Reviewerly/exHarmony huggingface dataset:
Minimum Information Standards in Chemistry: A Call for Better Research Data Management Practices

Abstract Research data management (RDM) is needed to assist experimental advances and data collection in the chemical sciences. Many funders require RDM because experiments are often paid for by taxpayers and the resulting data should be deposited sustainably for posterity. However, paper notebooks are still common in laboratories and research data is often stored in proprietary and/or dead‐end file formats without experimental context. Data must mature beyond a mere supplement to a research paper. Electronic lab notebooks (ELN) and laboratory information management systems (LIMS) allow researchers to manage data better and they simplify research and publication. Thus, an agreement is needed on minimum information standards for data handling to support structured approaches to data reporting. As digitalization becomes part of curricular teaching, future generations of digital native chemists will embrace RDM and ELN as an organic part of their research.
SortingHat

Machine learning as a service scenario typically requires the client to trust the server and provide sensitive data in plaintext. However, with the recent improvements in fully homomorphic encryption (FHE) schemes, many such applications can be designed in a privacy-preserving way. In this work, we focus on such a problem, private decision tree evaluation (PDTE) --- where a server has a decision tree classification model, and a client wants to use the model to classify her private data without revealing the data or the classification result to the server. We present an efficient non-interactive design of PDTE, that we call SortingHat, based on FHE techniques. As part of our design, we solve multiple cryptographic problems related to FHE: (1) we propose a fast homomorphic comparison function where one input can be in plaintext format; (2) we design an efficient binary decision tree evaluation technique in the FHE setting, which we call homomorphic traversal, and apply it together with our homomorphic comparison to evaluate private decision tree classifiers, obtaining running times orders of magnitude faster than the state of the art; (3) we improve both the communication cost and the time complexity of transciphering, by applying our homomorphic comparison to the FiLIP stream cipher. Through a prototype implementation, we demonstrate that our improved transciphering solution runs around 400 times faster than previous works. We finally present a choice in terms of PDTE design: we present a version of SortingHat without transciphering that achieves significant improvement in terms of computation cost compared to prior works, and another version t-SortingHat with transciphering that has a communication cost about 20 thousand times smaller but comparable running time.
Actionable Explainable AI (AxAI): A Practical Example with Aggregation Functions for Adaptive Classification and Textual Explanations for Interpretable Machine Learning

In many domains of our daily life (e.g., agriculture, forestry, health, etc.), both laymen and experts need to classify entities into two binary classes (yes/no, good/bad, sufficient/insufficient, benign/malign, etc.). For many entities, this decision is difficult and we need another class called “maybe”, which contains a corresponding quantifiable tendency toward one of these two opposites. Human domain experts are often able to mark any entity, place it in a different class and adjust the position of the slope in the class. Moreover, they can often explain the classification space linguistically—depending on their individual domain experience and previous knowledge. We consider this human-in-the-loop extremely important and call our approach actionable explainable AI. Consequently, the parameters of the functions are adapted to these requirements and the solution is explained to the domain experts accordingly. Specifically, this paper contains three novelties going beyond the state-of-the-art: (1) A novel method for detecting the appropriate parameter range for the averaging function to treat the slope in the “maybe” class, along with a proposal for a better generalisation than the existing solution. (2) the insight that for a given problem, the family of t-norms and t-conorms covering the whole range of nilpotency is suitable because we need a clear “no” or “yes” not only for the borderline cases. Consequently, we adopted the Schweizer–Sklar family of t-norms or t-conorms in ordinal sums. (3) A new fuzzy quasi-dissimilarity function for classification into three classes: Main difference, irrelevant difference and partial difference. We conducted all of our experiments with real-world datasets.
The best of both worlds: integrating semantic features with expert features for defect prediction and localization

To improve software quality, just-in-time defect prediction (JIT-DP) (identifying defect-inducing commits) and just-in-time defect localization (JIT-DL) (identifying defect-inducing code lines in commits) have been widely studied by learning semantic features or expert features respectively, and indeed achieved promising performance. Semantic features and expert features describe code change commits from different aspects, however, the best of the two features have not been fully explored together to boost the just-in-time defect prediction and localization in the literature yet. Additional, JIT-DP identifies defects at the coarse commit level, while as the consequent task of JIT-DP, JIT-DL cannot achieve the accurate localization of defect-inducing code lines in a commit without JIT-DP. We hypothesize that the two JIT tasks can be combined together to boost the accurate prediction and localization of defect-inducing commits by integrating semantic features with expert features. Therefore, we propose to build a unified model, JIT-Fine, for the just-in-time defect prediction and localization by leveraging the best of semantic features and expert features. To assess the feasibility of JIT-Fine, we first build a large-scale line-level manually labeled dataset, JIT-Defects4J. Then, we make a comprehensive comparison with six state-of-the-art baselines under various settings using ten performance measures grouped into two types: effort-agnostic and effort-aware. The experimental results indicate that JIT-Fine can outperform all state-of-the-art baselines on both JIT-DP and JITDL tasks in terms of ten performance measures with a substantial improvement (i.e., 10%-629% in terms of effort-agnostic measures on JIT-DP, 5%-54% in terms of effort-aware measures on JIT-DP, and 4%-117% in terms of effort-aware measures on JIT-DL).
PLFace: Progressive Learning for Face Recognition with Mask Bias

