Reviewerly/exHarmony huggingface dataset:
Anomaly Detection Technology for Cloud Manufacturing System based on Data Denoising and Feature Optimization

Aiming at the problem that the traditional anomaly detection method based on threshold cannot effectively detect sensor numerical anomalies in cloud manufacturing system, this work proposes a new method to detect some sensor numerical anomalies form the industrial control system. It is the central part of a cloud manufacturing system. Firstly, this work constructs a Savitzky-Golay (S-G) filter to reduce data noises. Furthermore, an extreme learning machine based on genetic algorithm (GA-ELM) model is proposed to detect sensor numerical anomalies form the industrial control system. The genetic algorithm (GA) is used to reduce feature dimensions from 51 to 10 and the extreme learning machine algorithm (ELM) is used for classification to achieve the purpose of anomaly detection. Finally, using the public dataset called Secure Water Treatment (SWaT), the classification accuracy is 98.96%. It shows a better performance of the proposed method.
A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model

High-dimensional and incomplete (HDI) data holds tremendous interactive information in various industrial applications. A latent factor (LF) model is remarkably effective in extracting valuable information from HDI data with stochastic gradient decent (SGD) algorithm. However, an SGD-based LFA model suffers from slow convergence since it only considers the current learning error. To address this critical issue, this paper proposes a Nonlinear PID-enhanced Adaptive Latent Factor (NPALF) model with two-fold ideas: 1) rebuilding the learning error via considering the past learning errors following the principle of a nonlinear PID controller; b) implementing all parameters adaptation effectively following the principle of a particle swarm optimization (PSO) algorithm. Experience results on four representative HDI datasets indicate that compared with five state-of-the-art LFA models, the NPALF model achieves better convergence rate and prediction accuracy for missing data of an HDI data.
Acceleration of applying AI to open intelligent network using parallel simulation for RL training

The era of Beyond 5G and 6G networks will further accelerate the development of intelligent networks with network virtualization and the introduction of open networks. This is because, in the next-generation networks, AI-based network operation is essential to manage explosively increasing network devices and increasingly complex services. Network intelligence is built through several stages, among which the stage of training AI models until they guarantee plausible performance consumes the most time and resources of all stages. In this paper, we explain our experiments to shorten model training time and accomplish cost reduction. We build a training host compatible with O-RAN specifications and create pipelines for parallel simulation to train Reinforcement Learning (RL) models. For RL model training, when it is difficult to interact with the real environment, the model interacts with simulators that mimic the environment. Simulation parallelization is performed to reduce the time consumed in the step of collecting the experiences from the simulations. The parallel simulation requires more resources than sequential training procedures, but can be performed with less time for training. According to the public cloud usage rate policy, the usage time of resources has a greater impact on the total usage fee than the total amount of resources used. Therefore, it is important to find an optimal pipeline configuration that can reduce training time without increasing overall cost. In conclusion, we have confirmed the effect of reducing training time by up to 51% and cost up to 80% through experiments using sample scenarios.
The Preliminary Investigation on Micro-Credentials Practices in Malaysia

This COVID-19 has lately changed the way individuals study and teach by making it available at any time, from any location, and at a low cost. Traditional face-to-face teaching and learning are losing popularity as more students opt for hybrid or all-online learning. In today's fast-paced business, the advent of the gig economy needs the development of individuals and experts with specialised skill sets to fill increasingly specialised positions. Higher education providers needed a more dynamic and quick style of learning to match these demands, which Micro-Credential, a well-known player in 21st-century training and education, delivered. Micro-credentials are nanodegrees, also known as small qualifications, that demonstrate a person's talents, knowledge, and/or experience in a certain subject area or ability. After completing a micro-credentials course, the learner will receive a digital badge. The adoption of Micro-Credential courses in Malaysia was investigated using desktop research and a survey questionnaire in this study. This study examines how Micro-Credentials are used in Malaysia's top three public universities, as well as a poll of Malaysian students' Micro-Credentials habits. Micro-credentialing appears to be gaining popularity at Malaysian universities. The research will be broadened to collect and analyse data for the preliminary study, which will focus on learners' attainment of Digital Badges for Micro Credential Computing Courses using a quantitative research technique.
High-dimensional Feature Selection in Classification: A Length-Adaptive Evolutionary Approach

