Reviewerly/exHarmony huggingface dataset:
Visual detection and tracking algorithms for minimally invasive surgical instruments: A comprehensive review of the state-of-the-art

Minimally invasive surgical instrument visual detection and tracking is one of the core algorithms of minimally invasive surgical robots. With the development of machine vision and robotics, related technologies such as virtual reality, three-dimensional reconstruction, path planning, and human–machine collaboration can be applied to surgical operations to assist clinicians or use surgical robots to complete clinical operations. The minimally invasive surgical instrument vision detection and tracking algorithm analyzes the image transmitted by the surgical robot endoscope, extracting the position of the surgical instrument tip in the image, so as to provide the surgical navigation. This technology can greatly improve the accuracy and success rate of surgical operations. The purpose of this paper is to further study the visual detection and tracking technology of minimally invasive surgical instruments, summarize the existing research results, and apply it to the surgical robot project. By reading the literature, the author summarized the theoretical basis and related algorithms of this technology in recent years. Finally, the author compares the accuracy, speed and application scenario of each algorithm, and analyzes the advantages and disadvantages of each algorithm. The papers included in the review were selected through Web of Science, Google Scholar, PubMed and CNKI searches using the keywords: “object detection”, “object tracking”, “surgical tool detection”, “surgical tool tracking”, “surgical instrument detection” and “surgical instrument tracking” limiting results to the year range 1985–2021. Our study shows that this technology will have a great development prospect in the aspects of accuracy and real-time improvement in the future.
Edge-Aware Guidance Fusion Network for RGB–Thermal Scene Parsing

RGB–thermal scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing methods fail to perform good boundary extraction for prediction maps and cannot fully use high-level features. In addition, these methods simply fuse the features from RGB and thermal modalities but are unable to obtain comprehensive fused features. To address these problems, we propose an edge-aware guidance fusion network (EGFNet) for RGB–thermal scene parsing. First, we introduce a prior edge map generated using the RGB and thermal images to capture detailed information in the prediction map and then embed the prior edge information in the feature maps. To effectively fuse the RGB and thermal information, we propose a multimodal fusion module that guarantees adequate cross-modal fusion. Considering the importance of high-level semantic information, we propose a global information module and a semantic information module to extract rich semantic information from the high-level features. For decoding, we use simple elementwise addition for cascaded feature fusion. Finally, to improve the parsing accuracy, we apply multitask deep supervision to the semantic and boundary maps. Extensive experiments were performed on benchmark datasets to demonstrate the effectiveness of the proposed EGFNet and its superior performance compared with state-of-the-art methods. The code and results can be found at https://github.com/ShaohuaDong2021/EGFNet.
A systematic literature review on spam content detection and classification

The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. The time spent by people using social media is overgrowing, especially in the time of the pandemic. Users get a lot of text messages through social media, and they cannot recognize the spam content in these messages. Spam messages contain malicious links, apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection.
A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data

Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.
Incorporating sentimental analysis into development of a hybrid classification model

The Sentimental Analysis approach is typically used for analyzing a user's ideas, sentiments, and text subjectivity, all of which are expressed through text. Sentimental analysis, also known as "opinion mining," is a type of data mining that follows the concept of emotional analysis presented by people in a thoughtful manner. Based on historical evidence, websites are the most effective venue for soliciting customer feedback. Existing methodologies based on sentimental analysis are ineffective. As a result, a novel hybrid framework based on three classifiers, including SVM, logistic regression, and random forest, is proposed in this paper. Based on user feedback or historical data, the hybrid model serves as an effective classifier, assisting in the development of more accurate classification results. Furthermore, the proposed model has worked well and has been compared to other methods based on several performance metrics, such as accuracy, precision, recall, and recall.
Experimental Quantum Advantage with Quantum Coupon Collector

