Reviewerly/exHarmony huggingface dataset:
A large-scale dataset for end-to-end table recognition in the wild

Abstract Table recognition (TR) is one of the research hotspots in pattern recognition, which aims to extract information from tables in an image. Common table recognition tasks include table detection (TD), table structure recognition (TSR) and table content recognition (TCR). TD is to locate tables in the image, TCR recognizes text content, and TSR recognizes spatial &amp; ontology (logical) structure. Currently, the end-to-end TR in real scenarios, accomplishing the three sub-tasks simultaneously, is yet an unexplored research area. One major factor that inhibits researchers is the lack of a benchmark dataset. To this end, we propose a new large-scale dataset named Table Recognition Set ( TabRecSet ) with diverse table forms sourcing from multiple scenarios in the wild, providing complete annotation dedicated to end-to-end TR research. It is the largest and first bi-lingual dataset for end-to-end TR, with 38.1 K tables in which 20.4 K are in English and 17.7 K are in Chinese. The samples have diverse forms, such as the border-complete and -incomplete table, regular and irregular table (rotated, distorted, etc.). The scenarios are multiple in the wild, varying from scanned to camera-taken images, documents to Excel tables, educational test papers to financial invoices. The annotations are complete, consisting of the table body spatial annotation, cell spatial &amp; logical annotation and text content for TD, TSR and TCR, respectively. The spatial annotation utilizes the polygon instead of the bounding box or quadrilateral adopted by most datasets. The polygon spatial annotation is more suitable for irregular tables that are common in wild scenarios. Additionally, we propose a visualized and interactive annotation tool named TableMe to improve the efficiency and quality of table annotation.
Examining the relationship between TPACK and STEAM through a bibliometric study

This research is a bibliometric study that focuses on publications containing both terms “technological pedagogical content knowledge” (TPACK or TPCK) and “science, technology, engineering, arts, and mathematics” (STEAM). The former addresses knowledge that teachers are expected to gain, whereas the latter addresses a new integrating subject field. Both terms dominate contemporary education research, especially in relation to technology and its impact on education. These two terms have been researched individually by bibliometricians. The aim of this research is to examine articles that combine these terms, as there seems to be a lack of such studies. With the help of the Scopus platform, 2,608 articles published since 2007 were collected and analyzed.
Analysis of Government Policy Sentiment Regarding Vacation during the COVID-19 Pandemic Using the Bidirectional Encoder Representation from Transformers (BERT)

To address the COVID-19 situation in Indonesia, the Indonesian government has adopted a number of policies. One of them is a vacation-related policy. Government measures with regard to this vacation policy have produced a wide range of viewpoints in society, which have been extensively shared on social media, including YouTube. However, there has not been any computerized system developed to date that can assess people’s social media reactions. Therefore, this paper provides a sentiment analysis application to this government policy by employing a bidirectional encoder representation from transformers (BERT) approach. The study method began with data collecting, data labeling, data preprocessing, BERT model training, and model evaluation. This study created a new dataset for this topic. The data were collected from the comments section of YouTube, and were categorized into three categories: positive, neutral, and negative. This research yielded an F-score of 84.33%. Another contribution from this study regards the methodology for processing sentiment analysis in Indonesian. In addition, the model was created as an application using the Python programming language and the Flask framework. The government can learn the extent to which the public accepts the policies that have been implemented by utilizing this research.
CoGNN: Efficient Scheduling for Concurrent GNN Training on GPUs

Graph neural networks (GNNs) suffer from low GPU utilization due to frequent memory accesses. Existing concurrent training mechanisms cannot be directly adapted to GNNs because they fail to consider the impact of input irregularity. This requires pre-profiling the memory footprint of concurrent tasks based on input dimensions to ensure successful co-location on GPU. Moreover, massive training tasks generated from scenarios such as hyper-parameter tuning require flexible scheduling strategies. To address these problems, we propose CoGNN that enables efficient management of GNN training tasks on GPUs. Specifically, the CoGNN organizes the tasks in a queue and estimates the memory consumption of each task based on cost functions at operator basis. In addition, the CoGNN implements scheduling policies to generate task groups, which are iteratively submitted for execution. The experiment results show that the CoGNN can achieve shorter completion and queuing time for training tasks from diverse GNN models.
Optimizing Random Access to Hierarchically-Compressed Data on GPU