Feature selection is an essential technique which has been widely applied in data mining. Recent research has shown that a good feature subset can be obtained by using evolutionary computing (EC) approaches as a wrapper. However, most feature selection methods based on EC use a fixed-length encoding to represent feature subsets. When this fixed length representation is applied to high-dimensional data, it requires a large amount of memory space as well as a high computational cost. Moreover, this representation is inflexible and may limit the performance of EC because of a too huge search space. In this paper, we propose an Adaptive- Variable-Length Genetic Algorithm (A VLGA), which adopts a variable-length individual encoding and enables individuals with different lengths in a population to evolve in their own search space. An adaptive length changing mechanism is introduced which can extend or shorten an individual to guide it to explore in a better search space. Thus, A VLGA is able to adaptively concentrate on a smaller but more fruitful search space and yield better solutions more quickly. Experimental results on 6 high-dimensional datasets reveal that A VLGA performs significantly better than existing methods.
A Systematic Analysis of Federated Learning

Deep Learning is an emerging area which is applicable to deal with massive and high multi-dimensional data in various E-Commerce platforms. This data is called as Big Data which is available in the complex and unstructured format, in terms of Volume, Velocity, Validity, Veracity, Variety and Variability. The data used in such e-platforms, is revealed at multiple places affecting the User's private data and security. To overcome the issue of user's private data security, a new dimension of Deep Learning Approach is been introduced, which is called as “Federated Learning (FL)”. Federated Learning provides Privacy Preservation and security to the User's data, thereby enhancing the accuracy of the system. This paper discusses about FL, various types of FL along with the implementation results of FL with Matrix Factorization (For accurate recommendations) on Movielens 1M dataset [15]. The paper concludes with various FL threats, and applications.
Aspect-level Sentiment Classification with Multi-head-attention-based Multi-channel Graph Convolutional Networks

Aspect-level sentiment classification (ALSC) has received extensive attention due to its fine-grained characteristics. However, ALSC tasks are often plagued by complex sentence structures, and existing attention-based methods cannot fully utilize implicit syntactic information to deconstruct sentences. Therefore, we propose a model named MAMC-GCN (Multi-head-attention-based Multi-channel Graph Convolutional Networks), which integrates the dependency relations of sentences and uses the multi-head attention mechanism to improve the ability to extract key information related to aspect terms. Experiments on five benchmarks prove that our model can make excellent performance under a short training duration.
Stability and generalization of graph convolutional networks in eigen-domains

Graph Convolution Networks (GCNs) have been shown to be very effective in utilizing pair-wise relationships across samples. They have been successfully applied to solve various machine learning problems in practice. In many applications, the construction of GCNs involves more than one layer. However, their generalization and stability analysis are limited. The main aim of this paper is to analyze GCNs with two layers. The formulation is based on transductive semi-supervised learning and the filtering is done in the eigen-domain. We show the uniform stability of the neural network and the convergence of the generalization gap to zero. The analysis of two-layer GCN is more involved than the single-layer case and requires some new estimates of the neural network’s quantities. The analysis confirms the usefulness of GCNs. It also sheds light on the design of the neural network, for instance, how the data should be scaled to achieve the uniform stability of the learning process. Some experimental results on benchmark datasets are presented to illustrate the theory.
Edge-weight-Based link prediction in heterogeneous graph

Link prediction is to predict whether there is a link between two nodes in the graph, it is a very important application and plays a great role in various industries. In recent years, with the development of graph neural network technology, many algorithms make effort to study the expression of each node from the original graph data and use them to infer new links. However, most of the existing algorithms have a common problem when facing heterogeneous graphs, which is, they do not consider the weight of edges in graphs. Instead, they put all their energies into computing node features. Although a few algorithms such as RGCN are trying to take the influence of different link types into account while extracting node features, these implicit feature extractions do not start from the global information, but just calculate independently for each node. In other words, in these algorithms, even the same type of links will be abstracted into different features on different nodes. This is obviously inconsistent with reality. On the same map, the feature of the same link should be relatively fixed and should not be changed just because of different positions. In addition, when the current graph neural network algorithm is applied to link prediction, the link type to be predicted must be specified in advance, which makes the algorithm extremely inflexible. In order to solve these problems, we propose an edge weight calculation algorithm that extracts the edge feature from the whole graph. We also propose the edge-weight-based link prediction algorithm. By introducing edge weight into the MLP, there is no need to specify the target link type at the beginning of model training. It improves both the performance and efficiency of the link prediction model. Experiments on two datasets show that this edge-weight-based link prediction algorithm performs better than current algorithms and reaches SOTA.
A Two-Stage Active Learning Method for Image Classification

