Reviewerly/exHarmony huggingface dataset:
GCN for HIN via Implicit Utilization of Attention and Meta-Paths

Heterogeneous information network (HIN) embedding, aiming to map the structure and semantic information in a HIN to distributed representations, has drawn considerable research attention. Graph neural networks for HIN embeddings typically adopt a hierarchical attention (including node-level and meta-path-level attentions) to capture the information from meta-path-based neighbors. However, this complicated attention structure often cannot achieve the function of selecting meta-paths due to severe overfitting. Moreover, when propagating information, these methods do not distinguish direct (one-hop) meta-paths from indirect (multi-hop) ones. But from the perspective of network science, direct relationships are often believed to be more essential, which can only be used to model direct information propagation. To address these limitations, we propose a novel neural network method via <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">implicitly</i> utilizing attention and meta-paths, which can relieve the severe overfitting brought by the current over-parameterized attention mechanisms on HIN. We first use the multi-layer graph convolutional network (GCN) framework, which performs a discriminative aggregation at each layer, along with stacking the information propagation of direct linked meta-paths layer-by-layer, realizing the function of attentions for selecting meta-paths in an indirect way. We then give an effective relaxation and improvement via introducing a new propagation operation which can be separated from aggregation. That is, we first model the whole propagation process with well-defined probabilistic diffusion dynamics, and then introduce a random graph-based constraint which allows it to reduce noise with the increase of layers. Extensive experiments demonstrate the superiority of the new approach over state-of-the-art methods.
SER: Speech Emotion Recognition Application Based on Extreme Learning Machine

Nowadays, device control is commonly using the human body feature or voice recognition technology. To expand the functionality of voice recognition, plenty of researchers have developed speech emotion recognition. By recognizing sound emotions, a system can provide better and beneficial decision-making output. This paper describes the development of an application that is able to recognize speech emotions using Extreme Learning Machine (ELM). We use the dataset from Toronto Emotional Speech Set (TESS). The dataset contains 2800 data points (audio files) in total and has high quality audio that focused on female voices to ensure the reliability of the data. The Speech Emotion Recognition application was design as web-based application that used Golang and Python which built with Extreme Learning Machine and Random Forest to recognize speech emotions. As a result, the functionality test shows that the application was able to satisfy 6 out of 6 requirements, and the accuracy test shows an accuracy value of 100&#x0025; by identifying 70 out of 70 test data.
TAD-Net: An approach for real-time action detection based on temporal convolution network and graph convolution network in digital twin shop-floor

<ns4:p><ns4:bold>Background:</ns4:bold> Intelligent monitoring of human action in production is an important step to help standardize production processes and construct a digital twin shop-floor rapidly. Human action has a significant impact on the production safety and efficiency of a shop-floor, however, because of the high individual initiative of humans, it is difficult to realize real-time action detection in a digital twin shop-floor.</ns4:p><ns4:p> <ns4:bold>Methods:</ns4:bold> We proposed a real-time detection approach for shop-floor production action. This approach used the sequence data of continuous human skeleton joints sequences as the input. We then reconstructed the Joint Classification-Regression Recurrent Neural Networks (JCR-RNN) based on Temporal Convolution Network (TCN) and Graph Convolution Network (GCN). We called this approach the Temporal Action Detection Net (TAD-Net), which realized real-time shop-floor production action detection.</ns4:p><ns4:p> <ns4:bold>Results:</ns4:bold> The results of the verification experiment showed that our approach has achieved a high temporal positioning score, recognition speed, and accuracy when applied to the existing Online Action Detection (OAD) dataset and the Nanjing University of Science and Technology 3 Dimensions (NJUST3D) dataset. TAD-Net can meet the actual needs of the digital twin shop-floor.</ns4:p><ns4:p> <ns4:bold>Conclusions: </ns4:bold>Our method has higher recognition accuracy, temporal positioning accuracy, and faster running speed than other mainstream network models, it can better meet actual application requirements, and has important research value and practical significance for standardizing shop-floor production processes, reducing production security risks, and contributing to the understanding of real-time production action.</ns4:p>
A Novel Framework of Identifying Chinese Jargons for Telegram Underground Markets

