Reviewerly/exHarmony huggingface dataset:
First Attempts at Cryptanalyzing a (Toy) Block Cipher by Means of Quantum Optimization Approaches

The discovery of quantum algorithms that may impact cryptography is one of the main reasons for the rise of quantum computing. Currently, all quantum cryptanalysis techniques are purely theoretical and none of them can be executed on quantum devices already existing or available in the near term. So, this paper investigates the capability of presently existing quantum computers to attack a toy block cipher (namely the Heys cipher) using the Quantum Approximate Optimization Algorithm (QAOA) on gate-based quantum computers and a variant of the Quantum Annealing (QA) algorithm on analog quantum computers. Starting from a known-plaintext key recovery problem, we transform it into an instance of the MAX-SAT problem. For the QAOA approach, we propose two ways to implement it in a quantum circuit and try to solve it using publicly available IBM Q Experience quantum computers. For the QA approach, we compare two reductions into a Quadratic Unconstrained Binary Optimization (QUBO) instance by solving their output on D-Wave quantum annealers. The results suggest that the limited number of qubits (especially for gate-based quantum computers) requires the use of exponential algorithms to achieve the transformation of our problem into a MAX-SAT instance. Furthermore, despite encouraging simulation results on QAOA, the quantum circuits are too deep to work on nowadays (too-)noisy gate-based quantum computers. Finally, even if the QA algorithm seems better than QAOA on quantum hardware, it needs more complex instance transformations that reduce its scaling abilities. Our results show that the attack is not feasible on real hardware even in very simplified settings, at least as long as there is no significant improvement of quantum processors. This is an extended version of our paper at the International Conference on Computational Science (ICCS) 2022 (Phab et al., 2022) [1] with new results on the application of the QA algorithm to our problem.
Event-triggered reconfigurable reinforcement learning motion-planning approach for mobile robot in unknown dynamic environments

Deep reinforcement learning (DRL) is an essential technique for autonomous motion planning of mobile robots in dynamic and uncertain environments. In attempting to acquire a satisfactory DRL-based motion planning strategy, the mobile robots encountered several difficulties, including poor convergence, insufficient sample information, and low learning efficiency. These problems not only consume plenty of training time, but also bring a negative impact on motion planning performance. One promising research direction is to provide a more effective network framework for DRL-based policies. Along this line of thinking, our paper presents a novel DRL-based motion planning approach called Reconfigurable Structure of Deep Deterministic Policy Gradient (RS-DDPG) for mobile robots. To account for the poor convergence, the proposed approach first introduces an event-triggered reconfigurable actor–critic network framework for motion policy that adaptively changes its network structure to suppress the overestimation of action value. Then, the time convergence of the motion policy can be enhanced based on the value actions with minor valuation deviation. Afterwards, an adaptive reward mechanism is designed for reconfigurable networks to compensate for the lack of sample information. To deal with the problem of low learning efficiency, we developed a sample pretreatment method for the experience samples, which employs three novel techniques to improve the sample utilization, including a double experience memory buffer, a variable proportional sampling principle, and a similarity judgment mechanism. In extensive experiments, the proposed method outperforms the compared approaches.
Exploring Internal and External Interactions for Semi-Structured Multivariate Attributes in Job-Resume Matching

Job-resume matching (JRM) is the core of online recruitment services for predicting the matching degree between a job post and a resume. Most of the existing methods for JRM achieve a promising performance by simplifying this task as a matching between the free-text attributes in the job post and the resume. However, they neglect the contributions of the semistructured multivariate attributes such as education and salary, which will result in an unsuccessful prediction. To address this issue, we propose a novel approach to comprehensively explore the Internal and EXternal InTeractions for semistructured multivariate attributes in JRM, i.e., InEXIT. In detail, we first encode the key and the value of each attribute as well as its source into the same semantic space. Next, to explore the complex relationships among the multivariate attributes, we propose to hierarchically model the internal interactions among the multivariate attributes inside the job post and the resume, as well as the external interactions between the job post and the resume. In particular, a stepwise fusion mechanism is designed to respectively integrate the key embeddings and the source embeddings into the value embeddings so as to clearly indicate the key and the source of the value. Finally, we employ an aggregation matching layer to predict the matching degree. We quantify the improvements of InEXIT against the competitive baselines on a real-world dataset, showing a general improvement of 4.28%, 4.10%, and 3.56% over the state-of-the-art baseline in terms of AUC, accuracy, and F1 score, respectively.
The “Yelp-Ification” of the Dark Web: An Exploration of the Use of Consumer Feedback in Dark Web Markets

