Reviewerly/exHarmony huggingface dataset:
Keyword Extraction in Economics Literatures using Natural Language Processing

Using Natural Language Process (NLP) as an efficient way to research paper is important when user feedback is sparse or unavailable. The task of text mining research paper is challenging, mainly due to the problem of unique characteristics such as jargon. Nowadays, there exist many language models that learn deep semantic representations by being trained on huge corpora. In this paper, we specify the NLP pre-processing process with Economics journal paper and apply it to a deep learning model to extract keywords. Here, we focus on the strength of NLP when applied to an unknown field. The analysis result shows the possibility and potential usefulness of the relationship research between keywords in research papers.
Completeness in Information Systems Ontologies

Abstract In the domain of information systems ontologies, the notion of completeness refers to ontological contents by demanding that they be exhaustive with respect to the domain that the ontology aims to represent. The purpose of this paper is to analyze such a notion, by distinguishing different varieties of completeness and by questioning its consistency with the open-world assumption, which formally assumes the incompleteness of conceptualizations on information systems ontologies.
UUCT - HyMP: Towards Tracking Dispersed Crowd Groups from UAVs

Aerial tracking of dispersed crowd groups with a single target window is a novel and one of the most challenging problems in Computer Vision and Robotics. Considering crowd group as a multi-object tracking problem can often lead to computational burden and frequent target mismatch due to numerous occlusions, whereas a single window can efficiently focus on the target. Recent progress on single object tracking (SOT) algorithms is achieved by learning a generic discriminator model from object tracking datasets, continuously updated during the testing steps. However, while tracking a group of crowd with a single window, the rigid discriminator can not generalize frequent group reformation, binomial dispersion, and crowd shape changes due to less knowledge about human-to-human interactions. To alleviate the issues, we propose a novel photo-realistic Unreal UAV Crowd Tracking (UUCT) dataset, which benchmarks aerial crowd group movements into several attributes. Second, we formulate a novel algorithm, Hybrid Motion Pooling (HyMP), which extends the existing SOT algorithm, DiMP, by exploiting graph convolutional networks for learning human groups and low-rank bilinear pooling for capturing temporal group reformations end-to-end. Then, we compare HyMP with state-of-the-art (SOTA) trackers on UUCT to demonstrate HyMP's effectiveness in group tracking. Also, we illustrate the generalizability of HyMP by evaluating on the existing benchmarks. On average, HyMP outperforms SOTA approaches by 7.5% on UUCT and 4.3% on related datasets.
Implementation of Sentiment Analysis Movie Review based on IMDB with Naive Bayes Using Information Gain on Feature Selection

A movie review is an information that forms an evaluation relating to each aspect inherent in a movie. The information contained in the film can be concluded as the quality of a film experience from the audience, but the rating of 'inappropriate' with the context sentence makes the rating of the film 'not recommended'. This issue supports this research where a film should be classified by sentiment. This paper uses the Naïve Bayes method for classification, TF-IDF as the feature extraction, and Information Gain as the feature selection. However, the uses of Naïve Bayes to classify the word are conditionally independent of each other. This research uses TF-IDF and Information Gain to prevent this problem. From the evaluation results on the testing process, the maximum performance results are 84.50% for precision, 88.27% for precision, 88.27% for recall, and 86.34% for f1-score. From the evaluation results, it can be concluded that using TF-IDF, Information Gain, and Naïve Bayes can produce precision, recall, and f1-score value which is good enough.
Portrait Thangka Image Retrieval via Figure Re-identification

Recognizing figures in portrait Thangka images is fundamental to the appreciation of this unique Tibetan art. In this paper, we focus on the problem of portrait Thangka image retrieval from the perspective of re-identifying the figures in the images. Based on state-of-the-art re-identification methods, we further improve them by exploiting several tricks. We also investigate the impact of different cropping methods to evaluate the contribution of different features. Our evaluation results on an annotated portrait Thangka image dataset collected by ourselves demonstrate the necessity of further study on this challenging problem. We will release the data and code to promote the research on Thangka.
A New Coding Scheme for Line Segment Clipping in E2

This contribution presents a new coding scheme based on Cohen-Sutherland line segment clipping algorithm, which enables to distinguish all possible cases easily. It leads to more efficient algorithm for a line segment clipping in E2. It also presents importance of a detailed analysis in algorithm development, if the algorithm robustness and efficiency is required.
Interdisciplinary Collaboration in Augmented Reality Development - A Process Model