GPU's powerful computational capacity holds great potentials for processing hierarchically-compressed data without decompression in data science domain. Unfortunately, existing GPU approaches offer only traversal-based data analytics; random access is extremely inefficient, substantially limiting their utility. To solve this problem, we develop a novel and broadly applicable optimization that enables efficient random access to hierarchically-compressed data without decompression in GPU memory. We address three major challenges for enabling efficient random access to compressed data on GPUs. The first challenge is designing GPU data structures that support random access. The second challenge is efficiently generating data structures on GPU. Generating data structures for random access is costly on the CPU, and the inefficiency increases dramatically when PCIe data transmission is incorporated. The third challenge is query processing on compressed data in GPU memory. Random accesses, including data updates, result in significant conflicts between massive threads. To solve the first challenge, we propose and modify a number of compressed data structures, including indexing within the complicated GPU memory hierarchy. To address the second challenge, we develop a two-phase process for generating these data structures on the GPU. To handle the third challenge, we propose a double-parsing design to avoid data conflicts. We evaluate our solution on two GPU platforms using five real-world datasets. Experiments show that the random access operations on GPU can achieve 65.04x average speedup compared to the state-of-the-art method.
STRONGHOLD: Fast and Affordable Billion-Scale Deep Learning Model Training

Deep neural networks (DNNs) with billion-scale parameters have demonstrated impressive performance in solving many tasks. Unfortunately, training a billion-scale DNN is out of the reach of many data scientists because it requires high-performance GPU servers that are too expensive to purchase and maintain. We present STRONGHOLD, a novel approach for enabling large DNN model training with no change to the user code. STRONGHOLD scales up the largest trainable model size by dynamically offloading data to the CPU RAM and enabling the use of secondary storage. It automatically determines the minimum amount of data to be kept in the GPU memory to minimize GPU memory usage. Compared to state-of-the-art offloading-based solutions, STRONGHOLD improves the trainable model size by 1.9x~6. Sx on a 32GB V100 GPU, with 1.2x~3.7x improvement on the training throughput. It has been deployed into production to successfully support large-scale DNN training.
Accelerating Elliptic Curve Digital Signature Algorithms on GPUs

The Elliptic Curve Digital Signature Algorithm (ECDSA) is an essential building block of various cryptographic protocols. In particular, most blockchain systems adopt it to ensure transaction integrity. However, due to its high computational intensity, ECDSA is often the performance bottleneck in blockchain transaction processing. Recent work has accelerated ECDSA algorithms on the CPU; in contrast, success has been limited on the GPU, which has great potential for parallelization but is challenging for implementing elliptic curve functions. In this paper, we propose RapidEC, a GPU-based ECDSA implementation for SM2, a popular elliptic curve. Specifically, we design architecture-aware parallel primitives for elliptic curve point operations, and parallelize the processing of a single SM2 request as well as batches of requests. Consequently, our GPU-based RapidEC outperformed the state-of-the-art CPU-based algorithm by orders of magnitude. Additionally, our GPU-based modular arithmetic functions as well as point operation primitives can be applied to other computation tasks.
Charter: Identifying the Most-Critical Gate Operations in Quantum Circuits via Amplified Gate Reversibility

When quantum programs are executed on noisy intermediate-scale quantum (NISQ) computers, they experience hardware noise; consequently, the program outputs are often erroneous. To mitigate the adverse effects of hardware noise, it is necessary to understand the effect of hardware noise on the program output and more fundamentally, understand the impact of hardware noise on specific regions within a quantum program. Identifying and optimizing regions that are more noise-sensitive is the key to expanding the capabilities of NISQ computers. Toward achieving that goal, we propose Charter, a novel technique to pinpoint specific gates and regions within a quantum program that are the most affected by the hardware noise and that have the highest impact on the program output. Using Charter's methodology, programmers can obtain a precise understanding of how different components of their code affect the output and optimize those components without the need for non-scalable quantum simulation on classical computers.
Specific Radar Emitter Identification Using 1D-CBAM-ResNet

With the development of the multifunction radar, the traditional specific emitter identification (SEI) can no longer meet the needs of the observe-orient-decide-act (OODA) closed loop, and most identification networks are in the process of converting one-dimensional (1D) radar emitter signals into two-dimensional (2D) signals to adapt the network input, which easily misses information. To address the above problems, this paper adopts a 1D convolutional residual neural network with the convolutional block attention module (1D-CBAM-ResNet) for automatic learning and single-step identification of 1D inter-mediate frequency (IF) signals to improve SEI accuracy. The model combines the 1D residual building unit (1D-RBV) with the 1D convolutional block attention module (1D-CBAM) to effectively aggregate channel and spatial information to accurately capture fingerprint features within the pulse. The simulation results demonstrate that the overall identification accuracy of the algorithm for 10 emitters of the same type reaches 93.03%, which proves the effectiveness and feasibility of the module.
Scaling Graph 500 SSSP to 140 Trillion Edges with over 40 Million Cores

