Reviewerly/exHarmony huggingface dataset:
GOZDE: A novel metaheuristic algorithm for global optimization

This study proposes a new metaheuristic algorithm, called "Geometric Octal Zones Distance Estimation" (GOZDE) algorithm to solve global optimization problems. The presented GOZDE employs a search scheme with the information sharing between the zones considering the distance of the zones utilizing median values. The whole population represents the eight zones that are the combination of different search strategies to guide knowledge dissemination from one zone to others in the search space. To demonstrate the effectiveness of the proposed optimizer, it is compared with two classes of metaheuristics, which are (1) GA, PSO, DE, CS and HS as the classical metaheuristics and (2) BWO, SSA, MVO, HHO, ChOA, AOA and EBOwithCMAR as the up-to-date metaheuristics. The search capability of the proposed algorithm is tested on two different numerical benchmark sets including low and high dimensional problems. The developed algorithm is also adapted to ten real-world applications to handle constraint optimization problems. In addition, to further analyse the results of the proposed algorithm, three well-known statistical metrics, Friedman, Wilcoxon rank-sum and Whisker-Box statistical tests are conducted. The experimental results statistically show that GOZDE is significantly better than, or at least comparable to the twelve metaheuristic algorithms with outstanding performance in solving numerical functions and real-world optimization problems.
Camera model identification based on forensic traces extracted from homogeneous patches

A crucial challenge in digital image forensics is to identify the source camera model used to generate given images. This is of prime importance, especially for Law Enforcement Agencies in their investigations of Child Sexual Abuse Material found in darknets or seized storage devices. In this work, we address this challenge by proposing a solution that is characterized by two main contributions. It relies on the extraction of rather small homogeneous regions that we extract very efficiently from the integral image, and on a hierarchical classification approach with convolutional neural networks as the underlying models. We rely on homogeneous regions as they contain camera traces that are less distorted than regions with high-level scene content. The hierarchical approach that we propose is important for scaling up and making minimal modifications when new cameras are added. Furthermore, this scheme performs better than the traditional single classifier approach. By means of thorough experimentation on the publicly available Dresden data set, we achieve an accuracy of 99.01% with 5-fold cross-validation on the ‘natural’ subset of this data set. To the best of our knowledge, this is the best result ever reported for Dresden data set.
Learning missing instances in latent space for incomplete multi-view clustering

Real world objects can usually be described from multiple views. How to combine multiple views to make better use of these data has attracted a lot of attentions, and numerous multi-view clustering algorithms have been proposed. However, the data in real scenes are often incomplete. We propose an incomplete multi-view clustering method (MISS), which simultaneously learns Missing Instances and Self-representations in latent Space. Instead of filling missing instances in the original data space, we learn missing instances and self-representations in the latent space so that we can grasp the features of missing instances, while preserving the original latent spatial structure of the data. In addition, a novel fusion matrix is introduced. The missing instances are represented by linear combinations of the known instances in the latent space. This fusion matrix will be learned simultaneously according to the learning of self-representation in the latent space for multi-view clustering. Furthermore, a completeness constraint is proposed to guarantee that learning direction of missing instances is close to the original data as possible. Because of the focus on the features of missing instances and the keeping of the original data structure, MISS can not only handle randomly data missing, where some instances are randomly removed from each view, but also continuous data missing, i.e., all instances of some clusters are missing completely. Experimental results on seven datasets show that MISS achieves improvements over the state-of-the-art incomplete multi-view clustering methods.
HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements

Recently, using machine learning for performance tuning of cloud database (CDB) service has shown great potentials. However, facing personalized requirements such as various restrictions for tuning with very different workloads, pre-trained models may mismatch or recommend suboptimal configurations given a new workload. On the other hand, if the system tunes configurations in an online fashion, the system will suffer from the cold start problem, resulting in long tuning time and performance fluctuation. To accommodate these problems, we propose an online CDB tuning system called HUNTER. The key feature of HUNTER is a hybrid architecture, which uses samples generated by Genetic Algorithm to warm-start the finer grained exploration of deep reinforcement learning. Meanwhile, we employ Principal Component Analysis, Random Forest, and Fast Exploration Strategy to reduce the search space and the update time of the learning model. In addition, we further propose a clone and parallelization scheme to stress-test workloads on multiple cloned CDB instances (CDBs), resulting in faster and safer configuration exploration. Extensive trials on CDB with public and real-world workloads demonstrate that, given the same time budget and resources, HUNTER improves performance and considerably decreases recommendation time compared to state-of-the-art tuning systems, with accelerations of up to 2.8× and 22.8× utilizing 1 and 20 cloned CDBs, respectively.
FogAdapt: Self-supervised domain adaptation for semantic segmentation of foggy images

