Reviewerly/exHarmony huggingface dataset:
Few-Shot Image Classification: Current Status and Research Trends

Conventional image classification methods usually require a large number of training samples for the training model. However, in practical scenarios, the amount of available sample data is often insufficient, which easily leads to overfitting in network construction. Few-shot learning provides an effective solution to this problem and has been a hot research topic. This paper provides an intensive survey on the state-of-the-art techniques in image classification based on few-shot learning. According to the different deep learning mechanisms, the existing algorithms are divided into four categories: transfer learning based, meta-learning based, data augmentation based, and multimodal based methods. Transfer learning based methods transfer useful prior knowledge from the source domain to the target domain. Meta-learning based methods employ past prior knowledge to guide the learning of new tasks. Data augmentation based methods expand the amount of sample data with auxiliary information. Multimodal based methods use the information of the auxiliary modal to facilitate the implementation of image classification tasks. This paper also summarizes the few-shot image datasets available in the literature, and experimental results tested by some representative algorithms are provided to compare their performance and analyze their pros and cons. In addition, the application of existing research outcomes on few-shot image classification in different practical fields are discussed. Finally, a few future research directions are identified.
NeutronStar: Distributed GNN Training with Hybrid Dependency Management

GNN's training needs to resolve issues of vertex dependencies, i.e., each vertex representation's update depends on its neighbors. Existing distributed GNN systems adopt either a dependencies-cached approach or a dependencies-communicated approach. Having made intensive experiments and analysis, we find that a decision to choose one or the other approach for the best performance is determined by a set of factors, including graph inputs, model configurations, and an underlying computing cluster environment. If various GNN trainings are supported solely by one approach, the performance results are often suboptimal. We study related factors for each GNN training before its execution to choose the best-fit approach accordingly. We propose a hybrid dependency-handling approach that adaptively takes the merits of the two approaches at runtime. Based on the hybrid approach, we further develop a distributed GNN training system called NeutronStar, which makes high performance GNN trainings in an automatic way. NeutronStar is also empowered by effective optimizations in CPU-GPU computation and data processing. Our experimental results on 16-node Aliyun cluster demonstrate that NeutronStar achieves 1.81X-14.25X speedup over existing GNN systems including DistDGL and ROC.
Pre-trained transformers: an empirical comparison

Pre-trained transformers have rapidly become very popular in the Natural Language Processing (NLP) community, surpassing the previous state of the art in a wide variety of tasks. While their effectiveness is indisputable, these methods are expensive to fine-tune on the target domain due to the high number of hyper-parameters; this aspect significantly affects the model selection phase and the reliability of the experimental assessment. This paper serves a double purpose: we first describe five popular transformer models and survey their typical use in previous literature, focusing on reproducibility; then, we perform comparisons in a controlled environment over a wide range of NLP tasks. Our analysis reveals that only a minority of recent NLP papers that use pre-trained transformers reported multiple runs (20%), standard deviation or statistical significance (10%), and other crucial information, seriously hurting replicability and reproducibility. Through a vast empirical comparison on real-world datasets and benchmarks, we also show how the hyper-parameters and the initial seed impact results, and highlight the low models' robustness.
Scalable semantic-enhanced supervised hashing for cross-modal retrieval

Cross-modal retrieval is a classic scenario that can describe the same semantics from multiple angles and provide us with more abundant and diversified information ; thus, it has abundant applications in information retrieval, data mining, and machine learning. Generally, supervised information can enhance the ability to learn hash features, and obtain better retrieval accuracy than unsupervised methods. However, how to effectively solve the discrete binary constraint problem, and how to retain more semantic information in the process of hash learning, which constitute important parts of cross-modal retrieval, still have some challenges. To mitigate these problems, in this work, we propose a novel cross-modal hashing approach, called, scalable pairwise embedding constraint hashing (SPECH). The SPECH approach employs the loss of likelihood similarity technique using pairwise sample data to measure the semantic similarity of heterogeneous modal samples. In light of this, the SPECH approach can maximize the utilization of available data from heterogeneous modal samples for training models. In addition, we further enhance the discriminative capability of hash codes and reduce the intraclass differences in heterogeneous modal samples, by embedding the more discriminative semantic label attribute space regression into the to-be-learned process of hash codes. Comprehensive experiments on three benchmark datasets show that the proposed SPECH approach achieves significantly better retrieval accuracy and outperforms several state-of-the-art approaches on cross-modal retrieval applications.
DSNN: A DenseNet-Based SNN for Explainable Brain Disease Classification