Law enforcement are tasked with confronting a variety of crimes on a daily basis, ranging from traditional offenses, such as burglary or assault, to online crimes, such as identity theft. The expansion of the internet has provided a new realm for investigatory consideration as buyers seek the tools of the crime as well as illegal products online. Since the inception of the Silk Road, the Dark Web’s first darknet marketplace, buyers and sellers have worked together to distribute and obtain products and services that were previously only available offline. For example, drugs, weapons, consumer data such as credit cards numbers, and other illicit products are all available for purchase on the Dark Web. Early on, Ross Ulbricht, founder of the original Silk Road Dark Web marketplace, incorporated consumer “reviews” on illegal websites to protect customers and to ensure that sellers were not taking advantage of potential buyers. The nature of illicit markets, where websites, vendors, or markets themselves can be online one day and gone the next, present challenges to developing a complete understanding as to how these networks operate. This study builds on existing research by providing an exploratory examination of the prevalence of consumer feedback and review indicators on approximately 50 Dark Web marketplaces. Findings indicate that more than half of explored sites displayed consumer reviews on their postings. Policies incorporating feedback indicators may be implemented to both identify and target for investigation and prosecution highly rated and high-profile sellers on Dark Web markets.
Speech Recognition for Air Traffic Control via Feature Learning and End-to-End Training

In this work, we propose a new automatic speech recognition (ASR) system based on feature learning and an end-to-end training procedure for air traffic control (ATC) systems. The proposed model integrates the feature learning block, recurrent neural network (RNN), and connectionist temporal classification loss to build an end-to-end ASR model. Facing the complex environments of ATC speech, instead of the handcrafted features, a learning block is designed to extract informative features from raw waveforms for acoustic modeling. Both the SincNet and 1D convolution blocks are applied to process the raw waveforms, whose outputs are concatenated to the RNN layers for the temporal modeling. Thanks to the ability to learn representations from raw waveforms, the proposed model can be optimized in a complete end-to-end manner, i.e., from waveform to text. Finally, the multilingual issue in the ATC domain is also considered to achieve the ASR task by constructing a combined vocabulary of Chinese characters and English letters. The proposed approach is validated on a multilingual real-world corpus (ATCSpeech), and the experimental results demonstrate that the proposed approach outperforms other baselines, achieving a 6.9% character error rate.
Effects of language mismatch in automatic forensic voice comparison using deep learning embeddings

In forensic voice comparison, deep learning has become widely popular recently. It is mainly used to learn speaker representations, called embeddings or embedding vectors. Speaker embeddings are often trained using corpora mostly containing widely spoken languages. Thus, language dependency is an important factor in automatic forensic voice comparison, especially when the target language is linguistically very different from that the model is trained on. In the case of a low-resource language, developing a corpus for forensic purposes containing enough speakers to train deep learning models is costly. This study aims to investigate whether a model pre-trained on multilingual (mostly English) corpus can be used on a target low-resource language (here, Hungarian), not represented by the model. Often multiple samples are not available from the offender (unknown speaker). Samples are therefore compared pairwise with and without speaker enrollment for suspect (known) speakers. Two corpora are used that were developed especially for forensic purposes and a third that is meant for traditional speaker verification. Speaker embedding vectors are extracted by the x-vector and ECAPA-TDNN techniques. Speaker verification was evaluated in the likelihood-ratio framework. A comparison is made between the language combinations (modeling, LR calibration, and evaluation). The results were evaluated by Cllrmin and EER metrics. It was found that the model pre-trained on a different language but on a corpus with a significant number of speakers can be used on samples with language mismatch. Sample duration and speaking style also seem to affect the performance.
Sensitivity Analysis of Project using Machine Learning

This paper expects to lead a writing survey of patterns and techniques for machine learning utilized for the Sensitivity Analysis of our Project Sensitivity analysis permits to assess how the subsequent presentation of the venture at various upsides of given factors expected for computation. This kind of examination to decide the most basic factors that have the best effect on the plausibility and adequacy of the undertaking.
Adam-assisted quantum particle swarm optimization guided by length of potential well for numerical function optimization