An increasing number of communication and computational schemes with quantum advantages have recently been proposed, which implies that quantum technology has fertile application prospects. However, demonstrating these schemes experimentally continues to be a central challenge because of the difficulty in preparing high-dimensional states or highly entangled states. In this study, we introduce and analyze a quantum coupon collector protocol by employing coherent states and simple linear optical elements, which was successfully demonstrated using realistic experimental equipment. We showed that our protocol can significantly reduce the number of samples needed to learn a specific set compared with the classical limit of the coupon collector problem. We also discuss the potential values and expansions of the quantum coupon collector by constructing a quantum blind box game. The information transmitted by the proposed game also broke the classical limit. These results strongly prove the advantages of quantum mechanics in machine learning and communication complexity.
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding

Structured document understanding has attracted considerable attention and made significant progress recently, owing to its crucial role in intelligent document processing.However, most existing related models can only deal with the document data of specific language(s) (typically English) included in the pre-training collection, which is extremely limited.To address this issue, we propose a simple yet effective Language-independent Layout Transformer (LiLT) for structured document understanding.LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained textual models.Experimental results on eight languages have shown that LiLT can achieve competitive or even superior performance on diverse widely-used downstream benchmarks, which enables language-independent benefit from the pre-training of document layout structure.Code and model are publicly available at https://github.com/jpWang/LiLT.
A Tractable First-Order Probabilistic Logic

Tractable subsets of first-order logic are a central topic in AI research. Several of these formalisms have been used as the basis for first-order probabilistic languages. However, these are intractable, losing the original motivation. Here we propose the first non-trivially tractable first-order probabilistic language. It is a subset of Markov logic, and uses probabilistic class and part hierarchies to control complexity. We call it TML (Tractable Markov Logic). We show that TML knowledge bases allow for efficient inference even when the corresponding graphical models have very high treewidth. We also show how probabilistic inheritance, default reasoning, and other inference patterns can be carried out in TML. TML opens up the prospect of efficient large-scale first-order probabilistic inference.
Cross-Domain Image Matching with Deep Feature Maps

We investigate the problem of automatically determining what type of shoe left an impression found at a crime scene. This recognition problem is made difficult by the variability in types of crime scene evidence (ranging from traces of dust or oil on hard surfaces to impressions made in soil) and the lack of comprehensive databases of shoe outsole tread patterns. We find that mid-level features extracted by pre-trained convolutional neural nets are surprisingly effective descriptors for this specialized domains. However, the choice of similarity measure for matching exemplars to a query image is essential to good performance. For matching multi-channel deep features, we propose the use of multi-channel normalized cross-correlation and analyze its effectiveness. Our proposed metric significantly improves performance in matching crime scene shoeprints to laboratory test impressions. We also show its effectiveness in other cross-domain image retrieval problems: matching facade images to segmentation labels and aerial photos to map images. Finally, we introduce a discriminatively trained variant and fine-tune our system through our proposed metric, obtaining state-of-the-art performance.
Sharing of tripartite nonlocality by multiple observers measuring sequentially at one side

Standard tripartite nonlocality and genuine tripartite nonlocality can be detected by the violations of Mermin inequality and Svetlichny inequality, respectively. Since tripartite quantum nonlocality has novel applications in quantum information and quantum computation, it is important to investigate whether more than three observers can share tripartite nonlocality, simultaneously. In the present study we answer this question in the affirmative. In particular, we consider a scenario where three spin-$\frac{1}{2}$ particles are spatially separated and shared between Alice, Bob and multiple Charlies. Alice performs measurements on the first particle; Bob performs measurements on the second particle and multiple Charlies perform measurements on the third particle sequentially. In this scenario we investigate how many Charlies can simultaneously demonstrate standard tripartite nonlocality and genuine tripartite nonlocality with single Alice and single Bob. The interesting result revealed by the present study is that at most six Charlies can simultaneously demonstrate standard tripartite nonlocality with single Alice and single Bob. On the other hand, at most two Charlies can simultaneously demonstrate genuine tripartite nonlocality with single Alice and single Bob. Hence, the present study shows that standard tripartite nonlocality can be simultaneously shared by larger number of Charlies compared to genuine tripartite nonlocality in the aforementioned scenario, which implies that standard tripartite nonlocality is more effective than genuine tripartite nonlocality in the context of simultaneous sharing by multiple observers.
Benchmarking question answering systems