Aims: Brain diseases refer to intracranial tissue and organ inflammation, vascular diseases, tumors, degeneration, malformations, genetic diseases, immune diseases, nutritional and metabolic diseases, poisoning, trauma, parasitic diseases, etc. Taking Alzheimer's disease (AD) as an example, the number of patients dramatically increases in developed countries. By 2025, the number of elderly patients with AD aged 65 and over will reach 7.1 million, an increase of nearly 29% over the 5.5 million patients of the same age in 2018. Unless medical breakthroughs are made, AD patients may increase from 5.5 million to 13.8 million by 2050, almost three times the original. Researchers have focused on developing complex machine learning (ML) algorithms, i.e., convolutional neural networks (CNNs), containing millions of parameters. However, CNN models need many training samples. A small number of training samples in CNN models may lead to overfitting problems. With the continuous research of CNN, other networks have been proposed, such as randomized neural networks (RNNs). Schmidt neural network (SNN), random vector functional link (RVFL), and extreme learning machine (ELM) are three types of RNNs. Methods: We propose three novel models to classify brain diseases to cope with these problems. The proposed models are DenseNet-based SNN (DSNN), DenseNet-based RVFL (DRVFL), and DenseNet-based ELM (DELM). The backbone of the three proposed models is the pre-trained "customize" DenseNet. The modified DenseNet is fine-tuned on the empirical dataset. Finally, the last five layers of the fine-tuned DenseNet are substituted by SNN, ELM, and RVFL, respectively. Results: Overall, the DSNN gets the best performance among the three proposed models in classification performance. We evaluate the proposed DSNN by five-fold cross-validation. The accuracy, sensitivity, specificity, precision, and F1-score of the proposed DSNN on the test set are 98.46% ± 2.05%, 100.00% ± 0.00%, 85.00% ± 20.00%, 98.36% ± 2.17%, and 99.16% ± 1.11%, respectively. The proposed DSNN is compared with restricted DenseNet, spiking neural network, and other state-of-the-art methods. Finally, our model obtains the best results among all models. Conclusions: DSNN is an effective model for classifying brain diseases.
The emerging potential of interactive virtual reality in drug discovery

The potential of virtual reality (VR) to contribute to drug design and development has been recognized for many years. A recent advance is to use VR not only to visualize and interact with molecules, but also to interact with molecular dynamics simulations 'on the fly' (interactive molecular dynamics in VR, IMD-VR), which is useful for flexible docking and examining binding processes and conformational changes.The authors use the term 'interactive VR' to refer to software where interactivity is an inherent part of the user VR experience e.g. in making structural modifications or interacting with a physically rigorous molecular dynamics (MD) simulation, as opposed to using VR controllers to rotate and translate the molecule for enhanced visualization. Here, they describe these methods and their application to problems relevant to drug discovery, highlighting the possibilities that they offer in this arena.The ease of viewing and manipulating molecular structures and dynamics, using accessible VR hardware, and the ability to modify structures on the fly (e.g. adding or deleting atoms) - and for groups of researchers to work together in the same virtual environment - makes modern interactive VR a valuable tool to add to the armory of drug design and development methods.
Blockchain for Governments: The Case of the Dubai Government

Blockchain technology is an innovative technology with the potential of transforming cities by augmenting the building of resilient societies and enabling the emergence of more transparent and accountable governments. To understand the capabilities of blockchain, as well as its impact on the public sector, this study conducted a review of blockchain technology and its implementations by various governments around the globe. E-government evolution is analyzed, based on empirical evidence from a Dubai government entity in the United Arab Emirates (UAE), which has utilized blockchain technology for developing end-user services, relevant to the public sector. Benefits achieved and challenges to overcome in such blockchain-based pilot deployments are discussed. The findings of this study offer new insights for practitioners involved in bringing in innovations for the benefit of society, using blockchain technology. Furthermore, it provides insights into policy actions to be developed to address the future challenges and to improve already existing e-government policies. The results of this research will benefit all blockchain-based pilot deployments by providing guidance and knowledge on this immature yet developing technology.
Model Selection Using K-Means Clustering Algorithm for the Symmetrical Segmentation of Remote Sensing Datasets

