Reviewerly/exHarmony huggingface dataset:
Aggregation models in ensemble learning: A large-scale comparison

In this work we present a large-scale comparison of 21 learning and aggregation methods proposed in the ensemble learning, social choice theory (SCT), information fusion and uncertainty management (IF-UM) and collective intelligence (CI) fields, based on a large collection of 40 benchmark datasets. The results of this comparison show that Bagging-based approaches reported performances comparable with XGBoost, and significantly outperformed other Boosting methods. In particular, ExtraTree-based approaches were as accurate as both XGBoost and Decision Tree-based ones while also being more computationally efficient. We also show how standard Bagging-based and IF-UM-inspired approaches outperformed the approaches based on CI and SCT. IF-UM-inspired approaches, in particular, reported the best performance (together with standard ExtraTrees), as well as the strongest resistance to label noise (together with XGBoost). Based on our results, we provide useful indications on the practical effectiveness of different state-of-the-art ensemble and aggregation methods in general settings.
Experimental Demonstration of Composite Pulses on IBM’s Quantum Computer

We perform comprehensive experimental tests of various composite pulse sequences using one of the open-access IBM quantum processors, based on superconducting transmon qubits. We implement explicit pulse control of the qubit by making use of the opportunity of low-level access to the backend, provided by IBM Quantum. We obtain the excitation profiles for a huge variety of broadband, narrowband, and passband composite pulses, producing any prechosen target probabilities, ranging from zero to one. We also test universal composite pulses that compensate errors in any experimental parameter. In all experiments, we find excellent agreement between theoretical and experimental excitation profiles. This proves both the composite pulses as a very efficient and flexible quantum control tool and the high quality of the IBM quantum processor. As an extreme example, we test and observe a pronounced narrowband excitation profile for a composite sequence of as many as 1001 pulses.
Toward Trustworthy and Privacy-Preserving Federated Deep Learning Service Framework for Industrial Internet of Things

In this article, we propose a trustworthy privacy-preserving federated learning (FL)-based deep learning (DL) service framework for Industrial Internet of Things-enabled systems. FL mitigates the privacy issues of the traditional collaborative learning model by aggregating multiple locally trained models without sharing any datasets among the participants. Nevertheless, the FL-based DL (FDL) model cannot be trusted as it is susceptible to intermediate results and data structure leakage during the model aggregation process. The proposed framework introduces an edge and cloud-powered service-oriented architecture identifying the key components and a service model for residual networks-based FDL with differential privacy for generating trustworthy locally trained models. The service model decomposes the functionality of the overall FDL process as services to ensure trustworthy execution through privacy preservation. Finally, we develop a privacy-preserving local model aggregation mechanism for FDL. We perform several experiments to assess the performance of the proposed framework.
Feature selection and classification in mammography using hybrid crow search algorithm with Harris hawks optimization

The purpose of this study is to develop a hybrid algorithm for feature selection and classification of masses in digital mammograms based on the Crow search algorithm (CSA) and Harris hawks optimization (HHO). The proposed CSAHHO algorithm finds the best features depending on their fitness value, which is determined by an artificial neural network. Using an artificial neural network and support vector machine classifiers, the best features determined by CSAHHO are utilized to classify masses in mammograms as benign or malignant. The performance of the suggested method is assessed using 651 mammograms. Experimental findings show that the proposed CSAHHO tends to be the best as compared to the original CSA and HHO algorithms when evaluated using ANN. It achieves an accuracy of 97.85% with a kappa value of 0.9569 and area under curve AZ = 0.982 ± 0.006. Furthermore, benchmark datasets are used to test the feasibility of the suggested approach and then compared with four state-of-the-art algorithms. The findings indicate that CSAHHO achieves high performance with the least amount of features and support to enhance breast cancer diagnosis.
Variational Quantum Computation of Molecular Linear Response Properties on a Superconducting Quantum Processor