As one of the most popular instant messaging (IM) software, Telegram has reached 500 million monthly active users (MAU) up to January 2021. Nevertheless, the characteristics of safety and openness have also made it a popular platform for transactions in underground markets. Moreover, cybercriminals usually use jargons instead of sensitive terms when they communicate in Telegram groups. Nevertheless, jargons identification relies on time-consuming and lagging manual work currently. To solve this problem, this paper proposes a Chinese Jargons Identification Framework (CJI-Framework) to identify jargons automatically. Firstly, we collect chat history from targeted Telegram groups to build a corpus called TUMCC, which is the first Chinese corpus in jargons identification field. Secondly, we extract seven brand-new features which can be classified into three categories: Vectors-based Features (VF), Lexical analysis-based Features (LF), and Dictionary analysis-based Features (DF), to distinguish between Chinese jargons and commonly-used words. Furthermore, we use a word vectors projection method and a transfer learning method to improve the quality of word vectors generated from the corpora. In our experiments, the CJI-Framework reaches a remarkable jargons identification performance with an F1-score of 89.66%. This work provides a method of identifying Chinese jargons for Telegram underground markets effectively and will be helpful for cybercrime investigation. It can also be helpful to jargons identification related to other similar communication platforms and languages.
QAM Signal Classification and Timing Jitter Identification Based on Eye Diagrams and Deep Learning

Radio spectrum awareness is an important topic to overcome many challenges, such as spectrum utilization and sharing appearing with the development of technologies in wireless communications. Some practical tasks of radio spectrum awareness are related to signal detection and identification for improving the system’s reliability, efficiency, and security. Eye diagrams are essential for measurement tools used by engineers to simulate, evaluate and debug systems. Eye diagrams reflect many vital parameters for signal integrity degradation, such as timing jitter, crosstalk, and inter-symbol interference. Therefore, using an eye diagram containing a valuable feature from the system could be helpful for spectrum awareness tasks. In this paper, we use deep learning to study and identify classes within quadrature amplitude modulation using eye diagrams and explored related impacts to enable radio spectrum awareness. Our results show that deep learning neural networks capable of classifying quadrature amplitude modulation types with eye diagrams at presenting timing jitter and varying signal-to-noise ratios.
Hierarchical Prototype Networks for Continual Graph Representation Learning

Despite significant advances in graph representation learning, little attention has been paid to the more practical continual learning scenario in which new categories of nodes (e.g., new research areas in citation networks, or new types of products in co-purchasing networks) and their associated edges are continuously emerging, causing catastrophic forgetting on previous categories. Existing methods either ignore the rich topological information or sacrifice plasticity for stability. To this end, we present Hierarchical Prototype Networks (HPNs) which extract different levels of abstract knowledge in the form of prototypes to represent the continuously expanded graphs. Specifically, we first leverage a set of Atomic Feature Extractors (AFEs) to encode both the elemental attribute information and the topological structure of the target node. Next, we develop HPNs to adaptively select relevant AFEs and represent each node with three levels of prototypes. In this way, whenever a new category of nodes is given, only the relevant AFEs and prototypes at each level will be activated and refined, while others remain uninterrupted to maintain the performance over existing nodes. Theoretically, we first demonstrate that the memory consumption of HPNs is bounded regardless of how many tasks are encountered. Then, we prove that under mild constraints, learning new tasks will not alter the prototypes matched to previous data, thereby eliminating the forgetting problem. The theoretical results are supported by experiments on five datasets, showing that HPNs not only outperform state-of-the-art baseline techniques but also consume relatively less memory. Code and datasets are available at https://github.com/QueuQ/HPNs.
Efficient parallel execution of block transactions in blockchain

Miners and validators in current blockchains serially execute block transactions. Such serial execution cannot efficiently utilize modern multi-core resources, consequently hampering system throughput. We propose three approaches to improve blockchain throughput by introducing parallel execution of block transactions. We present a static analysis-based DiPETrans approach that groups the block transactions into independent shards and executes them parallelly in a distributed fashion using a leader-follower method. DiPETrans is empirically evaluated with 5 million actual transactions from the Ethereum blockchain.
Towards accelerated greedy sampling and reconstruction of bandlimited graph signals

We study the problem of sampling and reconstructing spectrally sparse graph signals where the objective is to select a subset of nodes of prespecified cardinality that ensures interpolation of the original signal with the lowest possible reconstruction error. This task is of critical importance in Graph signal processing (GSP) and while existing methods generally provide satisfactory performance, they typically entail a prohibitive computational cost when it comes to the study of large-scale problems. Thus, there is a need for accelerated and efficient methods tailored for high-dimensional and large-scale sampling and reconstruction tasks. To this end, we first consider a non-Bayesian scenario and propose an efficient iterative node sampling procedure that in the noiseless case enables exact recovery of the original signal from the set of selected nodes. In the case of noisy measurements, a bound on the reconstruction error of the proposed algorithm is established. Then, we consider the Bayesian scenario where we formulate the sampling task as the problem of maximizing a monotone weak submodular function, and propose a randomized-greedy algorithm to find a sub-optimal subset of informative nodes. We derive worst-case performance guarantees on the mean-square error achieved by the randomized-greedy algorithm for general non-stationary graph signals.
DSEORA: Integration of Deep Learning and Metaheuristics for Web Page Recommendation Based on Search Engine Optimization Ranking