The importance of unsupervised clustering methods is well established in the statistics and machine learning literature. Many sophisticated unsupervised classification techniques have been made available to deal with a growing number of datasets. Due to its simplicity and efficiency in clustering a large dataset, the k-means clustering algorithm is still popular and widely used in the machine learning community. However, as with other clustering methods, it requires one to choose the balanced number of clusters in advance. This paper’s primary emphasis is to develop a novel method for finding the optimum number of clusters, k, using a data-driven approach. Taking into account the cluster symmetry property, the k-means algorithm is applied multiple times to a range of k values within which the balanced optimum k value is expected. This is based on the uniqueness and symmetrical nature among the centroid values for the clusters produced, and we chose the final k value as the one for which symmetry is observed. We evaluated the proposed algorithm’s performance on different simulated datasets with controlled parameters and also on real datasets taken from the UCI machine learning repository. We also evaluated the performance of the proposed method with the aim of remote sensing, such as in deforestation and urbanization, using satellite images of the Islamabad region in Pakistan, taken from the Sentinel-2B satellite of the United States Geological Survey. From the experimental results and real data analysis, it is concluded that the proposed algorithm has better accuracy and minimum root mean square error than the existing methods.
Leukocytes Image Classification Using Optimized Convolutional Neural Networks

• Developed a fast and accurate hybrid approach for leukocyte classification. • Achieved fast convergence of hyperparameters with a random search approach. • Demonstrated superior performance of an optimized convolutional neural network. Hematologic diseases and blood disorders can be studied through the microscopic or chemical examination of blood smear images. Many researchers work on identifying, counting, and classifying different types of blood cells as a theoretical and practical problem that is crucial for disease diagnosis and treatment planning. There are various approaches to classify blood cells such as thresholding, morphological operators, segmentation, edge-based techniques, region-based techniques, and hybrid approaches. Each of these techniques has several limitations in effectively classifying different types of cells; however, methods based on deep learning (DL) have remarkably contributed to the progress of blood cell classification by combining feature extraction, feature selection, and classification into one interconnected step. This study develops a hybrid approach of DL and optimization for accurate and efficient classification of four types of leukocytes: neutrophils, eosinophils, lymphocytes, and monocytes. Model hyperparameters are optimized using grid search (GS) and random search (RS), in which a convolutional neural network (CNN) is used to classify leukocytes. CNNs work through pattern recognition to detect significant features that help distinguish different classes. The blood cell count and detection (BCCD) dataset provides basic information, but the data is insufficient and highly unbalanced for CNNs to accurately classify the images, so the data is augmented to improve model performance. This segmentation-free optimized CNN achieved a classification accuracy of 97% for the validation set, which includes 2,487 cell images, and 99% for the training set, which includes 9,966 cell images. The model reached a sensitivity and specificity of 94% and 98%, respectively. RS accelerates the process of hyperparameter optimization while achieving the same accuracy as GS. The results are compared with the results accomplished by recent CNN models on the BCCD database using seven performance measures and demonstrate the superior performance and competence of the proposed method. This research study develops a fast and accurate approach for leukocyte classification and can be beneficial for other image classification tasks and help clinicians in diagnosing blood diseases.
High-utility itemsets mining based on binary particle swarm optimization with multiple adjustment strategies

As an essential data mining task, high-utility itemset mining (HUIM) has attracted a lot of research. With the increase of dataset size, traditional exact HUIM algorithms are faced with exponential growth search space, which is unacceptable for some algorithms. As HUIM can be treated as a combinatorial optimization problem, Evolutionary Computation (EC) based HUIM approaches have been proposed and shown promise performance in mining HUIs. However, the existing EC-based HUIM approaches usually only find part of the HUIs in a limitation time or discovering all the HUIs is usually time-consuming. In this study, an improved binary particle swarm optimization for HUIM (HUIM-IBPSO) is proposed with multiple adjustment strategies to address these problems. In HUIM-IBPSO, a particle movement direction adjustment strategy is presented to keep the same HUIs during the evolution process. In order to utilize the repeated HUIs more efficiently and enhance the search ability, the strategy of local exploration is proposed in HUIM-IBPSO. A restart strategy for the population is developed in HUIM-IBPSO with the purpose to avoid the premature convergence before discovering any HUIs. To mine HUIs more efficiently, particle modify strategy and fitness value hash strategy are introduced in HUIM-IBPSO. A comprehensive comparison with five state-of-the-art EC-based HUIM algorithms and three precise HUIM algorithms on real datasets shows that the designed model outperforms them in terms of the number of HUIs found, the speed at which they converge, and the duration of execution (runtime).
PACMAN

