Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN MEDIA PEMBELAJARAN TEMATIK “FUN THINKERS BOOK” TEMA BERBAGAI PEKERJAAN

The purpose of this study was to determine the validity of the product of developing thematic learning media Fun Thinkers Book on the theme of various fourth grade elementary school jobs. This research used research and development methods used to produce products and test the feasibility of media Fun Thinkers Book. This study used the ADDIE development model (Analysis, Design, Development, Implementation, and Evaluation. The problem was the lack of thematic learning media, when learning only used student books and LKS only so students are less interested, feel bored and make concentration The students only lasted a little while.The data were obtained through questionnaires, observations, and interviews.This study was conducted at the fourth grade in 3 elementary schools in the District of Pucakwangi, Pati, this study produced valid media with appropriate criteria in accordance with the results of the teacher response questionaire the percentage is 97.5% with very decent criteria and the results of the student questionaire responded 98.6% with very feasible criteria.
Semantic Unsupervised Automatic Keyphrases Extraction by Integrating Word Embedding with Clustering Methods

Increasingly, the web produces massive volumes of texts, alone or associated with images, videos, photographs, together with some metadata, indispensable for their finding and retrieval. Keywords/keyphrases that characterize the semantic content of documents should be, automatically or manually, extracted, and/or associated with them. The paper presents a novel method to address the problem of the automatic unsupervised extraction of keywords/phrases from texts, expressed both in English and in Italian. The main feature of this approach is the integration of two methods that have given interesting results: word embedding models, such as Word2Vec or GloVe able to capture the semantics of words and their context, and clustering algorithms, able to identify the essence of the terms and choose the more significant one(s), to represent the contents of a text. In the paper, the datasets used are presented, together with the method implemented and the results obtained. These results will be discussed, commented, and compared with those obtained in previous experimentations, using TextRank, Rapid Automatic Keyword Extraction (RAKE), and TF-IDF.
Warehouse-Oriented Optimal Path Planning for Autonomous Mobile Fire-Fighting Robots

In order to achieve the fastest fire-fighting purpose, warehouse autonomous mobile fire-fighting robots need to make an overall optimal planning based on the principle of the shortest time for their traveling path. A<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>∗</mml:mi></mml:math> algorithm is considered as a very ideal shortest path planning algorithm, but the shortest path is not necessarily the optimal path for robots. Furthermore, the conventional A<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>∗</mml:mi></mml:math> algorithm is affected by the search neighborhood restriction and the theoretical characteristics, so there are many problems, which are closing to obstacles, more inflection points, more redundant points, larger total turning angle, etc. Therefore, A<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>∗</mml:mi></mml:math> algorithm is improved in eight ways, and the inflection point prior strategy is adopted to compromise Floyd algorithm and A<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mi>∗</mml:mi></mml:math> algorithm in this paper. According to the criterion of the inflection point in this paper, the path inflection point arrays are constructed and traveling all path nodes are replaced by traveling path inflection points for the conventional Floyd algorithm backtracking, so it greatly reduces the backtracking time of the smooth path. In addition, this paper adopts the method of the extended grid map obstacle space in path planning safety distance. According to the relationship between the actual scale of the warehouse grid map and the size of the robot body, the different safe distance between the planning path and the obstacles is obtained, so that the algorithm can be applied to the safe path planning of the different size robots in any map environments. Finally, compared with the conventional A<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mi>∗</mml:mi></mml:math> algorithm, the improved algorithm reduces by 7.846% for the path length, reduces by 71.429% for the number of the cumulative turns, and reduces by 75% for the cumulative turning angle through the experiment. The proposed method can ensure robots to move fast on the planning path and ultimately achieve the goal of reducing the number of inflection points, reducing the cumulative turning angle, and reducing the path planning time.
A electronic voting protocol based on blockchain and homomorphic signcryption

Summary Compared with traditional voting methods, electronic voting can effectively avoid the phenomenon of fraud for personal gains in various links, it is faster and more accurate in the tallying stage. However, many electronic voting systems have many problems such as inability to verify ballots, easy to be forged, and low computing efficiency. We propose an electronic voting protocol based on homomorphic signcryption and blockchain. The protocol makes the voting process public through blockchain and replaces the traditional trusted third party with the smart contract. It uses the homomorphic encryption algorithm and the homomorphic signcryption algorithm to encrypt and sign the ballot and uses their aggregation properties to perform homomorphic tally on the encrypted votes. This not only reduces the excessive burden on the voters but also improves the voting efficiency. At the same time, it can satisfy the security of electronic voting, and the amount of calculation is small, so it is more convenient and flexible to use in large‐scale voting.
Improved Target Detection Algorithm Based on Libra R-CNN

