Reviewerly/exHarmony huggingface dataset:
Recognize highly similar sewing gestures by the robot

The autonomous and efficient learning of sewing gestures by robots will bring great convenience to the garment industry. To improve the accuracy of robots in detecting sewing gestures with high similarity, three detection models based on deep learning are proposed in the paper. First, in order to improve the detection accuracy and detection speed of sewing gestures under complex backgrounds, we added a dense connection layer to the low-resolution network layer of YOLO-V3 to enhance the transmission and reuse rate of image features. Secondly, a deeper ResNet50 residual network is introduced to replace the VGG16 basic network in the original SSD model. The feature pyramid structure is used to fuse high-level semantic features and low-level semantic features, which can improve the detection accuracy of small-sized sewing gestures. Finally, the parallel spatial-temporal dual-stream network separately extracts the temporal feature and the spatial feature of sewing gestures. The fusion of time feature and space feature improves the detection accuracy of the coherent sewing gesture. The results show that the suggested three models can effectively detect four sewing gestures with high similarity. Among them, the spatial-temporal two-stream convolutional neural network has the highest detection accuracy. The improved SSD model has faster detection speed than the improved YOLO-V3 model and other mainstream algorithms.
Pattern Matching Based Metric for Recommending Ordered Items

At present, we all know that the Recommendation System is essential. Our lives are continuously impacted by the Recommendation System. Its main objective is to suggest a relevant item or item list as per the user&#x2019;s requirement. In many cases, it recommends the user&#x2019;s desired item or item list based on rating prediction, and this prediction accuracy is considered to be the system&#x2019;s actual accuracy. But can the rating prediction accuracy be considered the system&#x2019;s true accuracy in ordered items prediction? Rating prediction system even after predicting a near-exact rating, there could be a difference between the actual item list and the predicted item list. We attempted to find answers to these issues by working with the College Recommendation System. We have used different machine learning-based models in our work for rating prediction. And we have measured the correlation between the actual item list and the predicted item list using the Longest Common Subsequence algorithm. Our analysis showed that the rating prediction accuracy does not always reflect the system&#x2019;s actual accuracy in the scenario of ordered items prediction. The accuracy of the system should be verified by how closely the predicted item list matches the actual item list when recommending ordered items. A pattern-matching algorithm like - Longest Common Subsequence can be considered as an accuracy metric in this context.
Integrated 5G MEC System and Its Application in Intelligent Video Analytics

Along with commercial use globally, 5G system is getting more and more attention from various vertical industries for its excellent network capabilities, where one of the key enabling technologies is Multi-access Edge Computing (MEC). MEC is a network solution that provides services and functions required by users on the nearby edge computing platform, ensuring low latency, high stability and sufficient capability, and hence is more suitable for real-time scenarios compared to the conventional fully central cloud-based paradigm. The integrated 5G MEC system brings the connectivity, computing and applications together in a converged ecosystem that will enable differentiated service innovations and empower intelligent transformation of vertical industries. In this paper, we introduce the principle and architecture of the integrated 5G MEC system, and propose an innovative application of 5G MEC based intelligent video analytics. By performing an on-site experiment, we validate the feasibility and performance of the proposed scheme, and then discuss the features and applicable scopes between edge-based and conventional cloud-based paradigms.
A novel random walk strategy for network embedding using community aware and node influence biasing

Network embedding (NE), a central issue for graph deep learning research, intends to represent nodes, edges, sub-graphs, and other information into a low-dimensional vector space. A highly accurate NE algorithm benefits significantly in developing AI applications related to social platforms such as node classification and link prediction. Existing studies mainly focus on preserving the local and global information of nodes. The propagation of bias information is not fully considered according to the desires of the nodes. Incorporating community aware and node influence into the random walk process for NE issues are not addressed. This work proposes a highly accurate NE algorithm, namely Community Aware and Node Influence Biased Embedding (CNBE), to deal with the NE problem of social networks. CNBE recognizes the community information and the random walk with node influence bias to preserve the global structure of nodes and local neighborhood information of reserved nodes. By random walk sampling of predefined rules in CNBE, we can get the sequence of nodes that reserved the local and global neighborhood node information. Then the SkipGram model is applied to learn the low-dimensional vector representation of nodes. Experiments on three real-world networks indicate that our proposed algorithm method outperforms three state-of-the-art network embedding approaches.
Deformation Modelling for a Physics-Based Perception System