The SSSP kernel was first introduced into the Graph 500 benchmark in 2017. However, there has been no result from a full-scale world-top supercomputer. The primary reason is the poor work-inefficiency of existing algorithms at large scales. In this paper, we propose an SSSP implementation for The Newest Generation Sunway Supercomputer,including an SSSP algorithm to achieve work-efficiency, along with an adaptive dense/sparse-mode selection approach to achieve communication-efficiency. Our implementation reaches 7638 GTEPS, with 103158 processors (over 40 million cores), and achieves 3.7× in performance and 512× in graph size compared with the current top one on the Graph 500 SSSP list. Based on our experience of running extreme-scale SSSP, we uncover the root cause of its poor scalability: the weight distribution allows edges with weights close to zero, making the SSSP tree deeper on larger graphs. We further explore a scalability-friendly weight distribution by setting a non-zero lower bound to the edge weights.
GraphScale: Scalable Bandwidth-Efficient Graph Processing on FPGAs

Recent advances in graph processing on FPGAs promise to alleviate performance bottlenecks with irregular memory access patterns. Such bottlenecks challenge performance for a growing number of important application areas like machine learning and data analytics. While FPGAs denote a promising solution through flexible memory hierarchies and massive parallelism, we argue that current graph processing accelerators either use the off-chip memory bandwidth inefficiently or do not scale well across memory channels. In this work, we propose GraphScale, a scalable graph processing framework for FPGAs. For the first time, Graph-Scale combines multi-channel memory with asynchronous graph processing (i. e., for fast convergence on results) and a com-pressed graph representation (i. e., for efficient usage of memory bandwidth and reduced memory footprint). GraphScale solves common graph problems like breadth-first search, PageRank, and weakly -connected components through modular user-defined functions, a novel two-dimensional partitioning scheme, and a high-performance two-level crossbar design.
Mobile Learning to promote Renewable Energy Education at the Secondary Education level in developing countries.

Abstract Education is a fundamental component in advancing society towards a cleaner and sustainable energy consumption route. To achieve and promote the Sustainable development Goals (SDG’s) education will play a pivotal role in creating awareness and promotion of Renewable energy concepts amongst the general public. Malaysia and Bangladesh are two ambitious countries that has implemented various policies to harness energy from renewable sources but lacked to achieve their targets. This is mainly due to lack of RE education and interest and awareness amongst the public. Recent studies have shown a significant lack of RE curriculum specially in the secondary level of education in most of the developing countries. This lack of curriculum can directly impact the societal process to shift to a sustainable green energy future due to the lack of interest, awareness in students for RE career and importance. This gap of introducing RE education into the secondary education curriculum can be fulfilled by using Mobile Learning (M-Learning) methods and technologies as means to educate secondary schools students about RE. Both the countries have a population well equipped with smart devices and internet access thus equipping people with greater mobility, communication, and access to resources all over the world via the Internet, which can be used potentially to incorporate within the education sector as a mean for teaching and learning. Thus, this paper aims to highlight the educational strategies, theories and components that are associated with mobile learning and propose a mobile learning system that can be used to incorporate RE education in secondary education level in countries like Malaysia and Bangladesh.
Unconstrained vocal pattern recognition algorithm based on attention mechanism

Deep learning-based voiceprint recognition methods rely heavily on adequate datasets, especially those closer to the natural environment and more complex under unconstrained conditions. Yet, the data types of open-source speech datasets are too homogeneous nowadays, and there are some differences with the address collected in natural application environments. For few Chinese datasets used, this paper proposes and produces an unconstrained Chinese speech dataset with richer data types closer to those collected in a natural environment. To address the inadequate extraction of acoustic features in the unconstrained speech dataset, a new two-dimensional convolutional residual network structure based on the attention mechanism is designed and applied to acoustic feature extraction. The residual block structure in the residual network is improved by the SE module and the CBAM module to obtain the SE-Cov2d and CSA-Cov2d models respectively. Finally, it is experimentally demonstrated that the attention mechanism can help the network focus on more critical feature information and fuse more differentiated features in feature extraction.
Semantic Technology and Anthropomorphism

