Reviewerly/exHarmony huggingface dataset:
Bioinspired Bare Bones Mayfly Algorithm for Large-Scale Spherical Minimum Spanning Tree

Mayfly algorithm (MA) is a bioinspired algorithm based on population proposed in recent years and has been applied to many engineering problems successfully. However, it has too many parameters, which makes it difficult to set and adjust a set of appropriate parameters for different problems. In order to avoid adjusting parameters, a bioinspired bare bones mayfly algorithm (BBMA) is proposed. The BBMA adopts Gaussian distribution and Lévy flight, which improves the convergence speed and accuracy of the algorithm and makes better exploration and exploitation of the search region. The minimum spanning tree (MST) problem is a classic combinatorial optimization problem. This study provides a mathematical model for solving a variant of the MST problem, in which all points and solutions are on a sphere. Finally, the BBMA is used to solve the large-scale spherical MST problems. By comparing and analyzing the results of BBMA and other swarm intelligence algorithms in sixteen scales, the experimental results illustrate that the proposed algorithm is superior to other algorithms for the MST problems on a sphere.
Efficient Lattice-Based Blind Signatures via Gaussian One-Time Signatures

Lattice-based blind signature schemes have been receiving some recent attention lately. Earlier efficient 3-round schemes (Asiacrypt 2010, Financial Cryptography 2020) were recently shown to have mistakes in their proofs, and fixing them turned out to be extremely inefficient and limited the number of signatures that a signer could send to less than a dozen (Crypto 2020). In this work we propose a round-optimal, 2-round lattice-based blind signature scheme which produces signatures of length 150 KB. The running time of the signing protocol is linear in the maximum number signatures that can be given out, and this limits the number of signatures that can be signed per public key. Nevertheless, the scheme is still quite efficient when the number of signatures is limited to a few dozen thousand, and appears to currently be the most efficient lattice-based candidate.
Shapley Feature Selection

Feature selection is a popular topic. The main approaches to deal with it fall into the three main categories of filters, wrappers and embedded methods. Advancement in algorithms, though proving fruitful, may be not enough. We propose to integrate an explainable AI approach, based on Shapley values, to provide more accurate information for feature selection. We test our proposal in a real setting, which concerns the prediction of the probability of default of Small and Medium Enterprises. Our results show that the integrated approach may indeed prove fruitful to some feature selection methods, in particular more parsimonious ones like LASSO. In general the combination of approaches seems to provide useful information which feature selection algorithm can improve their performance with.
Automatic Ethnicity Classification from Middle Part of the Face Using Convolutional Neural Networks

In the field of face biometrics, finding the identity of a person in an image is most researched, but there are other, soft biometric information that are equally as important, such as age, gender, ethnicity or emotion. Nowadays, ethnicity classification has a wide application area and is a prolific area of research. This paper gives an overview of recent advances in ethnicity classification with focus on convolutional neural networks (CNNs) and proposes a new ethnicity classification method using only the middle part of the face and CNN. The paper also compares the differences in results of CNN with and without plotted landmarks. The proposed model was tested using holdout testing method on UTKFace dataset and FairFace dataset. The accuracy of the model was 80.34% for classification into five classes and 61.74% for classification into seven classes, which is slightly better than state-of-the-art, but it is also important to note that results in this paper are obtained by using only the middle part of the face which reduces the time and resources necessary.
Blockchain: A potential technology to improve the performance of collaborative emergency management with multi-agent participation

As the complexity of emergencies continues to increase, responding to them often exceeds the capabilities of any single public agency, multi-agent collaborative participation is an inevitable tendency in the development of emergency management. In the collaborative emergency response, it is important to support various responders with a unified platform to assist in decision-making, distribution of resources, and coordination of emergency operation. In recent years, blockchain has provided strong support for the innovation of emergency management mechanisms with its technical advantages such as distributed storage, decentralization, consensus algorithms, tamper-proof, traceability, and smart contract. This study firstly reviews the development of blockchain and its application in emergency management. Secondly, the connotation and key dilemmas of the multi-agent collaborative emergency management mechanism are expounded from two aspects of the multi-agent participation and the construction of a collaborative mechanism. Then, the possibility of using blockchain to facilitate the innovation of multi-agent collaborative emergency management mechanism is analyzed from the aspects of emergency management information sharing, resource storage and distribution, the rights and obligations of multiple subjects, supervision, reward and punishment. Finally, a conceptual model of a multi-agent collaborative emergency management mechanism based on alliance chain and public chain is proposed. The application of blockchain provides a new perspective and motivation for the innovation of emergency management system. This study provides a new method for improving the multi-agent collaborative emergency management mechanism, and is an important supplement and innovation to existing emergency management studies.
Cefuzz: An Directed Fuzzing Framework for PHP RCE Vulnerability

