Reviewerly/exHarmony huggingface dataset:
Match Card Game For Education About Traditional Kingdom in Indonesia

Indonesia is one of the country that has an old history that can be tracked until 1000 years, even before Indonesia got invaded by Netherlands. Many kingdoms that rise and fall or replaced by another kingdom, and many rulers that have bloodline with another rulers in different kingdom. Each kingdom has different culture background that based on different religion, where we can see about their culture development from kingdoms from the past. Game education is one of the tools for learning that can be used for modern entertainment game that give education point or knowledge so the player can learn while playing the game. Match card game is a game where player match the image with the same image and finish it by matching all the cards with the same image. In this game, by borrowing match game mechanic, we modified it with player need to choose a Question Card and match it with the correct Answer Card. There is 3 stages in the game that separate it with Hindu’s kingdoms, buddha kingdoms and Muslim kingdoms in Indonesia. Player then will got a questioner about the topic in the game. The result we got that the players has improvement about the old kingdoms in Indonesia. And we got positive reaction about the game and they interested to play it again and gain a higher score than before.
A Curriculum Learning Approach for Multi-Domain Text Classification Using Keyword Weight Ranking

Text classification is a well-established task in NLP, but it has two major limitations. Firstly, text classification is heavily reliant on domain-specific knowledge, meaning that a classifier that is trained on a given corpus may not perform well when presented with text from another domain. Secondly, text classification models require substantial amounts of annotated data for training, and in certain domains, there may be an insufficient quantity of labeled data available. Consequently, it is essential to explore methods for efficiently utilizing text data from various domains to improve the performance of models across a range of domains. One approach for achieving this is through the use of multi-domain text classification models that leverage adversarial training to extract domain-shared features among all domains as well as the specific features of each domain. After observing the varying distinctness of domain-specific features, our paper introduces a curriculum learning approach using a ranking system based on keyword weight to enhance the effectiveness of multi-domain text classification models. The experimental data from Amazon reviews and FDU-MTL datasets show that our method significantly improves the efficacy of multi-domain text classification models adopting adversarial learning and reaching state-of-the-art outcomes on these two datasets.
Pelican Optimization Algorithm with Deep Learning for Aspect based Sentiment Analysis on Asian Low Resource Languages

Recently, sentiment analysis (SA) has become more popular as it is crucial to moderate and examine the data from the internet. It contains several applications, such as social media monitoring, market research, and opinion mining. Aspect Based Sentiment Analysis (ABSA) is a domain of SA that manages sentiment at a better level. ABSA classifies sentiment in terms of all the aspects for obtaining superior insights as sentiment expressed. A major contribution has been developed in ABSA, and then this progress can be restricted only to some languages with suitable resources. One common method is to utilize machine learning (ML) approaches, namely Neural Networks (NN), Support Vector Machines (SVM), and Naive Bayes (NB), together with Asian and low language-specific resources. These resources offer data on the sentiment polarity (neutral, positive, or negative) of phrases and words that are generally utilized in low-resource languages. In this aspect, this study develops a new Pelican Optimization Algorithm with Deep Learning for ABSA (POADL-ABSA) on Asian and Low Resource Languages. The proposed POADL-ABSA technique focuses on the detection and classification of sentiments. To accomplish this, the POADL-ABSA technique encompasses various levels of operations such as pre-processed, feature vector conversion, and classification. In addition, the POADL-ABSA technique employs the BERT model for feature vector extraction. Besides, attention-based bi-directional long short-term memory (ABiLSTM) system was used for the recognition and classification of sentiments. Finally, the POA was utilized for optimum hyperparameter selection of the ABiLSTM model, and it helps in attaining enhanced sentiment classification results. To ensure the improvised performance of the IAOADL-ABSA technique, an extensive experimental outcome the IAOADL-ABSA technique surpassed other models with acc{u}_y , pre{c}_n , rec{a}_l , and {F}_{score} of 98.72%, 98.71%, 98.72%, and 98.71%, respectively. Therefore, the IAOADL-ABSA technique can be employed for accurate classification results.
Low-Noise Quantum Frequency Conversion of Photons from a Trapped Barium Ion to the Telecom O-band