The development of Augmented Reality (AR) applications still poses several challenges for research and development. Apart from the complex modeling of interaction concepts comprising physical and virtual objects, the collaboration of different actors within the development process is particularly demanding. In AR development, authoring tools facilitate the modeling of User Interfaces (UI) and special frameworks simplify the implementation of AR-specific functionalities, but collaborative processes between UI designers and software developers are still underrepresented. We present a process model for interdisciplinary collaboration in AR development, based on several components which convert modeled UIs into collated artifacts that facilitate a shared understanding of the desired application. A conducted empirical study demonstrated the suitability of generated artifacts for the process model, since no specialized prior knowledge was needed to correctly interpret process artifacts, which thus serves the cross-role understanding of the desired application as a basis for interdisciplinary collaboration.
Hybrid Variational Autoencoder for Recommender Systems

E-commerce platforms heavily rely on automatic personalized recommender systems, e.g., collaborative filtering models, to improve customer experience. Some hybrid models have been proposed recently to address the deficiency of existing models. However, their performances drop significantly when the dataset is sparse. Most of the recent works failed to fully address this shortcoming. At most, some of them only tried to alleviate the problem by considering either user side or item side content information. In this article, we propose a novel recommender model called Hybrid Variational Autoencoder (HVAE) to improve the performance on sparse datasets. Different from the existing approaches, we encode both user and item information into a latent space for semantic relevance measurement. In parallel, we utilize collaborative filtering to find the implicit factors of users and items, and combine their outputs to deliver a hybrid solution. In addition, we compare the performance of Gaussian distribution and multinomial distribution in learning the representations of the textual data. Our experiment results show that HVAE is able to significantly outperform state-of-the-art models with robust performance.
Radar Signal Recognition Based on Deep Residual Network with Attention Mechanism

To address the problem of poor recognition success rate (RSR) of low probability of intercept (LPI) radar signals such as polyphase codes at low signal-to-noise ratio (SNR), In this paper, we propose a method of deep neural network with attention mechanism for modulation recognition of radar signals. The method first transforms the received radar signal into a two-dimensional time-frequency image(TFI), and then we obtain a binary time-frequency image by threshold segmentation, and resize TFIs through bicubic interpolation. While a deep residual network with Convolutional Block Attention Module (CBAM) is designed to recognize the radar signals. We take advantage of the attention mechanism to find the effective feature information in the noise and increase the effective information weight to further improve the recognition success rate of radar signals under low SNR. Finally, the average RSR of the method is nearly 96% for 7 signals at -6dB.
Quantum Computing and the Design of the Ultimate Accelerator

Quantum computers are domain-specific accelerators that can solve commercially important problems that are beyond the capability of conventional computers. Quantum computing is at an inflection point where small systems with a few dozen qubits have been demonstrated and the number of qubits is expected to increase to several hundred over the coming years. However, the qubit quality is likely to remain quite low making it difficult to execute programs reliably. Furthermore, scaling the designs to a large number of qubits would require specialized hardware that operates at cryogenic conditions. This article describes the compiler and hardware support for enabling reliable and scalable quantum computers.
Vacuum-gap-based lumped element Josephson parametric amplifier

Abstract We propose a lumped element Josephson parametric amplifier with vacuum-gap-based capacitor. The capacitor is made of quasi-floating aluminum pad and on-chip ground. We take a fabrication process compatible with air-bridge technology, which makes our design adaptable for future on-chip integrated quantum computing system. Further engineering the input impedance, we obtain a gain above 20 dB over 162-MHz bandwidth, along with a quasi quantum-limit noise performance. This work should facilitate the development of quantum information processing and integrated superconducting circuit design.
DeepRare: Generic Unsupervised Visual Attention Models

Visual attention selects data considered as “interesting” by humans, and it is modeled in the field of engineering by feature-engineered methods finding contrasted/surprising/unusual image data. Deep learning drastically improved the models efficiency on the main benchmark datasets. However, Deep Neural Networks-based (DNN-based) models are counterintuitive: surprising or unusual data are by definition difficult to learn because of their low occurrence probability. In reality, DNN-based models mainly learn top-down features such as faces, text, people, or animals which usually attract human attention, but they have low efficiency in extracting surprising or unusual data in the images. In this article, we propose a new family of visual attention models called DeepRare and especially DeepRare2021 (DR21), which uses the power of DNNs’ feature extraction and the genericity of feature-engineered algorithms. This algorithm is an evolution of a previous version called DeepRare2019 (DR19) based on this common framework. DR21 (1) does not need any additional training other than the default ImageNet training, (2) is fast even on CPU, (3) is tested on four very different eye-tracking datasets showing that DR21 is generic and is always within the top models on all datasets and metrics while no other model exhibits such a regularity and genericity. Finally, DR21 (4) is tested with several network architectures such as VGG16 (V16), VGG19 (V19), and MobileNetV2 (MN2), and (5) it provides explanation and transparency on which parts of the image are the most surprising at different levels despite the use of a DNN-based feature extractor.
Can Global, Extended, and Repeated Ransomware Attacks Overcome the User's Status Quo Bias and Cause a Switch of System?