Recent successes in learning-based image classification rely heavily on a large number of annotated training samples, which often require considerable human effort. In this paper, we propose a new two-stage active learning (AL) method for image classification with a query strategy considering both uncertainty and diversity. In the first stage, the uncertainty is used to determine the candidate set. In the second stage, the candidate set is clustered, and the nearest sample from the cluster center is selected to increase the diversity of samples. Our method uses Poly-1 loss as the classification loss and Binary Cross Entropy (BCE) as the binary loss to distinguish between the labeled samples and the unlabeled samples. We train the two classifiers in a joint way and evaluate our method on the CIFAR10 and CIFAR100 datasets. The rich experimental results show that the proposed method outperforms the state-of-the-art AL methods in image classification tasks.
Effective-aggregation Graph Convolutional Network for Imbalanced Classification

Classification is a common task and can be achieved by learning a predictive model from a labeled training dataset. However, the imbalanced data distribution makes the model tend to favor the majority class, which reduces the classification performance. Unlike traditional classification models, graph convolutional networks (GCNs) can extract useful feature information from unlabeled data. In this paper, a novel framework for imbalanced classification named effective-aggregation graph convolutional network (EGCN) is proposed. First, a graph generator constructs graph-structured data using both labeled and unlabeled data. Then, an aggregation control unit (ACU) is performed to improve the effectiveness of aggregation. ACU uses local estimation density to limit the aggregation of inter-class edges from a local perspective, and it enhances the aggregation of the minority class from a global perspective based on the imbalance ratio. Finally, the prediction results are obtained by a graph convolutional network. Experimental results on several real-world datasets show that EGCN has promising performance.
A Low Delay AES Round Transform Architecture Using Constant Matrix Multiplications Merging Technologies

This paper presents a low delay architecture design method for the hardware implementation of the Advanced Encryption Standard (AES) algorithm. The proposed architecture is designed by using constant matrix multiplications (CMM) merging technologies. To reduce the area cost in hardware implementations, S-box/InvS-box is usually implemented with composite field arithmetic (CFA) technologies. In this paper, CMM in CFA-based S-box/InvS-box are further merged with constant coefficient multiplications in MixColumns/ InvMixColumns, which can also be expressed as CMM forms. By the merging, the delay of the hardware implementation of encryption round transform is reduced at the cost of slight area cost increasing, and both delay and area cost are reduced in hardware implementations of decryption round transform. Hardware complexities analysis indicates that our designs have less delay compared with previous works.
An Encryption Method of Multiple Images and Multiple Region of Interest Based on DNA Sequence and Chaotic System

Most of the existing image encryption methods based on the region of interest (ROI) use traditional methods to manually screen regions, which are notoriously difficult to implement serialized and cannot handle multiple ROI well. In addition, the low dimensional Logistic map widely used has the shortcoming of small key space and poor security. Furthermore, DNA computing, which is popular in current research, also has problems such as fixed rules and low sensitivity. Therefore, this paper improves the advanced deep learning object detection algorithm YOLOV4 tiny to process multiple ROI more quickly and accurately and then designs an elegant encryption strategy for multiple images and multiple ROI combining Logistic map, Chen system, and DNA computing. Specifically, we calculate the initial value of the Logistic map from the multiple ROI, which significantly increases the keyspace and key sensitivity. At the same time, we utilize the Chen system to design a unique DNA coding and computing rule for each ROI dynamically to improve the pixel-level scrambling and diffusion. Finally, we combine multiple images into a larger picture, and use SHA256 to calculate its hash value as the initial value to generate a chaotic sequence to complete row-column scrambling, and realize serialized multiple images encryption. Simulation experiments and security analysis results indicate that our method has good encryption and decryption effects, as well as high security and robustness, and can resist statistical attacks and brute force attacks.
A Study on Forced Alignment Error Patterns in Kaldi

Forced alignment is one of the fundamental pieces in speech recognition, and we generally overlook the errors in the forced alignment process. It is extremely time consuming to look for phoneme level or word level alignment errors, and most importantly the alignment error detection is subjective to the listener. In this paper, we seek to identify error patterns in forced alignment, particularly the alignment skewness. We define an alignment skewness scoring function to capture the overall skewness of the alignment. Four different hypotheses, the first one on the overall skewness of the forced aligner, the second one on the difference in forced alignment between clean speech and noisy speech, the third one on male vs female recordings, a fourth one on the alignment difference between the word preceding silence vs random word, are formulated. One sample t-test for the first hypothesis and two sample t-test for the next three hypotheses are conducted respectively on a Kaldi trained forced alignment on Voxforge dataset. We report a left skew on the overall forced alignment output compared to the manually labelled ground truth and, no statistically significant difference in alignment skewness between clean speech vs noisy speech, male vs female alignment, and the word preceding silence vs random word alignment.
Comprising Survey of Steganography &amp; Cryptography: Evaluations, Techniques and Trends in Future Research

