Reviewerly/exHarmony huggingface dataset:
Evolving multi-label fuzzy classifier

Multi-label classification has attracted much attention in the machine learning community to address the problem of assigning single samples to more than one (not necessarily non-overlapping) class at the same time. We propose an evolving multi-label fuzzy classifier (EFC-ML) which is able to self-adapt and self-evolve its structure with new incoming multi-label samples in an incremental, single-pass manner. It is based on a multi-output Takagi–Sugeno type architecture, where for each class a separate consequent hyper-plane is defined, which yields flexibility for partially approximating the respective classes in a binary [0,1]-regression context. The learning procedure embeds a locally weighted incremental correlation-based algorithm combined with (conventional) recursive fuzzily weighted least squares and Lasso-based regularization. Locality is important to avoid the out-masking effect of single class labels in one or more rules; the correlation-based part ensures that the interrelations between class labels, a specific well-known property in multi-label classification for improved performance, are preserved properly; the Lasso-based regularization reduces the curse of dimensionality effects in the case of a higher number of inputs. Antecedent learning is achieved by product-space clustering and conducted for all class labels together, which yields a single rule base (opposed to related techniques such as one-versus-rest or classifier chaining, achieving multiple different rule bases, one per class), allowing a compact knowledge view and thus enabling better interpretable insights. Furthermore, our approach comes with an online active learning (AL) strategy for updating the classifier on just a (smaller) number of selected samples, which in turn makes the approach applicable for scarcely labelled streams in applications, where the annotation effort is typically expensive. It is based on three essential concepts: novelty content in the antecedent space, uncertainty due to ambiguity in the consequent (output) space and parameter instability reduction, and these in combination with an upper-allowed selection budget (which could be predefined by a user). Our approach was evaluated on several data sets from the MULAN repository and showed significantly improved classification accuracy and average precision trend lines compared to (evolving) one-versus-rest or classifier chaining concepts. A significant result was that, due to the online AL method, a 90% reduction in the number of samples used for classifier updates had little effect on the accumulated accuracy trend lines compared to a full update in most data set cases.
Efficient Depth Fusion Transformer for Aerial Image Semantic Segmentation

Taking depth into consideration has been proven to improve the performance of semantic segmentation through providing additional geometry information. Most existing works adopt a two-stream network, extracting features from color images and depth images separately using two branches of the same structure, which suffer from high memory and computation costs. We find that depth features acquired by simple downsampling can also play a complementary part in the semantic segmentation task, sometimes even better than the two-stream scheme with the same two branches. In this paper, a novel and efficient depth fusion transformer network for aerial image segmentation is proposed. The presented network utilizes patch merging to downsample depth input and a depth-aware self-attention (DSA) module is designed to mitigate the gap caused by difference between two branches and two modalities. Concretely, the DSA fuses depth features and color features by computing depth similarity and impact on self-attention map calculated by color feature. Extensive experiments on the ISPRS 2D semantic segmentation dataset validate the efficiency and effectiveness of our method. With nearly half the parameters of traditional two-stream scheme, our method acquires 83.82% mIoU on Vaihingen dataset outperforming other state-of-the-art methods and 87.43% mIoU on Potsdam dataset comparable to the state-of-the-art.
A third generation genetic algorithm NSGAIII for task scheduling in cloud computing

Task scheduling in the cloud is perceived as a difficult Multi-objective optimization problem. It refers to the assignment of user tasks on the available cloud virtual machines. This problem can be solved effectively by combining two or more approaches for improving task execution and increasing the use of resources. In this article, a third-generation Multi-objective optimization method called Non-dominated Sorting Genetic Algorithm (NSGA-III) was used for the first time to our knowledge to schedule a set of user tasks on a set of available virtual machines (VMs) in the cloud based on a new Multi-objective adaptation function to minimize the runtime (TE), the power consumption (CE), and the cost (cout). Furthermore, the performance of NSGAIII was compared with the performance of its previous version, Non-dominated Sorting Genetic Algorithm (NSGAII) where NSGAIII results outperform the results of NSGAII. The experimental results of the proposed method are encouraging, as they are used to show their effectiveness in solving such problems.
A robust voice spoofing detection system using novel CLS-LBP features and LSTM