This paper studies the synergies between memory corruption vulnerabilities and speculative execution vulnerabilities. We leverage speculative execution attacks to bypass an important memory protection mechanism, ARM Pointer Authentication, a security feature that is used to enforce pointer integrity. We present PACMAN, a novel attack methodology that speculatively leaks PAC verification results via micro-architectural side channels without causing any crashes. Our attack removes the primary barrier to conducting control-flow hijacking attacks on a platform protected using Pointer Authentication.
Disentangling the relationship between employees’ dynamic capabilities, innovative work behavior and job performance in public hospitals

Purpose The purpose of this study is to investigate the relationship between innovative work behavior and job performance among physicians in Iraq’s public hospitals. It also quantifies the impact of employees’ dynamic capabilities on innovative work behavior. It further examines the mediating role of innovative work behavior in the relationship between dynamic capabilities and of employees’ job performance. Design/methodology/approach The survey had a total of 332 respondents, but only 173 responses were used after data screening. To determine the relationship between the variables, the study uses structural equation modeling via partial least squares (PLS-SEM). Findings This study indicates that innovative work behavior has a positive relationship with job performance among physicians in Iraq’s public hospitals. It also reveals that employees’ dynamic capabilities are important determinants of innovative work behavior. This research further shows that innovative work behavior has a positive mediating role in the relationship between dynamic capabilities and job performance among physicians in Iraq’s public hospitals. Originality/value This study implies that innovative work behavior can improve physicians’ job performance while the employees’ dynamic capabilities can improve innovative work behavior. Besides, innovative work behavior can amplify the impact of employees’ dynamic capabilities on job performance. Thus, Iraq’s public hospitals should make a concerted effort to promote innovative work behavior to improve physicians’ job performance.
Adaptive track estimation on a radar array system for space surveillance

The increasing population of resident space objects is currently fostering many Space Surveillance and Tracking (SST) initiatives, which are based on the use of ground sensors. Italy contributes to the European SST Support Framework with the BIstatic RAdar for LEo Survey (BIRALES). Orbit determination from BIRALES observations relies on the estimation of the angular profiles of the object crossing the sensor field of view by processing the receiver raw data. Based on the receiver array configuration, its field of view is currently populated with many independent beams. However, the results of the currently adopted orbit determination process are negatively affected by the simultaneous presence of multiple grating lobes in the receiver gain pattern and by the signal quality. Within this context, this work proposes a paradigm shift in BIRALES data processing by introducing a multiple signal classification (MUSIC) approach. First, in the track estimation phase, multiple signal directions of arrival are determined, at any time instant, from the data correlation matrix. The multiplicity of the solutions is due to the array geometry and yields ambiguity in track reconstruction. For the catalogued objects, this ambiguity can be solved by exploiting the availability of their orbital estimates. For the uncatalogued ones, it is solved by clustering the candidate signal directions of arrival and identifying the correct track using slant range, Doppler shift and signal to noise ratio measurements. Although introduced for BIRALES, the applicability of the method extends to any SST survey radar with an array receiver and with the ability to correlate the signals detected by each receiver element. Finally, algorithm performances and robustness are assessed on a large set of synthetic passes as well as on real measurements.
2QAN

Simulating quantum systems is one of the most important potential applications of quantum computers. The high-level circuit defining the simulation needs to be compiled into one that complies with hardware limitations such as qubit architecture (connectivity) and instruction (gate) set. General-purpose quantum compilers work at the gate level and have little knowledge of the mathematical properties of quantum applications, missing further optimization opportunities. Existing application-specific compilers only apply advanced optimizations in the scheduling procedure and are restricted to the CNOT or CZ gate set. In this work, we develop a compiler, named 2QAN, to optimize quantum circuits for 2-local qubit Hamiltonian simulation problems, a framework which includes the important quantum approximate optimization algorithm (QAOA). In particular, we exploit the flexibility of permuting different operators in the Hamiltonian (no matter whether they commute) and propose permutation-aware techniques for qubit routing, gate optimization and scheduling to minimize compilation overhead. 2QAN can target different architectures and different instruction sets. Compilation results on four applications (up to 50 qubits) and three quantum computers (namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen) show that 2QAN outperforms state-of-the-art general-purpose compilers and application-specific compilers. Specifically, 2QAN can reduce the number of inserted SWAP gates by 11.5X, reduce overhead in hardware gate count by 68.5X, and reduce overhead in circuit depth by 21X. Experimental results on the Montreal device demonstrate that benchmarks compiled by 2QAN achieve the highest fidelity.