Simulating response properties of molecules is crucial for interpreting experimental spectroscopies and accelerating materials design. However, it remains a long-standing computational challenge for electronic structure methods on classical computers. While quantum computers hold the promise to solve this problem more efficiently in the long run, existing quantum algorithms requiring deep quantum circuits are infeasible for near-term noisy quantum processors. Here, we introduce a pragmatic variational quantum response (VQR) algorithm for response properties, which circumvents the need for deep quantum circuits. Using this algorithm, we report the first simulation of linear response properties of molecules including dynamic polarizabilities and absorption spectra on a superconducting quantum processor. Our results indicate that a large class of important dynamical properties such as Green's functions are within the reach of near-term quantum hardware using this algorithm in combination with suitable error mitigation techniques.
Sparse Tensor-based Point Cloud Attribute Compression

Surveillance videos can capture a variety of realistic events and also anomalies. Due to an increase in the crime rate in public areas, surveillance cameras are adopted in a very large number. But as these crimes/public disputes are rare to occur at a specific location, human monitors are idle most of the time. Hence, there is a justified need to develop intelligent systems for anomaly detection. There are several seminal deepneural architectures proposed in this field of anomaly detection ranging from using deep learning as a feature extraction tool to complete end-to-end deep-learning-based anomaly detection models. Any practical anomaly detection model must be generic in detecting a spectrum of anomalous events; however, several models can detect only specific types of anomalies. Further, several models are not amenable to distributed training over many machines on large streaming data, which is typical in a video surveillance system. In this paper, we discuss the techniques to detect anomalies in real-time by exploring recent architectures in the literature and analyze and explore ways we can improve the detection accuracy of the model. We propose a batching methodology that improves the existing model's area under the curve by 2%.
ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches

Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding, and requires careful hyperparameter tuning, potentially leading to suboptimal robustness evaluations. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. The dataset is built by first optimizing a set of adversarial patches against an ensemble of models, using a state-of-the-art attack that creates transferable patches. The corresponding patches are then randomly rotated and translated, and finally applied to the ImageNet data. We use ImageNet-Patch to benchmark the robustness of 127 models against patch attacks, and also validate the effectiveness of the given patches in the physical domain (i.e., by printing and applying them to real-world objects). We conclude by discussing how our dataset could be used as a benchmark for robustness, and how our methodology can be generalized to other domains. We open source our dataset and evaluation code at https://github.com/pralab/ImageNet-Patch.
ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals

A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices. Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses.
CNN-Based Broad Learning for Cross-Domain Emotion Classification

Cross-domain emotion classification aims to leverage useful information in a source domain to help predict emotion polarity in a target domain in a unsupervised or semi-supervised manner.Due to the domain discrepancy, an emotion classifier trained on source domain may not work well on target domain.Many researchers have focused on traditional cross-domain sentiment classification, which is coarse-grained emotion classification.However, the problem of emotion classification for cross-domain is rarely involved.In this paper, we propose a method, called convolutional neural network (CNN) based broad learning, for cross-domain emotion classification by combining the strength of CNN and broad learning.We first utilized CNN to extract domain-invariant and domain-specific features simultaneously, so as to train two more efficient classifiers by employing broad learning.Then, to take advantage of these two classifiers, we designed a co-training model to boost together for them.Finally, we conducted comparative experiments on four datasets for verifying the effectiveness of our proposed method.The experimental results show that the proposed method can improve the performance of emotion classification more effectively than those baseline methods.
BAT: Block and token self-attention for speech emotion recognition

