Reviewerly/exHarmony huggingface dataset:
Reliable and Efficient Anytime Skeleton Learning

Skeleton Learning (SL) is the task for learning an undirected graph from the input data that captures their dependency relations. SL plays a pivotal role in causal learning and has attracted growing attention in the research community lately. Due to the high time complexity, anytime SL has emerged which learns a skeleton incrementally and improves it overtime. In this paper, we first propose and advocate the reliability requirement for anytime SL to be practically useful. Reliability requires the intermediately learned skeleton to have precision and persistency. We also present REAL, a novel Reliable and Efficient Anytime Learning algorithm of skeleton. Specifically, we point out that the commonly existing Functional Dependency (FD) among variables could make the learned skeleton violate faithfulness assumption, thus we propose a theory to resolve such incompatibility. Based on this, REAL conducts SL on a reduced set of variables with guaranteed correctness thus drastically improves efficiency. Furthermore, it employs a novel edge-insertion and best-first strategy in anytime fashion for skeleton growing to achieve high reliability and efficiency. We prove that the skeleton learned by REAL converges to the correct skeleton under standard assumptions. Thorough experiments were conducted on both benchmark and real-world datasets demonstrate that REAL significantly outperforms the other state-of-the-art algorithms.
MHAD: Multi-Human Action Dataset

This paper presents a framework for a multi-action recognition method. In this framework, we introduce a new approach to detect and recognize the action of several persons within one scene. Also, considering the scarcity of related data, we provide a new data set involving many persons performing different actions in the same video. Our multi-action recognition method is based on a three-dimensional convolution neural network, and it involves a preprocessing phase to prepare the data to be recognized using the 3DCNN model. The new representation of data consists in extracting each person’s sequence during its presence in the scene. Then, we analyze each sequence to detect the actions in it. The experimental results proved to be accurate, efficient, and robust in real-time multi-human action recognition.
What Influences the Features of Post-Editese? A Preliminary Study

While a number of studies have shown evidence of translationese phenomena, that is, statistical differences between original texts and translated texts (Gellerstam, 1986), results of studies searching for translationese features in postedited texts (what has been called "posteditese" (Daems et al., 2017)) have presented mixed results.This paper reports a preliminary study aimed at identifying the presence of post-editese features in machine-translated post-edited texts and at understanding how they differ from translationese features.We test the influence of factors such as post-editing (PE) levels (full vs. light), translation proficiency (professionals vs. students) and text domain (news vs. literary).Results show evidence of post-editese features, especially in light PE texts and in certain domains.
Instructions, Interfaces, and Interoperable Data: The RIMMF Experience with RDA Revisited

This article presents a case study of RIMMF, a software tool developed to improve the orientation and training of catalogers who use Resource Description and Access (RDA) to maintain bibliographic data. The cataloging guidance and instructions of RDA are based on the Functional Requirements conceptual models that are now consolidated in the IFLA Library Reference Model, but many catalogers are applying RDA in systems that have evolved from inventory and text-processing applications developed from older metadata paradigms. The article describes how RIMMF interacts with the RDA Toolkit and RDA Registry to offer cataloger-friendly multilingual data input and editing interfaces.
Comparison of Automatic and Expert Teachers’ Rating of Computerized English Listening-Speaking Test

Through Many-Facet Rasch analysis, this study explores the rating differences between 1 computer automatic rater and 5 expert teacher raters on scoring 119 students in a computerized English listening-speaking test. Results indicate that both automatic and the teacher raters demonstrate good inter-rater reliability, though the automatic rater indicates less intra-rater reliability than college teacher and high school teacher raters under the stringent infit limits. There&amp;rsquo;s no central tendency and random effect for both automatic and human raters. This research provides evidence for the automatic rating reform of the computerized English listening-speaking test (CELST) in Guangdong NMET and encourages the application of MFRM in actual score monitoring.
Vision reconstruction based on planar laser with nonrestrictive installation position and posture relative to 2D reference

