Reviewerly/exHarmony huggingface dataset:
Multiple-Granularity Graph for Document-Level Relation Extraction

As an important branch of relation extraction, document-level relation extraction aims to extract relations among entities in the document. Compared with sentence-level relation extraction, document-level relation extraction relies on the implicit semantic connections between sentences to obtain relation between entities. In this paper, we propose Multiple-Granularity Graph (MGG) to extract relations within document, which uses encoding module, graph construction module, relation inference module and classification module. In the graph construction module, we define four node types, which are mention node, entity node, sentence node and document node. Based on these nodes, seven edge types are defined, which are Mention-Mention edge, Mention-Edge edge, Mention-Sentence edge, Mention-Document edge, Entity-Sentence edge, Sentence-Sentence edge and Entity-Entity edge. We then use path reasoning mechanism with attention mechanism to calculate the representation of Entity-Entity edge. Finally we judge the relation between entities by classification module. The experiment result shows that this method has achieved 62.77%, 63.11% and 62.94% in accuracy, recall and F1 value on the task of document-level relation extraction. Compared with other traditional methods, MMG can significantly improve the performance of document-level relation extraction.
Image Forgery Detection Using Supervised Learning Algorithm

The pictures on the Internet and the photos-sharing platform are extremely possible to be edited with other universal alterations like compression, resizing, or screening. So, image forgery detection algorithms are used to detect such manipulations on images. A framework is presented to improve the reliability of image forgery detection. The main stage in the structure is to take into consideration the image performance of the software selected. Hence, utilize a camcorder model recognition based on CNN. JPEG is an image compressor that enables us to investigate the alteration and is the most popular sort of deliberate and involuntary concealment of picture forgeries. Consequently, a variety of two qualities compressed and uncompressed images is provided to the qualified CNN. To better decipher our trained CNN, we initially recommended thorough monitoring of the layers but also an experimental evaluation of the impact of the learning features. This examination will take us to a stronger and more precise approach. Furthermore, in an image forgery detection scenario, we will use this upgraded detection algorithm and show impressive outcomes in the identification and demonstration of the locations where the image has been forged or manipulated.
Safety Helmet Wearing Detection Based on A Lightweight YOLOv4 Algorithm

Safety helmet wearing plays a major role in protecting the safety of workers in industry and construction, so a real-time helmet wearing detection technology is very necessary. This paper makes a lightweight improvement on the original YOLOv4 algorithm and obtains a real-time and efficient safety helmet wearing detection algorithm. The lightweight YOLOv4 uses GhostNet as the backbone network for reducing the number of parameters. The adoption of the coordinate attention mechanism makes the model focused more on the region of interest and effectively distinguishes the background and foreground. Weighted bi-directional feature pyramid network (BiFPN) is introduced to combine with PANet as the enhanced feature extraction network of improved YOLOv4 to further process multi-scale information, enhance feature fusion and improve detection accuracy. Replace the general 3 × 3 convolution with the depthwise separable convolution to further reduce the network structure parameters and improve the inference speed. The experimental data show that the mAP of the improved YOLOv4 model reaches 91.30%. Compared with the original YOLOv4, the accuracy is only reduced by about 1%, but the speed is 45% higher, and the model size is 197M lower. Compared with YOLOv4-Tiny, the accuracy is improved by about 10%.
Website Development for Alumni of Education Information Technology and Computer

&#x0D; ABSTRACT&#x0D; Information technology is all forms of technology that help humans in terms of creating, storing, or disseminating information. Along with the times, information technology is more associated with high-speed computing and communication such as computers and mobile phones. The Institute of Teacher Training and Education of the Indonesian Teachers Association (IKIP-PGRI) Pontianak as an educational institution that requires information technology to assist activities within the Pontianak IKIP-PGRI environment. Based on the results of observations by conducting interviews, it is known that the study program has an information system website that contains information on study programs, but in the information system there are several shortcomings, namely the news and job vacancies have not been updated and there is no special alumni data information system to manage alumni data. Based on these problems, the researcher intends to develop a web-based information system for the alumni database using the R&amp;D method and the ADDIE approach. The results of the system development were validated by 2 system experts to see the percentage of feasibility. The alumni information system has been tested for feasibility in terms of usability, functionality, and visual communication aspects. The overall average value in these aspects is 4.20 and 4.65, both values ​​are included in the "Very Good" criteria.&#x0D; .
Adv-Cut Paste: Semantic adversarial class specific data augmentation technique for object detection

