Reviewerly/exHarmony huggingface dataset:
SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis

We study the problem of novel view synthesis of objects from a single image. Existing methods have demonstrated the potential in single-view view synthesis. However, they still fail to recover the fine appearance details, especially in self-occluded areas. This is because a single view only provides limited information. We observe that man-made objects usually exhibit symmetric appearances, which introduce additional prior knowledge. Motivated by this, we investigate the potential performance gains of explicitly embedding symmetry into the scene representation. In this paper, we propose SymmNeRF, a neural radiance field (NeRF) based framework that combines local and global conditioning under the introduction of symmetry priors. In particular, SymmNeRF takes the pixel-aligned image features and the corresponding symmetric features as extra inputs to the NeRF, whose parameters are generated by a hypernetwork. As the parameters are conditioned on the image-encoded latent codes, SymmNeRF is thus scene-independent and can generalize to new scenes. Experiments on synthetic and real-world datasets show that SymmNeRF synthesizes novel views with more details regardless of the pose transformation, and demonstrates good generalization when applied to unseen objects. Code is available at: https://github.com/xingyi-li/SymmNeRF .
Patient multi-relational graph structure learning for diabetes clinical assistant diagnosis

The rapid accumulation of electronic health records (EHRs) and the advancements in data analysis technology have laid the foundation for research and clinical decision-making in the healthcare community. Graph neural networks (GNNs), a deep learning model family for graph embedding representations, have been widely used in the field of smart healthcare. However, traditional GNNs rely on the basic assumption that the graph structure extracted from the complex interactions among the EHRs must be a real topology. Noisy connections or false topology in the graph structure leads to inefficient disease prediction. We devise a new model named PM-GSL to improve diabetes clinical assistant diagnosis based on patient multi-relational graph structure learning. Specifically, we first build a patient multi-relational graph based on patient demographics, diagnostic information, laboratory tests, and complex interactions between medicines in EHRs. Second, to fully consider the heterogeneity of the patient multi-relational graph, we consider the node characteristics and the higher-order semantics of nodes. Thus, three candidate graphs are generated in the PM-GSL model: original subgraph, overall feature graph, and higher-order semantic graph. Finally, we fuse the three candidate graphs into a new heterogeneous graph and jointly optimize the graph structure with GNNs in the disease prediction task. The experimental results indicate that PM-GSL outperforms other state-of-the-art models in diabetes clinical assistant diagnosis tasks.
News Portal Website Measurement Analysis Using ISO/IEC 25010 And McCall Methods

Information system is an asset for a company when the information system is managed properly. In the past, newspapers have become one of the main sources for people who want to get the latest news about what is happening around them. But now, the important role of newspapers is gradually being replaced by online-based news portals that can deliver news very quickly. PT Info Pena Indonesia itself utilizes an information technology in carrying out the process of managing and disseminating the news they produce with the help of a web portal called Jitunews. This research is quantitative in nature, namely explaining the use of survey methods by distributing questionnaires to users of the Jitunews news portal site for data about the characteristics of the ISO/IEC 25010 and McCall methods. The results of the research conducted on the backend and frontend pages show that the Jitunews website deserves a "Good" interpretation value. However, the security and functional suitability variables contained on the frontend page have the lowest values of all the variables studied, namely 74.43% and 77.50% with the interpretation of "Enough". This can be input for stakeholders to improve the quality of the website in order to increase user satisfaction with the Jitunews news website portal.
Collective photon emission of two correlated atoms in free space

By preparing two distant emitters in entangled Dicke states via detection of a single photon, the subsequent spatiotemporal photon emission is investigated. Depending on the parity of the established quantum state, emission patterns for the second scattered photon are observed featuring spatial superradiance as well as subradiance. We employ ultrafast single-photon resolving cameras with high spatial resolution disclosing the spatiotemporal emission characteristics. By recording the first photon in one direction and the second photon in another, revealing the spatial two-photon cross-correlation function ${g}^{(2)}({\mathbf{r}}_{1},{\mathbf{r}}_{2})$, we characterize the collective spontaneous emission behavior of two ions in free space. We explain the observed contrast of ${g}^{(2)}({\mathbf{r}}_{1},{\mathbf{r}}_{2})$ considering independently derived experimental parameters. Our results show how the detection of a single photon can profoundly modify the collective spontaneous emission dynamics of an atomic ensemble.
CoDerainNet: Collaborative Deraining Network for Drone-View Object Detection in Rainy Weather Conditions