Automatic Speaker Verification (ASV) systems are vulnerable to a variety of voice spoofing attacks, e.g., replays, speech synthesis, etc. The imposters/fraudsters often use different voice spoofing attacks to fool the ASV systems to achieve certain objectives, i.e., bypass the security of someone’s home or stealing money from a bank account, etc. To counter such fraudulent activities on the ASV systems, we propose a robust voice spoofing detection system capable of effectively detecting multiple types of spoofing attacks. For this purpose, we propose a novel feature descriptor Center Lop-Sided Local Binary Patterns (CLS-LBP) for audio representation. CLS-LBP effectively analyzes the audios bidirectionally to better capture the artifacts of synthetic speech, microphone distortions of replay, and dynamic speech attributes of the bonafide signal. The proposed CLS-LBP features are used to train the long short-term memory (LSTM) network for detection of both the physical- (replay) and logical-access attacks (speech synthesis, voice conversion). We employed the LSTM due to its effectiveness to better process and learn the internal representation of sequential data. More specifically, we obtained an equal error rate (EER) value of 0.06% on logical-acess (LA) while 0.58% on physical-access (PA) attacks. Additionally, the proposed system is also capable of detecting the unseen voice spoofing attacks and also robust enough to classify among the cloning algorithms used to synthesize the speech. Performance evaluation on the ASVspoof 2019 corpus signify the effectiveness of the proposed system in terms of detecting the physical- and logical-access attacks over existing state-of-the-art voice spoofing detection systems.
An Oversampling Method for Class Imbalance Problems on Large Datasets

Several oversampling methods have been proposed for solving the class imbalance problem. However, most of them require searching the k-nearest neighbors to generate synthetic objects. This requirement makes them time-consuming and therefore unsuitable for large datasets. In this paper, an oversampling method for large class imbalance problems that do not require the k-nearest neighbors’ search is proposed. According to our experiments on large datasets with different sizes of imbalance, the proposed method is at least twice as fast as 8 the fastest method reported in the literature while obtaining similar oversampling quality.
All-round improvement in DCT-based blind image watermarking with visual enhancement via denoising autoencoder

Discrete cosine transform (DCT)-based watermarking has received great success in the copyright protection of digital images. This study exploited the orthonormal expansion properties of DCT to achieve superior performance in computational efficiency and watermarking efficacy. In particular, we reformulated the relative modulation to allow the modification of paired DCT coefficients as if operable in the spatial domain. To account for human visual characteristics, we also adjusted the embedding strength adaptively according to image entropy. The use of a range regulation mechanism enabled perfect watermark retrieval. The pursuit of an ideal balance between robustness and imperceptibility could be benefited from the grey wolf optimizer. Using the above-discussed techniques, the proposed watermarking scheme demonstrates superior robustness in terms of bit error rate and normalized correlation coefficient with the peak signal-to-noise ratio tuned at around 38 dB. Compared to the regular DCT approach, the computational acceleration is on the scale of N2/(log2N)2, where N signifies the side length of the image block under processing. Furthermore, for the type of character-style watermark, we explored the feasibility of using a denoising autoencoder (DAE) to enhance the retrieved watermark comprehensibility. The proposed DAE was found to be capable of contributing a 60% reduction of the bit error rate, thus making the recovered watermark visually more recognizable.
A Novel Feature Set Extraction Based on Accelerometer Sensor Data for Improving the Fall Detection System

Because falls are the second leading cause of injury deaths, especially in the elderly according to WHO statistics, there have been a lot of studies on developing a fall detection and warning system. Many approaches based on wearable sensors, cameras, Infrared sensors, radar, etc., have been proposed to detect falls efficiently. However, it still faces many challenges due to noise and no clear definition of fall activities. This paper proposes a new way to extract 44 features based on the time domain, frequency domain, and Hjorth parameters to deal with this. The effect of the proposed feature set has been evaluated on several classification algorithms, such as SVM, k-NN, ANN, J48, and RF. Our method achieves a relative high performance (F1-Score metric) in detecting fall and non-fall activities, i.e., 95.23% (falls), 99.11% (non-falls), and 96.16% (falls), 99.90% (non-falls) for the MobileAct 2.0 and UP-Fall datasets, respectively.
Development and Validation of a Questionnaire to Measure Digital Skills of Chinese Undergraduates

To keep sustainable development in a digital society, it is essential for young people to possess certain digital skills. Levels of the digital skills of Chinese undergraduates take a certain role in the process of educational digitalization promoted by the Chinese government, becoming an important concern for Chinese universities and policymakers. However, few valid and reliable instruments are available for the assessment of the digital skills of undergraduates in China. Thus, developing, and testing the reliability and validity of a questionnaire to measure the digital skills of Chinese undergraduates are necessary. Based on previous literature and situations in the Chinese educational context, this study developed and validated a questionnaire to assess the digital skills of Chinese undergraduates. Through factor analysis approaches, the internal factor structure of the questionnaire was explored, and its reliability and validity were verified. Development and validation of a questionnaire, in which 6 factors were extracted, were described: access to and management of digital content, use of digital means, communication of digital content, creation of digital content, digital empathy, and digital safety. The questionnaire was applied to the first sample of 222 undergraduates and the second sample of 231 undergraduates selected randomly from a university located in the east of China. An exploratory factor analysis (EFA) through SPSS 26.0 with the first sample was used to determine the internal factor structure of the whole questionnaire, which showed the expected congruency between items and dimensions. Then a confirmatory factor analysis (CFA) through Mplus 8.3 with the second sample was utilized to check model-data fit of the questionnaire, which showed a good fit between them. Convergent validity and discriminant validity of the questionnaire were also verified. The resulting questionnaire emerged as a useful tool for carrying out nationwide studies on digital skills in higher education generally or in different disciplines specifically in China in the future.
FaaSnap

