Reviewerly/exHarmony huggingface dataset:
Chinese Dialect Speech Recognition Based on End-to-end Machine Learning

With the development of End-to-end neural network, End-to-end speech recognition has achieved comparable performance with traditional speech recognition methods. The End-to-end speech recognition model only needs the speech features of the input and the text information of the output. This paper takes advantage of the End-to-end method and uses the dataset provided by the Oriental Language Recognition Challenge to build a Chinese dialect recognition system for Sichuanese, Hokkien, Shanghainese and Cantonese. Dialect data belongs to low-resource languages. In this paper, in view of the lack of dialect data resources, a method of adding unrelated languages for joint training and adding Chinese language model for joint decoding is proposed for dialect speech recognition. The model has a relative improvement of 12% in Character Error Rate compared with the Baseline systerm.
A Face Mask Detection Algorithm Based on YOLOv5

As a new machine learning method, deep learning has been widely used in computer vision. YOLOv5, a target detection algorithm based on deep learning, has a good detection effect. In the case of COVID-19, masks should be worn correctly in public places. Therefore, it is urgent to design an accurate and effective face mask detection algorithm. To solve the problem of mask-wearing detection, a face mask detection algorithm based on YOLOv5 is proposed. The main research contents include training of the YOLOv5 model, verification of face mask detection function, and analysis and comparison of detection effects of three different sizes of detection models: YOLOv5s, YOLOv5m and YOLOv5l. The proposed model realizes the mask detection function and obtains the advantages and disadvantages of different scale models through performance evaluation. The maximum mAP of the model reached 88.1%, with good detection accuracy.
DGNN: Denoising graph neural network for session-based recommendation

Session-based recommendation, which predicts the user’s following interactions by modeling the anonymous user’s interaction history, is a challenging sub-direction in recommender systems. Session data is noisy and contains unnecessary items. Currently, the primary solution to reduce noise is to use the attention mechanism, which does not eliminate noise very well, because the attention mechanism is a sort of soft pruning. We propose a novel denoising framework, named DGNN (Denoising Graph Neural Network). Different from soft pruning with the attention mechanism, DGNN implements hard pruning on graph structures, eliminating noise interference more thoroughly. Among DGNN, according to the individual needs of each node in the graph, we propose a scheme that can adaptively adjust the pruning threshold, enabling each node to cope with different degrees of noise. Extensive experiments show that DGNN can beat all baselines and achieves remarkable results on four benchmarks.
Analysis of Completion Time at Information Technology Department using Artificial Neural Network

Many people stated that education is the key to a better life since it is a very important need for humans. Since many people already realize the importance of education, many people take the college level of education. However, during this pandemic, academic activities were disrupted because the government required educational institutions to conduct learning at home. Therefore, every university has provided information technology services to deal with the IT issues. However, in practice, there are still shortcomings in the web-based learning management system (LMS) because the traffic load received is very large. Therefore, it is highly demanded to estimate the IT service completion time. This paper proposes a Multi-Layer Feed-Forward Artificial Neural Network (MFANN) as a prediction-methods for the time completion. The experimental results show that the $\mathrm{R}^{2}$ percentage of MFANN is 16.69%.
DragStream: An Anomaly And Concept Drift Detector In Univariate Data Streams

Anomaly detection in data streams comes with different technical challenges due to the data nature. The main challenges include storage limitations, the speed of data arrival, and concept drifts. In the literature, methods for mining data streams in order to detect anomalies have been proposed. While some methods focus on tackling a specific issue, other methods handle diverse problems but may have high complexity (time and memory). In the present work, we propose DragStream, a novel subsequence anomaly and concept drift detection algorithm for univariate data streams. DragStream extends the subsequence anomaly detection method for time series data Drag to streaming data. Furthermore, the new method is inspired by the well-known Matrix Profile, Drag, and MILOF which are respectively point and subsequence anomaly detection methods for time series and data streams. We conducted intensive experiments and statistical analysis to evaluate the performance of the proposed approach against existing methods. The results show that our method is competitive in performance while being linear in time and memory complexity. Finally, we provide an open-source implementation of the new method.
Design of SIBI Sign Language Recognition Using Artificial Neural Network Backpropagation

Humans communicate with one another in the form of language. However, in social life, not all humans, as well as deaf people, can communicate verbally well. They tend to communicate with others using sign language. One of the sign language systems in Indonesia is SIBI. In its application, there are many obstacles when deaf or speech-impaired people communicate with normal people. This happens because of the lack of understanding of sign language in people who do not have special needs. The existence of a sign language translator system can overcome these obstacles. The development of this system utilizes the Neural Network Backpropagation algorithm as its classification algorithm. This research aims to find the best combination of its parameters based on the obtained accuracy.
Language Variations: A New Trend of Language Use Among Teenagers in Social Media