Manipulation tasks require capturing the deformations undergone by the manipulated object. In order to handle various sorts and magnitudes of deformations, physical deformation models have been employed based on FEM. By resorting to linear elasticity with a volumetric formulation, these models simulate large strains while handling volumetric effects. For perception, such modelling provides physically consistent regularization to registered deformations, especially over areas unobserved by the sensing system. Besides elasticity modelling, fracture models are investigated to cope with potential cracks or tears of the manipulated objects as well as collision models to deal with multiple interacting objects or interactions with the environment. Also, these models have proven to be computationally efficient and are thus suitable for dynamic manipulation. This chapter is based on the works presented in [1, 2].
Research on the development of computer simulation technology in the context of blockchain

Block chain is, along with the rising popularity of digital encryption currency such as COINS and gradually rise a new decentralized architecture and distributed computing paradigm, decentralization, time-series data, collective maintenance and programmable and safety reliable, has caused the government departments, financial institutions, science and technology enterprises and capital market attaches great importance and wide public concern. As the world accelerates into the “blockchain economic era”, blockchain technology has shown a spurt of growth. As a disruptive underlying technology, it has been continuously applied in various industries and attracted more and more heed from all over the world. Blockchain + simulation industry has also become a new trend in the development of simulation technology. By deconstructing the core elements of blockchain, this paper puts forward the infrastructure model of blockchain system, expounds and analyzes what blockchain is, the blockchain’s development tendency, their relationship between blockchain and virtual simulation, and gives examples for analysis.
Receptor Status Prediction in Breast Cancer Patients Using Machine Learning Pipeline on DNA Methylation Data

In this study, we show that DNA methylation markers can be used for the early detection of breast cancer subtypes based on the status of Estrogen, Progesterone and HER2 receptors. A machine learning approach is used to predict breast cancer patients’ receptor status. DNA methylation datasets created using Illumina Hypermethylation 450K platform from four different studies from NCBI GEO and TCGA-BRCA are collated to create a dataset of 1514 samples. Of these, 1260 samples passed quality control and had receptor status information available, and these 1260 samples were used for the training and testing of the SVM model. We analysed the prognostic value of DNA methylation-based receptor status prediction by comparing the survival outcomes with the traditional methods. We observe that patients whose classification status does not match with those of traditional methods exhibit lower survival probability. This could possibly be due to incorrect classification and treatment plans by the traditional IHC method. This suggests that the proposed DNA methylation-based classifier is reliable for receptor status prediction.
3-Dimensional convolutional neural networks for predicting StarCraft Ⅱ results and extracting key game situations

In real-time strategy games, players collect resources, control various units, and create strategies to win. The creation of winning strategies requires accurately analyzing previous games; therefore, it is important to be able to identify the key situations that determined the outcomes of those games. However, previous studies have mainly focused on predicting game results. In this study, we propose a methodology to predict outcomes and to identify information about the turning points that determine outcomes in StarCraft Ⅱ, one of the most popular real-time strategy games. We used replay data from StarCraft Ⅱ that is similar to video data providing continuous multiple images. First, we trained a result prediction model using 3D-residual networks (3D-ResNet) and replay data to improve prediction performance by utilizing in-game spatiotemporal information. Second, we used gradient-weighted class activation mapping to extract information defining the key situations that significantly influenced the outcomes of the game. We then proved that the proposed method outperforms by comparing 2D-residual networks (2D-ResNet) using only one time-point information and 3D-ResNet with multiple time-point information. We verified the usefulness of our methodology on a 3D-ResNet with a gradient class activation map linked to a StarCraft Ⅱ replay dataset.
FPGA Implementation of Elliptic Curve Point Multiplication Over Galois Field

Safeguarding data from unauthorized access became a major issue in various aspects of daily life. To secure this, Elliptic curve cryptography plays a vital role. Elliptic curve cryptography (ECC) processor was built using a binary field in this project. The Montgomery ladder, as well as unified point addition algorithm, are considered in EC Point Multiplication. The ECC Processor’s conclusive operations are ECPM to generate a public key. The elliptical curve forms a group point addition, to reduce the operation and minimize the area size in the processor. The proposed designs are simulated and synthesized in VIVADO 2018 tool.
PolySense Flashcards: A Machine-Learning-Enhanced Platform for Learning the Multiple Senses of Academic Vocabulary

An architecture for a flashcard system for learning vocabulary is presented here that feeds directly off the experimental work on vocabulary acquisition. In principle, experiments in vocabulary acquisition based on traditional inferential statistics applied to small once-off collected sets of data informs vocabulary pedagogy as teachers are exposed to experimental results via training courses and practical teaching-oriented literature. Recently, however, in many domains of practical application this traditional statistics approach has been replaced by machine learning algorithms feeding off large continuous streams of real time data. Here it is proposed that a flashcard app that integrates such machine learning support into its architecture can serve as an adaptive experimental platform for continual pedagogical improvement. Flashcards for learning the multiple senses of high-frequency academic vocabulary is chosen as an important initial exploratory domain of application.
Phase change materials: the 'silicon' for analog photonic computing?