The necessity of making the Semantic Web more accessible for lay users, alongside the uptake of interactive systems and smart assistants for the Web, have spawned a new generation of RDF-based question answering systems. However, fair evaluation of these systems remains a challenge due to the diffe rent type of answers that they provide. Hence, repeating current published experiments or even benchmarking on the same datasets remains a complex and time-consuming task. We present a novel online benchmarking platform for question answering (QA) that relies on the FAIR principles to support the fine-grained evaluation of question answering systems. We detail how the platform addresses the fair benchmarking platform of question answering systems through the rewriting of URIs and URLs. In addition, we implement different evaluation metrics, measures, datasets and pre-implemented systems as well as methods to work with novel formats for interactive and non-interactive benchmarking of question answering systems. Our analysis of current frameworks shows that most of the current frameworks are tailored towards particular datasets and challenges but do not provide generic models. In addition, while most frameworks perform well in the annotation of entities and properties, the generation of SPARQL queries from annotated text remains a challenge.
Merit-guided dynamic feature selection filter for data streams

Learning from ephemeral data streams has garnered the interest of both researchers and practitioners towards adaptive learning techniques. Despite the convincing results obtained thus far, most of the current research still overlooks that the relevance of features may change throughout the learning process. Scenarios where features become - or cease to be - relevant to the learning task are called feature drifting data streams, and the identification of which features are relevant becomes even more challenging when the feature space is high-dimensional. To select relevant features during the progress of data streams, we propose a merit-guided and classifier-independent dynamic feature selection algorithm named DynamIc SymmetriCal Uncertainty Selection for Streams (DISCUSS). We evaluate our proposal on both synthetic and real-world datasets and show that DISCUSS can boost kNN and Naive Bayes classifiers' accuracy rates on high-dimensional data streams, while at the expense of limited processing time and memory space. Finally, the drawbacks of the proposed method are assessed, and possible future works on the topic are also discussed.
3D human pose estimation with siamese equivariant embedding

In monocular 3D human pose estimation a common setup is to first detect 2D positions and then lift the detection into 3D coordinates. Many algorithms suffer from overfitting to camera positions in the training set. We propose a siamese architecture that learns a rotation equivariant hidden representation to reduce the need for data augmentation. Our method is evaluated on multiple databases with different base networks and shows a consistent improvement of error metrics. It achieves state-of-the-art cross-camera error rate among algorithms that use estimated 2D joint coordinates only.
Compiling Bayesian Network Classifiers into Decision Graphs

We propose an algorithm for compiling Bayesian network classifiers into decision graphs that mimic the input and output behavior of the classifiers. In particular, we compile Bayesian network classifiers into ordered decision graphs, which are tractable and can be exponentially smaller in size than decision trees. This tractability facilitates reasoning about the behavior of Bayesian network classifiers, including the explanation of decisions they make. Our compilation algorithm comes with guarantees on the time of compilation and the size of compiled decision graphs. We apply our compilation algorithm to classifiers from the literature and discuss some case studies in which we show how to automatically explain their decisions and verify properties of their behavior.
Verification of RNN-Based Neural Agent-Environment Systems

We introduce agent-environment systems where the agent is stateful and executing a ReLU recurrent neural network. We define and study their verification problem by providing equivalences of recurrent and feed-forward neural networks on bounded execution traces. We give a sound and complete procedure for their verification against properties specified in a simplified version of LTL on bounded executions. We present an implementation and discuss the experimental results obtained.
Deep Learning With Asymmetric Connections and Hebbian Updates