Ransomware attack effectiveness has increased causing far reaching consequences that are not fully understood. The ability to disrupt core services, the global reach, extended duration, and the repetition has increased their ability to harm organizations. One aspect that needs to be understood better is the effect on the user. The user in the current environment is exposed to new technologies that might be adopted, but there are also habits of using existing systems. The habits have developed over time with trust increasing in the organization in contact directly and the institutions supporting it. This research explores whether the global, extended, and repeated RW attacks reduce the trust and inertia sufficiently to change long-held habits in using information systems. The model tested measures the effect of the RW attack on the e-commerce status quo to evaluate if it is significant enough to overcome the users resistance to change.
African buffalo optimized multinomial softmax regression based convolutional deep neural network for software fault prediction

Software fault prediction is an essential part of the software quality assurance to detect faulty software modules depending on software measurement data. This models area of the research generates software engineers focus to development activities on fault-prone code for increasing the software quality. Many existing designed for improving the software quality by various methods. The software failure cause prediction accuracy and defect detection rate of the conventional techniques were not sufficient. Introduced an African Buffalo Optimized Multinomial Softmax Regression based Convolutional Deep Neural Learning (ABOMSR-CDNL). ABOMSR-CDNL model to enhance the software reliability through predicting root cause of software failure at earlier stage. ABOMSR-CDNL Model comprises four layers, namely input layer, two hidden layers and output layer and software program codes, event log files as an input layer then transmits the software program codes to the hidden layer 1. Construct the projects portfolio with help of optimal parameters selected from event log files then sent to the hidden layer 2. This method reduce the amount of time taken for examining the failure behaviour of system application. ABOMSR-CDNL Model applied multinomial softmax regression analysis in hidden layer 2 this objective of determining the cause of higher accuracy the result is sent to the output layer. Result illustrates ABOMSR-CDNL Model is increasing the accuracy, false positive rate and minimized the software failure identification time.
Searchable Encryption System for Big Data Storage

Big data cloud platforms provide users with on-demand configurable computing, storage resources to users, thus involving a large amount of user data. However, most of the data is processed and stored in plaintext, resulting in data leakage. At the same time, simple encrypted storage ensures the confidentiality of the cloud data, but has the following problems: if the encrypted data is downloaded to the client and then decrypted, the search efficiency will be low. If the encrypted data is decrypted and searched on the server side, the security will be reduced. Data availability is finally reduced, and indiscriminate protection measures make the risk of data leakage uncontrollable. To solve the problems, based on searchable encryption and key derivation, a cipher search system is designed in this paper considering both data security and availability, and the use of a search encryption algorithm that supports dynamic update is listed. Moreover, the system structure has the advantage of adapting different searchable encryption algorithm. In particular, a user-centered key derivation mechanism is designed to realize file-level fine-grained encryption. Finally, extensive experiment and analysis show that the scheme greatly improves the data security of big data platform.
Enabling Large-Scale Federated Learning over Wireless Edge Networks

Major bottlenecks of large-scale Federated Learning (FL) networks are the high costs for communication and computation. This is due to the fact that most of current FL frameworks only consider a star network topology where all local trained models are aggregated at a single server (e.g., a cloud server). This causes significant overhead at the server when the number of users are huge and local models&#x0027; sizes are large. This paper proposes a novel edge network architecture which decentralizes the model aggregation process at the server, thereby significantly reducing the aggregation latency of the whole network. In this architecture, we propose a highly-effective in-network computation protocol consisting of two components. First, an in-network aggregation process is designed so that the majority of aggregation computations can be offloaded from cloud server to edge nodes. Second, a joint routing and resource allocation optimization problem is formulated to minimize the aggregation latency for the whole system at every learning round. The problem turns out to be NP-hard, and thus we propose a polynomial time routing algorithm which can achieve near optimal performance with a theoretical bound. Numerical results show that our proposed framework can dramatically reduce the network latency, up to 4.6 times. Furthermore, this framework can significantly decrease cloud&#x0027;s traffic and computing overhead by a factor of <tex>$K$</tex>/ M, where <tex>$K$</tex> is the number of users and <tex>$M$</tex> is the number of edge nodes, in comparison with conventional baselines.
A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images