This paper presents FogAdapt, a novel approach for domain adaptation of semantic segmentation for dense foggy scenes. Although significant research has been directed to reduce the domain shift in semantic segmentation, adaptation to scenes with adverse weather conditions remains an open question. Large variations in the visibility of the scene due to weather conditions, such as fog, smog, and haze, exacerbate the domain shift, thus making unsupervised adaptation in such scenarios challenging. We propose a self-entropy and multi-scale information augmented self-supervised domain adaptation method (FogAdapt) to minimize the domain shift in foggy scenes segmentation. Supported by the empirical evidence that an increase in fog density results in high self-entropy for segmentation probabilities, we introduce a self-entropy based loss function to guide the adaptation method. Furthermore, inferences obtained at different image scales are combined and weighted by the uncertainty to generate scale-invariant pseudo-labels for the target domain. These scale-invariant pseudo-labels are robust to visibility and scale variations. We evaluate the proposed model on real clear-weather scenes to real foggy scenes adaptation and synthetic non-foggy images to real foggy scenes adaptation scenarios. Our experiments demonstrate that FogAdapt significantly outperforms the current state-of-the-art in semantic segmentation of foggy images. Specifically, by considering the standard settings compared to state-of-the-art (SOTA) methods, FogAdapt gains 3.8% on Foggy Zurich, 6.0% on Foggy Driving-dense, and 3.6% on Foggy Driving in mIoU when adapted from Cityscapes to Foggy Zurich.
NFTGAN: Non-Fungible Token Art Generation Using Generative Adversarial Networks

Digital arts have gained an unprecedented level of popularity with the emergence of non-fungible tokens (NFTs). NFTs are cryptographic assets that are stored on blockchain networks and represent a digital certificate of ownership that cannot be forged. NFTs can be incorporated into a smart contract which allows the owner to benefit from a future sale percentage. While digital art producers can benefit immensely with NFTs, their production is time consuming. Therefore, this paper explores the possibility of using generative adversarial networks (GANs) for automatic generation of digital arts. GANs are deep learning architectures that are widely and effectively used for synthesis of audio, images, and video contents. However, their application to NFT arts have been limited. In this paper, a GAN-based architecture is implemented and evaluated for novel NFT-style digital arts generation. Results from the qualitative case study indicate that the generated artworks are comparable to the real samples in terms of being interesting and inspiring and they were judged to be more innovative than real samples.
Chemistry beyond the Hartree–Fock energy via quantum computed moments

Abstract Quantum computers hold promise to circumvent the limitations of conventional computing for difficult molecular problems. However, the accumulation of quantum logic errors on real devices represents a major challenge, particularly in the pursuit of chemical accuracy requiring the inclusion of electronic correlation effects. In this work we implement the quantum computed moments (QCM) approach for hydrogen chain molecular systems up to H $$_6$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow /><mml:mn>6</mml:mn></mml:msub></mml:math> . On a superconducting quantum processor, Hamiltonian moments, $$\langle H^p\rangle$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>⟨</mml:mo><mml:msup><mml:mi>H</mml:mi><mml:mi>p</mml:mi></mml:msup><mml:mo>⟩</mml:mo></mml:mrow></mml:math> are computed with respect to the Hartree–Fock state, which are then employed in Lanczos expansion theory to determine an estimate for the ground-state energy which incorporates electronic correlations and manifestly improves on the direct energy measurement. Post-processing purification of the raw QCM data takes the estimate below the Hartree–Fock energy to within 99.9% of the exact electronic ground-state energy for the largest system studied, H $$_6$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow /><mml:mn>6</mml:mn></mml:msub></mml:math> . Calculated dissociation curves indicate precision at about 10mH for this system and as low as 0.1mH for molecular hydrogen, H $$_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow /><mml:mn>2</mml:mn></mml:msub></mml:math> , over a range of bond lengths. In the context of stringent precision requirements for chemical problems, these results provide strong evidence for the error suppression capability of the QCM method, particularly when coupled with post-processing error mitigation. While calculations based on the Hartree–Fock state are tractable to classical computation, these results represent a first step towards implementing the QCM method in a quantum chemical trial circuit. Greater emphasis on more efficient representations of the Hamiltonian and classical preprocessing steps may enable the solution of larger systems on near-term quantum processors.
Deep learning features encode interpretable morphologies within histological images

