Reviewerly/exHarmony huggingface dataset:
An Effective Random Forest Approach for Mining Graded Multi-Label Data

The graded multi-label classification (GMLC) is an extension of multi-label classification.Whilst a multilabel classifier is limited to predicting the set of relevant labels, a graded multi-label classifier predicts the degree of relevance of a set of given labels.A key challenge of this learning problem consists of modelling the dependencies among the labels to improve the predictive accuracy.The algorithm adaptation-based solutions, which modify the algorithms directly to handle GMLC were proven effective in modeling these dependencies in comparison to the transformation-based models which reduce the graded multi-label datasets into a set of multi-class or binary datasets.In this paper, we propose an adaptation of random forest algorithm (GML_DT) with an adapted CART (classification and regression trees).The adapted algorithm is based on a modified formula for the Gini Index which fully models the label dependencies.The performance of the new model was tested on a 101 benchmark datasets and compared against the most influential methods for GMLC.The evaluation metrics considered in the experimental study are proper to the graded multi-label setting, i.e., hamming loss and vertical 0-1 loss.The experimental results show that the proposed model outperforms the considered models for the graded multi-label performance metrics.The increase in performance and overall accuracy is quantified by a decrease in the hamming loss of more than 13% and a decrease in vertical 0-1 loss that exceeds 10% when compared to the state-of-the-art models.
A Min-Max Workload Scheduling Technique Using Soft-Computing Approach in Multi-Cloud Platform

Maximizing resource utilization by minimizing service level agreement (SLA) violation is considered to be extremely difficult process for scheduling of realtime workload on multi-cloud platform.In particular the proposed work considers both energy and performance as SLA minimization constraint for scheduling of workload in multi-cloud platform.The scheduling optimization of SLA minimization is done as single objective function using dragonfly soft computing approach.Further, the scheduling optimization of min-max is done as multi-objective for maximizing resource utilization with minimal SLA violation using dragonfly soft computing approach.The results show that the min-max workload scheduling (MMWS) has improved the resource utilization by 16.02%, 7.92%, and 2.52% respectively for montage workload and 9.92%, 4.07%, and 1.11% for SIPHT workload in comparison to the existing workload-scheduling adaptive-dragonfly algorithm (WS-ADA), reliability multi cloud-scheduler (REL-MC) and service-level agreement-based workload-scheduling (SLA-WS) method respectively.The results show that the MMWS has reduced the SLA violation rate by 81.92%, 69.23%, and 30.11% respectively for montage workload and 84.57%, 61.32%, and 12.62% for SIPHT workload in comparison to the existing WS-ADA, REL-MC and SLA-WS methods respectively.Hence, from the experiment outcomes it is shown that the proposed MMWS technique reduces SLA violation with improved resource efficiency in comparison with WS-ADA, REL-MC and SLA-WS.
A Block-Based Image Characteristics Robust Watermarking with Optimal Embeddable AC Coefficient

Due to extensive Internet usage, digital media can be quickly altered, copied, and distributed via information networks.As a result, digital information's illicit reproduction became a serious issue.As a means of copyright protection for digital media, digital watermarking has been demonstrated to be effective.Based on a discrete cosine transform (DCT), an improved robust watermarking technique considering image characteristics, a 1-D linear transformation, and an optimal embeddable AC coefficient is proposed in this paper.The host image is first divided into 8 x 8 non-overlapping block and block selection for DCT transformation is performed.The variance in each block is used to identify the embedding watermark location.A linear map is used to scramble the watermark image, which is embedded into the AC coefficients which has a maximum difference between embeddable and non-embeddable non-zero AC coefficients.Experiment results show that the suggested approach can withstand a range of a nonmalicious attack with very satisfactory results for BER close to 0, and NC near to 1.
AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes

The existing resource allocation policy for application instances in Kubernetes cannot dynamically adjust according to the requirement of business, which would cause an enormous waste of resources during fluctuations. Moreover, the emergence of new cloud services puts higher resource management requirements. This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA - the adaptive horizontal pod auto-scaling system. Based on a robust decomposition forecasting algorithm and performance training model, AHPA offers an optimal pod number adjustment plan that could reduce POD resources and maintain business stability. Since being deployed in April 2021, this system has expanded to multiple customer scenarios, including logistics, social networks, AI audio and video, e-commerce, etc. Compared with the previous algorithms, AHPA solves the elastic lag problem, increasing CPU usage by 10% and reducing resource cost by more than 20%. In addition, AHPA can automatically perform flexible planning according to the predicted business volume without manual intervention, significantly saving operation and maintenance costs.
Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework

As a novel distributed learning paradigm, federated learning (FL) faces serious challenges in dealing with massive clients with heterogeneous data distribution and computation and communication resources. Various client-variance-reduction schemes and client sampling strategies have been respectively introduced to improve the robustness of FL. Among others, primal-dual algorithms such as the alternating direction of method multipliers (ADMM) have been found being resilient to data distribution and outperform most of the primal-only FL algorithms. However, the reason behind remains a mystery still. In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm. While this explains the inherent robustness of federated ADMM, the vanilla version of it lacks the ability to be adaptive to the degree of client heterogeneity. Besides, the global model at the server under client sampling is biased which slows down the practical convergence. To go beyond ADMM, we propose a novel primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model. In addition, FedVRA unifies several representative FL algorithms in the sense that they are either special instances of FedVRA or are close to it. Extensions of FedVRA to semi/un-supervised learning are also presented. Experiments based on (semi-)supervised image classification tasks demonstrate superiority of FedVRA over the existing schemes in learning scenarios with massive heterogeneous clients and client sampling.
ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification

Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for learning node/graph representations without labels. However, in practice, the underlying class distribution of unlabeled nodes for the given graph is usually imbalanced. This highly imbalanced class distribution inevitably deteriorates the quality of learned node representations in GCL. Indeed, we empirically find that most state-of-the-art GCL methods cannot obtain discriminative representations and exhibit poor performance on imbalanced node classification. Motivated by this observation, we propose a principled GCL framework on Imbalanced node classification (ImGCL), which automatically and adaptively balances the representations learned from GCL without labels. Specifically, we first introduce the online clustering based progressively balanced sampling (PBS) method with theoretical rationale, which balances the training sets based on pseudo-labels obtained from learned representations in GCL. We then develop the node centrality based PBS method to better preserve the intrinsic structure of graphs, by upweighting the important nodes of the given graph. Extensive experiments on multiple imbalanced graph datasets and imbalanced settings demonstrate the effectiveness of our proposed framework, which significantly improves the performance of the recent state-of-the-art GCL methods. Further experimental ablations and analyses show that the ImGCL framework consistently improves the representation quality of nodes in under-represented (tail) classes.
AMOM: Adaptive Masking over Masking for Conditional Masked Language Model

Transformer-based autoregressive (AR) methods have achieved appealing performance for varied sequence-to-sequence generation tasks, e.g., neural machine translation, summarization, and code generation, but suffer from low inference efficiency. To speed up the inference stage, many non-autoregressive (NAR) strategies have been proposed in the past few years. Among them, the conditional masked language model (CMLM) is one of the most versatile frameworks, as it can support many different sequence generation scenarios and achieve very competitive performance on these tasks. In this paper, we further introduce a simple yet effective adaptive masking over masking strategy to enhance the refinement capability of the decoder and make the encoder optimization easier. Experiments on 3 different tasks (neural machine translation, summarization, and code generation) with 15 datasets in total confirm that our proposed simple method achieves significant performance improvement over the strong CMLM model. Surprisingly, our proposed model yields state-of-the-art performance on neural machine translation (34.62 BLEU on WMT16 EN to RO, 34.82 BLEU on WMT16 RO to EN, and 34.84 BLEU on IWSLT De to En) and even better performance than the AR Transformer on 7 benchmark datasets with at least 2.2x speedup. Our code is available at GitHub.
Metric Multi-View Graph Clustering

Graph-based methods have hitherto been used to pursue the coherent patterns of data due to its ease of implementation and efficiency. These methods have been increasingly applied in multi-view learning and achieved promising performance in various clustering tasks. However, despite their noticeable empirical success, existing graph-based multi-view clustering methods may still suffer the suboptimal solution considering that multi-view data can be very complicated in raw feature space. Moreover, existing methods usually adopt the similarity metric by an ad hoc approach, which largely simplifies the relationship among real-world data and results in an inaccurate output. To address these issues, we propose to seamlessly integrates metric learning and graph learning for multi-view clustering. Specifically, we employ a useful metric to depict the inherent structure with linearity-aware of affinity graph representation learned based on the self-expressiveness property. Furthermore, instead of directly utilizing the raw features, we prefer to recover a smooth representation such that the geometric structure of the original data can be retained. We model the above concerns into a unified learning framework, and hence complements each learning subtask in a mutual reinforcement manner. The empirical studies corroborate our theoretical findings, and demonstrate that the proposed method is able to boost the multi-view clustering performance.
Detecting VoIP Data Streams: Approaches Using Hidden Representation Learning