Data augmentation has been a prevalent approach in improving the performance of deep learning models against slight variations in data. Adversarial learning is one such form of data augmentation. In this work, we aim to introduce a framework to generate harder examples for a specific object class and an adversarial attack for the object detection task. We have also presented our study on the effect of training against such generated harder examples and adversarial samples in object detection. We have applied this adversarial learning technique to a YOLOv3 model and due to the nature of the attack, we demonstrated a substantial improvement in average precision (AP) for a single class of the COCO dataset. As per the literature, we are the first to introduce this kind of class-specific data augmentation strategy in object detection. With our approach, we have shown an improvement of 23.34% in AP for Cat class and 3.1% on overall mAP of YOLOv3 model on clean validation data, while 43.5% improvement in AP for the Cat class on the composite images with class-specific adversarial samples.
Modelling Multi-relations for Convolutional-based Knowledge Graph Embedding

Representation learning of knowledge graphs aims to embed entities and relations into low-dimensional vectors. Most existing works only consider the direct relations or paths between an entity pair. It is considered that such approaches disconnect the semantic connection of multi-relations between an entity pair, and we propose a convolutional and multi-relational representation learning model, ConvMR. The proposed ConvMR model addresses the multi-relation issue in two aspects: (1) Encoding the multi-relations between an entity pair into a unified vector that maintains the semantic connection. (2) Since not all relations are necessary while joining multi-relations, we propose an attention-based relation encoder to automatically assign weights to different relations based on semantic hierarchy. Experimental results on two popular datasets, FB15k-237 and WN18RR, achieved consistent improvements on the mean rank. We also found that ConvMR is efficient to deal with less frequent entities.
SignNet II: A Transformer-Based Two-Way Sign Language Translation Model

The role of a sign interpreting agent is to bridge the communication gap between the hearing-only and Deaf or Hard of Hearing communities by translating both from sign language to text and from text to sign language. Until now, much of the AI work in automated sign language processing has focused primarily on sign language to text translation, which puts the advantage mainly on the side of hearing individuals. In this article, we describe advances in sign language processing based on transformer networks. Specifically, we introduce SignNet II, a sign language processing architecture, a promising step towards facilitating two-way sign language communication. It is comprised of sign-to-text and text-to-sign networks jointly trained using a dual learning mechanism. Furthermore, by exploiting the notion of sign similarity, a metric embedding learning process is introduced to enhance the text-to-sign translation performance. Using a bank of multi-feature transformers, we analyzed several input feature representations and discovered that keypoint-based pose features consistently performed well, irrespective of the quality of the input videos. We demonstrated that the two jointly trained networks outperformed their singly-trained counterparts, showing noteworthy enhancements in BLEU-1 - BLEU-4 scores when tested on the largest available German Sign Language (GSL) benchmark dataset.
SPN2D-GAN: Semantic Prior Based Night-to-Day Image-to-Image Translation

Existing image-to-image translation approaches can deal with simple scenes or styles effectively, such as summer-to-winter, horses-to-zebra, and photo-to-map. Although a great progress has been made by GAN-based methods recently, the performance of night-to-day (N2D) translation remains unsatisfactory due to imbalanced/poor visibility, and thus leading to translation ambiguity. To improve the quality of N2D translation, we propose an unpaired translation scheme based on a semantic prior generator, namely SPN2D-GAN, in a weakly- supervised manner with consideration of both image and semantic information. Specifically, we design a novel N2D generator, which can adopt the semantic information of images as prior knowledge to generate more reasonable and realistic results. Also, we suggest adjusting the brightness of nighttime images to boost the visibility, so that the generator can better extract content information. Moreover, the proposed SPN2D-GAN translates images by enforcing the distribution of daytime images in both image and semantic domains on final outputs. Besides, the cycle consistency is employed to preserve the fidelity between translations from two directions. Extensive experimental results are provided to reveal the effectiveness of our design, and demonstrate its superior performance over other state-of-the-art N2D translation approaches both quantitatively and qualitatively.
Leveraging Lithium Niobate on Insulator Technology for Photonic Analog Computing