At the moment, multimedia data protection has become a critical activity. It is primarily utilized when information confidentiality is vital, which can be accomplished through cryptography and Steganography. These methods conceal not only the message itself but also its existence. These techniques have a variety of services in various sectors, including defense, medicine, and online transactions. Both approaches must have an ample key space resistant to brute force searches, low execution time complexity/high speed, and the ability to generate substantial confusion and diffusion for adequate security. Various encryption and steganography techniques have been employed to safeguard confidential digital data from those attempting to gain access illegally. This research paper includes an in-depth analysis of existing types of cryptography and steganography techniques and recent advances to both categories in numerous modalities, most notably image and audio data. The research highlights the security improved by applying various cryptography and Steganography techniques. Also, a brief classification of the multiple methods used in both approaches is done. In the case of cryptography, this classification is based on classic and modern techniques. Apart from cryptography, various existing steganography techniques are classified based on retrieval, embedding domain, adaptive nature, and theory or a key idea.
A Recommendation Method For Fusion of Multiple HIN Meta-Paths

With the rapid development of information technology and the Internet, people have stridden into the era of "information overload". The personalized recommendation is an effective way to solve the problem of "information overload" and plays a pivotal role in people's daily life. Feature extraction based on heterogeneous information networks can improve the performance of recommendation systems. This paper considers different search paths between nodes and proposes a recommendation method that integrates multiple heterogeneous information network meta-paths. Combining different meta-paths in a personalized nonlinear way, the network representation learning vector is generated through the meta-path and deepwalk algorithm. The embeddings of users and items are spliced and MLP is used for training and scoring prediction to improve the performance of the recommender system. The experimental results show that on the Yelp dataset and Douban Book dataset, the proposed model is better than other algorithms. In terms of improving the recommendation effectiveness, the MAE and RMSE are reduced by 5.4%~15%, which has good effectiveness and interpretability.
Single Low-Lighting Image Enhancement via Self-Supervised Intensity Map Estimation

In this paper, a single low-light image enhancement method based on self-supervised intensity map estimation is proposed. This method combines camera response model and data-driven model. To control the monotonicity, a camera response function is proposed to generate the initial intensity map model between different exposure ratio images. Then an end-to-end self-supervised network learned a final pixel-wise intensity map considering the smoothness of image, color distortion and brightness. Specially, any exposure ratio images can be produced from the learned pixel-wise map. In order to preserve the natural of brightness in the enhanced image, two virtual images are generated from the learned pixel-wise map. Then, the final enhanced image is generated by fusing the two virtual images and one low-light image. Experimental results have shown that the proposed method can brighten low-light images while persevering the visibility of images.
An Unconstrained Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks

Large-scale undirected weighted networks are usually found in big data-related research fields. It can naturally be quantified as a symmetric high-dimensional and incomplete (SHDI) matrix for implementing big data analysis tasks. A symmetric non-negative latent-factor-analysis (SNL) model is able to efficiently extract latent factors (LFs) from an SHDI matrix. Yet it relies on a constraint-combination training scheme, which makes it lack flexibility. To address this issue, this paper proposes an unconstrained symmetric nonnegative latent-factor-analysis (USNL) model. Its main idea is two-fold: 1) The output LFs are separated from the decision parameters via integrating a nonnegative mapping function into an SNL model; and 2) Stochastic gradient descent (SGD) is adopted for implementing unconstrained model training along with ensuring the output LFs nonnegativity. Empirical studies on four SHDI matrices generated from real big data applications demonstrate that an USNL model achieves higher prediction accuracy of missing data than its peers, as well as highly competitive computational efficiency.
An Adapted Google's PageRank Algorithm for Hadith Sequencing

It is common that curious users from various walks of life approach a hadith expert and put forward a query, seeking advice with reference to hadiths. In such a scenario, systematically recalling relevant hadiths from human memory or referring to web resources and manually sequencing a list of hadiths is a time consuming process. Hadith data repositories are voluminous, detailed and store over 10,000 sahih (authentic) hadiths recorded in the six books called Kutub al-Sittah. As a pioneering step, to address this issue, we have initiated developing an algorithm which can be incorporated into a web or mobile application. In this contribution we introduce a theme called hadith sequencing and propose a novel hadith sequencing algorithm by adapting Google's Page Rank algorithm and report our preliminary findings.