Trapped ions are one of the leading candidates for scalable and long-distance quantum networks because of their long qubit coherence time, high-fidelity single- and two-qubit gates, and their ability to generate photons entangled with the qubit state of the ion. One method for creating ion-photon entanglement is to exploit optical transitions from the 6P1/2 to 6S1/2 levels, which naturally emit spin-photon entangled states. But these optical transitions typically lie in the ultraviolet and visible wavelength regions of the spectrum. These wavelengths exhibit significant fiber-optic propagation loss, thereby limiting the transfer of quantum information to tens of meters. Quantum frequency conversion is essential to convert these photons to telecom wavelengths so that they can propagate over long distances in fiber-based networks, as well as for compatibility with the vast number of telecom-based optoelectronic components. Here, we generate O-band telecom photons via a low-noise quantum frequency conversion scheme from photons emitted from the 6P1/2 to 6S1/2 dipole transition of a trapped barium ion. We use a two-stage quantum frequency conversion scheme to achieve a frequency shift of 375.4 THz between the input visible photon and the output telecom photon, achieving a conversion efficiency of 11%. We attain a signal-to-background ratio of over 100 for the converted O-band telecom photon with background noise of less than 15 counts/s. These results are an important step toward achieving trapped ion quantum networks over long distances for distributed quantum computing and quantum communication.
Efficient Dicke-state distribution in a network of lossy channels

We analyze the generation of entanglement in a multipartite optical network. We generalize the twin-field strategy to the multipartite case and show that our protocol has advantageous rate-loss scalings of distributing $W$ states and Dicke states over the star networks. We provide precise theoretical formulas and quantitative performance analyses. Also, analysis of the same protocol using Gaussian states as resources, which is a typical setup in many experimental tests, is provided. We numerically study the feasibility of the protocol in realistic experimental conditions with imperfect detectors. We observe that the advantage depends on the chosen fidelity and is affected by the dark counts of the detector.
Accurate and real-time object detection in crowded indoor spaces based on the fusion of DBSCAN algorithm and improved YOLOv4-tiny network

Abstract Real-time object detection is an integral part of internet of things (IoT) application, which is an important research field of computer vision. Existing lightweight algorithms cannot handle target occlusions well in target detection tasks in indoor narrow scenes, resulting in a large number of missed detections and misclassifications. To this end, an accurate real-time multi-scale detection method that integrates density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm and the improved You Only Look Once (YOLO)-v4-tiny network is proposed. First, by improving the neck network of the YOLOv4-tiny model, the detailed information of the shallow network is utilized to boost the average precision of the model to identify dense small objects, and the Cross mini-Batch Normalization strategy is adopted to improve the accuracy of statistical information. Second, the DBSCAN clustering algorithm is fused with the modified network to achieve better clustering effects. Finally, Mosaic data enrichment technique is adopted during model training process to improve the capability of the model to recognize occluded targets. Experimental results show that compared to the original YOLOv4-tiny algorithm, the mAP values of the improved algorithm on the self-construct dataset are significantly improved, and the processing speed can well meet the requirements of real-time applications on embedded devices. The performance of the proposed model on public datasets PASCAL VOC07 and PASCAL VOC12 is also better than that of other advanced lightweight algorithms, and the detection ability for occluded objects is significantly improved, which meets the requirements of mobile terminals for real-time detection in crowded indoor environments.
NLP Models Behind RASA Stack

This paper brings about the foundation of a platform for conversational AI the Rasa platform. This Rasa stack contains a block of open source machine learning tools exclusively used in intend to create a contextual chatbots and assistants. The services hold by this platform undergoes a major classification of powerful APIs and embedded together with Rasa stack which includes Rasa core and Rasa NLU in the form of an event stream discussed throughout this paper and also the algorithm involved in building upon this platform. Its ingredients include the Bag of words algorithm helping in simplifying representation used in the NLP, CRFs – Conditional Random Field used in statistical modelling and machine learning platforms and also advanced technology such as LTSM neural networks. This paper discusses all the algorithms involved in building up the platform and also the result produced in building up the student assistant chatbot using this platform. It also encourages the use of this RASA platform for the user required custom format as per their requirements and also promotes to contribute in developing the platform for better efficiency of the platform to function.
Fine-Grained Code Clone Detection with Block-Based Splitting of Abstract Syntax Tree