With the development of science and technology, artificial intelligence has been widely used in the transportation field, and research on the symmetry of artificial intelligence has become increasingly more in-depth. Traffic sign detection based on deep learning has the problems of different target shapes and high variability in the number of targets between different labels. To solve these problems from a lack of symmetry, the idea of applying the concept of balanced data and the deformable positioning region to a target recognition network is proposed. The research is based on the improvement of the Libra R-CNN. Aiming at the problem that the difficult-to-distinguish target in target detection has a high impact on detection, the idea of generating increasingly more diverse indistinguishable samples during training is proposed to improve the detection accuracy, which is verified by experiments. The experiment is carried out on the MS COCO 2017 and traffic sign datasets. The improved Libra R-CNN is 3 percentage points better than the unimproved Libra R-CNN’s mean Average Precision (mAP). A large number of comparative experimental results show that the improved network is effective.
A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction

Grammatical Error Correction (GEC) is concerned with correcting grammatical errors in written text.Current GEC systems, namely those leveraging statistical and neural machine translation, require large quantities of annotated training data, which can be expensive or impractical to obtain.This research compares techniques for generating synthetic data utilized by the two highest scoring submissions to the restricted and low-resource tracks in the BEA-2019 Shared Task on Grammatical Error Correction.
Face Recognition based Attendance System

The management of the attendance can be a great burden on the teachers if it is done by hand.To resolve this problem, smart and auto attendance management system is being utilized.By utilizing this framework, the problem of proxies and students being marked present even though they are not physically present can easily be solved.This system marks the attendance using live video stream.The frames are extracted from video using OpenCV.The main implementation steps used in this type of system are face detection and recognizing the detected face, for which dlib is used.After these, the connection of recognized faces ought to be conceivable by comparing with the database containing student's faces.This model will be a successful technique to manage the attendance of students.
RobertNLP at the IWPT 2020 Shared Task: Surprisingly Simple Enhanced UD Parsing for English

This paper presents our system at the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies.Using a biaffine classifier architecture (Dozat and Manning, 2017) which operates directly on fine-tuned RoBERTa embeddings, our parser generates enhanced UD graphs by predicting the best dependency label (or absence of a dependency) for each pair of tokens in the sentence.We address label sparsity issues by replacing lexical items in relations with placeholders at prediction time, later retrieving them from the parse in a rule-based fashion.In addition, we ensure structural graph constraints using a simple set of heuristics.On the English blind test data, our system achieves a very high parsing accuracy, ranking 1 st out of 10 with an ELAS F1 score of 88.94 %.
Grammatical Error Correction Using Pseudo Learner Corpus Considering Learner’s Error Tendency

Recently, several studies have focused on improving the performance of grammatical error correction (GEC) tasks using pseudo data.However, a large amount of pseudo data are required to train an accurate GEC model.To address the limitations of language and computational resources, we assume that introducing pseudo errors into sentences similar to those written by the language learners is more efficient, rather than incorporating random pseudo errors into monolingual data.In this regard, we study the effect of pseudo data on GEC task performance using two approaches.First, we extract sentences that are similar to the learners' sentences from monolingual data.Second, we generate realistic pseudo errors by considering error types that learners often make.Based on our comparative results, we observe that F 0.5 scores for the Russian GEC task are significantly improved.
Combined LA-ICP-MS and cryo-SEM-EDS: An improved technique for quantitative analysis of major, minor, and trace elements in fluid inclusions in halite