The active-vision-based reconstruction is an advanced approach to characterize the spatial object. In the active vision, the planar laser is a kind of the moderate structured light for the robustness and adaptivity in the complicated illumination environment. However, as the planar laser is determined by 3 unknown degrees of freedom, the laser sensor should be calibrated in the immovable coordinate frame of reference (CFR), or be pre-calibrated in the coordinate frame of the camera (CFC) and moved with CFC. In the study, as the main improvement, the laser plane is attached to the simple 2D reference without strict constraints of the relative installation position and posture. The laser plane is desired to be flexibly moved and registered in CFC. The calibration is modeled by the closed loop of the camera, the cylindrical object, the planar laser and the 2D reference, in which the camera is twice considered as the bridge between the cylindrical object and the 2D reference. The calibration contributes the planar laser that is determined as a constant vector in CFR. Furthermore, the reconstruction is performed by the calibrated laser plane and refined by the optimization method. The method errors of 1.263, 1.517, 1.633, and 1.803 mm are evaluated by the experiments, which indicate the flexibility of the method and applicable prospects in the spatial reconstruction field.
Mobile manipulator control through gesture recognition using IMUs and Online Lazy Neighborhood Graph search

Gesture-based control potentially eliminates the need for wearisome physical controls and facilitates easy interaction between a human and a robot. At the same time, it is intuitive and enables a natural means of control. In this paper, we present and evaluate a framework for gesture recognition using four wearable Inertial Measurement Units (IMUs) to indirectly control a mobile robot. Six gestures involving different hand and arm motions are defined. A novel algorithm based on an Online Lazy Neighborhood Graph (OLNG) search is used to recognise and classify the gestures online. A software framework is developed to control a robotic platform through integrating our gesture recognition algorithm with a Robot Operating System (ROS), which is in turn used to trigger predefined robot behaviours. Experiments show that the framework is able to correctly detect and classify six different gestures in real time with average success rates of 81.61 % and 81.67 %, while keeping the false-positive rate low by designing and using only 126 training samples.
MALICIOUS URL RECOGNITION AND DETECTION USING ATTENTION-BASED CNN-LSTM

A malicious Uniform Resource Locator (URL) recognition and detection method based on the combination of Attention mechanism with Convolutional Neural Network and Long Short-Term Memory Network (Attention-Based CNN-LSTM), is proposed.Firstly, the WHOIS check method is used to extract and filter features, including the URL texture information, the URL string statistical information of attributes and the WHOIS information, and the features are subsequently encoded and pre-processed followed by inputting them to the constructed Convolutional Neural Network (CNN) convolution layer to extract local features.Secondly, in accordance with the weights from the Attention mechanism, the generated local features are input into the Long-Short Term Memory (LSTM) model, and subsequently pooled to calculate the global features of the URLs.Finally, the URLs are detected and classified by the SoftMax function using global features.The results demonstrate that compared with the existing methods, the Attention-based CNN-LSTM mechanism has higher accuracy for malicious URL detection.
Atmospheric correction algorithm for short-wave channels of the MSU-MR device of the Meteor-M no. 2 satellite

The problem of atmospheric correction for short-wave channels of a multispectral low-resolution scanning device installed on the Meteor-M No. 2 satellite is considered. To solve the problem the existing atmospheric correction algorithms are investigated. The developed atmospheric correction algorithm is based on the use of special Look-up Tables generated by the authors. Look-up Tables contain information about reflectance of the satellite device channels for various atmospheric conditions and observation geometry. The results of atmospheric correction for the first channel of the device were verified. Verification showed a high correlation with the reference reflectance, which is the data from the EUMETSAT portal Surface Albedo Validation Sites. An additional, verification of the present algorithm was also performed with the first channel data of the AVHRR device MetOp-A satellite. The correlation of the reference values and the results of atmospheric correction of both satellite devices are comparable.
Flexible method for accurate calibration of large-scale vision metrology system based on virtual 3-D targets and laser tracker

For the purpose of obtaining high-precision in stereo vision calibration, a large-size precise calibration target, which can cover more than half of the field of view is vital. However, large-scale calibration targets are very difficult to fabricate. Based on the idea of error tracing, a high-precision calibration method for vision system with large field of view by constructing a virtual 3-D calibration target with a laser tracker was proposed in this article. A virtual 3-D calibration target that covers the whole measurement space can be established flexibly and the measurement precision of the vision system can be traceable to the laser tracker. First, virtual 3-D targets by calculating rigid body transformation with unit quaternion method were constructed. Then, the high-order distortion camera model was taken into consideration. Besides, the calibration parameters were solved with Levenberg–Marquardt optimization algorithm. In the experiment, a binocular stereo vision system with the field of view of 4 × 3 × 2 m 3 was built for verifying the validity and precision of the proposed calibration method. It is measured that the accuracy with the proposed method can be greatly improved comparing with traditional plane calibration method. The method can be widely used in industrial applications, such as in the field of calibrating large-scale vision-based coordinate metrology, and six-degrees of freedom pose tracking system for dimensional measurement of workpiece, as well as robotics geometrical accuracy detection and compensation.
Pengembangan Modul Fisika Online Berbasis Web pada Materi Usaha dan Energi