Current static detection technology for web application vulnerabilities relies highly on specific vulnerability patterns, while dynamic analysis technology has the problem of low vulnerability coverage. In order to improve the ability to detect unknown web application vulnerabilities, this paper proposes a PHP Remote Command/Code Execution (RCE) vulnerability directed fuzzing method. Our method is a combination of static and dynamic methods. First, we obtained the potential RCE vulnerability information of the web application through fine-grained static taint analysis. Then we performed instrumentation for the source code of the web application based on the potential RCE vulnerability information to provide feedback information for fuzzing. Finally, a loop feedback web application vulnerability automatic verification mechanism was established in which the vulnerability verification component provides feedback information, and the seed mutation component improves the vulnerability test seed based on the feedback information. On the basis of this method, the prototype system Cefuzz (Command/Code Execution Fuzzer) is implemented. Thorough experiments show that, compared with the existing web application vulnerability detection methods, Cefuzz significantly improves the verification effect of RCE vulnerabilities, discovering 13 unknown vulnerabilities in 10 popular web CMSes.
e-BMI: A gait based smart remote BMI monitoring framework implementing edge computing and incremental machine learning

Body Mass Index (BMI) is a significant indicator for assessing health risk factors. Being obese can be associated with certain health conditions like diabetes, heart disease, etc. and being underweight can influence weaker immunity, fertility, etc. With increasing age, the effect of BMI on health significantly increases. Therefore monitoring the weight category is of prime importance and a lot of people often do not measure the BMI at a regular interval and unknowingly move into the BMI of risk category. However, smartphones are widely available around the world and used during the day-to-day activity which gives us immense opportunity to track the gait of a person timely with help of the motion sensors embedded in it which may be useful in assisted living in elderly people. We have assessed 30 people from underweight, normal and overweight category with consent and found distinct patterns in the three categories. On the other hand, with advancements in edge computing techniques, it has become easier to simplify most of the tracked data locally to the device level reducing the load on the cloud server. Utilizing these, we have used machine learning algorithms and obtained 98.6% detection accuracy. The model could be easily deployed at a global scale after testing the hypothesis on a wider spectrum of human subjects.
Evolving Search Space for Neural Architecture Search

Automation of neural architecture design has been a coveted alternative to human experts. Various search methods have been proposed aiming to find the optimal architecture in the search space. One would expect the search results to improve when the search space grows larger since it would potentially contain more performant candidates. Surprisingly, we observe that enlarging search space is unbeneficial or even detrimental to existing NAS methods such as DARTS, ProxylessNAS, and SPOS. This counterintuitive phenomenon suggests that enabling existing methods to large search space regimes is non-trivial. However, this problem is less discussed in the literature.We present a Neural Search-space Evolution (NSE) scheme, the first neural architecture search scheme designed especially for large space neural architecture search problems. The necessity of a well-designed search space with constrained size is a tacit consent in existing methods, and our NSE aims at minimizing such necessity. Specifically, the NSE starts with a search space subset, then evolves the search space by repeating two steps: 1) search an optimized space from the search space subset, 2) refill this subset from a large pool of operations that are not traversed. We further extend the flexibility of obtainable architectures by introducing a learnable multi-branch setting. With the proposed method, we achieve 77.3% top-1 retrain accuracy on ImageNet with 333M FLOPs, which yielded a state-of-the-art performance among previous auto-generated architectures that do not involve knowledge distillation or weight pruning. When the latency constraint is adopted, our result also performs better than the previous best-performing mobile models with a 77.9% Top-1 retrain accuracy. Code is available at https://github.com/orashi/NSENAS.
Comparing Sampling Strategies for Tackling Imbalanced Data in Human Activity Recognition