Code clone detection aims to find similar code fragments and gains increasing importance in the field of software engineering. There are several types of techniques for detecting code clones. Text-based or token-based code clone detectors are scalable and efficient but lack consideration of syntax, thus resulting in poor performance in detecting syntactic code clones. Although some tree-based methods have been proposed to detect syntactic or semantic code clones with decent performance, they are mostly time-consuming and lack scalability. In addition, these detection methods can not realize fine-grained code clone detection. They are unable to distinguish the concrete code blocks that are cloned. In this paper, we design Tamer, a scalable and fine-grained tree-based syntactic code clone detector. Specifically, we propose a novel method to transform the complex abstract syntax tree into simple subtrees. It can accelerate the process of detection and implement the fine-grained analysis of clone pairs to locate the concrete clone parts of the code. To examine the detection performance and scalability of Tamer, we evaluate it on a widely used dataset BigCloneBench. Experimental results show that Tamer outperforms ten state-of-the-art code clone detection tools (i.e., CCAligner, SourcererCC, Siamese, NIL, NiCad, LVMapper, Deckard, Yang2018, CCFinder, and CloneWorks).
Task-Oriented Surveillance Framework for Virtual Emotion Informatics in Polygon Spaces

Since the advent of high speed communications and reliable computing devices, it is indispensable to study how to process information and computations appropriately to deal with the requested tasks. In particular, for task-oriented environment, the virtual emotion informatics can be deliberated, which covers a series of detection, data sharing, transmission, governance, and management supported by numerous heterogeneous devices. In this study, we design a task-oriented surveillance framework for virtual emotion informatics through a cooperation of mobile robots and smart UAVs in polygon spaces. The devised framework essentially provides task-oriented virtual emotion security for the promising virtual emotion informatics. Also, we make a formal definition of the problem which pursues to minimize the number of system devices such that task-oriented virtual emotion security is satisfied in polygon spaces. To solve the problem, three different schemes are proposed and are performed through expansive simulations with various settings and scenarios in polygon spaces. Then their performances are evaluated with detailed discussions. In addition, we suggest promising research challenges and open issues toward new frontiers in virtual emotion informatics and its applications.
Online Book Selling Dapp Based on Smart Contracts

Smart contracts are the wave of the future. The idea behind them is to allow people who don’t know or trust each other to do business together without the need for a middleman. Since their appearance in the blockchain, smart contracts extended usability for a wide range of applications, from financial transactions to everyday agreements. This work explored mainly the smart contract and addressed the problem of lengthy and costly payment processes, including some obstructive steps to settle a transaction and additional fees to pay for payment gateways. To shorten this lengthy process, we implemented data exchange between a smart contract and the user interface that supports custom ether and token purchases in a form of an online book-selling system. Our results indicate the viability and efficiency of the Dapp. Finally, we have demonstrated through this work that smart contracts can extend the usability of blockchain technology to other fields.
SocialNER: A Training Dataset for Named Entity Recognition in Short Social Media Texts

Named entity recognition (NER) is one of the many natural language processing tasks in which deep learning has demonstrated superior performance. The effectiveness of deep learning models strongly depends on the quality of the training data. However, the training phase requires large amounts of labeled data. This paper presents SocialNer, a training dataset for deep learning models intended for the detection of named entities in short texts of social networks. SocialNer is constructed by merging information extracted from different challenge datasets of other natural language processing tasks (entity linking, question answering, information retrieval, etc.); and enriched with entity type, part of speech tag, and chunk tag. A well-tailored annotation procedure is adopted to ensure the quality of the dataset and an analysis of the constructed dataset is presented.
Efficient Robust Watermarking Based on Structure-Preserving Quaternion Singular Value Decomposition

Quaternion singular value decomposition (QSVD) is a robust technique of digital watermarking that extracts high quality watermarks from watermarked images with low distortion. However, the existing QSVD-based watermarking schemes face the obstacle of “explosion of complexity” and have much room for improvement in terms of real-time, invisibility, and robustness. In this paper, we overcome such obstacle by introducing a new real structure-preserving QSVD algorithm and propose a novel QSVD-based watermarking scheme with high efficiency. Secret information is transmitted blindly by incorporating two new strategies: coefficient pair selection and adaptive embedding. The highly correlated coefficient pairs determined by the normalized cross-correlation method reduce the impact of embedding by reducing the maximum modification of the coefficient values, resulting in high fidelity of the watermarked image. Large-size 8-color binary watermark and QR code effectively verify that the proposed watermarking scheme can resist various image attacks in numerical experiments. Two keys designed by Logistic chaotic map ensure the security of the watermarking system. Under the premise of considering the correlation of color channels, the proposed watermarking scheme not only performs well in real-time and invisibility, but also has satisfactory advantages in robustness compared with the state-of-the-art methods.
Spatial-Temporal Synchronous Transformer for Skeleton-Based Hand Gesture Recognition