Chalcogenide phase change materials (PCMs) are a class of alloys exhibiting gigantic optical property contrast upon structural transition from an amorphous to a crystalline state. The structural transition is also nonvolatile and does not require constant power supply to maintain its optical state. These unique behaviors qualify PCMs as a novel functional material enabling various on-chip and free-space re-programmable optical computing network architectures. Here we present monolithic integration of PCMs with integrated photonics and metasurface optics leveraging standard silicon foundry facilities, and the demonstration of electrically programmable photonic devices for on-chip optical routing, memory, and computing functions
Spam Detection using Word Embedding-based LSTM

Spam email is a well-known problem in network communications. A large number of spam emails in mailbox caused inconvenience and might lead to cybercrime. Effective spam mail detection is one of challenges research topics. This paper proposed a new spam mail detection approach that can be applied for sentiment analysis. A dataset of 5,572 messages was tested using Word embedding Techniques such as Bag of words, Hashing, and Long short-term memory network Algorithm (LSTM). The results showed the proposed method performance are equal 98%, 93%, 95%, and 98% interns of precision, recall, F1-score, and accuracy respectively.
Real-time supply chain tracing using blockchain from cloud-based computing portal

Supply chain management is the approach applied to track products, including the transformation process. Real-time tracing techniques are considered to enhance the accuracy and performance rate of the tracing process.The main contribution of this work consists of a review of real-time tracing techniques in supply management with focus on cloud computing, Information extracted supports the development of a taxonomy of evaluation and classification processes , process stages and the role blockchain technology plays in improving tracing and data handling. This work is based on secondary research into articles related to real-time tracing techniques and supply chain management.We contribute to the body of knowledge for tracing in supply chain management through an up-to-date snapshot of the extent and breadth of research in the field and through clear identification of remaining challenges to guide future research.
Using Score-based Structure Learning to Computationally Learn Direct Influence between Hierarchical Dynamic Bayesian Networks

Numerous fields of science have investigated stochastic processes which are partially observable. However, the discovery and analysis of the interaction between, and the influence upon each other, of several of these processes, have not been probed extensively. This paper uses probabilistic structure learning in an attempt to learn influence relationships between stochastic processes that are partially observed. These processes are represented by hierarchical dynamic Bayesian networks (HDBNs). To track the direct influence between the these processes, we provide an algorithm that extends the BIC structure score as well as the cumbersome (greedy hill-climbing) local search procedure. Our method leverages the temporal nature of the HDBN through the use of assembles thereby surpassing the standard approach that treats each process as a single variable. The derived BIC-score for HDBN families is clearly shown to be theoretically decomposable and empirically consistent.
Statistically Near-Optimal Hypothesis Selection

Hypothesis Selection is a fundamental distribution learning problem where given a comparator-class <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{Q}=\{q_{1}, \ldots, q_{n}\}$</tex> of distributions, and a sampling access to an unknown target distribution <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> , the goal is to output a distribution <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> such that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathsf{TV}(p, q)$</tex> is close to opt, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathsf{opt}=\min\nolimits_{i}\{\mathsf{TV}(p, q_{i})\}$</tex> and TV (.,.) denotes the total-variation distance. Despite the fact that this problem has been studied since the 19th century, its complexity in terms of basic resources, such as number of samples and approximation guarantees, remains unsettled (this is discussed, e.g., in the charming book by Devroye and Lugosi '00). This is in stark contrast with other (younger) learning settings, such as PAC learning, for which these complexities are well understood. We derive an optimal 2-approximation learning strategy for the Hypothesis Selection problem, outputting <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> such that, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathsf{TV}(p, q)\leq 2\cdot\text{opt}+\varepsilon$</tex> , with a (nearly) optimal sample complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tilde{O}(\log n/\varepsilon^{2})$</tex> . This is the first algorithm that simultaneously achieves the best approximation factor and sample complexity: previously, Bousquet, Kane, and Moran (COLT ‘19) gave a learner achieving the optimal 2-approximation, but with an exponentially worse sample complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tilde{O}(\sqrt{n}/\varepsilon^{2.5})$</tex> , and Yatracos (Annals of Statistics '85) gave a learner with optimal sample complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\log n/\varepsilon^{2})$</tex> but with a sub-optimal approximation factor of 3. We mention that many works in the Density Estimation (a.k.a., Distribution Learning) literature use Hypothesis Selection as a black box subroutine. Our result therefore implies an improvement on the approximation factors obtained by these works, while keeping their sample complexity intact. For example, our result improves the approximation factor of the algorithm of Ashtiani, Ben-David, Harvey, Liaw, and Mehrabian (JACM '20) for agnostic learning of mixtures of gaussians from 9 to 6, while maintaining its nearly-tight sample complexity.
Learning Deep ReLU Networks Is Fixed-Parameter Tractable

