Reviewerly/exHarmony huggingface dataset:
Improving Hypernymy Prediction via Taxonomy Enhanced Adversarial Learning

Hypernymy is a basic semantic relation in computational linguistics that expresses the “is-a” relation between a generic concept and its specific instances, serving as the backbone in taxonomies and ontologies. Although several NLP tasks related to hypernymy prediction have been extensively addressed, few methods have fully exploited the large number of hypernymy relations in Web-scale taxonomies.In this paper, we introduce the Taxonomy Enhanced Adversarial Learning (TEAL) for hypernymy prediction. We first propose an unsupervised measure U-TEAL to distinguish hypernymy with other semantic relations. It is implemented based on a word embedding projection network distantly trained over a taxonomy. To address supervised hypernymy detection tasks, the supervised model S-TEAL and its improved version, the adversarial supervised model AS-TEAL, are further presented. Specifically, AS-TEAL employs a coupled adversarial training algorithm to transfer hierarchical knowledge in taxonomies to hypernymy prediction models. We conduct extensive experiments to confirm the effectiveness of TEAL over three standard NLP tasks: unsupervised hypernymy classification, supervised hypernymy detection and graded lexical entailment. We also show that TEAL can be applied to non-English languages and can detect missing hypernymy relations in taxonomies.
Strong Equivalence for Epistemic Logic Programs Made Easy

Epistemic Logic Programs (ELPs), that is, Answer Set Programming (ASP) extended with epistemic operators, have received renewed interest in recent years, which led to a flurry of new research, as well as efficient solvers. An important question is under which conditions a sub-program can be replaced by another one without changing the meaning, in any context. This problem is known as strong equivalence, and is well-studied for ASP. For ELPs, this question has been approached by embedding them into epistemic extensions of equilibrium logics. In this paper, we consider a simpler, more direct characterization that is directly applicable to the language used in state-of-the-art ELP solvers. This also allows us to give tight complexity bounds, showing that strong equivalence for ELPs remains coNP-complete, as for ASP. We further use our results to provide syntactic characterizations for tautological rules and rule subsumption for ELPs.
Translating with Bilingual Topic Knowledge for Neural Machine Translation

The dominant neural machine translation (NMT) models that based on the encoder-decoder architecture have recently achieved the state-of-the-art performance. Traditionally, the NMT models only depend on the representations learned during training for mapping a source sentence into the target domain. However, the learned representations often suffer from implicit and inadequately informed properties. In this paper, we propose a novel bilingual topic enhanced NMT (BLTNMT) model to improve translation performance by incorporating bilingual topic knowledge into NMT. Specifically, the bilingual topic knowledge is included into the hidden states of both encoder and decoder, as well as the attention mechanism. With this new setting, the proposed BLT-NMT has access to the background knowledge implied in bilingual topics which is beyond the sequential context, and enables the attention mechanism to attend to topic-level attentions for generating accurate target words during translation. Experimental results show that the proposed model consistently outperforms the traditional RNNsearch and the previous topic-informed NMT on Chinese-English and EnglishGerman translation tasks. We also introduce the bilingual topic knowledge into the newly emerged Transformer base model on English-German translation and achieve a notable improvement.
Development of automatic healthcare instruction system via movement gesture sensor for paralysis patient

This paper presented an automatic healthcare system where the system able to help and facilitates the paralysis patient to complete their daily life. When a patient suffers from a paralysis attack, the whole or partial of their body maybe disabled to move which means their movement is restricted and they also barely to communicate with anyone because they are unable to speak like a normal person. It will be hard for medical staff to understand what they want to convey and in helping them to manage their daily needs such as eating, drinking, bathing and etc. By developing this project, the health officer can assist the paralyzed patient when they are alerted by the message from patient via GSM network. There are several instruction of movement gesture sensor presented in this paper in order to assist health officer in helping the paralyzed patient to complete their needs. Whenever the patient gives the simple hand movement instruction, then it will be delivered through SMS and the alerted notice will be display on notification board to alert the health officers for assisting the patient.
Visualization and Interpretation Tool for Expert Systems Based on Fuzzy Cognitive Maps

This paper presents a python library that includes a toolkit with the aim of improving the interpretability of expert systems based on fuzzy cognitive maps through improvements in the visualization and representation of the graphs that can be drawn using the variables of the resulting models. The motivation for the development of the library arises from the need to improve the interpretability of the aforementioned expert systems, given that their multilayer and extracted from experts’ knowledge nature can make them very difficult to interpret even for the expert user. Throughout the paper, the reader will be introduced to the basic features of fuzzy logic and fuzzy cognitive maps, and the different developed tools will be defined and exemplified.
Assessing variable importance in clustering: a new method based on unsupervised binary decision trees