FaaSnap is a VM snapshot-based platform that uses a set of complementary optimizations to improve function cold-start performance for Function-as-a-Service (FaaS) applications. Compact loading set files take better advantage of prefetching. Per-region memory mapping tailors page fault handling depending on the contents of different guest VM memory regions. Hierarchical overlapping memory-mapped regions simplify the mapping process. Concurrent paging allows the guest VM to start execution immediately, rather than pausing until the working set is loaded. Altogether, FaaSnap significantly reduces guest VM page fault handling time on the critical path and improves overall function loading performance. Experiments on serverless benchmarks show that it reduces end-to-end function execution by up to 3.5x compared to state-of-the-art, and on average is only 3.5% slower than snapshots cached in memory. Moreover, we show that FaaSnap is resilient to changes of working set and remains efficient under bursty workloads and when snapshots are located in remote storage.
Efficient image denoising technique using the meshless method: Investigation of operator splitting RBF collocation method for two anisotropic diffusion-based PDEs

Images taken and stored digitally are often degraded by noise, so that the perceived image quality is significantly decreased in the presence of noise and human gaze behavior is affected by this noise as well. In recent years, image denoising has been modeled as partial differential equations (PDEs), which can be said to eliminate noise as well as preserve image detail is one of their main advantages. Numerical simulation of these PDEs, which are often nonlinear, high-order and high-dimensional, is one of the most challenging topics in this field. In this paper, one of the most powerful meshless numerical approaches, the operator splitting radial basis function (RBF) collocation method, is introduced to solve two modified versions of the Perona-Malik (PM) equation. The method splits each problem into several equations each of which is solved in one direction so as to make the major problem solvable in a smaller size. Furthermore, being in one direction makes it possible to solve the equations without any need for domain decomposition and its complexities. An unconditionally stable method (Crank-Nicolson) is used for discretizing time in this paper. Using meshless approaches in the image processing problems makes sense since the structure of digital images consist of pixels that corresponds to the distributed nodes of the meshless method so the complex time consuming domain construction of the finite element methods is avoided to get higher speeds in obtaining the desired denoised results. Some images with different noise levels are denoised using the presented method in the section of numerical results to show the desirable performance of the method. Also, the quality of the denoised images are concluded in tables which can be used to compare the obtained results.
SBSim: A Sentence-BERT Similarity-Based Evaluation Metric for Indian Language Neural Machine Translation Systems

Machine translation (MT) outputs are widely scored using automatic evaluation metrics and human evaluation scores. The automatic evaluation metrics are expected to be easily computable and a reflection of human evaluation. Traditional string-based metrics such as BLEU, ChrF++ scores, are widely used to evaluate MT systems, but fail to account for synonyms that appear in the state-of-the-art neural machine translation (NMT) systems, owing to their inability to evaluate paraphrases. While similarity-based metrics such as Yisi, BERTScore address this issue, these metrics need to be modified to better evaluate morphologically rich Indian languages such as, Tamil and Hindi. The current work proposes a novel and individual sentence-BERT based similarity (SBSim) metric, that makes use of a paraphrase-BERT model and sentence-level embedding to evaluate NMT outputs. The effectiveness of the BLEU, ChrF++, Yisi, BERTScore, and the proposed SBSim are evaluated on English-to-Tamil and English-to-Hindi NMT outputs. The sentence-level metric correlation of the proposed SBSim metric with respect to human scores is observed to outperform the existing metrics with a correlation of 0.9123 and 0.9052 for English-to-Tamil and English-to-Hindi NMT systems, respectively. Further, the average metric correlation of the SBSim metric is also observed to be the highest with a value of 0.9801 and 0.9836 for these NMT systems, respectively. The proposed metric is also evaluated on WMT2020 dataset and reports the highest correlation of 0.7129 with the human scores.
Modeling user micro-behaviors and original interest via Adaptive Multi-Attention Network for session-based recommendation