Abstract Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&amp;E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&amp;E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture). While many studies have incorporated CNN features into predictive models, there has been little empirical study of their properties. We show such features can be construed as abstract morphological genes (“mones”) with strong independent associations to biological phenotypes. Many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC = $$97.1\% \pm 2.8\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>97.1</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>2.8</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC = $$99.2\% \pm 0.12\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>99.2</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>0.12</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> ). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&amp;E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values. Our work also demonstrates mones can be interpreted without using a classifier as a proxy.
SmartSAGE

Graph neural networks (GNNs) can extract features by learning both the representation of each objects (i.e., graph nodes) and the relationship across different objects (i.e., the edges that connect nodes), achieving state-of-the-art performance in various graph-based tasks. Despite its strengths, utilizing these algorithms in a production environment faces several challenges as the number of graph nodes and edges amount to several billions to hundreds of billions scale, requiring substantial storage space for training. Unfortunately, state-of-the-art ML frameworks employ an in-memory processing model which significantly hampers the productivity of ML practitioners as it mandates the overall working set to fit within DRAM capacity. In this work, we first conduct a detailed characterization on a state-of-the-art, large-scale GNN training algorithm, GraphSAGE. Based on the characterization, we then explore the feasibility of utilizing capacity-optimized NVMe SSDs for storing memory-hungry GNN data, which enables large-scale GNN training beyond the limits of main memory size. Given the large performance gap between DRAM and SSD, however, blindly utilizing SSDs as a direct substitute for DRAM leads to significant performance loss. We therefore develop SmartSAGE, our software/hardware co-design based on an in-storage processing (ISP) architecture. Our work demonstrates that an ISP based large-scale GNN training system can achieve both high capacity storage and high performance, opening up opportunities for ML practitioners to train large GNN datasets without being hampered by the physical limitations of main memory size.
Approaches to manage the user experience process in Agile software development: A systematic literature review

Software development companies use Agile methods to develop their products or services efficiently and in a goal-oriented way. But this alone is not enough to satisfy user demands today. It is much more important nowadays that a product or service should offer a great user experience — the user wants to have some positive user experience while interacting with the product or service. An essential requirement is the integration of user experience methods in Agile software development. Based on this, the development of positive user experience must be managed. We understand management in general as a combination of a goal, a strategy, and resources. When applied to UX, user experience management consists of a UX goal, a UX strategy, and UX resources. We have conducted a systematic literature review (SLR) to analyse suitable approaches for managing user experience in the context of Agile software development. We have identified 49 relevant studies in this regard. After analysing the studies in detail, we have identified different primary approaches that can be deemed suitable for UX management. Additionally, we have identified several UX methods that are used in combination with the primary approaches. However, we could not identify any approaches that directly address UX management. There is also no general definition or common understanding of UX management. To successfully implement UX management, it is important to know what UX management actually is and how to measure or determine successful UX management.
Unlearning Protected User Attributes in Recommendations with Adversarial Training