The development of digitization methods for line drawings – especially in the area of electrical engineering – relies on the availability of publicly available training and evaluation data. This paper presents such an image set along with annotations. The dataset consists of \(1152\) images of \(144\) circuits by \(12\) drafters and \(48\,539\) annotations. Each of these images depicts an electrical circuit diagram taken by consumer grade cameras under varying lighting conditions and perspectives. A variety of different pencil types and surface materials has been used. For each image, all individual electrical components are annotated with bounding boxes and one out of \(45\) class labels. In order to simplify a graph extraction process, different helper symbols like junction points and crossovers are introduced, while texts are annotated as well. The geometric and taxonomic problems arising from this task as well as the classes themselves and statistics of their appearances are stated. The performance of a standard Faster RCNN on the dataset is provided as an object detection baseline.
Siamese Network-Based Few-Shot Learning for Classification of Human Peripheral Blood Leukocyte

In the present study, we propose a Siamese network-based few-shot learning method to classify rare human peripheral blood leukocyte images. The proposed Siamese network contains two identical convolutional neural networks (CNNs) and a logistic regression network (LRN). The CNNs are utilized to extract image features and the LRN is responsible for comparing the similarity of these features. To train the Siamese network, two leukocyte samples that belong to the same category make up genuine pairs and two leukocyte samples from disparate categories make up impostor pairs. A strategy for generating sample pairs is designed for this study to ensure the number of genuine pairs and impostor pairs is equal. After the Siamese network accomplishes the training process, with the purpose of evaluating the classification performance of the proposed Siamese network, an appropriate support set is selected to test the samples in the query. The numerical results show that the Siamese network can overcome the scarcity and imbalance of datasets used in this research, and the average classification accuracy of rare leukocyte images can achieve 89.7%, which is potential to apply the Siamese network-based few-shot learning method for addressing the issue of rare leukocyte images recognition in medicine.
Revisiting Text Guide, a Truncation Method for Long Text Classification

The quality of text classification has greatly improved with the introduction of deep learning, and more recently, models using attention mechanism. However, to address the problem of classifying text instances that are longer than the length limit adopted by most of the best performing transformer models, the most common method is to naively truncate the text so that it meets the model limit. Researchers have proposed other approaches, but they do not appear to be popular, because of their high computational cost and implementation complexity. Recently, another method called Text Guide has been proposed, which allows for text truncation that outperforms the naive approach and simultaneously is less complex and costly than earlier proposed solutions. Our study revisits Text Guide by testing the influence of certain modifications on the method’s performance. We found that some aspects of the method can be altered to further improve performance and confirmed several assumptions regarding the dependence of the method’s quality on certain factors.
Multi-modal discrete tensor decomposition hashing for efficient multimedia retrieval

In the research field of multimedia retrieval, unsupervised multi-modal hashing has received widespread attention because of its high retrieval efficiency, low storage cost and semantic label independence. However, there are still several problems that need to be resolved: 1) All existing methods are based on matrix factorizations, which has limited capability on fusing heterogeneous multi-modal features under the unsupervised learning paradigm. 2) Most methods adopt two-step learning strategy to separately learn the hash codes and functions, which may generate sub-optimal hash functions. 3) The optimization strategies used by most methods produce significant quantization loss and computation cost. In this paper, an efficient unsupervised Multi-modal Discrete Tensor Decomposition Hashing (MDTDH) approach is proposed to solve the above problems. Specifically, all multi-modal features are first stacked together into a three-dimensional tensor after nonlinear mapping, and then it is decomposed further into a core tensor and two factor matrices by Tucker decomposition. A series of hash functions are learned simultaneously by mapping the nonlinear features of the training instances to their corresponding hash codes. To reduce the quantization loss and computation cost, a fast discrete optimization strategy is proposed to generate hash codes directly without relaxing quantization loss. Extensive experimental results on three benchmarks demonstrate that our proposed MDTDH obtains superior performance than state-of-the-art unsupervised baselines, and even defeats several supervised baselines. Our source codes and testing datasets are available at https://github.com/XizeWu/MDTDH.