This research is based on the language variation used among teenagers in one of the social media platforms, Twitter.This research is focused on the language variations created by changing the form of the words in Bahasa which has become a new trend on Twitter.The purpose of this study is to find out the patterns, meanings, and factors that cause language variations among teenagers.A descriptive qualitative method was used in conducting this research.The data collected is based on the words referring to the new trend mentioned in Twitter's tweets.The data were analyzed by using language variations theory proposed by Akmajian (1998) andAdams (2009).The finding shows that a new trend of language use appears to dominate the language use among teenagers which is nowadays triggered by the development of communication technology rapidly growing.The development in the way they communicate is shown as their identity as they are involved as the participants of the community of practice.
AMD-DBSCAN: An Adaptive Multi-density DBSCAN for datasets of extremely variable density

DBSCAN has been widely used in density-based clustering algorithms. However, with the increasing demand for Multi-density clustering, previous traditional DSBCAN can not have good clustering results on Multi-density datasets. In order to address this problem, an adaptive Multi-density DBSCAN algorithm (AMD-DBSCAN) is proposed in this paper. An improved parameter adaptation method is proposed in AMD-DBSCAN to search for multiple parameter pairs (i.e., Eps and MinPts), which are the key parameters to determine the clustering results and performance, therefore allowing the model to be applied to Multi-density datasets. Moreover, only one hyperparameter is required for AMD-DBSCAN to avoid the complicated repetitive initialization operations. Furthermore, the variance of the number of neighbors (VNN) is proposed to measure the difference in density between each cluster. The experimental results show that our AMD-DBSCAN reduces execution time by an average of 75% due to lower algorithm complexity compared with the traditional adaptive algorithm. In addition, AMD-DBSCAN improves accuracy by 24.7% on average over the state-of-the-art design on Multi-density datasets of extremely variable density, while having no performance loss in Single-density scenarios.
Robust Indoor Target Tracking Based on Track Matching

Radar-based indoor target tracking has received a lot of attention in recent years. Due to the occlusions caused by furniture or building structures, the tracker could only produce several short tracks. In this paper, a track matching scheme is proposed for indoor target tracking, where the Hankel matrix completion technique is utilized to estimate the missing data and the rank of the Hankel matrix is used for track association. The proposed algorithm doesn't rely on the prior knowledge of the dynamic model, which could be an effective complement to the existing tracking frameworks.
Track-Before-Detect Method for Targets with Appearance Time Uncertainty

Traditional velocity filtering based track-before-detect (VF - TBD) methods jointly process multiple successive frames in a processing batch. However, the target appearance time is uncertain in practice. When a target appears after the first frame of the current processing batch, energy integration may be corrupted by the noise-only frames and performance loss may occur. In this paper, a VF - TBD method to detect and track targets with uncertain appearance time is proposed. The number of jointly processed frames is adjusted by matching the target appearance time. Each measurement cell in every informative frame is converted to Cartesian coordinates and predicted according to a hypothetical velocity. Measurement of every cell in each informative frame is added onto the cell closest to the predicted position. Effective energy integration and superior performance can be obtained since the interference of noise-only frames can be well eliminated. An additional target appearance time estimation can be supplied. Procedures for the proposed method are derived in detail. Simulation experiments show the effectiveness of the proposed algorithm compared with conventional VF - TBD methods.
Mobile Big Data Analytics Using Deep Learning and Apache Spark

The new mobile big data is the outcome of the widespread use of mobile devices like smartphones and IoT devices. Without employing suitable analytical and learning methodologies to extract crucial facts and hidden designs from the data, collecting MBDs is not economically viable. We drew on the work of other academics who published their findings between 2015 and 2021 for this analysis. This white paper gives an introduction to deep learning in MBD analysis and a straightforward training exercise, and it verifies the viability of customizable learning architectures via Apache Spark. Certain deep learning tasks, in particular, are carried out using guided iterations. Many Spark staff members have been let go. Recent progress has been made due in large part to the availability of "big data." An expert deep model is constructed by having each Spark worker train a fractional deep model on a shared MBD and then averaging the range of all Midway models. For instance, in the business world, platforms like Apache Hadoop and Apache Spark have become increasingly well-known in recent years. The importance of efficient big data analytics in solving AI-related problems is becoming more and more apparent. His Spark infrastructure now includes the multi-computational package MLlib. Although the library can be used to do many different types of AI computations, the Spark architecture is particularly well-suited to very slow and computationally costly methods like deep learning.
The Effect of Talent Management on Organizational Innovation of the Telecommunications Companies in Jordan

