Reviewerly/exHarmony huggingface dataset:
MAKE:Knowledge Graph Embedding via Multi-Attention neural network

Knowledge graph embedding is a popular method to solve the incompleteness of knowledge graph. At present, research on knowledge graph embedding based on neural network has achieved remarkable results, but most models ignore the influence of the correlation among with subject entity and relation and object entity within triple. Existing attention-based models take into account the effect of correlation, but perform moderately. In this paper, we propose a multi-attention neural network-based embedding model, named MAKE, which utilizes a novel multi-attention mechanism to generate feature maps of triples by computing correlations within triple. To fully exploit the performance of the multi-attention mechanism, MAKE uses a trainable batch normalization method and a novel composite loss function to improve the model learning ability. Evaluation results on FB15K-237 and WN18RR standard datasets show that our MAKE achieves better performance than previous state-of-the-art knowledge graph embedding models.
Heirarchical Federated Learning in Delay Sensitive Communication Networks

The impact of local averaging on the performance of federated learning (FL) systems is studied in the presence of communication delay between the clients and the parameter server. We focus on a hierarchical FL (HFL) setting where clients are assigned into different groups, each having its own local parameter server (LPS). The number of local and global communication rounds in our work is randomly determined by the (different) delays experienced by each group of clients. Specifically, the number of local averaging rounds are tied to a wall-clock time period coined the sync time S, after which the LPSs synchronize their models by sharing them with a global parameter server (GPS). Such sync time <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> is then reapplied until a global wall-clock time is exhausted. First, an upper bound on the deviation between the updated model at each LPS with respect to that available at the GPS is derived. This is then used to derive the convergence bounds of our proposed HFL setting, at each LPS and at the GPS. The bounds showcase the effects of the whole system's parameters, including the number of groups, the number of clients per group, and the value of S. Our results show that optimizing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> is especially crucial in heterogeneous systems in which the number of clients per group and their delay statistics are different. In addition to showing the necessary need of collaboration for under-performing groups, optimizing the value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> promotes fairness among groups, and allows one to deal with delay-sensitive FL applications in which the training time is restricted.
Extraction of Single Chemical Structure from Handwritten Complex Chemical Structure with Junction Point Based Segmentation

In chemistry domain, lots of chemical compounds were represented by complex graphics. These complex graphics can be a combination of basic shapes from geometry like a circle, line, hexagon, etc. When such complex graphics have drawn with free handwriting, it was very difficult to recognize it for inputting it into a computer system. Hence the recognition of handwritten complex chemical structure (HCCS) has challenges in the segmentation of each chemical symbol or structure. Here in this paper, we had proposed an approach to segment the isolated chemical structures from HCCS. We had considered only the chemical structures made up with benzene rings. The proposed approach extracts isolated benzene structure from HCCS based on junction point base segmentation technique. Skeletonization of the input image has improved the accuracy of the junction point detection process. Here the skeletonization of an image has been achieved with morphological thinning. The proposed approach accepts the scanned image of HCCS as an input, and it produced the images of isolated chemical structures as an output.
ORCA computing: the route from NISQ processors to fault-tolerance

Photonics is a powerful platform for quantum computing that offers high clock rates and room-temperature guided-wave interconnects and circuits, without the need for electromagnetic shielding, high vacuum systems or atom trapping and cooling. But generating the entangled states needed for quantum computing with linear optics requires active multplexing, with detection followed by fast feedforward. I will describe ORCA’s approach to addressing this challenge, along with our roadmap to move from NISQ processors to fault-tolerant quantum computers.
Observing a Seismic Cycle at Sea

Scientists organized a trio of expeditions to document the buildup of stress leading to a large earthquake on a seafloor fault, developing innovations for successful seagoing research in the process.
The role of FDI uncertainty on financial development: evidence from panel data

The financial sector is one of the most crucial sectors in an economy that requires development considering the role it plays in any of the economic structures. In view of that, this study investigates the role financial globalisation uncertainty and the Global Financial Crisis 2007 plays in relation to the financial development of some selected leading African economies. To support the model, the study includes economic growth and trade openness as control variables in the research. The empirical co-integration result using Westerlund and Edgerton (2007) confirmed the presence of long-run relationship among the variables. Our result from the dynamic common correlated effect estimator also reveals that GDP, FGU and trade openness are positively related to the financial development. However, the Global Financial Crisis 2007 shows a negative effect towards financial development. Thus, proper policy implications are discussed in the final section of the study.
A Deep Neural Network Optimized by a Genetic Algorithm to Improve Arabic Sentiment Classification