The outbreak of the COVID-19 coronavirus epidemic has promoted the development of masked face recognition (MFR). Nevertheless, the performance of regular face recognition is severely compromised when the MFR accuracy is blindly pursued. More facts indicate that MFR should be regarded as a mask bias of face recognition rather than an independent task. To mitigate mask bias, we propose a novel Progressive Learning Loss (PLFace) that achieves a progressive training strategy for deep face recognition to learn balanced performance for masked/mask-free faces recognition based on margin losses. Particularly, our PLFace adaptively adjusts the relative importance of masked and mask-free samples during different training stages. In the early stage of training, PLFace mainly learns the feature representations of mask-free samples. At this time, the regular sample embeddings shrink to the prototype. In the later stage of training, PLFace converges on mask-free samples and further focuses on masked samples until the masked sample embeddings are also gathered in the center of the class. The entire training process emphasizes the paradigm that normal samples shrink first and masked samples gather afterward. Extensive experimental results on popular regular and masked face benchmarks demonstrate the superiority of our PLFace over state-of-the-art competitors.
Combining spectral total variation with dynamic threshold neural P systems for medical image fusion

Synthesis of medical images is one of the indispensable tasks today because of its applications in clinical diagnosis. Composite images often suffer from problems such as poor contrast, loss of detail, and low light intensity. The reason for the above problem is that the input image is of poor quality, and the fusion rules are not really effective. In this paper, we propose a new image synthesis model to simultaneously solve the problems mentioned above. Firstly, the input image is enhanced because the input image’s quality significantly affects the fusion image’s quality. Next, the Spectral total variation (STV) method is utilized to decompose input images into a base layer and a series of detail layers. An adaptive rule based on the Chameleon Swarm Algorithm (CSA) algorithm is proposed for the synthesis of the base layers. This rule ensures that the synthesized image has good quality in terms of brightness and contrast. To ensure that the details are preserved in the synthesized image, we propose an effective fusion rule for detail layers based on the Dynamic threshold neural P systems (DTNPS). Finally, the base and detail layers that have been composited are summed together to create the composite image. Six evaluation indexes, seven state-of-the-art image synthesis algorithms, and 132 medical images were used to evaluate. The results show that our image synthesis model is more efficient than the current latest image synthesis methods.
Unimon qubit

Superconducting qubits seem promising for useful quantum computers, but the currently wide-spread qubit designs and techniques do not yet provide high enough performance. Here, we introduce a superconducting-qubit type, the unimon, which combines the desired properties of increased anharmonicity, full insensitivity to dc charge noise, reduced sensitivity to flux noise, and a simple structure consisting only of a single Josephson junction in a resonator. In agreement with our quantum models, we measure the qubit frequency, ω01/(2π), and increased anharmonicity α/(2π) at the optimal operation point, yielding, for example, 99.9% and 99.8% fidelity for 13 ns single-qubit gates on two qubits with (ω01, α) = (4.49 GHz, 434 MHz) × 2π and (3.55 GHz, 744 MHz) × 2π, respectively. The energy relaxation seems to be dominated by dielectric losses. Thus, improvements of the design, materials, and gate time may promote the unimon to break the 99.99% fidelity target for efficient quantum error correction and possible useful quantum advantage with noisy systems.
Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection

Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D detection has brought significant improvement, e.g., Pseudo-LiDAR methods. However, the existing methods usually apply non-end-to-end training strategies and insufficiently leverage the LiDAR information, where the rich potential of the LiDAR data has not been well exploited. In this paper, we propose the Cross-Modality Knowledge Distillation (CMKD) network for monocular 3D detection to efficiently and directly transfer the knowledge from LiDAR modality to image modality on both features and responses. Moreover, we further extend CMKD as a semi-supervised training framework by distilling knowledge from large-scale unlabeled data and significantly boost the performance. Until submission, CMKD ranks $$1^{st}$$ among the monocular 3D detectors with publications on both KITTI test set and Waymo val set with significant performance gains compared to previous state-of-the-art methods. Our code will be released at https://github.com/Cc-Hy/CMKD .
Bitcoin and S&amp;P500: Co-movements of high-order moments in the time-frequency domain