Quantitative multi-element analyses of single fluid inclusions in halite and other sedimentary minerals can provide information on the origin and chemical evolution of ancient surface waters on Earth. Integrated laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) and cryogenic-scanning electron microscopy-energy dispersive spectroscopy (cryo-SEM-EDS) were used here for the quantitative analysis of fluid inclusions in halite. Single phase fluid inclusions in modern and ancient halite were analyzed using a 193 nm ArF excimer laser ablation system coupled with a quadrupole mass spectrometer to test a new calibration technique using magnesium (Mg) as an internal standard. Mg concentrations obtained by cryo-SEM-EDS analyses of fluid inclusions were used to convert LA-ICP-MS concentration ratios into absolute elemental concentrations. Mg concentrations of ancient fluid inclusions from cryo-SEM-EDS analyses were reproducible to better than 5% relative standard deviation (RSD). Comparison between the chemical composition of modern Dead Sea brine measured using ICP-OES (optical emission spectroscopy) and the composition of fluid inclusions in Dead Sea halite formed from those brines, shows that fluid inclusions in halite faithfully record the chemistry of the brines from which they precipitated. Overall LA-ICP-MS analytical precision for major ions K, Ca, S in SO4 (above 50 mmol/kg H2O) is better than 10% RSD and accuracies range from 2% to 21%. Mean concentrations of Li, B, Sr, Rb and Ba agree within 7% of their expected values and are reproducible within 15%, whereas Cs concentrations above detection limit are typically reproducible to within 15 to 25% RSD. For trace elements in seawater, such as U and Mo, quantitative analyses in fluid inclusions are achieved at concentrations above 20 nmol/kg H2O. The results from this study confirm that the precision and accuracy of major and minor elemental analysis is improved with Mg as an internal standard instead of Na and Cl used in previous studies. Controlled, optimized ablation of >30 μm fluid inclusions in halite improved the accuracy and precision and reduced the overall limit of detection (LOD) by one order of magnitude compared to previous studies. Wide ranges of LODs, between 0.7 nmol/kg H2O and 10 mmol/kg H2O, reflect variations in inclusion volume and elemental concentrations. Analytical accuracies obtained for major elements demonstrate that cryo-SEM-EDS and LA-ICP-MS are complementary microbeam techniques for chemical analysis of individual fluid inclusions in halite.
Generation of hybrid Greenberger-Horne-Zeilinger entangled states of particlelike and wavelike optical qubits in circuit QED

Hybrid entanglement between particlelike and wavelike optical qubits has drawn increasing attention because such hybrid entanglement is a key resource in establishing hybrid quantum networks and connecting quantum processors with different encoding qubits. For convenience, we define ``particlelike optical qubits'' as PO qubits and ``wavelike optical qubits'' as WO qubits. In this work, we propose a method to create a hybrid Greenberger-Horne-Zeilinger (GHZ) entangled state of $n$ PO qubits and $n$ WO qubits, by using $2n$ microwave cavities coupled to a superconducting flux qutrit. The two logic states of a PO qubit here are represented by the vacuum state and the single-photon state of a cavity (or represented by the rotated states of the vacuum state and the single-photon state), while the two logic states of a WO qubit are indicated by the two coherent states of a cavity. The procedure for preparing the GHZ state consists of only a few basic operations, and the circuit resources are significantly reduced because of using only one flux qutrit as the coupler. The GHZ-state preparation time does not depend on the number of qubits, and the GHZ state is deterministically generated since no measurement is made. In addition, the intermediate higher-energy level of the qutrit during the entire operation is virtually excited and thus decoherence from this level is greatly suppressed. This proposal is quite general and can be extended to create the proposed hybrid GHZ state, by using a $\mathrm{\ensuremath{\Lambda}}$-type natural or artificial atom coupled to $2n$ microwave or optical cavities. As an example, our numerical simulation demonstrates that within current circuit-QED technology, the hybrid GHZ state of two PO qubits and two WO qubits can be prepared with a high fidelity.
Boolean learning under noise-perturbations in hardware neural networks

A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate. Multiple systems have recently implemented some or all of these operations, yet the focus was placed on addressing technological challenges. Fundamental questions regarding learning in hardware neural networks remain largely unexplored. Noise in particular is unavoidable in such architectures, and here we investigate its interaction with a learning algorithm using an opto-electronic recurrent neural network. We find that noise strongly modifies the system's path during convergence, and surprisingly fully decorrelates the final readout weight matrices. This highlights the importance of understanding architecture, noise and learning algorithm as interacting players, and therefore identifies the need for mathematical tools for noisy, analogue system optimization.
I Am Guessing You Can't Recognize This: Generating Adversarial Images for Object Detection Using Spatial Commonsense (Student Abstract)

Can we automatically predict failures of an object detection model on images from a target domain? We characterize errors of a state-of-the-art object detection model on the currently popular smart mobility domain, and find that a large number of errors can be identified using spatial commonsense. We propose øurmodel , a system that automatically identifies a large number of such errors based on commonsense knowledge. Our system does not require any new annotations and can still find object detection errors with high accuracy (more than 80% when measured by humans). This work lays the foundation to answer exciting research questions on domain adaptation including the ability to automatically create adversarial datasets for target domain.
Appearance-and-Dynamic Learning With Bifurcated Convolution Neural Network for Action Recognition