Deep learning has improved the state-of-the-art in sentiment analysis for various languages, including Arabic.One aspect that can affect the performance of deep learning-based sentiment classification is the optimization method used for training the neural network.The conventional optimization method is carried out by a backpropagation (BP) algorithm that relies on gradient descent to find the minimum of a cost function.However, BP has the tendency to converge into local minima instead of global minima since neural networks generate complex error surfaces for even simple problems.In this study, for the purpose of improving the Arabic sentiment classification, we propose to use a genetic algorithm (GA) to train a deep neural network (DNN).GA is a meta-heuristic optimization algorithm inspired by the theory of natural evolution.The algorithm is expected to improve the classifier's performance due to its capability to reach optimal or near-optimal solutions.The proposed method uses Arabic sentiment lexicons to extract various features considering different aspects for text representation.The effectiveness of the proposed method is evaluated by analyzing its performance, versus a DNN trained with BP algorithm.The experimental results show that the proposed method can present better F1-measure of 90.7% for Arabic sentiment classification than traditional BP-based DNN.
Objective characterization of saxophone mouthpiece playability

In single-reed instruments, the mouthpiece and reed have an essential effect on instrument playability, since they largely determine the range of lip force and blowing pressure required to produce notes. This paper quantitatively examines the effect of three different mouthpiece designs (Selmer S80, Concept, and Spirit) on the range of lip force and blowing pressure combinations in which tone can be produced on the alto saxophone for notes ranging from D3 to D5. Average lip force, blowing pressure, loudness, and tuning are investigated as functions of notes and for three different reed strengths. Characteristics such as lip-force/blowing-pressure playability area (total playability area, TPA) and correctly tuned playability area (CTPA) are determined for different reed strengths and note values, and change of tuning as a function of notes is investigated. Apparent differences were observed between mouthpieces of different designs and even between mouthpieces with extremely similar tip openings. The Spirit mouthpiece is louder but requires higher lip force and more adaptation of lip force and of blowing pressure over note ranges. The Concept, with a remarkably similar tip opening and facing length as the S80, allows for a smoother transition between the low and high registers of the instrument and has better tuning stability. The new parameters < TPA > and < CTPA > are introduced as summarizing indicators of mouthpiece characteristics, averaged over note range and reed strength range. Such parameters can be used to define mouthpiece playability objectively and can form important input for parameter-based design improvement or customization.
Cost Optimization - A Recommendation Analysis of Azure Workloads

In a rapidly modernizing world, cloud computing has emerged as an innovative and ground-breaking technology. It is a sector that is experiencing rapid growth, accelerated by the COVID-19 pandemic and remote work environment. Many businesses have started to make the transition from traditional storage systems to the cloud. However, the transition can be opaque and result in an excessive amount of spending on cloud resources. Additionally, these resources are often underutilized, further contributing to cloud waste. This paper seeks to address this problem by designing a comprehensive algorithm based on a detailed analysis of the Microsoft Azure 2019 virtual machine (VM) traces. We first analyzed the cost of approximately 2.7 million VMs and identified that many VMs are under utilized and over consume resources. Then, using a policy-oriented algorithm we constructed a list of VMs that were considered to be "wasteful". Finally, we implemented a recommendation algorithm to sort through this constructed list and recommend lower prices and options for users without interfering with the purpose and function of the VM. Our results show that if all users were to follow our recommendations, the potential cost reduction is over \$1.4 million. We conclude with a discussion of the implications of this research and make recommendations for future studies.
Adaptive Spherical Simplex-Radial Cubature Kalman Filter for Spacecraft Formation Flying Relative Orbit Determination

For spacecraft formation flying, the exact measurement noise statistical characteristics, which are required in conventional cubature Kalman filter, are generally hard to acquire in advance on account of the existing space random interference. To this end, a noise statistical estimator based adaptive spherical simplex-radial cubature Kalman filter is proposed. The posterior mean and covariance in the nonlinear Kalman filtering framework are calculated by means of the spherical simplex-radial cubature rule approximating the nonlinear Gaussian weighted function. Meanwhile, based on the maximum a posterior estimation principle, the noise statistical characteristics are estimated online using the estimator, so as to achieve high filtering accuracy in the case of inaccurate noise. Simulation results show that the proposed filter has the ability to obtain higher relative orbit determination accuracy with inaccurate measurement noise statistical characteristics.
Iterated Orthogonal Simplex Cubature Kalman Filter for Target Tracking