Transformers have achieved great success in many artificial intelligence fields, such as computer vision (CV), audio processing and natural language processing (NLP). In speech emotion recognition (SER), transformer-based architectures usually compute attention in a token-by-token (frame-by-frame) manner, but this approach lacks adequate capacity to capture local emotion information and is easily affected by noise. This paper proposes a novel SER architecture, referred to as block and token self-attention (BAT), that splits a mixed spectrogram into blocks and computes self-attention by combining these blocks with tokens, which can alleviate the effect of local noise while capturing authentic sentiment expressions. Furthermore, we present a cross-block attention mechanism to facilitate information interaction among blocks while integrating a frequency compression and channel enhancement (FCCE) module to smooth the attention biases between blocks and tokens. BAT achieves 73.2% weighted accuracy (WA) and 75.2% unweighted accuracy (UA) on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset, surpassing the results of previously developed state-of-the-art approaches with the same dataset partitioning operation. Further experimental results reveal that our proposed method is also well suited for cross-database and cross-domain tasks, achieving 89% WA and 87.4% UA on Emo-DB and producing a top-1 recognition accuracy of 88.32% with only 15.01 Mb of parameters on the CIFAR-10 image dataset under a scenario with no data augmentation or pretraining.
Disentangling Identity and Pose for Facial Expression Recognition

Facial expression recognition (FER) is a challenging problem because the expression component is always entangled with other irrelevant factors, such as identity and head pose. In this work, we propose an identity and pose disentangled facial expression recognition (IPD-FER) model to learn more discriminative feature representation. We regard the holistic facial representation as the combination of identity, pose and expression. These three components are encoded with different encoders. For identity encoder, a well pre-trained face recognition model is utilized and fixed during training, which alleviates the restriction on specific expression training data in previous works and makes the disentanglement practicable on in-the-wild datasets. At the same time, the pose and expression encoder are optimized with corresponding labels. Combining identity and pose feature, a neutral face of input individual should be generated by the decoder. When expression feature is added, the input image should be reconstructed. By comparing the difference between synthesized neutral and expressional images of the same individual, the expression component is further disentangled from identity and pose. Experimental results verify the effectiveness of our method on both lab-controlled and in-the-wild databases and we achieve state-of-the-art recognition performance.
Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go?

Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises-quadratic or exponential increases in computational time with quantum parallelism and thus offer a huge leap forward in the computation of Machine Learning algorithms. This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML). We applied QML methods such as Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) to detect Software Supply Chain (SSC) attacks. Due to the access limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow Quantum. We evaluated the performance of QML in terms of processing speed and accuracy and finally, compared with its classical counterparts. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks.
Training the Maritime Security Operations Centre Teams

A Security Operation Centre (SOC) is a powerful and versatile infrastructure for cybersecurity due to the capabilities of monitoring and improving the security posture of an organization. While they found great diffusion in companies to defend IT/OT infrastructures, their employment in the maritime domain is still narrow but required. Nevertheless, SOC analysts working in traditional SOCs may be unprepared to operate proficiently in the maritime environment due to its context-specific features. They require specific training to fully exploit these newfound requirements. In this work, we leverage the NICE framework to outline the profile definition of a SOC operator in terms of required knowledge and skills. This profile allowed us to define the requirements of a training program tailored for maritime SOC operators. Moreover, we show how this program can be fulfilled with targeted hands-on exercises. An example exercise set in a representative scenario highlights that we are able to train the specific skills with metrics for evaluating their proficiency.
Efficient and Effective Feature Space Exploration for Testing Deep Learning Systems

Assessing the quality of Deep Learning (DL) systems is crucial, as they are increasingly adopted in safety-critical domains. Researchers have proposed several input generation techniques for DL systems. While such techniques can expose failures, they do not explain which features of the test inputs influenced the system’s (mis-) behaviour. DeepHyperion was the first test generator to overcome this limitation by exploring the DL systems’ feature space at large. In this article, we propose DeepHyperion-CS , a test generator for DL systems that enhances DeepHyperion by promoting the inputs that contributed more to feature space exploration during the previous search iterations. We performed an empirical study involving two different test subjects (i.e., a digit classifier and a lane-keeping system for self-driving cars). Our results proved that the contribution-based guidance implemented within DeepHyperion-CS outperforms state-of-the-art tools and significantly improves the efficiency and the effectiveness of DeepHyperion . DeepHyperion-CS exposed significantly more misbehaviours for five out of six feature combinations and was up to 65% more efficient than DeepHyperion in finding misbehaviour-inducing inputs and exploring the feature space. DeepHyperion-CS was useful for expanding the datasets used to train the DL systems, populating up to 200% more feature map cells than the original training set.
Domain Generalization by Joint-Product Distribution Alignment