Collaborative filtering algorithms capture underlying consumption patterns, including the ones specific to particular demographics or protected information of users, e.g. gender, race, and location. These encoded biases can influence the decision of a recommendation system (RS) towards further separation of the contents provided to various demographic subgroups, and raise privacy concerns regarding the disclosure of users' protected attributes. In this work, we investigate the possibility and challenges of removing specific protected information of users from the learned interaction representations of a RS algorithm, while maintaining its effectiveness. Specifically, we incorporate adversarial training into the state-of-the-art MultVAE architecture, resulting in a novel model, Adversarial Variational Auto-Encoder with Multinomial Likelihood (Adv-MultVAE), which aims at removing the implicit information of protected attributes while preserving recommendation performance. We conduct experiments on the MovieLens-1M and LFM-2b-DemoBias datasets, and evaluate the effectiveness of the bias mitigation method based on the inability of external attackers in revealing the users' gender information from the model. Comparing with baseline MultVAE, the results show that Adv-MultVAE, with marginal deterioration in performance (w.r.t. NDCG and recall), largely mitigates inherent biases in the model on both datasets.
Lyapunov equation in open quantum systems and non-Hermitian physics

The continuous-time differential Lyapunov equation is widely used in linear optimal control theory, a branch of mathematics and engineering. In quantum physics, it is known to appear in Markovian descriptions of linear (quadratic Hamiltonian, linear equations of motion) open quantum systems, typically from quantum master equations. Despite this, the Lyapunov equation is seldom considered a fundamental formalism for linear open quantum systems. In this work we aim to change that. We establish the Lyapunov equation as a fundamental and efficient formalism for linear open quantum systems that can go beyond the limitations of various standard quantum master equation descriptions, while remaining of much less complexity than general exact formalisms. This also provides valuable insights for non-Hermitian quantum physics. In particular, we derive the Lyapunov equation for the most general number conserving linear system in a lattice of arbitrary dimension and geometry, connected to an arbitrary number of baths at different temperatures and chemical potentials. Three slightly different forms of the Lyapunov equation are derived via an equation of motion approach, by making increasing levels of controlled approximations, without reference to any quantum master equation. Then we discuss their relation with quantum master equations, positivity, accuracy and additivity issues, the possibility of describing dark states, general perturbative solutions in terms of single-particle eigenvectors and eigenvalues of the system, and quantum regression formulas. Our derivation gives a clear understanding of the origin of the non-Hermitian Hamiltonian describing the dynamics and separates it from the effects of quantum and thermal fluctuations. Many of these results would have been hard to obtain via standard quantum master equation approaches.
Economic Growth, Exchange Rate and Remittance Nexus: Evidence from Africa

This paper examined the nexus between economic growth and exchange rate, remittances, trade, and agricultural output based on data sourced from 1980 to 2018 for 10 selected African economies. We employed both the Dumitrescu and Hurlin time-domain Granger causality test and the Croux and Reusens frequency domain Granger causality test. Results from the time-domain test suggests that causality only exists between economic growth and both exchange rate and trade, with no significant relationship between economic growth and both remittances and agricultural output. When we employed frequency domain model in our analysis, the results suggested that there is a bi-directional temporary and permanent causality between economic growth and exchange rate, trade, agriculture, and remittances. Our results suggest the validity of both the J-Curve and Marshall–Lerner hypotheses in the studied economies. Our study offers some relevant policy implications.
Evaluation of Online Teaching Quality Based on Facial Expression Recognition

In 21st-century society, with the rapid development of information technology, the scientific and technological strength of all walks of life is increasing, and the field of education has also begun to introduce high and new technologies gradually. Affected by the epidemic, online teaching has been implemented all over the country, forming an education model of “dual integration” of online and offline teaching. However, the disadvantages of online teaching are also very obvious; that is, teachers cannot understand the students’ listening status in real-time. Therefore, our study adopts automatic face detection and expression recognition based on a deep learning framework and other related technologies to solve this problem, and it designs an analysis system of students’ class concentration based on expression recognition. The students’ class concentration analysis system can help teachers detect students’ class concentration and improve the efficiency of class evaluation. In this system, OpenCV is used to call the camera to collect the students’ listening status in real-time, and the MTCNN algorithm is used to detect the face of the video to frame the location of the student’s face image. Finally, the obtained face image is used for real-time expression recognition by using the VGG16 network added with ECANet, and the students’ emotions in class are obtained. The experimental results show that the method in our study can more accurately identify students’ emotions in class and carry out a teaching effect evaluation, which has certain application value in intelligent education fields, such as the smart classroom and distance learning. For example, a teaching evaluation module can be added to the teaching software, and teachers can know the listening emotions of each student in class while lecturing.