Session-based recommendation aims to recommend the next item that matches the user’s preferences based on short-term limited session information. It does not need user’s account or other privacy information and can be applied widely on most e-commerce platforms. Many efforts have made great achievements. However, the following two insights are often neglected. First, a user’s micro-behaviors, such as item browsing, item clicking, item collecting, item carting and purchasing, offer detailed information and can help infer the user’s intention. Second, the first item of a session usually implies the user’s original interest. These insights motivate us to propose a novel model named AMAN (Adaptive Multi-Attention Network) which considers both user micro-behaviors and original interest for session-based recommendation. AMAN contains three key components: Directed Graph Attention Network (DGAN) learns item representation from item-level and adaptively captures items’ associations in the same micro-behavior sequence; Transformer-based Operation-level Attention Network (TOAN) learns item representation from operation-level and adaptively captures items’ associations across different micro-behavior sequences; Micro-behavior Co-Attention Network (MBCAN) learns micro-behavior sequence representation and adaptively captures the co-dependence among different micro-behavior sequences from item-level, and explicitly models the first item that reflects the user’s original interest. Experimental results on Yoochoose, Taobao14 and Taobao15 benchmark datasets show that AMAN outperforms the state-of-the-art baselines, justifying the effectiveness of incorporating user micro-behaviors and original interest for session-based recommendation.
Spatial Distribution-based Imbalanced Undersampling

Undersampling is one of the most popular techniques for dealing with class-imbalance problems. Various undersampling methods have emerged over the past few decades. Each of them exhibits the superiority in some scenarios. However, selecting representative majority-class samples such that the structures of the selected groups are maintained according to the underlying imbalanced distribution remains a challenge. For this purpose, this paper proposes Spatial Distribution-based UnderSampling (SDUS) for imbalanced learning. SDUS uses a supervised constructive process to learn majority-class local patterns in terms of sphere neighborhoods (SPN). Two sample selection strategies, specifically, a top-down strategy and a bottom-up strategy, are proposed for maintaining the distribution pattern of original data in selecting majority-class sample subsets from different perspectives. SDUS introduces an ensemble technique that improves learning performance by utilizing the diversity caused by the randomness of the local-pattern learning process. Numerical experiments on 38 typical datasets from KEEL repository and 13 state-of-the-art comparison methods demonstrate the effectiveness of SDUS in maintaining the underlying distribution characteristics for imbalanced undersampling.
Multi-object tracking with an adaptive generalized labeled multi-Bernoulli filter

The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process. Further, the information on locations where the objects appear, their detection probabilities, and the statistics of the sensor's false alarms significantly influence the tracking accuracy of the filter. However, this information is usually assumed to be known and provided by the users. In this paper, we propose an adaptive generalized labeled multi-Bernoulli (GLMB) filter which can track multiple objects without prior knowledge of the aforementioned information. Experimental results show that the performance of the proposed filter is comparable to an ideal GLMB filter supplied with correct information of the tracking scenarios.
Instance importance-Aware graph convolutional network for 3D medical diagnosis

Automatic diagnosis of 3D medical data is a significant goal of intelligent healthcare. By exploiting the abundant pathological information of 3D data, human experts and algorithms can provide accurate predictions for patients. Considering the high cost of collecting exhaustive annotations for 3D data, a sustainable alternative is to develop diagnosis algorithms with merely patient-level labels. Motivated by the fact that 2D slices of 3D data hold explicit diagnostic efficacy, we propose the Instance Importance-aware Graph Convolutional Network (I2GCN) under the multi-instance learning (MIL). Specifically, we first calculate the instance importance of each slice towards diagnosis using a preliminary MIL classifier, which is further utilized to promote the refined diagnosis branch. In the refined diagnosis branch, we devise the Instance Importance-aware Graph Convolutional Layer (I2GCLayer) to exploit complementary features in both importance-based and feature-based topologies. Moreover, to alleviate the deficient supervision of 3D dataset, we propose the importance-based Sub-Graph Augmentation (SGA) to effectively regularize the framework training. Extensive experiments confirm the effectiveness of our method with different organs and modals on the CC-CCII and PROSTATEx datasets, which outperforms state-of-the-art methods by a large margin. The source code is available at https://github.com/CityU-AIM-Group/I2GCN.
Verbal-Person Nets: Pose-Guided Multi-Granularity Language-to-Person Generation

Person image generation conditioned on natural language allows us to personalize image editing in a user-friendly manner. This fashion, however, involves different granularities of semantic relevance between texts and visual content. Given a sentence describing an unknown person, we propose a novel pose-guided multi-granularity attention architecture to synthesize the person image in an end-to-end manner. To determine what content to draw at a global outline, the sentence-level description and pose feature maps are incorporated into a U-Net architecture to generate a coarse person image. To further enhance the fine-grained details, we propose to draw the human body parts with highly correlated textual nouns and determine the spatial positions with respect to target pose points. Our model is premised on a conditional generative adversarial network (GAN) that translates language description into a realistic person image. The proposed model is coupled with two-stream discriminators: 1) text-relevant local discriminators to improve the fine-grained appearance by identifying the region-text correspondences at the finer manipulation and 2) a global full-body discriminator to regulate the generation via a pose-weighting feature selection. Extensive experiments conducted on benchmarks validate the superiority of our method for person image generation.