Human resources at school is good enough in utilizing technology, information, and communication online including in the field of education. Conditions on the ground show that in some schools in the learning is based online, but only limited scope blog and quipper school. For that we need to develop web-based online physics module on business material and energy. This research is R &amp; D (Research and Development) model of ADDIE (Analysis, Design, Development, Implementation and Evaluation). The results showed that the level of feasibility according to the expert and the response of student’s who are on the criteria worth with a percentage of 85% and 72.5% respectively. The web-based online physics module can be used as a self-learning resource. This product, should be developed for other high school physics materials
An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network

The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user’s head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user’s head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems.
North Sámi morphological segmentation with low-resource semi-supervised sequence labeling

Semi-supervised sequence labeling is an effective way to train a low-resource morphological segmentation system.We show that a feature set augmentation approach, which combines the strengths of generative and discriminative models, is suitable both for graphical models like conditional random field (CRF) and sequence-to-sequence neural models.We perform a comparative evaluation between three existing and one novel semi-supervised segmentation methods.All four systems are language-independent and have open-source implementations.We improve on previous best results for North Sámi morphological segmentation.We see a relative improvement in morph boundary F1-score of 8.6% compared to using the generative Morfessor FlatCat model directly and 2.4% compared to a seq2seq baseline.Our neural sequence tagging system reaches almost the same performance as the CRF topline. TiivistelmäPuoliohjattu sekvenssiluokitus on tehokas tapa opettaa morfologinen pilkontajärjestelmä kielelle, jolle on saatavilla niukasti lingvistisiä resursseja.Osoitamme, että generatiivisen mallin tuottamien piirteiden käyttäminen soveltuu paitsi graafisille malleille kuten ehdollinen satunnaiskenttä (CRF), myös sekvenssistäsekvenssiin (seq2seq) -neuroverkkomalleille.Vertailemme kolmea olemassaolevaa ja yhtä uutta puoliohjattua menetelmää.
DUSTBot: A duplex and stealthy P2P-based botnet in the Bitcoin network

As the root cause of illegal cyber activities, botnets are evolving continuously over the last two decades. Current researches on botnet command and control mechanism based on blockchain network suffer from high economic cost, single point of failure, and limited scalability. In this paper, we present DUSTBot, a novel P2P botnet model based on Bitcoin transactions to prepare for new cyber threats. Specifically, a covert, duplex, and low-cost command and control (C&C) channel in the Bitcoin network is presented in our work. DUSTBot uses the Bitcoin main network as the downstream channel while using the Bitcoin testnet as the upstream channel. Furthermore, the peer list exchange algorithm based on the Ethereum block hash proposed in this paper is effective against routing table poisoning attack and P2P botnet crawling. The robustness of DUSTBot against node removal is studied through constructing the botnet with a P2P simulator. We deploy the implementation of DUSTBot on cloud platforms to test its feasibility and performance. Moreover, the stealthiness of DUSTBot and the effectiveness of the proposed peer list exchange algorithm are evaluated. The results demonstrate the feasibility, performance, stealthiness, and robustness of DUSTBot. In the end, possible countermeasures are discussed to mitigate similar threats in the future.
Intrinsic Motivation for Encouraging Synergistic Behavior

We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks, which are tasks where multiple agents must work together to achieve a goal they could not individually. Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own. Thus, we propose to incentivize agents to take (joint) actions whose effects cannot be predicted via a composition of the predicted effect for each individual agent. We study two instantiations of this idea, one based on the true states encountered, and another based on a dynamics model trained concurrently with the policy. While the former is simpler, the latter has the benefit of being analytically differentiable with respect to the action taken. We validate our approach in robotic bimanual manipulation tasks with sparse rewards; we find that our approach yields more efficient learning than both 1) training with only the sparse reward and 2) using the typical surprise-based formulation of intrinsic motivation, which does not bias toward synergistic behavior. Videos are available on the project webpage: https://sites.google.com/view/iclr2020-synergistic.
Multilayer ToI Detection Approach for Nested NER