In this paper, an iterated orthogonal simplex cubature Kalman filter (IOSCKF) for target tracking is proposed. The Gaussian weighted integral is decomposed into two parts, including the spherical integral and radial integral. The former is calculated approximately by the spherical simplex rule, while the latter is computed using the second-order Gauss-Laguerre quadrature rule. The above two rules are combined, and the simplex cubature rule is derived. The cubature points, which are extracted from the proposed rule, are transformed using the given orthogonal matrix to reduce the high order error terms. The transformed points and weights are embedded into the nonlinear Kalman filter framework. The measured value is used iteratively to correct the measurement update using the Gauss-Newton iteration until the termination condition is satisfied, and the IOSCKF is achieved. For target tracking with linear state equation, the time update is reduced to that of conventional Kalman filter, and the simplified IOSCKF (SIOSCKF) is obtained. The simulation results show that IOSCKF can achieve higher accuracy, and the simplified form SIOSCKF could provide the same accuracy as IOSCKF with less computational complexity.
Perching Control for UAV Based on Feedback Control and Visual Servo

This paper presents a perching control strategy for a novel tiltable Unmanned Aerial Vehicle (UAV). The control scheme is based on the position-based visual servo and double-loop control method. Firstly, the dynamic model and perching strategy are established according to specific configuration of the UAV. Secondly, in controller design, proportional-integral-derivative (PID) control method is adopted, and a scheme that fuses image data and inertial measurement unit (IMU) data is designed for feedback in the control loop. Finally, the simulation results show the effectiveness of the perching control strategy proposed in this paper.
Deep Flow-Guided Video Dehazing Based on Multi-scale Recurrent Network

We present a deep flow-guided video dehazing method based on multi-scale recurrent network dehazing. First, optical flow estimation is used for extracting inter-frame information between adjacent frames. Next, the adjacent frames and the target frames are aligned after deformation operation. Finally, the aligned frames are fed into a multi-scale recurrent network for dehazing, which employs an encoder-decoder structure that incorporates residual blocks. Moreover, this method introduces a hybrid loss method including hard flow example mining loss, contrastive regularization loss and bright channel loss to improve the training accuracy. To further improve the generalization ability, we added real datasets to train the network, which is different from the previous training method that used only synthetic datasets. Extensive experimental results demonstrate that the proposed video defogging algorithm outperforms other mainstream algorithms.
Federated Learning for Industrial Entity Extraction

Entity extraction in the industrial field is an important part of the realization of digital transformation in the industrial field. The construction of entity extraction model in the industrial field requires a large amount of data from various parties. However, due to the security and privacy issues of the data, the data in the industrial field often exists in the form of islands, so it is almost impossible to integrate the data scattered among various parties. Therefore, this paper proposes a federated learning framework to assist parties in industry to overcome data silos and collaborate in building entity extraction models. The solution to the Non-IID problem in federal learning is to find an index to measure the data performance of all participants. Participants with relatively good data performance have a higher weight in the aggregation stage, while participants with relatively poor data performance have a lower weight in the aggregation stage. In this paper, an aggregation update method FedData is proposed to improve the performance of federated learning in data Non-IID scenarios. The method measures the data performance of each participant based on the aggregate test performance of each participant’s local model on the private data of other participants and assigns aggregate weights to each participant based on this. The experimental results show that the framework can make the participants who cannot cooperate in modeling jointly build the entity extraction model without being constrained by data security and privacy issues, so as to achieve better results. Moreover, the aggregation update method proposed in this paper has better performance than FedAvg in the scenario where the data is not independent and equally distributed.
Pyramid Channel Attention Combined Adaptive Multi-column Network for SISR

Deep convolutional neural networks (CNN) have achieved excellent performance in the single image super-resolution (SISR) task. Blindly stacking network depth cannot effectively utilize the shallow features and equal treatment of features from different stages seriously hinders network’s representational ability. To address the problems, we present a novel network structure called pyramid channel attention combined adaptive multi-column network (PCA-AMCN) for SISR. Specifically, adaptive multi-column block (AMCB) is proposed to extract local features and adaptively handle the interlayer and interlayer features, which contains three columns made up from different convolution kernels, and each column is weighted by a learnable adaptive parameter in the same receptive field and eventually fused by residual skip connection. Furthermore, pyramid channel attention (PCA) is designed to implement channel attention mechanism, which can progressively infer attention weights more accurately through the pyramid structure. The experimental results on benchmark datasets demonstrate the superiority of PCA-AMCN in comparison with other state-of-the-art methods.
PENGEMBANGAN BAHAN AJAR MENULIS ILMIAH BERBASIS KETERAMPILAN 4C DI PRODI PENDIDIKAN BAHASA INDONESIA UNIVERSITAS BILLFATH,