Information retrieval is an indispensable part of a user’s experience over the Web. With more than half a million websites being created every single day, it becomes of greater significance that relevant search results are displayed in response to the user query. This paper discusses deep learning inspired classification-based model using Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) and Ant Colony Optimization (ACO) based Ranking Algorithm which produces very accurate and precise results. Firstly, a trained classification-based model of URLs is prepared using a large corpus of a labelled dataset and then a query-driven ranking process is carried out using Ant Colony Optimization incorporating Jaccard’s similarity index. Results show improved accuracy as well as precision in the relevant search results over a clustering-based approach of grouping the URLs. An accuracy of 86.90% has been achieved by the proposed method.
Analysis of the Necessity of Quantum Computing Capacity Development for National Defense and Homeland Security

Prior work has discussed the significant benefits of the use of quantum computing systems in an array of different fields. These systems have already demonstrated the potential to expedite processing in key areas, such as factoring large integers, as compared to their classical counterparts. The specific value of quantum factoring has been demonstrated for cryptography. It will render one of the most prevalent encryption techniques, RSA encryption – which is dependent on computational systems being incapable of determining the prime factors of large integers, effectively obsolete. However, defeating encryption is not the only prospective use or value of quantum computing for national defense and homeland security purposes. This paper analyzes the need to invest resources in quantum systems and quantum computing technology development due to the potential threats they pose and the benefits that technological innovation in this area is poised to provide. The benefits of quantum computing in a variety of national defense and homeland security application areas are discussed and the implications of investment – as well as failure to invest – in this technology are considered.
An area-efficient multiply-accumulation architecture and implementations for time-domain neural processing

In our work, a new area-efficient multiply-accumulation scheme for time-domain neural processing named differential multiply-accumulation is proposed. Our new scheme reduces hardware resources utilization of multiply-accumulation with suppressing the increasing computational time resulting from the time-multiplexing. As a result, 2,048 neurons of fully connected CBM and RC-CBM were synthesized for a single field-programmable gate array (FPGA).
Potential Threat of Face Swapping to eKYC with Face Registration and Augmented Solution with Deepfake Detection

It is necessary to develop an efficient and secure mechanism to verify customers digitally for various online transactions. Integrating biometric solutions into the online user registration and verification processes is a promising trend for electronic Know Your Customer (eKYC) systems. However, Deepfake or face manipulation techniques may become a threat for eKYC with face authentication. In this paper, we introduce this potential attack of Deepfake on eKYC by swapping and manipulating faces between source and target faces. We then propose to augment the security for current eKYC systems with Deepfake detection. We conduct the experiments on the 10K video clips in the private test of Deepfake Detection Challenge 2020, and our method, following the Capsule-forensics approach, achieves the Logloss score of 0.5189, among the top 6% best results among the 2114 teams worldwide. This result demonstrates that our deepfake detection algorithm can be a promising method to provide extra protection for eKYC solutions with face registration and authentication.
Anomaly Detection for the Automated Visual Inspection of PET Preform Closures

Recent advances in supervised Machine Learning have enabled the automated visual inspection of increasingly complex products. The economic feasibility of developed methods is, however, limited by their requirement for large amounts of labeled training data. As an alternative, Anomaly Detection (AD) methods have been proposed, which do not rely on large-scale datasets and often require defect-free images only. In our work, we benchmark and adapt proposed state-of-the-art AD as well as Anomaly Segmentation (AS) methods to the Polyethylene Terephthalate (PET) preform closure inspection task in a high-throughput setting. We furthermore compare AD/AS methods with fully supervised baselines and show that, in the low-data regime, comparable AS performance can be achieved. Moreover, fully supervised baselines are outperformed with respect to AD by our adapted methods. We also conduct ablation studies to quantify the effects of proposed modifications, and show that they reduce memory footprint by a factor of 3 while maintaining AD/AS performance. Together, this demonstrates the suitability of AD/AS methods for the automated visual inspection of complex objects such as transparent PET preform closures in high-throughput settings.
Force-Based Foot Gesture Navigation in Virtual Reality

Navigation is a primary interaction in virtual reality. Previous research has explored different forms of artificial locomotion techniques for navigation, including hand gestures and body motions. However, few studies have investigated force-based foot gestures as a locomotion technique. We present three force-based foot gestures (Foot Fly, Foot Step and Foot Teleportation) for navigation in a virtual environment, relying on surface electromyography sensors readings from leg muscles. A pilot study comparing our techniques with controller-based techniques indicates that force-based foot gestures can provide a fun and engaging alternative. Of all six input techniques evaluated, Foot Fly was often most preferred despite requiring more exertion than the Controller Fly technique.
A Study of College English Translation Teaching in the Age of Artificial Intelligence