Nested entities commonly exist in news articles and biomedical corpora. The performance of nested NER is still a great challenge in the field of named entity recognition (NER). Unlike the structural models in previous work, this paper presents a comprehensive study of nested NER by means of text-of-interest (ToI) detection. This paper presents a novel ToI-CNN with dual transformer encoders (ToI-CNN + DTE) model for this solution. We design a directional self-attention mechanism to encode contextual representation over the whole-sentence in the forward and backward directions. The features of the entities are extracted from the contextual token representations by a convolutional neural network. Moreover, we use HAT pooling operation to convert the various length ToIs to a fixed length vector and connect with a fully connected network for classification. The layer where the nested entities are located can be evaluated by multi-task learning jointly with layer classification. The experimental results show that our model achieves excellent performance in F1 score, training cost and layer evaluation on the nested NER datasets.
The Effect of Blended Learning on Nursing Students’ Knowledge

Introduction: Blended learning is assumed to improve the students’ understanding of learning at nursing school especially in the current industrial revolution 4.0 era. The combination of classroom and online activities is expected to provide better study outcomes. This research aimed to compare the knowledge scores of the nursing students in blended learning concerning a research and statistics course at a private faculty of nursing.Methods: The knowledge scores resulted from the student’s score in their mid and final examinations (0-100). A total of 474 respondents who were third year students involved in the study. This study applied a pre-experimental design. Due to the data having a significant non-normal value (p value &lt; 0.001), this study further compared the mean-rank of the students’ score using a Wilcoxon test analysis.Results: The results of this study revealed that there was a significant difference (p value &lt; 0.0001) between the students’ score in the mid (Median 72.5; SD 9.82) and final exam examinations (Median 86.66; SD 4.75). This means that the students had a better score in their final exam.Conclusion: Applying a blended learning method was a positive experience for the nursing students in terms of knowledge. It is needed to explore the students’ understanding and interest in their learning process using blended learning.
An Adversarial Deep Hybrid Model for Text-Aware Recommendation with Convolutional Neural Networks

The standard matrix factorization methods for recommender systems suffer from data sparsity and cold-start problems. Thus, in real-world scenarios where items are commonly associated with textual data such as reviews, it becomes necessary to build a hybrid recommendation model that can fully utilize the text features. However, existing methods in this area either cannot extract good features from the texts due to their order–insensitive document modeling approaches or fail to learn the hybrid model in an effective way due to their complexity of inferring the latent vectors. To this end, we propose a deep hybrid recommendation model which seamlessly integrates matrix factorization with a Convolutional Neural Network (CNN), a powerful text feature extraction tool with the capability of detecting the information of word orders. Unlike previous works which use content features as prior knowledge to regularize the latent vectors, we combine CNN into MF in an additive manner to allow training CNN with direct learning signals. Furthermore, we propose an adversarial training framework to learn the hybrid recommendation model, where a generator model is built to learn the distribution over the pairwise ranking pairs while training a discriminator to distinguish generated (fake) and real item pairs. We conduct extensive experiments on three real-world datasets to demonstrate the effectiveness of our proposed model against state-of-the-art methods in various recommendation settings.
PANTOGRAPHY APPLICATION WITH REAL-TIME PLC BASED ON IMAGE PROCESSING IN GANTRY ROBOT SYSTEM

Programmable Logic Controllers (PLC) is one of the indispensable products of automation industry. PLCs, which are widely used in many fields of programming, make our lives easier in daily life. In this study, after determining the coordinates of the image taken from the camera with the help of image processing algorithm, the servo drives of cartesian machine are controlled and processed in X-Y plane with Rexroth PLC in real time. In this application, it is aimed to obtain the image again by subtracting the boundary coordinates of the image taken with the camera in MatLab environment. To achieve this goal, the servo drives of the cartesian machine were controlled and the application was realized. Two different methods were applied for the image taken in Matlab environment. The skeleton of the image was taken for the first method and then the boundary coordinates were determined. For the second method, the contour of the image was first extracted. The coordinates of the contour image were not directly used. These coordinates have been tried to be optimized. In this study, it is aimed to draw a desired image by drawing border coordinates in Matlab with Rexroth PLC on a cartesian machine. As a result of the test studies, it was seen that the drawing process was carried out properly (according to the coordinates received) as a result of the analysis and measurement processes (metric caliper and measurement methods).