Benefiting from the advances in object detection in remote sensing, detecting objects in images captured by drones has achieved promising performance in recent years. However, drone-view object detection in rainy weather conditions (Rainy DroneDet) remains a challenge, as small-sized objects blurred by rain streaks offer a little valuable information for robust detection. In this paper, we propose a Collaborative Deraining Network called “CoDerainNet”, which simultaneously and interactively trains a deraining subnetwork and a droneDet subnetwork to improve the accuracy of Rainy DroneDet. Furthermore, we propose a Collaborative Teaching paradigm called “ColTeaching”, which leverages rain-free features extracted by the Deraining Subnetwork and teaches the DroneDet Subnetwork such features, to remove rain-specific interference in features for DroneDet. Due to the lack of an existing dataset for Rainy DroneDet, we built three drone datasets, including two synthetic datasets, namely RainVisdrone and RainUAVDT, and one real drone dataset, called RainDrone. Extensive experiment results on the three rainy datasets show that CoDerainNet can significantly reduce the computational costs of state-of-the-art (SOTA) object detectors while maintaining detection performance comparable to these SOTA models.
A two-layer consortium blockchain with transaction privacy protection based on sharding technology

In the traditional consortium blockchain system, all nodes jointly participate in the process of transaction consensus, so that the confidentiality of transaction data within and between organizations in the consortium cannot be protected; and the throughput of the existing consortium blockchain system is still low. In response to the above problems, we propose a two-layer consortium blockchain with transaction privacy protection based on sharding technology. The blockchain consists of a sharding layer and a root consensus layer, which supports internal transactions of distributed interactive organizations, confidential transactions across shards, and public transactions across shards. At the sharding layer, we shard the nodes and their corresponding UTXOs, and ensure the confidentiality of transaction data by authorizing nodes in different shards to access different transaction data. And through the root consensus layer, the processing reliability, tamper-proof and traceability of confidential transactions across shards and internal transactions are guaranteed. The system throughput is improved by parallel processing of transactions using sharding technology, and the blockchain system with an improved Byteball architecture inside the shard, which further improves the transaction throughput of the system. And through analysis, it shows that the consortium blockchain proposed in this scheme is reasonable and feasible.
Balanced incremental deep reinforcement learning based on variational autoencoder data augmentation for customer credit scoring

Incremental deep reinforcement learning has been successfully applied to different fields in the real-world. However, training deep reinforcement learning models with the data set generated in a certain stage may lead to catastrophic forgetting of trained models, that is, when training models on the new data set, the performance of models will be seriously degraded on the old data set. Therefore, we construct the balanced incremental deep Q-network based on variational autoencoder data augmentation (BIDQN-VADA) model for customer credit scoring. First, the original training set is processed by the random undersampling method to obtain the balanced training set, in which the initial deep Q-network model is trained. Then, the balanced training subset with a fixed number of samples is selected from the original training set, and these samples are augmented by the variational autoencoder data augmentation technology to obtain the balanced augmented training subset. Finally, the balanced training subset and balanced augmented training subset are stored in data stream cache according to the first-in first-out principle for incrementally updating parameters of the deep Q-network model. In order to verify the performance of the model, we conduct a series of experiments on eight real-world customer credit scoring data sets. The experimental results show that the BIDQN-VADA model can achieve the best customer credit scoring performance by combining the balance, incremental and data augmentation process at the same time. More importantly, the performance of the BIDQN-VADA model is significantly better than the other seven classification models.
Genuine Explanation and the Strong Minimalist Thesis

Abstract The goal of theoretical inquiry is explanation: Why this, and not that? In the study of language, search for explanatory theory proceeds at two levels: for individual languages (a generative grammar in the broad sense) and for the general faculty of language fl ( ug ), the latter apparently a true species property, common to humans and without significant analogue in the animal world. ug must meet several conditions: learnability, evolvability, coverage. These conditions appear to conflict, and are far more severe than had earlier been supposed. A solution to the conundrum would be satisfaction of smt for ug combined with recourse to language-independent principles of computational efficiency, with diversity sequestered in components of language subject to simple algorithmic search. For the first time, hopes for such an outcome seem to be on the horizon, with significant implications if the hopes can be realized. I will outline some current work on these topics.
An Anomaly-based Detection System for Monitoring Kubernetes Infrastructures