The use of voice-over-IP technology has rapidly expanded over the past several years, and has thus become a significant portion of traffic in the real, complex network environment. Deep packet inspection and middlebox technologies need to analyze call flows in order to perform network management, load-balancing, content monitoring, forensic analysis, and intelligence gathering. Because the session setup and management data can be sent on different ports or out of sync with VoIP call data over the Real-time Transport Protocol (RTP) with low latency, inspection software may miss calls or parts of calls. To solve this problem, we engineered two different deep learning models based on hidden representation learning. MAPLE, a matrix-based encoder which transforms packets into an image representation, uses convolutional neural networks to determine RTP packets from data flow. DATE is a density-analysis based tensor encoder which transforms packet data into a three-dimensional point cloud representation. We then perform density-based clustering over the point clouds as latent representations of the data, and classify packets as RTP or non-RTP based on their statistical clustering features. In this research, we show that these tools may allow a data collection and analysis pipeline to begin detecting and buffering RTP streams for later session association, solving the initial drop problem. MAPLE achieves over ninety-nine percent accuracy in RTP/non-RTP detection. The results of our experiments show that both models can not only classify RTP versus non-RTP packet streams, but could extend to other network traffic classification problems in real deployments of network analysis pipelines.
Hypernetworks for Zero-Shot Transfer in Reinforcement Learning

In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.
Long Legal Article Question Answering via Cascaded Key Segment Learning (Student Abstract)

Current sentence-level evidence extraction based methods may lose the discourse coherence of legal articles since they tend to make the extracted sentences scattered over the article. To solve the problem, this paper proposes a Cascaded Answer-guided key segment learning framework for long Legal article Question Answering, namely CALQA. The framework consists of three cascaded modules: Sifter, Reader, and Responder. The Sifter transfers a long legal article into several segments and works in an answer-guided way by automatically sifting out key fact segments in a coarse-to-fine approach through multiple iterations. The Reader utilizes a set of attention mechanisms to obtain semantic representations of the question and key fact segments. Finally, considering it a multi-label classification task the Responder predicts final answers in a cascaded manner. CALQA outperforms state-of-the-art methods in CAIL 2021 Law dataset.
MTDiag: An Effective Multi-Task Framework for Automatic Diagnosis

Automatic diagnosis systems aim to probe for symptoms (i.e., symptom checking) and diagnose disease through multi-turn conversations with patients. Most previous works formulate it as a sequential decision process and use reinforcement learning (RL) to decide whether to inquire about symptoms or make a diagnosis. However, these RL-based methods heavily rely on the elaborate reward function and usually suffer from an unstable training process and low data efficiency. In this work, we propose an effective multi-task framework for automatic diagnosis called MTDiag. We first reformulate symptom checking as a multi-label classification task by direct supervision. Each medical dialogue is equivalently converted into multiple samples for classification, which can also help alleviate the data scarcity problem. Furthermore, we design a multi-task learning strategy to guide the symptom checking procedure with disease information and further utilize contrastive learning to better distinguish symptoms between diseases. Extensive experimental results show that our method achieves state-of-the-art performance on four public datasets with 1.7%~3.1% improvement in disease diagnosis, demonstrating the superiority of the proposed method. Additionally, our model is now deployed in an online medical consultant system as an assistant tool for real-life doctors.
Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning

Capturing high dynamic range (HDR) images (videos) is attractive because it can reveal the details in both dark and bright regions. Since the mainstream screens only support low dynamic range (LDR) content, tone mapping algorithm is required to compress the dynamic range of HDR images (videos). Although image tone mapping has been widely explored, video tone mapping is lagging behind, especially for the deep-learning-based methods, due to the lack of HDR-LDR video pairs. In this work, we propose a unified framework (IVTMNet) for unsupervised image and video tone mapping. To improve unsupervised training, we propose domain and instance based contrastive learning loss. Instead of using a universal feature extractor, such as VGG to extract the features for similarity measurement, we propose a novel latent code, which is an aggregation of the brightness and contrast of extracted features, to measure the similarity of different pairs. We totally construct two negative pairs and three positive pairs to constrain the latent codes of tone mapped results. For the network structure, we propose a spatial-feature-enhanced (SFE) module to enable information exchange and transformation of nonlocal regions. For video tone mapping, we propose a temporal-feature-replaced (TFR) module to efficiently utilize the temporal correlation and improve the temporal consistency of video tone-mapped results. We construct a large-scale unpaired HDR-LDR video dataset to facilitate the unsupervised training process for video tone mapping. Experimental results demonstrate that our method outperforms state-of-the-art image and video tone mapping methods. Our code and dataset are available at https://github.com/cao-cong/UnCLTMO.
A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least Significant Bit Substitution

A new data hiding technique was presented to conceal the privacy of patient’s diagnosis and personal sensitive information in an electrocardiogram (ECG) signal. Instead of hiding one data bit in a host block at a time by traditional coefficient adjustment technique (CAT), we embed two data bits in the same size of the block via the proposed enhanced CAT (ECAT) method. The resultant payload provided by ECAT is nearly twice as large as that provided by the CAT scheme. In addition, if ECAT equipped with the least significant bit (LSB) substitution, then a large number of bits can be hidden in the ECG hosts while the resulting perceived quality is good. Simulations revealed that both the average payload and SNR and the proposed method are superior to those of the CAT-based schemes and existed ECG steganographic methods. The application of our method can be found in portable (or wearable) measurement devices.
Enabling Non-linear Quantum Operations Through Variational Quantum Splines

The postulates of quantum mechanics impose only unitary transformations on quantum states, which is a severe limitation for quantum machine learning algorithms. Quantum Splines (QSplines) have recently been proposed to approximate quantum activation functions to introduce non-linearity in quantum algorithms. However, QSplines make use of the HHL as a subroutine and require a fault-tolerant quantum computer to be correctly implemented. This work proposes the Generalised Hybrid Quantum Splines (GHQSplines), a novel method for approximating non-linear quantum activation functions using hybrid quantum-classical computation. The GHQSplines overcome the highly demanding requirements of the original QSplines in terms of quantum hardware and can be implemented using near-term quantum computers. Furthermore, the proposed method relies on a flexible problem representation for non-linear approximation and it is suitable to be embedded in existing quantum neural network architectures. In addition, we provide a practical implementation of the GHQSplines using Pennylane and show that our model outperforms the original QSplines in terms of quality of fitting.
How Can Management Accounting Contribute to Risk Management Strategies Within Organization Case Study of Small and Medium Enterprises: Evidence from Emerging Countries

Purpose: Current study aimed at exploring the impact of management accounting in supporting risks management strategies within Small and Medium Enterprises in Jordan and GCC countries during the fiscal year 2021-2022. Theoretical framework: Competency of management accounting was chosen as the independent variable which included (Strategic Management, Professional Ethics and Values, Reporting and Control, Leadership, Technology and Analytics, Business Acumen and Operations), while the variable of risk management strategies was chosen to be the dependent variable. Design/methodology/approach: Through quantitative approach and distributing a questionnaire on (257) financial and accounting managers within Small and Medium Enterprises in Jordan and GCC countries. Findings: Based on screening the data through SPSS; results of study was able to identify and accept the main hypothesis which argued that "Management accounting competency has a statistically positive influence on risk management strategies within Small and Medium Enterprises in Jordan and GCC countries " with R= 0.877 and a variance of influence that reached 76.9%. As for sub-variables, it was seen through statistical processing that business acumen and operations and leadership were the most influential variables on risk management strategies with a high variance that indicated a statistical influence of 72% and 42.3% respectively. Research, Practical &amp; Social implications: Study recommended developing and increasing awareness of the importance of management accounting in organizational risk management by promoting the comprehensive and integrated perspective and abandoning the individual perspective of management accounting in the organization; further recommendations were presented in the study. Originality/Value: These finding could be used as a reference for Small and Medium Enterprises in Jordan and GCC countries