At present, artificial intelligence technology has made a major breakthrough. Big data, cloud computing, language recognition, deep learning and other artificial intelligence technologies are widely used in the field of education and teaching, which not only changes students' traditional learning methods, but also promotes the innovation of teachers' teaching methods and teaching functions. The application of artificial intelligence technology, especially language translation technology in language teaching, has brought new opportunities and challenges for the development of language teaching in colleges. In this paper, artificial intelligence and its development are briefly introduced; then the influence of artificial intelligence on college English translation teaching are analysed; finally, college English translation teaching under the background of artificial intelligence is expounded.
An improved Image Interpolation technique using OLA e-spline

Image upscaling aims to increase the resolution and size of a low resolution (LR) image in order to generate a high resolution (HR) image of high frequency (HF). There are several polynomial methods for obtaining a sharpened, upscaled HR image. The interpolated pixel is measured using a weighted average of the neighboring pixels within the image grid that blur at HF regions in these methods. Edge degradation is also caused by other edge-directed and learning-based upscaling methods, which produce blurring artifacts. A novel approach is proposed to fill these gaps. Using the concept of unsharp masking (USM), the LR image is blurred adaptively based on the region's local variance. The sharpened high pass filtered (HPF) image is then obtained by subtracting the adaptively blurred image from the LR image. According to USM, the HPF image is combined with the LR image via a gain factor optimized using the cuckoo search (CS) algorithm. To compensate for the loss caused by upscaling, this pre-processing step is performed prior to interpolation. Aside from that, the edge of the B-spline interpolated image is detected and expanded. Edge expansion of the upscaled image is performed to further restore the HF details and reduce zigzag artifacts introduced by upscaling while also preserving the edge boundary. The proposed method outperforms the Lanczos, Bicubic, and Bilinear schemes in terms of peak signal to noise ratio (PSNR) gain of 3.475, 8.3839, and 8.075 dB, respectively. In terms of performance, this method outperforms state-of-the-art techniques both objectively (PSNR and SSIM) and subjectively (visual quality).
A Comparison of Models Predicting One-Year Mortality at Time of Admission

Hospitalization provides an opportunity to address end-of-life care (EoLC) preferences if patients at risk of death can be accurately identified while in the hospital. The modified Hospital One-Year Mortality Risk (mHOMR) uses demographic and admission data in a logistic regression algorithm to identify patients at risk of death one year from admission.This project sought to validate mHOMR and identify superior models.The mHOMR model was validated using historical data from an academic health system. Alternative logistic regression and random forest (RF) models were developed using the same variables. Receiver operating characteristic (ROC) and precision recall curves were developed, and sensitivity, specificity, and positive and negative predictive values were compared over a range of model thresholds.The RF model demonstrated higher area under the ROC curve (0.950, 95% CI 0.947 - 0.954) as compared to the logistic regression models (0.818 [95% CI 0.812 - 0.825] and 0.841 [95% CI 0.836 - 0.847]). Area under the precision recall curve was higher with the random forest model compared to the logistic regression models (0.863 vs. 0.458 and 0.494, respectively). Across a range of thresholds, the RF model demonstrated superior sensitivity, equivalent specificity, and higher positive and negative predictive values.A machine learning RF model, using common demographic and utilization data available on hospital admission, identified inpatients at risk of death more effectively than logistic regression models using the same variables. Machine learning models have promise for identifying admitted patients with elevated one-year mortality risk, increasing opportunities to prompt discussion of EoLC preferences.
Matching sensor ontologies with unsupervised neural network with competitive learning

Sensor ontologies formally model the core concepts in the sensor domain and their relationships, which facilitates the trusted communication and collaboration of Artificial Intelligence of Things (AIoT). However, due to the subjectivity of the ontology building process, sensor ontologies might be defined by different terms, leading to the problem of heterogeneity. In order to integrate the knowledge of two heterogeneous sensor ontologies, it is necessary to determine the correspondence between two heterogeneous concepts, which is the so-called ontology matching. Recently, more and more neural networks have been considered as an effective approach to address the ontology heterogeneity problem, but they require a large number of manually labelled training samples to train the network, which poses an open challenge. In order to improve the quality of the sensor ontology alignment, an unsupervised neural network model is proposed in this work. It first models the ontology matching problem as a binary classification problem, and then uses a competitive learning strategy to efficiently cluster the ontologies to be matched, which does not require the labelled training samples. The experiment utilizes the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) and multiple real sensor ontology alignment tasks to test our proposal's performance. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matching strategies under different domain knowledge such as bibliographic and real sensor ontologies.