This study aimed to identify the impact of talent management on Organizational Innovation. The study population consisted of 370 employees distributed over three companies representing the telecommunications sector in Jordan. An electronic questionnaire was distributed via (Google Forms) and shared by talent managers at Jordanian telecommunications companies, and 257 responses were valid for statistical analysis. After conducting a statistical analysis, the study reached the presence of a statistically significant effect of talent management has a statistically significant effect on Organizational Innovation in Jordanian Telecommunications companies. The study recommended giving attention to talent management, as it represents the first driver of all work activities.
Reviewing the Literature of Internal Corporate Social Responsibility on Job Satisfaction

In the last decade a social responsibility for the environment, local communities, working conditions, and the ethical practices have become necessary for any organization that is aiming to be sustainable and successful in the business world. Also, job satisfaction reflects the positive and favorable attitudes of the workers that have a great effect on their performance. The purpose of this paper is to reviewing the literature and the associations among internal corporate social responsibility, job satisfaction and their dimensions.
BP-AKAA: Blockchain-enforced Privacy-preserving Authentication and Key Agreement and Access Control for IIoT

The Industrial Internet of Things (IIoT) links multiple subnets to accomplish more real-time, efficient, and high-class production. Authentication is an essential prerequisite for secure communication and data sharing between mutually untrusted subdomains.However, solving trust issues between subnets through third-party trusted servers inevitably introduces security and efficiency bottlenecks. In addition, the issue of not compromising the privacy of mutual authentication remains a challenge. Furthermore, key agreement and access control, as two follow-up steps of authentication, is non-negligible for achieving secure and efficient data sharing. Existing authentication works either require heavy computational overhead or lack necessary features for data sharing. Therefore, this paper proposed a blockchain-enforced cross-domain private-protected authentication and key agreement scheme supporting attribute-based access control, named BP-AKAA. To the best of our knowledge, this is the first scheme that simultaneously supports privacy authentication, key agreement, and access control. Non-interactive zero-knowledge proof technology is adopted to protect the identities of devices. In addition, with the assistance of distributed blockchain, the untrust issue of cross-domain authentication is solved.Performance analysis demonstrates that our scheme satisfies multiple functions, including cross-domain, privacy-preserving, and mutual authentication, and outperforms existing schemes in terms of key generation, authentication, and access control.
Virtual distillation with noise dilution

Virtual distillation is an error-mitigation technique that reduces quantum-computation errors without assuming the noise type. In scenarios where the user of a quantum circuit is required to additionally employ peripherals, such as delay lines, that introduce excess noise, we find that the error-mitigation performance can be improved if the peripheral, whenever possible, is split across the entire circuit; that is, when the noise channel is uniformly distributed in layers within the circuit. We show that under the multiqubit loss and Pauli noise channels respectively, for a given overall error rate, the average mitigation performance improves monotonically as the noisy peripheral is split~(diluted) into more layers, with each layer sandwiched between subcircuits that are sufficiently deep to behave as two-designs. For both channels, analytical and numerical evidence show that second-order distillation is generally sufficient for (near-)optimal mitigation. We propose an application of these findings in designing a quantum-computing cluster that houses realistic noisy intermediate-scale quantum circuits that may be shallow in depth, where measurement detectors are limited and delay lines are necessary to queue output qubits from multiple circuits.
Fast underwater image enhancement based on a generative adversarial framework

Underwater image enhancement is a fundamental requirement in the field of underwater vision. Along with the development of deep learning, underwater image enhancement has made remarkable progress. However, most deep learning-based enhancement methods are computationally expensive, restricting their application in real-time large-size underwater image processing. Furthermore, GAN-based methods tend to generate spatially inconsistent styles that decrease the enhanced image quality. We propose a novel efficiency model, FSpiral-GAN, based on a generative adversarial framework for large-size underwater image enhancement to solve these problems. We design our model with equal upsampling blocks (EUBs), equal downsampling blocks (EDBs) and lightweight residual channel attention blocks (RCABs), effectively simplifying the network structure and solving the spatial inconsistency problem. Enhancement experiments on many real underwater datasets demonstrate our model's advanced performance and improved efficiency.
Cyber Deception Against Zero-Day Attacks: A Game Theoretic Approach