Quantum-inspired particle swarm optimization (QPSO) is a powerful computation technique that introduces quantum mechanics theory into particle swarm optimization (PSO), which has been successfully applied to address complex and challenging functional optimization problems. However, QPSO still suffers from premature convergence and trapping in the local optima. In this paper, we propose a novel QPSO variant by guiding it with the length of the potential well (LPW) and assisting it with Adam (abbreviated as Adam-LGQPSO). To select the appropriate strategy according to the evolutionary states, an LPW-guided particle update strategy is proposed, by introducing a Gaussian random vector for enhancing the information sharing among particles and endowing the swarm with adaptive randomness. Meanwhile, a particle perturbation mechanism is developed to avoid getting into a search dilemma. To balance the exploration and exploitation, a diversity function is introduced and a diversity-based LPW strategy is designed, which helps the algorithm to better adapt to the complex and variable search environment. Finally, the Adam algorithm is integrated into the QPSO to assist the initialization of the population. The effectiveness of the proposed strategies is testified by a set of experiments. With the extensive experiments on the CEC 2017 test suite, we also show that Adam-LGQPSO achieves superior performance compared to several state-of-art metaheuristics.
A uniform-Laplace mixture distribution

We introduce a new stochastic model incorporating a mixture of uniform and Laplace distributions. We present basic theoretical properties of this model and discuss related computational issues of parameter estimation via expectation–maximization computational schemes. We check the performance of the estimation algorithm on synthetic data and provide a data example illustrating modeling potential of this novel methodology. A related model involving a mixture of uniform and exponential distributions is studied as well along the same lines.
MFPCDR: A Meta-Learning-Based Model for Federated Personalized Cross-Domain Recommendation

Cross-domain recommendation systems frequently require the use of rich source domain information to improve recommendations in the target domain, thereby resolving the data sparsity and cold-start problems, whereas the majority of existing approaches frequently require the centralized storage of user data, which poses a substantial risk of privacy breaches. Compared to traditional recommendation systems with centralized data, federated recommendation systems with multiple clients trained collaboratively have significant privacy benefits in terms of user data. While users’ interests are often personalized, meta-learning can be used to learn users’ personalized preferences, and personalized preferences can help models make recommendations in cold-start scenarios. We use meta-learning to learn the personalized preferences of cold-start users. Therefore, we offer a unique meta-learning-based federated personalized cross-domain recommendation model that discovers the personalized preferences for cold-start users via a server-side meta-recommendation module. To avoid compromising user privacy, an attention mechanism is used on each client to find transferable features that contribute to knowledge transfer while obtaining embeddings of users and items; each client then uploads the weights to the server. The server accumulates weights and delivers them to clients for update. Compared to traditional recommendation system models, our model can effectively protect user privacy while solving the user cold-start problem, as we use an attention mechanism in the local embedding module to mine the source domain for transferable features that contribute to knowledge transfer. Extensive trials on real-world datasets have demonstrated that our technique effectively guarantees speed while protecting user privacy.
An improved deep convolutionary neural network for bone marrow cancer detection using image processing

Bone Marrow Cancer is a type of cancer that develops in the stem cells of the bone marrow that are responsible for blood formation. AML(Acute Myeloid Leukaemia) and MM(Multiple Myeloma) are both types of malignancy that can affect bone marrow. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. Hence, in order to provide accurate treatment, a better diagnosis technique is required. In this regard, the suggested study has improved a unique technique, specifically classification and segmentation, allowing for the identification of more intricate disorders. The study's findings on the accuracy of cancer cell identification, as well as the decrease in the possibility of false alarm rates, continue to fall on the negative side of the outcome spectrum, according to the researchers. The hybrid approach has been proposed in this article that includes deep convolutional neural networks, hyperparameter sets utilising adaptive multi-objective CAT algorithms and other image processing techniques. To train the suggested model, it is critical to use previously processed cell images. The suggested model is then used to train an Optimized Convolutional Neural Network (OCNN), which is then used to determine the type of tumour identified in the bone area. The SN-AM datasets were thoroughly examined, and a number of presentation measures, such as accuracy (recall), F1-score, and specificity (specificity), were generated and analysed. When it came to predicting different types of cancer, researchers observed that they had an overall accuracy of 99.45 percent. In terms of recognizing the sorts of cancer cells, the proposed model surpassed all existing learning models.
TransAM: Transformer appending matcher for few-shot knowledge graph completion