Teaching materials are the most basic thing to help the implementation of learning objectives. Likewise with the Scientific Writing Techniques Course in the Indonesian Language Education Study Program, FKIP, Billfath University there are no special teaching materials provided by lecturers, this is the reason for this research. The aims of this study include how to develop 4C skills-based Scientific Writing teaching materials, and to determine the feasibility of using 4C skills-based Scientific Writing teaching materials. The development model used is ADDIE development. The stages are (1) analysis, (2) design, (3) planning, (4) implementation, and (5) evaluation. Data collection was carried out using a validation sheet. Data analysis techniques use qualitative analysis by describing. The results of the study stated that Scientific Writing teaching materials were suitable for use based on the results of the validation which contained aspects of: (1) the feasibility of the content of the material, (2) the feasibility of presentation, (3) the feasibility of language, and (4) the feasibility of graphics.
Robust Lane Tracking for Harsh Driving Environments

Recently, the industry for autonomous driving is expanding rapidly. Therefore for a safe system, it require high lane detection performance. In this paper, we propose robust lane detection and a tracking algorithm even though under harsh environments. High lane detection accuracy can make more safe Lane Keeping Assist System(LKAS).
Feature Interaction-Based Reinforcement Learning for Tabular Anomaly Detection

Deep learning-based anomaly detection (DAD) has been a hot topic of research in various domains. Despite being the most common data type, DAD for tabular data remains under-explored. Due to the scarcity of anomalies in real-world scenarios, deep semi-supervised learning methods have come to dominate, which build deep learning models and leverage a limited number of labeled anomalies and large-scale unlabeled data to improve their detection capabilities. However, existing works share two drawbacks. (1) Most of them simply treat the unlabeled samples as normal ones, ignoring the problem of label contamination, which is very common in real-world datasets. (2) Only very few works have designed models specifically for tabular data instead of migrating models from other domains to tabular data. Both of them will limit the model’s performance. In this work, we propose a feature interaction-based reinforcement learning for tabular anomaly detection, FIRTAD. FIRTAD incorporates a feature interaction module into a deep reinforcement learning framework; the former can model tabular data by learning a relationship among features, while the latter can effectively exploit available information and fully explore suspicious anomalies from the unlabeled samples. Extensive experiments on three datasets not only demonstrate its superiority over the state-of-art methods but also confirm its robustness to anomaly rarity, label contamination and unknown anomalies.
Research on dialect speech recognition based on DenseNet-CTC

At present, most of the speech recognition research is based on a wide range of regions, and there are few studies on speech recognition of urban dialects.The mainstream speech recognition methods are mostly based on ResNet network, using ResNet network as acoustic model and N-gram as language model.In this study, DenseNet is used as the basic network, and the data set of Zigong dialect subdivided by Sichuan dialect is taken as the research object of speech recognition.DenseNet-BiGRU + CTC is constructed as the acoustic model of speech recognition, and RNN is used as the speech recognition model of language model.Experiments show that the speech recognition model using DenseNet network as the basic network has higher accuracy than the model based on ResNet.Compared with the GRU-CTC network word error rate (WER) decreased by 3 %, compared with the DPCNN-Attention-CTC speech recognition method error rate decreased by 5 %.
Application of "PBL+TBL" Mixed Teaching Mode in Pedagogy Teaching

With the increasing use of Internet technology, the traditional education and teaching model has been impacted again and again, and in recent years, it has also been impacted by the COVID-19, as well as the national policy of vigorously promoting the combination of quality education and smart education.In the teaching process, there are more and more student-centered teaching methods for teachers.This paper proposes the application strategy of "PBL+TBL" mixed teaching mode in pedagogy curriculum teaching to cultivate students' high-level thinking and improve their independent learning ability.
Research on improving the quality and efficiency of interrogation in telecommunication network fraud cases

With the advancement of science and technology, telecommunication network fraud crime constantly updates the means of operation, completing the transformation of intelligence, syndication, and specialization, and has become the most prominent crime with the highest incidence, the largest losses, and the strongest public repercussions.The interrogation of such cases in practice suffers from a lack of professionals, takes a long time, is inefficient, and has other problems, but currently there is less research on it in China.By summarizing the differences between telecommunication network fraud cases and other ordinary cases and the special psychology of criminal suspects, combined with the actual interrogation, problems in the interrogation reality are found, and reasonable countermeasures to improve the quality and efficiency of interrogation are proposed with reference to the practice of telecommunication network fraud investigation.