Network monitoring is crucial to analyze infrastructure baselines and alert whenever an abnormal behavior is observed. However, human effort is limited in time and scope since many variables must be considered in real-time. In addition, infrastructures such as Kubernetes are complex by nature since they do not consider fixed equipment from which to gather data; instead, these infrastructures consider distributed, event-driven, and ephemeral containers that make it complicated to capture and track metrics. Artificial Intelligence models have demonstrated high detection rates for anomaly detection; therefore, there is a need to design and implement a global solution to collect complex data and orchestrate the whole Machine Learning Operations workflow. This document shares the findings and learnings from defining a cloud-native Artificial Intelligence infrastructure at Aligo to develop an anomaly-based detection system for monitoring on-premise Kubernetes infrastructures. After Chaos Engineering experiments, it is shown that the resulting deployed system is strong when alerting outliers and that an end-to-end infrastructure has been developed for conducting future Artificial Intelligence projects at the company.
Understanding Indicators of Compromise against Cyber-attacks in Industrial Control Systems: A Security Perspective

Numerous sophisticated and nation-state attacks on Industrial Control Systems (ICSs) have increased in recent years, exemplified by Stuxnet and Ukrainian Power Grid. Measures to be taken post-incident are crucial to reduce damage, restore control, and identify attack actors involved. By monitoring Indicators of Compromise (IOCs), the incident responder can detect malicious activity triggers and respond quickly to a similar intrusion at an earlier stage. However, to implement IOCs in critical infrastructures, we need to understand their contexts and requirements. Unfortunately, there is no survey paper in the literature on IOC in the ICS environment, and only limited information is provided in research articles. In this article, we describe different standards for IOC representation and discuss the associated challenges that restrict security investigators from developing IOCs in the industrial sectors. We also discuss the potential IOCs against cyber-attacks in ICS systems. Furthermore, we conduct a critical analysis of existing works and available tools in this space. We evaluate the effectiveness of identified IOCs’ by mapping these indicators to the most frequently targeted attacks in the ICS environment. Finally, we highlight the lessons to be learned from the literature and the future problems in the domain along with the approaches that might be taken.
Biomolecular and quantum algorithms for the dominating set problem in arbitrary networks

Abstract A dominating set of a graph $$G = (V, E)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>G</mml:mi> <mml:mo>=</mml:mo> <mml:mo>(</mml:mo> <mml:mi>V</mml:mi> <mml:mo>,</mml:mo> <mml:mi>E</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> is a subset U of its vertices V , such that any vertex of G is either in U , or has a neighbor in U . The dominating-set problem is to find a minimum dominating set in G . Dominating sets are of critical importance for various types of networks/graphs, and find therefore potential applications in many fields. Particularly, in the area of communication, dominating sets are prominently used in the efficient organization of large-scale wireless ad hoc and sensor networks. However, the dominating set problem is also a hard optimization problem and thus currently is not efficiently solvable on classical computers. Here, we propose a biomolecular and a quantum algorithm for this problem, where the quantum algorithm provides a quadratic speedup over any classical algorithm. We show that the dominating set problem can be solved in $$O(2^{n/2})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> queries by our proposed quantum algorithm, where n is the number of vertices in G . We also demonstrate that our quantum algorithm is the best known procedure to date for this problem. We confirm the correctness of our algorithm by executing it on IBM Quantum’s qasm simulator and the Brooklyn superconducting quantum device. And lastly, we show that molecular solutions obtained from solving the dominating set problem are represented in terms of a unit vector in a finite-dimensional Hilbert space.
Understanding repertoire sequencing data through a multiscale computational model of the germinal center

Sequencing of B-cell and T-cell immune receptor repertoires helps us to understand the adaptive immune response, although it only provides information about the clonotypes (lineages) and their frequencies and not about, for example, their affinity or antigen (Ag) specificity. To further characterize the identified clones, usually with special attention to the particularly abundant ones (dominant), additional time-consuming or expensive experiments are generally required. Here, we present an extension of a multiscale model of the germinal center (GC) that we previously developed to gain more insight in B-cell repertoires. We compare the extent that these simulated repertoires deviate from experimental repertoires established from single GCs, blood, or tissue. Our simulations show that there is a limited correlation between clonal abundance and affinity and that there is large affinity variability among same-ancestor (same-clone) subclones. Our simulations suggest that low-abundance clones and subclones, might also be of interest since they may have high affinity for the Ag. We show that the fraction of plasma cells (PCs) with high B-cell receptor (BcR) mRNA content in the GC does not significantly affect the number of dominant clones derived from single GCs by sequencing BcR mRNAs. Results from these simulations guide data interpretation and the design of follow-up experiments.
Scatter matrix decomposition for jointly sparse learning