Machine learning has experienced an unprecedented growth over the last decade with graphics processing units being instrumental to this success, used as accelerators for the computations required during training and inference in artificial neural networks. However, the increasing complexity of the employed neuromorphic models might pose issues in terms of required computing resources and energy to run these tasks. Optical solutions, especially exploiting integrated photonics, are promising to effectively run neuromorphic tasks, exploiting highspeed and low-power elements. In this paper, we propose to exploit cascaded low-loss and low-driving-voltage travelling wave Lithium Niobate on Insulator (LNOI) modulators to perform multiply-accumulate operations at high speed and low power consumption. Thanks to the moderate losses, the same input can be split to multiple weight modulators, which increases the energy efficiency. Simulations of computer vision tasks on well known datasets show that the proposed solution, notwithstanding the limitations due to the analog photonic physical layer, achieves a good accuracy with limited degradation with respect to traditional digital electronic implementations.
An Efficient Open Vote Network for Multiple Candidates

In 2010, Hao et al. proposed an efficient decentralized voting protocol known as Open Vote Network (OV-Net), which does not require a trusted third party to count the votes or a private channel to protect ballot secrecy. In the last decade, various studies have been conducted to solve some limitations of the OV-Net, such as fairness and robustness. However, an unresolved problem exists in the OV-Net (and its variants), which is its limited scalability for multiple candidates. The computational cost for tallying, which increases exponentially with the number of candidates, causes the scalability problem. Therefore, in this study, we solve this problem by proposing a variant of the OV-Net for multiple candidates and showing that the computational cost of tallying increases linearly with the number of candidates. Regarding security, we prove that our variant satisfies the ballot secrecy and dispute-freeness in formal security models, based on the decision Diffie-Hellman assumption and non-interactive zero-knowledge properties, respectively. Moreover, regarding the efficiency, we compare the performance of the traditional and proposed OV-Net from the theoretical standpoint and present experimental results to show that the proposed OV-Net variant is considerably more efficient than the traditional OV-Net as the numbers of voters and candidates increase.
Synthesis of Bases of Boolean Functions Based on Post Classes

The paper introduced the concept of Post's characteristic of Boolean functions. Five-dimensional Post's cube has been constructed. All empty Post's classes are described, and nonempty classes form the Post's lattice. An algorithm of finding the base's systems of Boolean function has been presented, and Post's characteristics have been found. Formulas for calculating the number of two-functional, three-functional, and four-functional bases of an arbitrary system of Boolean functions are obtained. The results for the class of Boolean functions arnost equals two, three, and four, indicating the exact number of one-functional, two-functional, three-functional, and four-function bases. This paper uses Boolean functions in tables, graphs, n-dimensional cubes. Boolean functions are also presented analytically based on formulas of Boolean algebras. This is required to find of bases of Boolean functions with Post's characteristic, performed by a developed software package to analyze Boolean functions.
Sentiment Analysis of Electric Power Domain based on Transfer Learning

Sentiment analysis is an essential part of the natural language processing domain. Currently, sentiment analysis is mainly applied to scenarios such as user tweets, film comments, and opinion analysis. These scenarios generally contain sufficient training data. However, for some scenarios that lack sufficient training data, their performance is often limited. For this reason, a sentiment analysis method based on transfer learning was proposed in this study. With self-attention and adversarial learning as the core framework, the method could be used to assist the learning of small-scale specific domain sentiment analysis by using sentiment data from the rest of the large-scale domains, thus achieving better sentiment analysis results in small-scale specific domains. In the experimental part, the electric power field was taken as an example. The relevant experimental results demonstrate the effectiveness of the proposed method.
Image Sampling for Machine Vision

Image sampling is one of the basic methods for image compression, which is efficient for image store, transmission, and applications. Existing sampling methods are designed for human-eye perception, which discard unconcerned information to decrease the amount of data considering the visual preference of human. However, these methods cannot adapt to the increasing machine vision tasks since there is a lot of redundant information for machine analysis to ensure the comfort of human eyes. In this paper, we propose an image sampling method for machine vision. We adopt a gray image to retain the main structural information of the image, and construct a concise color feature map based on the dominant channel of pixels to provide color information. Experiments on public datasets including COCO and ImageNet show that our sampling method can adapt to the characteristics of machine vision and greatly reduce the amount of data with little impact on the performance of mainstream computer vision algorithms.
Zero-Shot Sketch Based Image Retrieval Using Graph Transformer