We show that deep networks can be trained using Hebbian updates yielding similar performance to ordinary back-propagation on challenging image datasets. To overcome the unrealistic symmetry in connections between layers, implicit in back-propagation, the feedback weights are separate from the feedforward weights. The feedback weights are also updated with a local rule, the same as the feedforward weights-a weight is updated solely based on the product of activity of the units it connects. With fixed feedback weights as proposed in Lillicrap et al. (2016) performance degrades quickly as the depth of the network increases. If the feedforward and feedback weights are initialized with the same values, as proposed in Zipser and Rumelhart (1990), they remain the same throughout training thus precisely implementing back-propagation. We show that even when the weights are initialized differently and at random, and the algorithm is no longer performing back-propagation, performance is comparable on challenging datasets. We also propose a cost function whose derivative can be represented as a local Hebbian update on the last layer. Convolutional layers are updated with tied weights across space, which is not biologically plausible. We show that similar performance is achieved with untied layers, also known as locally connected layers, corresponding to the connectivity implied by the convolutional layers, but where weights are untied and updated separately. In the linear case we show theoretically that the convergence of the error to zero is accelerated by the update of the feedback weights.
Learning a bi-level adversarial network with global and local perception for makeup-invariant face verification

Makeup is widely used to improve facial attractiveness and is well accepted by the public. However, different makeup styles will result in significant facial appearance changes. It remains a challenging problem to match makeup and non-makeup face images. This paper proposes a learning from generation approach for makeup-invariant face verification by introducing a bi-level adversarial network (BLAN). To alleviate the negative effects from makeup, we first generate non-makeup images from makeup ones, and then use the synthesized non-makeup images for further verification. Specifically, there are two adversarial sub-networks on different levels in BLAN, with the one on pixel level for reconstructing appealing facial images and the other on feature level for preserving identity information. For the non-makeup image generation module, a two-path network that involves both global and local structures is applied to improve the synthesis quality. Moreover, we make the generator well constrained by incorporating multiple perceptual losses. All the modules are embedded in an end-to-end network and jointly reduce the sensing gap between makeup and non-makeup images. Experimental results on three benchmark makeup face datasets demonstrate that our method achieves state-of-the-art verification accuracy across makeup status and can produce photo-realistic non-makeup face images.
Analog circuit topology synthesis by means of evolutionary computation

Designing an analog electrical circuit requires substantial effort and time by a highly-skilled designer. Many tools have been devised to aid engineers in the design procedure, from simulators to parameter optimization tools, which largely assist in fine-tuning the parameter values of circuits with predetermined topologies. In this paper, we propose a new approach to circuit optimization that is capable of searching not only for the parameter but also the topological space. Starting from a high-level statement describing a desired circuit functionality, the proposed method can either improve a given circuit, or create a novel topology with given resources, based on the Darwinian theory of evolutionary selection. Our method uses a global parameter optimization method PSADE to speed up evolutionary convergence. We use the proposed algorithm (both single- and multi-objective) to successfully evolve passive, active, and BiCMOS circuits.
Circular Multi-Party Quantum Private Comparison with n-Level Single-Particle States

In this paper, a novel multi-party quantum private comparison (MQPC) protocol for equality comparison with n-level single-particle states is constructed, where the encoded particles are transmitted in a circular way. Here, n parties employ the qudit shifting operation to encode their private secrets and can compare the equality of their private secrets within one time execution of protocol. The proposed MQPC protocol can overcome both the outside attack and the participant attack. Specially, each party’s secret can be kept unknown to other parties and the third party (TP).
A Novel Method to Identify Initial Values of Chaotic Maps in Cybersecurity

Chaos theory has applications in several disciplines and is focusing on the behavior of dynamical systems that are highly sensitive to initial conditions. Chaotic dynamics are the impromptu behavior displayed by some nonlinear dynamical frameworks and have been used as a source of diffusion in cybersecurity for more than two decades. With the addition of chaos, the overall strength of communication security systems can be increased, as seen in recent proposals. However, there is a major drawback of using chaos in communication security systems. Chaotic communication security systems rely on private keys, which are the initial values and parameters of chaotic systems. This paper shows that these chaotic communication security systems can be broken by identifying those initial values through the statistical analysis of standard deviation and variance. The proposed analyses are done on the chaotic sequences of Lorenz chaotic system and Logistic chaotic map and show that the initial values and parameters, which serve as security keys, can be retrieved and broken in short computer times. Furthermore, the proposed model of identifying the initial values can also be applied on other chaotic maps as well.