A long-standing debate exists on whether robots need personality. With voice assistants (VAs) (i.e., Google Assistant and Apple's Siri) as the research context, this study employed the stimulus-organism-response (SOR) model and the theory of reasoned action to investigate how the personalities that VAs display (i.e., humanlike traits and behavior traits) influence perceived risk, perceived enjoyment, trust, attitude to use, and continued usage intention. The results show that when VAs have more humanlike linguistic traits, such as tone and phrasing, and more positive behavior traits, such as politeness and helpfulness, users enjoy using VAs more, have more trust in VAs, and display a greater willingness to continue using VAs. Unlike past studies focusing on technical aspects, the results of this study provide decision-makers with a new perspective, showing that using more humanlike designs and giving VAs unique personalities can build user trust and increase willingness to use VAs.
Systematic Survey: Secure and Privacy-Preserving Big Data Analytics in Cloud

ABSTRACTMassive data are being generated exponentially every day. Therefore, analytics over data is inevitable nowadays to gain meaningful insights. Big Data Analytics (BDA) is powerful in critical applications while making effective decisions. Since the data to be processed are enormous in local systems, it is getting stored and processed in cloud platforms. Most of the clouds are public which are third-party resources. Security and privacy are the greatest concerns when it comes to the cloud. In the Big Data era, secure and private BDA has acquired the center of attention. This survey analyzes the various security and privacy solutions for BDA in the cloud environment from the combined perspectives of the three main subsystems: secure access control, secure data storage, and secure and private learning. Various techniques are studied and presented in each subsystem. The primary aim of this paper is to provide an overview of secure and private BDA in the cloud. Research challenges and future research directions in this area have also been presented.KEYWORDS: Big data analyticsprivacy and securitysecure access controlsecure data storagesecure and private learningcloud computing AcknowledgmentThe authors would like to thank all the reviewers of the article.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingNo funding was received for this work.
Where you edit is what you get: Text-guided image editing with region-based attention

Leveraging the abundant knowledge learned from pre-trained multi-modal models like CLIP has recently proved to be effective for text-guided image editing. Though convincing results have been made when combining the image generator StyleGAN with CLIP, most methods need to train separate models for different prompts, and irrelevant regions are often changed after editing due to the lack of spatial disentanglement. We propose a novel framework that can edit different images according to different prompts in one model. Besides, an innovative region-based spatial attention mechanism is adopted to explicitly guarantee the locality of editing. Experiments mainly in the face domain verify the feasibility of our framework and show that when multi-text editing and local editing are accomplishable, our method can complete practical applications like sequential editing and regional style transfer.
Gaze prediction based on long short-term memory convolution with associated features of video frames

Gaze prediction is a key issue for visual perception research. It can be used to infer important regions in videos to reduce the amount of computation in learning and inference of various analysis tasks. Vanilla methods for dynamic video unable to extract valid features, and the motion information among dynamic video frames are ignored, which lead to poor prediction results. We propose a gaze prediction based on LSTM convolution with associated features of video frames (LSTM-CVFAF). Firstly, by adding learnable central prior knowledge, the proposed method can effectively and accurately extract the spatial information of each frame. Secondly, the LSTM is deployed to get temporal motion gaze features. Finally, the spatial and temporal motion information is fused to generate the gaze prediction maps of the dynamic video. Compared with the state-of-art models on DHF1K dataset, the CC, AUC-j, sAUC, NSS are separately increased by 5.1%, 0.6%, 38.2% and 0.5%.
Optimal Recommendation Models Based on Knowledge Representation Learning and Graph Attention Networks

Knowledge representation learning techniques process the knowledge graph, embedding entities and relationships into a continuous dense low-dimensional vector space, and providing the rich semantic association information between entities embedded in the knowledge graph to the recommendation module, improving recommendation performance and providing better interpretability. However, the knowledge representation learning techniques based on the translation model TransD with too many parameters and no association between entity representations are difficult to apply to large knowledge graph, as well as the problem is that most existing knowledge graph which based on recommendation systems ignore the different levels of importance that users attach to different relationships of items. To address these shortcomings, we propose an improved knowledge representation learning model Cluster TransD and a recommendation model Cluster TransD-GAT based on knowledge graph and graph attention networks, where the Cluster TransD model reduces the number of entity projections, makes the association between entity representations, reduces the computational pressure, and makes it better to be applied to the large knowledge graph, and the Cluster TransD-GAT model can capture the attention of different users to different relationships of items. Extensive comparison and ablation experiments on three real datasets show that the model proposed in this paper has significant performance improvements in terms of average ranking, accuracy and recall of the scoring function compared to other state-of-the-art models.