The performance of a zero-shot sketch-based image retrieval (ZS-SBIR) task is primarily affected by two challenges. The substantial domain gap between image and sketch features needs to be bridged, while at the same time the side information has to be chosen tactfully. Existing literature has shown that varying the semantic side information greatly affects the performance of ZS-SBIR. To this end, we propose a novel graph transformer based zero-shot sketch-based image retrieval (GTZSR) framework for solving ZS-SBIR tasks which uses a novel graph transformer to preserve the topology of the classes in the semantic space and propagates the context-graph of the classes within the embedding features of the visual space. To bridge the domain gap between the visual features, we propose minimizing the Wasserstein distance between images and sketches in a learned domain-shared space. We also propose a novel compatibility loss that further aligns the two visual domains by bridging the domain gap of one class with respect to the domain gap of all other classes in the training set. Experimental results obtained on the extended Sketchy, TU-Berlin, and QuickDraw datasets exhibit sharp improvements over the existing state-of-the-art methods in both ZS-SBIR and generalized ZS-SBIR.
Attention-Aware Learning for Hyperparameter Prediction in Image Processing Pipelines

Between the imaging sensor and the image applications, the hardware image signal processing (ISP) pipelines reconstruct an RGB image from the sensor signal and feed it into downstream tasks. The processing blocks in ISPs depend on a set of tunable hyperparameters that have a complex interaction with the output. Manual setting by image experts is the traditional way of hyperparameter tuning, which is time-consuming and biased towards human perception. Recently, ISP has been optimized by the feedback of the downstream tasks based on different optimization algorithms. Unfortunately, these methods should keep parameters fixed during the inference stage for arbitrary input without considering that each image should have specific parameters based on its feature. To this end, we propose an attention-aware learning method that integrates the parameter prediction network into ISP tuning and utilizes the multi-attention mechanism to generate the attentive mapping between the input RAW image and the parameter space. The proposed method integrates downstream tasks end-to-end, predicting specific parameters for each image. We validate the proposed method on object detection, image segmentation, and human viewing tasks.
ITCareerBot: A Personalized Career Counselling Chatbot

Nowadays, career counselling, which is a service designed to help people finding the right professional learning is emerging. In the information technology (IT) domain, this service is facing the challenge of changing very quickly in business environments, technologies and tools. Consequently, IT students and professionals often need additional knowledge and skills to fulfill market requirements and to target their professional goal in order to increase their opportunity for growth. This study aims at leveraging a specific type of chatbots to offer an intelligent and personalized advising learning services to job seekers as learners by providing information and recommendations of a learning path according to the market trends and learners’ profile. Firstly, a chatbot framework is developed based on a context-aware knowledge model and a recommendation method. Thus, the context-aware knowledge base and its instance were built by analyzing different data sources collected from professional social networks and online education platforms. Furthermore, the recommendation method defines how the chatbot responds to user messages based on the matching between the current job seekers’ skills and their career interest. Concerning research evaluation, the effectiveness of the proposed framework is validated by measuring the algorithms. Moreover, some efficient and satisfaction criteria were analyzed and evaluated based on the feedback from a survey.
Pseudo Label Rectification With Joint Camera Shift Adaptation and Outlier Progressive Recycling for Unsupervised Person Re-Identification

Person re-identification (re-ID) has many applications in intelligent transportation systems. Clustering-based methods, which alternate between the generation of pseudo labels via clustering and the optimization of the feature extractor, have obtained leading performance in unsupervised person re-ID. But there are still two issues not well addressed: 1) Most methods measure the feature similarity without considering the domain shift between cameras, degrading the clustering performance. 2) Outliers, which usually correspond to hard samples with large discrepancy from other images of the identical person, are in most cases directly excluded from the network training. To tackle the above issues, this paper proposes a plug-and-play pseudo label rectification framework, which jointly utilizes CAmera Shift adapTation module and Outlier progressive Recycling strategy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CASTOR$ </tex-math></inline-formula> ) to improve the quality of pseudo labels from both pre-clustering and post-clustering. Specifically, we first compute the camera similarity of two samples by utilizing a pretrained camera classification network and subtract the feature similarity by the camera similarity, the value of which is weighted in an exponential decay manner throughout the network training, in order to adaptively remedy the adverse impact of inter-camera distribution shift upon clustering. Besides, we carefully design an outlier progressive recycling strategy to reassign part of the outliers into the clustered groups to make full use of the useful information of outliers. Extensive experiments on three large scale unsupervised and unsupervised domain adaptive (UDA) person re-ID benchmarks validate the effectiveness of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CASTOR$ </tex-math></inline-formula> and its wide compatibility with the state-of-the-art clustering-based methods.