For a long time, learning spatiotemporal features with deep neural networks has been a difficult task in the field of computer vision. In this paper, we present a novel deep architecture, termed as Bifurcated Convolutional Neural Network (BifurcatedNet) to learn the discriminative video representation in an end-to-end manner. In our work, the BifurcatedNet is built upon the stacking bifurcated blocks that aim at simultaneously capturing the static appearance information and the temporal dynamic from input data. Specifically, the bifurcated block is composed of two separated branch, i.e., an appearance branch and a dynamic branch. The appearance branch employs 2D convolutional operation to obtain the spatial responses of image pixels or filters of each input frame, while the design of the dynamic branch is based on the spatio-temporal convolutional operation to exploit the temporal dynamic between pixels and filter response across multiple frames. Multiple experiments are conducted on two popular action recognition benchmarks: UCF101 and HMDB51. With only RGB input, the BifurcatedNet obtains the superior performance over the existing state-of-the-art models under the same experimental setting. The proposed BifurcatedNet is also implemented in a two-stream fashion by using both RGB and optical flow input, and still achieves the state-of-the-art performance, demonstrating the effectiveness of the network design. Furthermore, in order to evaluate the generalization ability, we conduct experiments on the Chalearn LAP IsoGD dataset and find that our model works well in gesture recognition tasks.
Compressive interference-based image encryption via sparsity constraints

We report a compressive interference-based encryption (IBE) method using sparsity constraints. In this method, two sparse phase-only masks (POMs) are generated through a modified phase retrieval algorithm under the constraints of two complementary binary amplitude masks (BAMs), and then fused into one ciphertext. For the decryption in the IBE system, the ciphertext bonded with one BAM is placed in the corresponding interference channel. Only if the correct ciphertext and BAMs are arranged at their positions properly, the expected information could be extracted. In contrast to previous IBE methods, the proposal not only reduces the number of ciphertext into one but also breaks the mathematical relation between the plaintext and the ciphertext, which is usually utilized by an illegal user. As a result, both the efficiency and security are reinforced. Some analysis and numerical simulations are conducted to prove the feasibility and effectiveness of the proposal.
Hierarchical Transformer Network for Utterance-Level Emotion Recognition

While there have been significant advances in detecting emotions in text, in the field of utterance-level emotion recognition (ULER), there are still many problems to be solved. In this paper, we address some challenges in ULER in dialog systems. (1) The same utterance can deliver different emotions when it is in different contexts. (2) Long-range contextual information is hard to effectively capture. (3) Unlike the traditional text classification problem, for most datasets of this task, they contain inadequate conversations or speech. (4) To better model the emotional interaction between speakers, speaker information is necessary. To address the problems of (1) and (2), we propose a hierarchical transformer framework (apart from the description of other studies, the “transformer” in this paper usually refers to the encoder part of the transformer) with a lower-level transformer to model the word-level input and an upper-level transformer to capture the context of utterance-level embeddings. For problem (3), we use bidirectional encoder representations from transformers (BERT), a pretrained language model, as the lower-level transformer, which is equivalent to introducing external data into the model and solves the problem of data shortage to some extent. For problem (4), we add speaker embeddings to the model for the first time, which enables our model to capture the interaction between speakers. Experiments on three dialog emotion datasets, Friends, EmotionPush, and EmoryNLP, demonstrate that our proposed hierarchical transformer network models obtain competitive results compared with the state-of-the-art methods in terms of the macro-averaged F1-score (macro-F1).
Screen-Cam Robust and Blind Watermarking for Tile Satellite Images

To achieve watermark synchronization from an image with perspective distortion is a challenge. This paper proposes a screen-cam robust and blind watermarking scheme for tile satellite images, which means we do not need any user interaction or additional information in watermark detection. To achieve this, at the watermark embedding side, we divide tiles into synchronization tiles and message tiles, and propose a discrete Fourier transform (DFT) based embedding method to cope with the quality degradation caused by screen-cam process. At the extraction side, based on the idea of template matching, we first use a synchronization response index to estimate an appropriate scale level and positions of embedded synchronization watermarks from the noise component, which is estimated by Wiener filter. Then, we propose verification, selection, and precise locations methods to ensure the effectiveness and accuracy of synchronization detection results. After that, we can extract the regions of message tiles. Finally, we extract watermark message based on the local max value from the DFT domain of the noise component. Experimental results demonstrate the validity of the proposed scheme against common attacks and screen-cam attack with a tripod as well as handhold shooting.