Reconnaissance activities precedent other attack steps in the cyber kill chain. Zero-day attacks exploit unknown vulnerabilities and give attackers the upper hand against conventional defenses. Honeypots have been used to deceive attackers by misrepresenting the true state of the network. Existing work on cyber deception does not model zero-day attacks. In this paper, we address the question of "How to allocate honeypots over the network?" to protect its most valuable assets. To this end, we develop a two-player zero-sum game theoretic approach to study the potential reconnaissance tracks and attack paths that attackers may use. However, zero-day attacks allow attackers to avoid placed honeypots by creating new attack paths. Therefore, we introduce a sensitivity analysis to investigate the impact of different zero-day vulnerabilities on the performance of the proposed deception technique. Next, we propose several mitigating strategies to defend the network against zero-day attacks based on this analysis. Finally, our numerical results validate our findings and illustrate the effectiveness of the proposed defense approach.
Ancestor regression in linear structural equation models

Summary We present a new method for causal discovery in linear structural equation models. We propose a simple technique based on statistical testing in linear models that can distinguish between ancestors and non-ancestors of any given variable. Naturally, this approach can then be extended to estimating the causal order among all variables. We provide explicit error control for false causal discovery, at least asymptotically. This holds true even under Gaussianity, where other methods fail due to non-identifiable structures. These Type I error guarantees come at the cost of reduced power. Additionally, we provide an asymptotically valid goodness-of-fit p-value for assessing whether multivariate data stem from a linear structural equation model.
Masked Face Recognition Using Histogram-Based Recurrent Neural Network

Masked face recognition (MFR) is an interesting topic in which researchers have tried to find a better solution to improve and enhance performance. Recently, COVID-19 caused most of the recognition system fails to recognize facial images since the current face recognition cannot accurately capture or detect masked face images. This paper introduces the proposed method known as histogram-based recurrent neural network (HRNN) MFR to solve the undetected masked face problem. The proposed method includes the feature descriptor of histograms of oriented gradients (HOG) as the feature extraction process and recurrent neural network (RNN) as the deep learning process. We have proven that the combination of both approaches works well and achieves a high true acceptance rate (TAR) of 99 percent. In addition, the proposed method is designed to overcome the underfitting problem and reduce computational burdens with large-scale dataset training. The experiments were conducted on two benchmark datasets which are RMFD (Real-World Masked Face Dataset) and Labeled Face in the Wild Simulated Masked Face Dataset (LFW-SMFD) to vindicate the viability of the proposed HRNN method.
Inference of Factors for Labor Productivity Growth Used Randomized Experiment and Statistical Causality

The study of causal dependencies in economics is fraught with great difficulties, that it is required to consider not only the object structure, but also take into account a huge number of factors acting on the object, about which nothing is either known or difficult to measure. In this paper, we attempt to overcome this problem and apply the theory of statistical causality for labor productivity management. We suggest new technology that provides the inference of causal relations between the special programs implemented in the company’s and employee’s labor productivity. The novelty of the proposed technology is that it is based on a hybrid object model, combines two models: 1—the structural object model about its functioning and development to provide a causal inference and prediction the effect of explicit factors; 2—the model based on observed data to clarify causality and to test it empirically. The technology provides integration of the theory of causal Bayesian networks, methods of randomized controlled experiments and statistical methods, allows under nonlinearity, dynamism, stochasticity and non-stationarity of the initial data, to evaluate the effect of programs on the labor effeciency. The difference between the proposed technology and others is that it ensures determination the synergistic effect of the action of the cause (program) on the effect—labor productivity in condition of hidden factors. The practical significance of the research is the results of its testing the proposed theoretical provisions, methods and technologies on actual data about food service company. The results obtained could contribute to the labor productivity growth over uncertainty of the external and internal factors and provide the companies sustainable development and its profitability growth.
A Benchmark for Dutch End-to-End Cross-Document Event Coreference Resolution

In this paper, we present a benchmark result for end-to-end cross-document event coreference resolution in Dutch. First, the state of the art of this task in other languages is introduced, as well as currently existing resources and commonly used evaluation metrics. We then build on recently published work to fully explore end-to-end event coreference resolution for the first time in the Dutch language domain. For this purpose, two well-performing transformer-based algorithms for the respective detection and coreference resolution of Dutch textual events are combined in a pipeline architecture and compared to baseline scores relying on feature-based methods. The results are promising and comparable to similar studies in higher-resourced languages; however, they also reveal that in this specific NLP domain, much work remains to be done. In order to gain more insights, an in-depth analysis of the two pipeline components is carried out to highlight and overcome possible shortcoming of the current approach and provide suggestions for future work.