We consider different approaches for assessing variable importance in clustering. We focus on clustering using binary decision trees (CUBT), which is a non-parametric top-down hierarchical clustering method designed for both continuous and nominal data. We suggest a measure of variable importance for this method similar to the one used in Breiman’s classification and regression trees. This score is useful to rank the variables in a dataset, to determine which variables are the most important or to detect the irrelevant ones. We analyze both stability and efficiency of this score on different data simulation models in the presence of noise, and compare it to other classical variable importance measures. Our experiments show that variable importance based on CUBT is much more efficient than other approaches in a large variety of situations.
AnnoFly: annotating <i>Drosophila</i> embryonic images based on an attention-enhanced RNN model

Abstract Motivation In the post-genomic era, image-based transcriptomics have received huge attention, because the visualization of gene expression distribution is able to reveal spatial and temporal expression pattern, which is significantly important for understanding biological mechanisms. The Berkeley Drosophila Genome Project has collected a large-scale spatial gene expression database for studying Drosophila embryogenesis. Given the expression images, how to annotate them for the study of Drosophila embryonic development is the next urgent task. In order to speed up the labor-intensive labeling work, automatic tools are highly desired. However, conventional image annotation tools are not applicable here, because the labeling is at the gene-level rather than the image-level, where each gene is represented by a bag of multiple related images, showing a multi-instance phenomenon, and the image quality varies by image orientations and experiment batches. Moreover, different local regions of an image correspond to different CV annotation terms, i.e. an image has multiple labels. Designing an accurate annotation tool in such a multi-instance multi-label scenario is a very challenging task. Results To address these challenges, we develop a new annotator for the fruit fly embryonic images, called AnnoFly. Driven by an attention-enhanced RNN model, it can weight images of different qualities, so as to focus on the most informative image patterns. We assess the new model on three standard datasets. The experimental results reveal that the attention-based model provides a transparent approach for identifying the important images for labeling, and it substantially enhances the accuracy compared with the existing annotation methods, including both single-instance and multi-instance learning methods. Availability and implementation http://www.csbio.sjtu.edu.cn/bioinf/annofly/ Supplementary information Supplementary data are available at Bioinformatics online.
Improvement of Hourly Surface Solar Irradiance Estimation Using MSG Rapid Scanning Service

The purpose of this work is to explore the effect of temporal sampling on the accuracy of the hourly mean Surface Solar Irradiance (SSI) estimation. An upgraded version of the Advanced Model for the Estimation of Surface Solar Irradiance from Satellite (AMESIS), exploiting data from the Meteosat Second Generation Rapid Scanning Service (MSG-RSS), has been used to evaluate the SSI. The assessment of the new version of AMESIS has been carried out against data from two pyranometers located in Southern (Tito) and Northern (Ispra) Italy at an altitude of 760 m and 220 m, respectively. The statistical analysis of the comparison between hourly mean SSI estimates based on temporal sampling every five minutes shows a quantitative improvement compared to those based on 15-minute sampling. In particular, for the whole dataset in Tito, the correlation increases from 0.979 to 0.998, the Root Mean Square Error (RMSE) decreases from 45.16 W/m2 to 13.19 W/m2 and the Mean Bias Error (MBE) is reduced from −0.67 W/m2 to −0.02 W/m2. For the whole dataset in Ispra, the correlation increases from 0.995 to 0.998, the RMSE decreases from 24.85 W/m2 to 15.59 W/m2, whereas the MBE increases from 3.84 W/m2 to 4.58 W/m2. This preliminary assessment shows that higher temporal sampling can improve SSI monitoring over areas featuring frequent and rapid solar irradiance variation.
Support mechanisms for cloud configuration using XML filtering techniques: A case study in SaaS