Human activity recognition (HAR) using wearable sensors is an increasingly active research topic in machine learning, aided in part by the ready availability of detailed motion capture data from smartphones, fitness trackers, and smartwatches. The goal of HAR is to use such devices to assist users in their daily lives in application areas such as healthcare, physical therapy, and fitness. One of the main challenges for HAR, particularly when using supervised learning methods, is obtaining balanced data for algorithm optimisation and testing. As people perform some activities more than others (e.g., walk more than run), HAR datasets are typically imbalanced. The lack of dataset representation from minority classes hinders the ability of HAR classifiers to sufficiently capture new instances of those activities. We introduce three novel hybrid sampling strategies to generate more diverse synthetic samples to overcome the class imbalance problem. The first strategy, which we call the distance-based method (DBM), combines Synthetic Minority Oversampling Techniques (SMOTE) with Random_SMOTE, both of which are built around the k-nearest neighbors (KNN). The second technique, referred to as the noise detection-based method (NDBM), combines SMOTE Tomek links (SMOTE_Tomeklinks) and the modified synthetic minority oversampling technique (MSMOTE). The third approach, which we call the cluster-based method (CBM), combines Cluster-Based Synthetic Oversampling (CBSO) and Proximity Weighted Synthetic Oversampling Technique (ProWSyn). We compare the performance of the proposed hybrid methods to the individual constituent methods and baseline using accelerometer data from three commonly used benchmark datasets. We show that DBM, NDBM, and CBM reduce the impact of class imbalance and enhance F1 scores by a range of 9-20 percentage point compared to their constituent sampling methods. CBM performs significantly better than the others under a Friedman test, however, DBM has lower computational requirements.
PF2RM: A Power Fault Retrieval and Recommendation Model Based on Knowledge Graph

Energy saving and emission reduction have become common concerns in countries around the world. In China, with the implementation of the new strategy of “carbon peak and neutrality” and the rapid development of the new smart grid infrastructure, the amount of data of actual power grid dispatching and fault analysis show exponential growth, which has led to phenomena such as poor supervision effectiveness and difficulty in handling faults in the process of grid operation and maintenance. Existing research on retrieval recommendation methods has had a lower accuracy rate at cold-start due to a small sample of user interactions. In addition, the cumulative learning of user personalization during general retrieval results in a poor perception of potential interest. By constructing a power knowledge graph, this paper presents a power fault retrieval and recommendation model (PF2RM) based on user-polymorphic perception. This model includes two methods: the power fault retrieval method (PFR) and the user-polymorphic retrieval recommendation method (UPRR). First, we take the power grid fault dispatching business as the core and reconstruct the ontology layer of the power knowledge graph. The PFR method is used to design the graph-neighbor fault entity cluster to enhance the polymerization degree of a fault implementation scenario. This method can solve the search cold-start recommendation problem. At the same time, the UPRR method aims to form user retrieval subgraphs of the past-state and current-state and make a feature matching for the graph-neighbor fault entity cluster, and then realize the accurate prediction of the user’s general search intention. The model is compared with other current classical models through the evaluation of multiple recommendation evaluation metrics, and the experimental results show that the model has a 3–8% improvement in the cold-start recommendation effect and 2–10% improvement in regular retrieval. The model has the best average recommendation performance in multiple metrics and has good results in fault analysis and retrieval recommendation. It plays a helpful role in intelligent operation and maintenance of the power grid and auxiliary decision-making, and effectively improves the reliability of the power grid.
CrossDet: Crossline Representation for Object Detection

Object detection aims to accurately locate and classify objects in an image, which requires precise object representations. Existing methods usually use rectangular anchor boxes or a set of points to represent objects. However, these methods either introduce background noise or miss the continuous appearance information inside the object, and thus cause incorrect detection results. In this paper, we propose a novel anchor-free object detection network, called Cross-Det, which uses a set of growing cross lines along horizontal and vertical axes as object representations. An object can be flexibly represented as cross lines in different combinations. It not only can effectively reduce the interference of noise, but also take into account the continuous object information, which is useful to enhance the discriminability of object features and find the object boundaries. Based on the learned cross lines, we propose a crossline extraction module to adaptively capture features of cross lines. Furthermore, we design a decoupled regression mechanism to regress the localization along the horizontal and vertical directions respectively, which helps to decrease the optimization difficulty because the optimization space is limited to a specific direction. Our method achieves consistently improvement on the PASCAL VOC and MS-COCO datasets. The experiment results demonstrate the effectiveness of our proposed method. Code can be available at: https://github.com/QiuHeqian/CrossDet.
Domain generalization and adaptation based on second-order style information