In this work, we address the problem of domain generalization for classification, where the goal is to learn a classification model on a set of source domains and generalize it to a target domain. The source and target domains are different, which weakens the generalization ability of the learned model. To tackle the domain difference, we propose to align a joint distribution and a product distribution using a neural transformation, and minimize the Relative Chi-Square (RCS) divergence between the two distributions to learn that transformation. In this manner, we conveniently achieve the alignment of multiple domains in the neural transformation space. Specifically, we show that the RCS divergence can be explicitly estimated as the maximal value of a quadratic function, which allows us to perform joint-product distribution alignment by minimizing the divergence estimate. We demonstrate the effectiveness of our solution through comparison with the state-of-the-art methods on several image classification datasets.
The geopolitics of infrastructuralized platforms: the case of Alibaba

Contemporary digital platforms have become increasingly infrastructuralized, and started to raise geopolitical tensions with their global expansion. Amidst the heightened geopolitical competition between the US and China, the growing power of Chinese infrastructuralized platforms has made them the center of recent geopolitical dynamics. Drawing from an exploratory case study, this paper discusses Alibaba, one of the most prominent Chinese Internet giants, as an infrastructuralized platform, and highlights its geopolitical struggles. Often perceived as an e-commerce company, Alibaba has become 'infrastructuralized': its now-massive digital empire has moved beyond e-commerce, expanding into almost every aspect of China's and global digital economy such as logistics, finance, offline retailing, and cloud computing. This paper traces three highly visible cases in Alibaba's global journey – its failed deal with MoneyGram in 2017, the uneven global journey of Alibaba Cloud, and the construction of the electronic World Trade Platform – to illustrate three key dimensions of the geopolitics of infrastructuralized platforms – namely, the geopolitics of everyday data, the geopolitics of the visibility-invisibility tension, and the geopolitics of modularity. By doing so, it contributes to the following two areas of scholarship. On the one hand, it contributes to the growing literature on 'infrastructures and platforms' by foregrounding the geopolitical dimensions of Chinese infrastructuralized platforms. On the other hand, it adds to the literature on the 'geopolitics of infrastructures' by bringing in a new type of infrastructure, complementing previous discussions on the geopolitics of traditional material infrastructures.
An optimized case-based software project effort estimation using genetic algorithm

Software development companies have long suffered from inaccurate estimation of their software projects. This in turn led to huge losses, especially in the financial resources available for the project as well as the time required to complete it. As a result of this, the research community has developed different methods for estimating effort in software projects in the hope of achieving high levels of accuracy and efficiency in the use of available resources. Among those methods that have proven to be accurate in estimating the effort of software projects is the use of machine learning (ML) techniques, especially the case-based reasoning technique (CBR). This technique is based on adapting previously successful solutions for similar software projects. However, the CBR technique suffers from a problem which is its multiple parameters that are difficult to be tuned. This justifies the importance of the adaptation and adjustment process as an essential part of CBR to produce accurate and efficient results with least absolute estimation error. In this paper, one of the most efficient multi-objective evolutionary techniques, the Genetic Algorithm (GA), are used to help find the best set of classical CBR parameters (feature selection, feature weighting, similarity measures, and k number of nearest neighbors) to produce the most accurate effort estimates for software projects. The proposed CBR-GA model showed the effectiveness of using the GA algorithm to search for the best combination of CBR parameters and thus improve its accuracy. This in turn is beneficial for project managers in the early financial planning phase for effort estimation and thus project cost control. To validate the proposed CBR-GA model, we used a set of public benchmark datasets available on PROMISE data repository, in addition we used a set of reliable evaluation metrics. The obtained results are promising in terms of accuracy and significance tests. This implies the importance of search-based techniques for tuning effort estimation methods.