Software-as-a-service (SaaS) has attracted substantial attention as a software delivery and service model in a cloud computing environment. SaaS delivery can help organizations significantly reduce the cost of using software, because the resources for running SaaS applications are shared among tenants (end users or organizations). However, multi-tenancy can considerably complicate SaaS development, deployment, and maintenance as a result of a large number of co-existing tenant-specific constraints or features. Manually configuring and maintaining tenant-specific features will increase the cost, introduce possible errors, and limit scalability and flexibility. The paper addresses the problem of large variations and complex configurations. Specifically, the objective is to develop mechanisms to support automatic multi-tenant software features analysis and matching for the purposes of efficient deployment and operations in the cloud. The emphasis of this paper is on the matching between the tenant-specific requirements and the SaaS features managed by the cloud provider. This paper proposes a novel approach for cloud feature matching using XML filtering techniques to support the process of multi-tenant SaaS deployment and management. Feature modeling has been widely used to capture requirements and constraints. On the other hand, XML filtering techniques are mature and have been adopted in various problem domains. We used Yfilter, a proven and XML filtering technique, to support two multi-tenant applications: (i) Identifying SaaS configurations (in XPath representations) that satisfy tenant-specific requirements and constraints (in XML notation); and (ii) Identifying tenants that have subscribed to a specific set of SaaS features. The applications can effectively facilitate SaaS subsequent management and operations due to various changes, e.g., functionalities, constraints, cost, etc. The experimental results demonstrate that the proposed approach can automatically and correctly identify cloud system configurations that match tenant-specific requirements or identify the group of tenants that have subscribed to a particular set of cloud features. In addition, the execution time of our proposed approach is only a small fraction compared to the existing approach using the formal method, e.g., FaMa, and the configuration space is also much smaller.
An End-to-End Human Segmentation by Region Proposed Fully Convolutional Network

Person segmentation in images has various applications, for example, smart home, human-computer interaction, and scene perception for self-driving cars, which are a key feature of the Internet of Things. Due to limitations in performance, such as accuracy and runtime, most traditional methods do not fulfill the practical requirements. Deep learning-based modern segmentation systems become prevalent. Fully convolutional network (FCN), as a classic image semantic segmentation method, directly optimizes the semantic map from the original image in a pixel-wise manner without using pixel-correlations or global object information. In this paper, we propose an efficient end-to-end person segmentation network structure fusing the person detection network with the FCN. The person detection network estimates the region of interest of persons and enforces the segmentation network to focus on the optimization of person segmentation. The loss function of the proposed network considers both the segmentation error and the detection bias error. In addition, the lightweight design of the detection network that optimizes only person bounding-box coordinates enables real-time person detection. The experimental comparison and analysis of several different networks on several datasets show the effectiveness of the proposed fusion strategy. The approach shows a promising practical application potential by fast running time and high segmentation accuracy.
Comparison of mouse and multi-touch for protein structure manipulation in a citizen science game interface

We developed a multi-touch interface for the citizen science video game Foldit, in which players manipulate 3D protein structures, and compared multi-touch and mouse interfaces in a 41-subject user study. We found that participants performed similarly in both interfaces and did not have an overall preference for either interface. However, results indicate that for tasks involving guided movement to dock protein parts, subjects using the multi-touch interface completed tasks more accurately with fewer moves, and reported higher attention and spatial presence. For tasks involving direct selection and dragging of points, subjects using the mouse interface performed fewer camera adjustments.
Rough-Fuzzy Circular Clustering for Color Normalization of Histological Images

Color disagreement among histological images may affect the performance of computer-aided histological image analysis. So, one of the most important and challenging tasks in histological image analysis is to diminish the color variation among the images, maintaining the histological information con tained in them. In this regard, the paper proposes a new circular clustering algorithm, termed as rough-fuzzy circular clustering. It integrates judiciously the merits of rough-fuzzy clustering and cosine distance. The rough-fuzzy circular clustering addresses the uncertainty due to vagueness and incompleteness in stain class definition, as well as overlapping nature of multiple contrasting histochemical stains. The proposed circular clustering algorithm incorporates saturation-weighted hue histogram, which considers both saturation and hue information of the given histological image. The efficacy of the proposed method, along with a comparison with other state-of-the-art methods, is demonstrated on publicly available hematoxylin and eosin stained fifty-eight benchmark histological images.
A Study on Multiple Barcode Detection from an Image in Business System

The pillar of automatic identification is Barcode technology which is used comprehensively in real time applications with various types of codes.The different types of codes and applications sometimes faces special problems, so the improvement of the effectiveness must be done persistently.This paper's proposal mainly deals with multiple product's barcode to be detected simultaneously.The proposed algorithm which will have implementation for supershop billing system and inventory management.The method will recognize the barcodes using image processing.Images will be taken using mobile camera sensor.It will detect 1D barcodes such as EAN-13 barcodes, Code-128 barcodes, 2D barcodes such as QR codes.Moreover, it will be angle invariant, requires less user interaction than usual and can be executed on available computers.This model helps consumers to minimize the time for the billing system in shopping.We have implemented this in Python IDE using OpenCV library.
The Use of Voki as A Media to Support the Enhancement of First Grade Students Speaking Skills at SMPN 4 Tambun Selatan