Few-shot knowledge graph completion (FSKGC) refers to predicting new facts for a new relation with only few-shot observed entity pairs (triples) as support set. Existing solutions to FSKGC mainly conduct the matching process over entity pair representations. Although effective, a major concern of these models is that the entity interactions are not fully explored, based on the observation that they usually generate the pair representation before the matching stage. Such a design inherently overlooks the fine-grained information from entity interactions, leading to performance decrements in one or three shot, which require matching models to capture more sufficient semantic meanings for prediction. To remedy this issue, in this paper, we explore the entity interactions within and between different instances, i.e., the co-occurrence of two entities, for FSKGC and propose our model named TransAM, Transformer Appending Matcher. TransAM solves the FSKGC problem by computing the probability of entity sequence with a well-designed transformer matching network. Specifically, TransAM appends query entity pair to serialized reference entity sequence and utilizes transformer to calculate the probability by capturing intra- and inter- triple entity interactions. To bridge the gap between transformer and the triple structure, TransAM introduces rotary operation to preserve the head and tail roles of entity within the triple and distinguishes different triples by a separated triple position encoding. Empirical studies on two public benchmark datasets NELL-One and Wiki-One show that TransAM outperforms existing metric-learning solutions in MRR and Hits@1 with both one- and three- shot settings, and achieves comparable results on five-shot setting. Datasets and code will be public available at https://github.com/gawainx/TransAM.
Toward computer-supported semi-automated timelines of future events

Abstract During a Futures Study, researchers analyze a significant quantity of information dispersed across multiple document databases to gather conjectures about future events, making it challenging for researchers to retrieve all predicted events described in publications quickly. Generating a timeline of future events is time-consuming and prone to errors, requiring a group of experts to execute appropriately. This work introduces NERMAP, a system capable of semi-automating the process of discovering future events, organizing them in a timeline through Named Entity Recognition supported by machine learning, and gathering up to 83% of future events found in documents when compared to humans. The system identified future events that we failed to detect during the tests. Using the system allows researchers to perform the analysis in significantly less time, thus reducing costs. Therefore, the proposed approach enables a small group of researchers to efficiently process and analyze a large volume of documents, enhancing their capability to identify and comprehend information in a timeline while minimizing costs.
FAS-Res2net: An Improved Res2net-Based Script Identification Method for Natural Scenes

Problems such as complex image backgrounds, low image quality, diverse text forms, and similar or common character layouts in different script categories in natural scenes pose great challenges to scene script identification. This paper proposes a new Res2Net-based improved script identification method, namely FAS-Res2Net. In the feature extraction part, the feature pyramid network (FPN) module is introduced, which is beneficial to aggregate the geometric feature information extracted by the shallow network and the semantic feature information extracted by the deep network. Integrating the Adaptive Spatial Feature Fusion (ASFF) module is beneficial to obtain local feature information for optimal weight fusion. In addition, the global feature information of the image is extracted by introducing the swin transformer coding block, which makes the extracted feature information more abundant. In the classification part, the convolutional classifier is used to replace the traditional Linear classification, and the classification confidence of each category is output, which improves the identification efficiency. The improved algorithm achieved identification rates of 94.7% and 96.0% on public script identification datasets SIW-13 and CVSI-2015, respectively, which verified the superiority of the method.
SD-CapsNet: A Siamese Dense Capsule Network for SAR Image Registration with Complex Scenes

SAR image registration is the basis for applications such as change detection, image fusion, and three-dimensional reconstruction. Although CNN-based SAR image registration methods have achieved competitive results, they are insensitive to small displacement errors in matched point pairs and do not provide a comprehensive description of keypoint information in complex scenes. In addition, existing keypoint detectors are unable to obtain a uniform distribution of keypoints in SAR images with complex scenes. In this paper, we propose a texture constraint-based phase congruency (TCPC) keypoint detector that uses a rotation-invariant local binary pattern operator (RI-LBP) to remove keypoints that may be located at overlay or shadow locations. Then, we propose a Siamese dense capsule network (SD-CapsNet) to extract more accurate feature descriptors. Then, we define and verify that the feature descriptors in capsule form contain intensity, texture, orientation, and structure information that is useful for SAR image registration. In addition, we define a novel distance metric for the feature descriptors in capsule form and feed it into the Hard L2 loss function for model training. Experimental results for six pairs of SAR images demonstrate that, compared to other state-of-the-art methods, our proposed method achieves more robust results in complex scenes, with the number of correctly matched keypoint pairs (NCM) at least 2 to 3 times higher than the comparison methods, a root mean square error (RMSE) at most 0.27 lower than the compared methods.