530 ppm), Au (11-30 ppb), Pt (47-95 ppb) and As (15-755 ppm) suggests komatiitic parentage of host rock.Discovery of polymetallic mineralization in serpentinized peridotite, a hitherto unknown geological environment, opens up scope for further research and enhancement of uranium resources in the SSZ.
Gamma-Nets: Generalizing Value Estimation over Timescale

Temporal abstraction is a key requirement for agents making decisions over long time horizons—a fundamental challenge in reinforcement learning. There are many reasons why value estimates at multiple timescales might be useful; recent work has shown that value estimates at different time scales can be the basis for creating more advanced discounting functions and for driving representation learning. Further, predictions at many different timescales serve to broaden an agent's model of its environment. One predictive approach of interest within an online learning setting is general value function (GVFs), which represent models of an agent's world as a collection of predictive questions each defined by a policy, a signal to be predicted, and a prediction timescale. In this paper we present Γ-nets, a method for generalizing value function estimation over timescale, allowing a given GVF to be trained and queried for arbitrary timescales so as to greatly increase the predictive ability and scalability of a GVF-based model. The key to our approach is to use timescale as one of the value estimator's inputs. As a result, the prediction target for any timescale is available at every timestep and we are free to train on any number of timescales. We first provide two demonstrations by 1) predicting a square wave and 2) predicting sensorimotor signals on a robot arm using a linear function approximator. Next, we empirically evaluate Γ-nets in the deep reinforcement learning setting using policy evaluation on a set of Atari video games. Our results show that Γ-nets can be effective for predicting arbitrary timescales, with only a small cost in accuracy as compared to learning estimators for fixed timescales. Γ-nets provide a method for accurately and compactly making predictions at many timescales without requiring a priori knowledge of the task, making it a valuable contribution to ongoing work on model-based planning, representation learning, and lifelong learning algorithms.
Reliable Multilabel Classification: Prediction with Partial Abstention

In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a subset of all labels. In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. We propose a formalization of MLC with abstention in terms of a generalized loss minimization problem and present first results for the case of the Hamming loss, rank loss, and F-measure, both theoretical and experimental.
Tiny-RetinaNet: a one-stage detector for real-time object detection

In this paper, we present a new one stage detector for object detection. In order to meet the requirements of real-time detection, we use MobileNetV2-FPN as backbone for feature extraction. The lightweight depthwise separable convolutions can improve the speed of detection and make the model smaller. In order to improve the accuracy of our small detection model, we add Stem Block into backbone and we add SEnet in front of two task-specific subnets. The stem block can reduce the information loss from raw input images. The SEnet can enhance useful features from backbone network and suppress features that are little use to two-specific tasks. Inspired by RetinaNet, we also use Focal Loss as our classification loss function. We measure our performance on PASCAL VOC2007 and PASCAL VOC2012. Our detector with 300300 input achieves 73.8% mAP on VOC2007 test, 71.4% mAP on VOC2012 test. And our detector can run at 97FPS and the number of parameters is only 7.7M that meets the requirements of real-time detection. The accuracy of our detector is close to SSD, our detector uses about only 1/3 parameters to SSD. Keywords: One-
Justification-Based Reliability in Machine Learning

With the advent of Deep Learning, the field of machine learning (ML) has surpassed human-level performance on diverse classification tasks. At the same time, there is a stark need to characterize and quantify reliability of a model's prediction on individual samples. This is especially true in applications of such models in safety-critical domains of industrial control and healthcare. To address this need, we link the question of reliability of a model's individual prediction to the epistemic uncertainty of the model's prediction. More specifically, we extend the theory of Justified True Belief (JTB) in epistemology, created to study the validity and limits of human-acquired knowledge, towards characterizing the validity and limits of knowledge in supervised classifiers. We present an analysis of neural network classifiers linking the reliability of its prediction on a test input to characteristics of the support gathered from the input and hidden layers of the network. We hypothesize that the JTB analysis exposes the epistemic uncertainty (or ignorance) of a model with respect to its inference, thereby allowing for the inference to be only as strong as the justification permits. We explore various forms of support (for e.g., k-nearest neighbors (k-NN) and ℓp-norm based) generated for an input, using the training data to construct a justification for the prediction with that input. Through experiments conducted on simulated and real datasets, we demonstrate that our approach can provide reliability for individual predictions and characterize regions where such reliability cannot be ascertained.
A Privacy-preserving Fuzzy Search Scheme Supporting Logic Query over Encrypted Cloud Data

Fuzzy keyword search is a necessary and important feature of information retrieval in modern cloud storage services since users with insufficient knowledge may input typos or keywords with inconsistent formats. However, most of the existing fuzzy search schemes adopt bloom filter and locality sensitive hashing which cannot resist Sparse Non-negative Matrix Factorization based attack (SNMF attack). To this end, we propose a new secure multi-keyword fuzzy search scheme for encrypted cloud data, our scheme leverages random redundancy method to handle the deterministic of bloom filter to resist SNMF attack. Besides the privacy, our scheme uses tree-based index construction to improve search efficiency and allows users to conduct complicated fuzzy search with logic operations “AND”, “OR” and “NOT”, which can meet more flexible and fine-grained query demands. The theoretical analysis and experiments on real-world data show the security and high performance of our scheme.
Lifted Fact-Alternating Mutex Groups and Pruned Grounding of Classical Planning Problems

In this paper, we focus on the inference of mutex groups in the lifted (PDDL) representation. We formalize the inference and prove that the most commonly used translator from the Fast Downward (FD) planning system infers a certain subclass of mutex groups, called fact-alternating mutex groups (fam-groups). Based on that, we show that the previously proposed fam-groups-based pruning techniques for the STRIPS representation can be utilized during the grounding process with lifted fam-groups, i.e., before the full STRIPS representation is known. Furthermore, we propose an improved inference algorithm for lifted fam-groups that produces a richer set of fam-groups than the FD translator and we demonstrate a positive impact on the number of pruned operators and overall coverage.