Orthogonal Linear Discriminant Analysis (OLDA) based on generalized Eigen-equation is widely used in the field of computer vision and pattern recognition. However, the performance of OLDA for feature extraction and classification needs to be improved as it lacks sparsity for better interpretation of the features. Moreover, computing the orthogonal sparse projections based on LDA is very difficult and is still unsolved. To solve these problems, in this paper, we propose a method called Jointly Sparse Orthogonal Linear Discriminant Analysis (JSOLDA). Different from the existing OLDA, JSOLDA is proposed from a novel viewpoint of scatter matrix decomposition. Theoretical analysis shows that OLDA can be derived by the constrained scatter matrix decomposition. In addition, by imposing L2,1-norm on the penalty term, the proposed JSOLDA can obtain the jointly sparse orthogonal projections to perform feature extraction. We also design an iterative algorithm to obtain the optimal solution. Systematic theoretical analysis between the OLDA and JSOLDA are uncovered. Both of convergence and computational complexity are also discussed. Experimental results on four data sets (i.e., COIL100, USPS, ICADAR2003 and CMU PIE) indicate that JSOLDA outperforms several well-known LDA-based and L2,1-norm based methods.
A Gridded Solar Irradiance Ensemble Prediction System Based on WRF-Solar EPS and the Analog Ensemble

The WRF-Solar Ensemble Prediction System (WRF-Solar EPS) and a calibration method, the analog ensemble (AnEn), are used to generate calibrated gridded ensemble forecasts of solar irradiance over the contiguous United States (CONUS). Global horizontal irradiance (GHI) and direct normal irradiance (DNI) retrievals, based on geostationary satellites from the National Solar Radiation Database (NSRDB) are used for both calibrating and verifying the day-ahead GHI and DNI predictions (GDIP). A 10-member ensemble of WRF-Solar EPS is run in a re-forecast mode to generate day-ahead GDIP for three years. The AnEn is used to calibrate GDIP at each grid point independently using the NSRDB as the “ground truth”. Performance evaluations of deterministic and probabilistic attributes are carried out over the whole CONUS. The results demonstrate that using the AnEn calibrated ensemble forecast from WRF-Solar EPS contributes to improving the overall quality of the GHI predictions with respect to an AnEn calibrated system based only on the deterministic run of WRF-Solar. In fact, the calibrated WRF-Solar EPS’s mean exhibits a lower bias and RMSE than the calibrated deterministic WRF-Solar. Moreover, using the ensemble mean and spread as predictors for the AnEn allows a more effective calibration than using variables only from the deterministic runs. Finally, it has been shown that the recently introduced algorithm of correction for rare events is of paramount importance to obtain the lowest values of GHI from the calibrated ensemble (WRF-Solar EPS AnEn), qualitatively consistent with those observed from the NSRDB.
Examining the relationship between blockchain capabilities and organizational performance in the Indian banking sector

Abstract Blockchain has enormous capabilities to transform the traditional business models in countless ways. Banks in India are building collaborative blockchain ecosystems to create an innovative business model and disrupt the traditional one to create further competitive advantages. The purpose of this study is to examine the relationship between blockchain capabilities (BCC), competitive advantages (CA) and organizational performance (OP). Further, to evaluate the mediating role of CA on the relationship between BCC and OP. In this context, scientific research model consisting of a hypothesis has been developed from the existing literature. The proposed model was tested using statistical data collected from Blockchain specialists, blockchain product marketing managers, experts of future and emergent technology and VP/AVP/Chief Manager/Branch head of banks/ financial Analyst/divisional Managers who are involved in planning and deployment of practical blockchain in banking/financial sector. Data was analyzed and tested through AMOS 22.0 and process macro using a sample of 289 responses. Our empirical result indicated that there is a significant positive relationship between BCC, CA and OP. Furthermore, relationship of BCC and OP partially mediated CA. This paper presents originality and contributes towards the body of knowledge on this subject to understand the relationship and mediation role of CA on the relationship between BCC and OP in Indian banking sector.
The effect of strategic intelligence, effective decision-making and strategic flexibility on logistics performance

This paper aims to investigate the impact of strategic intelligence on strategic flexibility, in addition, to identify the effective decision-making on Logistics Performance. Nevertheless, partial least squares (Smart -PLS- 4.0.8.7) are used to examine the hypotheses, the results indicate that strategic intelligence (organized thinking, strategic vision, foresight, partnership, and motivation) significantly affect Strategic flexibility. In addition, effective decision-making mediates the effect of strategic intelligence and strategic flexibility towards the performance of logistics companies. Moreover, the influence of effective decision-making on logistics performance is therefore clear and significant towards logistics performance. However, the study supports managers in enhancing sustainability practices in logistics organizations while also offering policy guidance to decision-makers.