We consider the problem of learning an unknown ReLU network with respect to Gaussian inputs and obtain the first nontrivial results for networks of depth more than two. We give an algorithm whose running time is a fixed polynomial in the ambient dimension and some (exponentially large) function of only the network's parameters. Our results provably cannot be obtained using gradient-based methods and give the first example of a class of efficiently learnable neural networks that gradient descent will fail to learn. Our bounds depend on the number of hidden units, depth, spectral norm of the weight matrices, and Lipschitz constant of the overall network (we show that some dependence on the Lipschitz constant is necessary). We also give a bound that is doubly exponential in the size of the network but is independent of spectral norm. In contrast, prior work for learning networks of depth three or higher requires exponential time in the ambient dimension, even when the above parameters are bounded by a constant. Additionally, all prior work for the depth-two case requires well-conditioned weights and/or positive coefficients to obtain efficient run-times. Our algorithm does not require these assumptions. Our main technical tool is a type of filtered PCA that can be used to iteratively recover an approximate basis for the subspace spanned by the hidden units in the first layer. Our analysis leverages new structural results on lattice polynomials from tropical geometry.
Detection of DIAG and LINE Patterns in PassPoints Graphical Passwords Based on the Maximum Angles of Their Delaunay Triangles

An alternative authentication method to traditional alphanumeric passwords is graphical password authentication, also known as graphical authentication, for which one of the most valuable cued-recall techniques is PassPoints. This technique stands out for its security and usability. However, it can be violated if the user follows a predefined pattern when selecting the five points in an image as their passwords, such as the DIAG and LINE patterns. Dictionary attacks can be built using these two patterns to compromise graphical passwords. So far, no reports have been found in the state of the art about any test capable of detecting graphical passwords with DIAG or LINE patterns in PassPoints. Studies carried out in other scenarios have shown the effectiveness of the characteristics of Delaunay triangulations in extracting information about the dependence between the points. In this work, graphical passwords formed by five randomly selected points on an image are compared with passwords whose points contain patterns of the DIAG or LINE type. The comparison is based on building for each password its Delaunay triangulation and calculating the mean value of the maximum angles of the triangles obtained; such a mean value is denoted by amadt. It is experimentally shown that in passwords containing DIAG and LINE patterns, the value of amadt is higher than the one obtained in passwords formed by random dots. From this result, it is proposed to use this amadt value as a statistic to build a test of means. This result constitutes the work's main contribution: The proposal of a spatial randomness test to detect weak graphic passwords that contain DIAG and LINE type patterns. The importance and novelty of this result become evident when two aspects are taken into account: First, these weak passwords can be exploited by attackers to improve the effectiveness of their attacks; second, there are no prior criteria to detect this type of weak password. The practical application of said test contributes to increasing PassPoints security without substantially affecting its efficiency.
Towards Distractibility Induced trust Management Using BlockChain for Edge Computing

Edge computing solves massive data processing problems by utilizing the storage and computing functions of edge devices. Due to the openness of edge computing, distracted devices inevitably participate in edge computing and affect the network computing performance, which degrades trust management of edge devices. To solve the above issue, this paper proposes a trust management framework based on blockchain for edge computing scenarios (ECTMF). First, the ECTMF framework slices users and provides corresponding services based on different requirements. It evaluates the trustworthiness of end devices and distinguishes distracted devices in edge computing. Then, the Proof of Monitor (PoM) consensus mechanism is proposed to continuously monitor distracted end devices in edge computing. Simulations validate the effectiveness and efficiency of the ECTMF and PoM.