Domain generalization (DG) and unsupervised domain adaptation (UDA) aim to solve the domain-shift problem that arises when the trained model is tested in the domain with different style distribution from the training data. Style Normalization and Restitution(SNR) has solved this problem to a certain extent and achieved the best performance. However, SNR ignores the discriminative information encoded in the appearance style information, which limits the performance of the model. In this paper, we propose Two-level Style Normalization and Restitution(Tl-SNR) to solve this problem. First, we use group whitening to introduce the appearance style information encoded in the second-order statistic into the SNR, which prepares for restituting the task-relevant discriminative information in the appearance information later. Secondly, we defined dynamic affine parameters, which improves the affine parameters in group whitening. It makes the model adjust adaptively according to the characteristics of the sample, so as to better exploit the capabilities of the model. Finally, we designed a Two-level Style Normalization and Restitution module based on the improved group whitening for domain generalization and unsupervised domain adaptation. Extensive experiments show that our method is effective. And our method outperforms state-of-the-art DG and UDA methods on four benchmarks.
SNARGs for $\mathcal{P}$ from LWE

We provide the first construction of a succinct non-interactive argument (SNARG) for all polynomial time deterministic computations based on standard assumptions. For <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> steps of computation, the size of the proof and the common random string (CRS) as well as the verification time are poly-logarithmic in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> . The security of our scheme relies on the hardness of the Learning with Errors (LWE) problem against polynomial-time adversaries. Previously, SNARGs based on standard assumptions could support bounded-depth computations and required sub-exponential hardness assumptions [Jawale-Kalai-Khurana-Zhang, STOC'21]. Along the way, we also provide the first construction of non-interactive batch arguments for N P based solely on the LWE assumption.
AI-Based Misogyny Detection from Arabic Levantine Twitter Tweets

Twitter is one of the social media platforms that is extensively used to share public opinions. Arabic text detection system (ATDS) is a challenging computational task in the field of Natural Language Processing (NLP) using Artificial Intelligence (AI)-based techniques. The detection of misogyny in Arabic text has received a lot of attention in recent years due to the racial and verbal violence against women on social media platforms. In this paper, an Arabic text recognition approach is presented for detecting misogyny from Arabic tweets. The proposed approach is evaluated using the Arabic Levantine Twitter Dataset for Misogynistic, and it gained recognition accuracies of 90.0% and 89.0% for binary and multi-class tasks, respectively. The proposed approach seems to be useful in providing practical smart solutions for detecting Arabic misogyny on social media.
Efficient Architecture Search for Continual Learning

Continual learning with neural networks, which aims to learn a sequence of tasks, is an important learning framework in artificial intelligence (AI). However, it often confronts three challenges: 1) overcome the catastrophic forgetting problem; 2) adapt the current network to new tasks; and 3) control its model complexity. To reach these goals, we propose a novel approach named continual learning with efficient architecture search (CLEAS). CLEAS works closely with neural architecture search (NAS), which leverages reinforcement learning techniques to search for the best neural architecture that fits a new task. In particular, we design a neuron-level NAS controller that decides which old neurons from previous tasks should be reused (knowledge transfer) and which new neurons should be added (to learn new knowledge). Such a fine-grained controller allows finding a very concise architecture that can fit each new task well. Meanwhile, since we do not alter the weights of the reused neurons, we perfectly memorize the knowledge learned from the previous tasks. We evaluate CLEAS on numerous sequential classification tasks, and the results demonstrate that CLEAS outperforms other state-of-the-art alternative methods, achieving higher classification accuracy while using simpler neural architectures.
An Attention-Based Spatiotemporal GGNN for Next POI Recommendation

The task of Point-of-Interest (POI) recommendation is to recommend the next interest locations for users. Gated Graph Neural Network (GGNN) has been proved to be effective on POI recommendation tasks. However, existing GGNN solutions rarely consider the spatiotemporal information between nodes in the sequence graph, which is essential for modeling user check-in behaviors in next POI recommendation. In this paper, we propose an attention-based spatiotemporal gated graph neural network model (ATST-GGNN) for next POI recommendation. Firstly, the user’s check-in sequence is represented as a graph structure. Secondly, we use spatiotemporal context information to dynamically update nodes in the sequence graph, and obtain the complex transfer relationships between the check-ins. Thirdly, each session is then represented as the composition of the long and short preference using an attention network. However, current short preference fails to model union-level sequential patterns, we improve the local embedding representation of graph nodes by window pooling method, as well as the global embedding representation of graph nodes by integrating it into attention mechanism. Finally, the objective function is constructed by cross entropy and the model parameters are learned. The experimental results show that the precision rate and mean reciprocal ranking of ATST-GGNN method are greatly improved compared with the state-of-art methods. It has good application prospect.