Interactions between stock and cryptocurrency markets have experienced shifts and changes in their dynamics. In this paper, we study the connection between S&P500 and Bitcoin in higher-order moments, specifically up to the fourth conditional moment, utilizing the time-scale perspective of the wavelet coherence analysis. Using data from 19 August 2011 to 14 January 2022, the results show that the co-movement between Bitcoin and S&P500 is moment-dependent and varies across time and frequency. There is very weak or even non-existent connection between the two markets before 2018. Starting 2018, but mostly 2019 onwards, the interconnections emerge. The co-movements between the volatility of Bitcoin and S&P500 intensified around the COVID-19 outbreak, especially at mid-term scales. For skewness and kurtosis, the co-movement is stronger and more significant at mid- and long-term scales. A partial-wavelet coherence analysis underlines the intermediating role of economic policy uncertainty (EPU) in provoking the Bitcoin-S&P500 nexus. These results reflect the co-movement between US stock and Bitcoin markets beyond the second moment of return distribution and across time scales, suggesting the relevance and importance of considering fat tails and return asymmetry when jointly considering US equity-Bitcoin trading or investments and the policy formulation for the sake of US market stability.
Occluded prohibited object detection in X-ray images with global Context-aware Multi-Scale feature Aggregation

Prohibited Object Detection (POD) in X-ray images plays an important role in protecting public safety. Automatic and accurate POD is required to relieve the working pressure of security inspectors. However, the existing methods cannot obtain a satisfactory detection accuracy, and especially, the problem of object occlusion also has not been solved well. Therefore, in this paper, according to the specific characteristics of X-ray images as well as low-level and high-level features of Convolutional Neural Network (CNN), different feature enhancement strategies have been elaborately designed for occluded POD. First, a learnable Gabor convolutional layer is designed and embedded into the low layer of the network to enhance the network's capability to capture the edge and contour information of object. A Spatial Attention (SA) mechanism is then designed to weight the output features of the Gabor convolutional layer to enhance the spatial structure information of object and suppress the background noises simultaneously. For the high-level features, Global Context Feature Extraction (GCFE) module is proposed to extract multi-scale global contextual information of object. And, a Dual Scale Feature Aggregation (DSFA) module is proposed to fuse these global features with those of another layer. To verify the effectiveness of the proposed modules, they are embedded into typical one-stage and two-stage object detection frameworks, i.e., Faster R-CNN and YOLO v5L, obtaining POD-F and POD-Y methods, respectively. The proposed methods have been extensively evaluated on three publicly available benchmark datasets, namely SIXray, OPIXray and WIXray. The experimental results show that, compared with existing methods, the proposed POD-Y method can achieve a state-of-the-art detection accuracy. And POD-F can also achieve a competitive detection performance among the two-stage detection methods.1
Feature learning network with transformer for multi-label image classification

The purpose of multi-label image classification task is to accurately assign a set of labels to the objects in images. Although promising results have been achieved, most of the existing methods cannot effectively learn multi-scale features, so it is difficult to identify small-scale objects from images. Besides, current attention-based methods tend to learn the most salient feature regions in images, but fail to excavate various potential useful features concealed by the most salient feature, thus limiting the further improvement of model performance. To address above issues, we propose a novel Feature Learning network based on Transformer to learn salient features and excavate potential useful features (FL-Tran). Specifically, in order to solve the problem that current methods are difficult to identify small-scale objects, we first present a novel multi-scale fusion module (MSFM) to align high-level features and low-level features to learn multi-scale features. Additionally, a spatial attention module (SAM) utilizing transformer encoder is introduced to capture salient object features in images to enhance the model performance. Furthermore, we devise a feature enhancement and suppression module (FESM) with the aim of excavating potential useful features concealed by the most salient features. By suppressing the most salient features obtained in current SAM layer, and then forcing subsequent SAM layer to excavate potential salient features in feature maps, FL-Tran model can learn various useful features more comprehensively. Extensive experiments on MS-COCO 2014, PASCAL VOC 2007, and NUS-WIDE datasets demonstrate that our proposed FL-Tran model outperforms current state-of-the-art methods.
Rapid solution of logical equivalence problems by quantum computation algorithm