Speaking skill is very crucial for every human being in order to be able to communicate and deliver his or her thoughts among themselves. However, many people around the world speak in vary languages; it depends on their origin. Consequently, many or most of them do not master or able to speak in those languages. So that, people around the world agreed to use some languages as international languages. These languages used as a medium of communication by people whose native languages are different or not mutually intelligible; people are used to say those languages as Lingua Francas. There are some languages that included in Lingua Francas i..e, English. This research is aimed to investigate and explore the use of VOKI to support the enhancement of first grade students’ speaking skills at SMPN 4 Tambun Selatan, Bekasi. The method used in this research is qualitative approach, and it applied Research and Development technique. In this case, the writer collected the data by using observation, structural interview, questionnaire and documentation. The interview was done for the students of first grade students’ speaking skills at SMPN 4 Tambun Selatan, Bekasi. Based on the result of the research, it was found that there were several factors which influence students’ inabilities to speak in English properly, they were students’ speaking ability was low or poor, time limitation, uncomfortable feeling in learning English, lack of motivation, lack of exposures, insufficient materials, and did not know how to start speaking, and lack of feedback. In addition, it was also found that Voki had some features that could be used to solve those problems of the research, the problems of students in speaking English properly. This research can be concluded that the use of Voki as a media to enhance students’ speaking skills to promote the extensive learning at SLTPN 4 Tambun Selatan is useful. It is proven by several problems that hinder the teaching learning process are solved.
FCNN-based axon segmentation for convection-enhanced delivery optimization

Glioblastoma multiforme treatment is a challenging task in clinical oncology. Convection- enhanced delivery (CED) is showing encouraging but still suboptimal results due to drug leakages. Numerical models can predict drug distribution within the brain, but require retrieving brain physical properties, such as the axon diameter distribution (ADD), through axon architecture analysis. The goal of this work was to provide an automatic, accurate and fast method for axon segmentation in electronic microscopy images based on fully convolutional neural network (FCNN) as to allow automatic ADD computation. The segmentation was performed using a residual FCNN inspired by U-Net and Resnet. The FCNN training was performed exploiting mini-batch gradient descent and the Adam optimizer. The Dice coefficient was chosen as loss function. The proposed segmentation method achieved results comparable with already existing methods for axon segmentation in terms of Information Theoretic Scoring ( $$0.98\%$$ ) with a faster training (5 h on the deployed GPU) and without requiring heavy post-processing (testing time was 0.2 s with a non-optimized code). The ADDs computed from the segmented and ground-truth images were statistically equivalent. The algorithm proposed in this work allowed fast and accurate axon segmentation and ADD computation, showing promising performance for brain microstructure analysis for CED delivery optimization.
Research on Hyperspectral Identification of Altered Minerals in Yemaquan West Gold Field, Xinjiang

Predictions of prospectivity based on remote sensing were developed using alteration mineral indicative hyperspectral mapping and remote sensing anomaly filtering, combined with geological characteristics and anomalous mineral field verification. Based on the results of the hyperspectral mineral mapping and the actual geological ground conditions, the results of mapping of altered minerals, such as chlorite, muscovite, kaolinite, and iron oxide were validated, and gold, silver, copper, nickel, and other geochemical anomaly areas were identified for verification work. The results of hyperspectral mineral extraction show that the mineral assemblage closely related to gold deposits in shear zones is muscovite + chlorite + epidote + kaolinite. This alteration mineral assemblage can be used as regional search criteria for shear zone gold mineralisation and was the basis for the discovery of mineralised hydrothermal alteration centres and delineation of four prospective targets. Established on a spectral prospectivity model of the study area, prospective ore-bearing areas have been delineated, which indicate the direction for further geological and mineral resource surveys.
Utility of peripheral blood smear in platelet count estimation

Background: There are several methods of platelet count used in hematology laboratory. These methods are manual counting, automated hematology analyzer counting, platelet count estimation by peripheral blood smear (PBS) method etc. Many diseases such as dengue, malaria, pregnancy induced hypertension etc. may leads to severe thrombocytopenia. Timely and precise diagnosis of platelet count plays very crucial role in critical care management of thrombocytopenia cases. The present study was undertaken to estimate platelet counts by PBS method and correlate them with results from automated hematology analyzer method.Methods: Study included one hundred randomly collected blood samples in EDTA anticoagulant vacutainer tubes. Each blood sample was processed for platelet count estimation with automated hematology analyzer and Leishman’s stained PBS examination. The statistical analysis was done by using Pearson's correlation test to access the agreement between both the methods.Results: The Pearson's correlation test showed significant positive correlation for platelet count estimation between both the methods. (r =0.9789).Conclusions: Platelet count estimation by PBS method is reliable and statistically significant when compared to hematology analyzer method. PBS platelet estimation method can be taken as early and rapid procedure for platelet assessment in critical severe thrombocytopenia cases. This method is simple, cheaper and can be done in rural hospital setup where automation is not available.