Capturing the long-range spatial-temporal correlation among joints of dynamic skeletal data efficiently is very challenging in hand gesture recognition (HGR). The flexibility of Transformer in modeling global dependencies among elements of any sequence makes it a perfect solution for skeleton-based HGR. However, the existing Transformer-based approaches only capture the correlation of intra-frame and inter-frame joints, respectively, without considering the relationship among different joints in several successive frames. In this paper, a novel spatial-temporal synchronous transformer (STST) method is proposed for skeleton-based HGR. The spatial-temporal chunks encoding module is proposed to encode the hand gesture skeleton sequence (HGSS) into several chunks, in which each chunk contains several consecutive frames to encode the relationship among spatial-temporal joints. Then, the encoding feature is fed into a spatial-temporal chunks transformer module and a temporal integration transformer module to model the spatial-temporal correlation of HGSSs, simultaneously, so that a more comprehensive understanding of the global and local spatial-temporal information can be achieved. In this way, the spatial-temporal information among joints can be efficiently extracted and utilized to better understand the semantics of gesture actions and then yield a higher recognition accuracy. Extensive experiments on SHREC’17 Track dataset and DHG-14/28 dataset show that the proposed method achieves the state-of-the-art performance compared with other representative methods.
Verifying C++ Dynamic Binding

We propose an approach for modular verification of programs written in an object-oriented language where, like in C++, the same virtual method call is bound to different methods at different points during the construction or destruction of an object. Our separation logic combines Parkinson and Bierman's abstract predicate families with essentially explicitly tracking each subobject's vtable pointer. Our logic supports polymorphic destruction. Virtual inheritance is not yet supported. We formalized our approach and implemented it in our VeriFast tool for semi-automated modular formal verification of C++ programs.
Back and Forth—On Automatic Exposure of Origin and Dissemination of Files on Windows

The number of Child Sexual Abuse Material (CSAM) cases has increased dramatically in recent years. This leads to the need to automate various steps in digital forensic processing, especially for CSAM investigations. For instance, if CSAM pictures are found on a device, the investigator aim at finding traces about the origin and possible further dissemination, respectively. In this article, we address this challenge with respect to the widespread Windows operating system. We model different common scenarios of system use by CSAM offenders in the scope of file inbound and outbound transfer channels. This gives us insights about digital traces in the Windows operating system and its applications to get knowledge about origin and possible destination of a file. We review available concepts and applications to support this issue. Furthermore, we develop a recursive-based approach and provide a prototype as plugin for the open source application Autopsy. We call our prototype AutoTrack . Our evaluation against the different models of Windows system usage reveals that Autotrack is superior to existing solutions and provides support for an investigator to find digital traces about the origin and possible further dissemination of files. We publish our AutoTrack plugin and thus provide full reproducibility of our approach.
An end-to-end multi-resolution feature fusion defogging network

Traditional convolutional neural networks work well on single-image defogging synthetic datasets, but for real-world images with different concentrations, it leads to incomplete defogging or distortion and loss of image detail information. In this paper, the authors propose an end-to-end single-image defogging network, which adopts an encoder–decoder structure, obtains a large perceptual field of view of high pixels through a large number of pooling operations in the U-Net network, and uses operations such as hopping connections to retain most of the image feature information. To achieve superior quality haze removal, the proposed method utilizes a bilateral grid to capture high-frequency information pertaining to the image edges in low-resolution pixels. Additionally, relevant haze-related features are extracted, and a local affine model is fitted within the bilateral space. Finally, the high and low pixel data are integrated with the extracted features to generate clear and vivid images. The authors compare the algorithm qualitatively and quantitatively with several state-of-the-art algorithms and show that the algorithm achieves better defogging results in both the SOTS dataset and real-world images, retains high-frequency image details, achieves higher peak signal-to-noise ratio, and performs better defogging in haze images with different concentrations.
Integrated Privacy Preservation with Novel Encoding and Encryption for Securing Video in Internet-of-Things

With the modernization of communication and security processes, the mechanism of video security system has also undergone a tremendous transformation leading to intelligence-incorporated video surveillance system. Review of existing literatures shows the presence of some of the concrete evolving security methodologies; however, they are never assessed on bigger scale of deployment scenario like that in Internet-of-Things (IoT). Therefore, the proposed scheme presents a novel computational analytical model where a unique encoding and simplified encryption scheme is introduced. The raw video feed from multiple location is stored in cloud server where they are indexed and subjected to sequences of security processing. The study outcome shows that proposed system to offer a potentially enhanced performance in comparison to existing scheme of video security.