We present a quantum computation algorithm that enables solving the problem of logical equivalence verification in exponentially less time than the classical deterministic computation. In this novel quantum algorithm, the oracles of the two evaluated functions are executed in series to yield a common target qubit which then interacts with an ancillary qubit. We found that the degree of entanglement (measured by the concurrence) of the target and ancillary qubits is a reliable witness for the logical equivalence property of the two functions. The steps number of the quantum algorithm is inversely proportional to the square of the standard error ϵ2 of the measured concurrence value, with no dependence on the input size ′n′ of each function. This corresponds to a number of evaluations of the two functions: O(ϵ−2) for the quantum algorithm compared with O(2n) for the classical approach. To assess the algorithm performance, two sets of experiments are conducted using the IBM Q Experience simulator for input sizes: 2 and 12 variables per function. While the former verifies that the results of the experiment are in a good match with the theory, the latter showcases the quantum supremacy of the presented algorithm. In particular, The latter shows that the quantum algorithm requires only 200 oracles queries compared with 213 queries for the classical algorithm.
Revisiting crowd counting: State-of-the-art, trends, and future perspectives

Crowd counting is an effective tool for situational awareness in public places. Automated crowd counting using images and videos is an interesting yet challenging problem that has gained significant attention in computer vision. Over the past few years, various deep learning methods have been developed to achieve state-of-the-art performance. The methods evolved over time vary in many aspects such as model architecture, input pipeline, learning paradigm, computational complexity, and accuracy gains etc. In this paper, we present a systematic and comprehensive review of the most significant contributions in the area of crowd counting. Although few surveys exist on the topic, our survey is most up-to date and different in several aspects. First, it provides a more meaningful categorization of the most significant contributions by model architectures, learning methods (i.e., loss functions), and evaluation methods (i.e., evaluation metrics). We chose prominent and distinct works and excluded similar works. We also sort the well-known crowd counting models by their performance over benchmark datasets. We believe that this survey can be a good resource for novice researchers to understand the progressive developments and contributions over time and the current state-of-the-art.
A federated feature selection algorithm based on particle swarm optimization under privacy protection

Feature selection is an important preprocessing technique in the fields of data mining and machine learning. With the promotion of privacy protection awareness, recently it becomes a very practical and challenging issue to select high-quality feature subsets while ensuring the privacy of all participants. However, there is a lack of research results on this issue, i.e., feature selection under privacy protection. Aiming at the issue, this paper proposes a federated feature selection framework for the first time. In the framework, inspiring by the idea of federated learning, a credible third participant is introduced to process and integrate optimal feature subsets from multiple participants. On the basis of the framework, a federated evolutionary feature selection algorithm based on particle swarm optimization is proposed to effectively solve feature selection problems with multiple participants under privacy protection. Two new operators satisfying the requirement of privacy protection, i.e., the feature assembling strategy with multi-participant cooperation and the swarm initialization strategy guided by assembling solution, are designed to improve the ability of the proposed algorithm. Compared with several typical assembling feature selection algorithms on 15 data sets, experimental results show that the proposed algorithm can significantly improve the classification accuracy of the feature subset selected by each participant, while protecting the privacy of data.
Temporal knowledge graphs reasoning with iterative guidance by temporal logical rules

Reasoning is essential for the development of large temporal knowledge graphs, which aim to infer new facts based on existing ones. Recent temporal knowledge graph reasoning methods mainly embed timestamps into low-dimensional spaces. These methods focus on entity reasoning, which cannot obtain the specific reasoning paths. More importantly, they ignore the logic and explanation of reasoning paths in temporal knowledge graphs (TKGs). To overcome this limitation, we propose a novel Temporal Logical reasoning Model, denoted as TLmod. This model represents a reasoning process that works through iterative guidance by temporal logical rules. More importantly, we propose two principles of temporal logical rules and define five types of temporal logical rules. Meanwhile, considering the diversity of temporal logical rules, we propose a pruning strategy for obtaining them and calculating the confidence score by combining traversing and random selection. Experimental results show that our model outperforms most metrics compared to prior state-of-the-art baselines across two benchmarks. In addition, analysis of the ablation experiment reveals the validity and importance of temporal logical rules in TKGs.
Enhancing the government accounting information systems using social media information: An application of text mining and machine learning

This study demonstrates a way of bringing an innovative data source, social media information, to the government accounting information systems to support accountability to stakeholders and managerial decision-making. Future accounting and auditing processes will heavily rely on multiple forms of exogenous data. As an example of the techniques that could be used to generate this needed information, the study applies text mining techniques and machine learning algorithms to Twitter data. The information is developed as an alternative performance measure for NYC street cleanliness. It utilizes Naïve Bayes, Random Forest, and XGBoost to classify the tweets, illustrates how to use the sampling method to solve the imbalanced class distribution issue, and uses VADER sentiment to derive the public opinion about street cleanliness. This study also extends the research to another social media platform, Facebook, and finds that the incremental value is different between the two social media platforms. This data can then be linked to government accounting information systems to evaluate costs and provide a better understanding of the efficiency and effectiveness of operations.