Reviewerly/exHarmony huggingface dataset:
Cervical Cancer Diagnosis Model Using Extreme Gradient Boosting and Bioinspired Firefly Optimization

Cervical cancer is frequently a deadly disease, common in females. However, early diagnosis of cervical cancer can reduce the mortality rate and other associated complications. Cervical cancer risk factors can aid the early diagnosis. For better diagnosis accuracy, we proposed a study for early diagnosis of cervical cancer using reduced risk feature set and three ensemble-based classification techniques, i.e., extreme Gradient Boosting (XGBoost), AdaBoost, and Random Forest (RF) along with Firefly algorithm for optimization. Synthetic Minority Oversampling Technique (SMOTE) data sampling technique was used to alleviate the data imbalance problem. Cervical cancer Risk Factors data set, containing 32 risks factor and four targets (Hinselmann, Schiller, Cytology, and Biopsy), is used in the study. The four targets are the widely used diagnosis test for cervical cancer. The effectiveness of the proposed study is evaluated in terms of accuracy, sensitivity, specificity, positive predictive accuracy (PPA), and negative predictive accuracy (NPA). Moreover, Firefly features selection technique was used to achieve better results with the reduced number of features. Experimental results reveal the significance of the proposed model and achieved the highest outcome for Hinselmann test when compared with other three diagnostic tests. Furthermore, the reduction in the number of features has enhanced the outcomes. Additionally, the performance of the proposed models is noticeable in terms of accuracy when compared with other benchmark studies for cervical cancer diagnosis using reduced risk factors data set.
O2NN: Optical Neural Networks with Differential Detection-Enabled Optical Operands

Optical neuromorphic computing has demonstrated promising performance with ultra-high computation speed, high bandwidth, and low energy consumption. The traditional optical neural network (ONN) architectures realize neuromorphic computing via electrical weight encoding. However, previous ONN design methodologies can only handle static linear projection with stationary synaptic weights, thus fail to support efficient and flexible computing when both operands are dynamically-encoded light signals. In this work, we propose a novel ONN engine O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NN based on wavelength-division multiplexing and differential detection to enable high-performance, robust, and versatile photonic neural computing with both light operands. Balanced optical weights and augmented quantization are introduced to enhance the representability and efficiency of our architecture. Static and dynamic variations are discussed in detail with a knowledge-distillation-based solution given for robustness improvement. Discussions on hardware cost and efficiency are provided for a comprehensive comparison with prior work. Simulation and experimental results show that the proposed ONN architecture provides flexible, efficient, and robust support for high-performance photonic neural computing with fully-optical operands under low-bit quantization and practical variations.
Semantic segmentation from remote sensor data and the exploitation of latent learning for classification of auxiliary tasks

In this paper we address three different aspects of semantic segmentation from remote sensor data using deep neural networks. Firstly, we focus on the semantic segmentation of buildings from remote sensor data and propose ICT-Net: a novel network with the underlying architecture of a fully convolutional network, infused with feature re-calibrated Dense blocks at each layer. Uniquely, the proposed network combines the localization accuracy and use of context of the U-Net network architecture, the compact internal representations and reduced feature redundancy of the Dense blocks, and the dynamic channel-wise feature re-weighting of the Squeeze-and-Excitation(SE) blocks. The proposed network has been tested on the INRIA and AIRS benchmark datasets and is shown to outperform other state of the art. Secondly, as the building classification is typically the first step of the reconstruction process, we investigate the relationship of the classification accuracy to the reconstruction accuracy. Due to the lack of (1) scene depth information, and (2) ground-truth (blueprints) for large urban-areas, the evaluation of the 3D reconstructions is not possible. Thus, we use boundary localization as a proxy to reconstruction accuracy and perform the evaluation in 2D. A comparative quantitative analysis of reconstruction accuracies corresponding to different classification accuracies confirms the strong correlation between the two. We present the results which show a consistent and considerable reduction in the reconstruction accuracy. Finally, we present the simple yet compelling concept of latent learning and the implications it carries within the context of deep learning. We posit that a network trained on a primary task (i.e. building classification) is unintentionally learning about auxiliary tasks (e.g. the classification of road, tree, etc.) which are complementary to the primary task. Although embedded in a trained network, this latent knowledge relating to the auxiliary tasks is never externalized or immediately expressed but instead only knowledge relating to the primary task is ever output by the network. We experimentally prove this occurrence of incidental learning on the pre-trained ICT-Net and show how sub-classification of the negative label is possible without further training/fine-tuning. We present the results of our experiments and explain how knowledge about auxiliary and complementary tasks – for which the network was never trained – can be retrieved and utilized for further classification. We extensively tested the proposed technique on the ISPRS benchmark dataset which contains multi-label ground truth, and report an average classification accuracy (F1 score) of 54.29% (SD=17.03) for roads, 10.15% (SD=2.54) for cars, 24.11% (SD=5.25) for trees, 42.74% (SD=6.62) for low vegetation, and 18.30% (SD=16.08) for clutter. The source code and supplemental material is publicly available at http://www.theICTlab.org/lp/2020ICTNet/.
Cognitive Robotics on 5G Networks

Emotional cognitive ability is a key technical indicator to measure the friendliness of interaction. Therefore, this research aims to explore robots with human emotion cognitively. By discussing the prospects of 5G technology and cognitive robots, the main direction of the study is cognitive robots. For the emotional cognitive robots, the analysis logic similar to humans is difficult to imitate; the information processing levels of robots are divided into three levels in this study: cognitive algorithm, feature extraction, and information collection by comparing human information processing levels. In addition, a multi-scale rectangular direction gradient histogram is used for facial expression recognition, and robust principal component analysis algorithm is used for facial expression recognition. In the pictures where humans intuitively feel smiles in sad emotions, the proportion of emotions obtained by the method in this study are as follows: calmness accounted for 0%, sadness accounted for 15.78%, fear accounted for 0%, happiness accounted for 76.53%, disgust accounted for 7.69%, anger accounted for 0%, and astonishment accounted for 0%. In the recognition of micro-expressions, humans intuitively feel negative emotions such as surprise and fear, and the proportion of emotions obtained by the method adopted in this study are as follows: calmness accounted for 32.34%, sadness accounted for 34.07%, fear accounted for 6.79%, happiness accounted for 0%, disgust accounted for 0%, anger accounted for 13.91%, and astonishment accounted for 15.89%. Therefore, the algorithm explored in this study can realize accuracy in cognition of emotions. From the preceding research results, it can be seen that the research method in this study can intuitively reflect the proportion of human expressions, and the recognition methods based on facial expressions and micro-expressions have good recognition effects, which is in line with human intuitive experience.
Combining SWAPs and Remote Toffoli Gates in the Mapping to IBM QX Architectures

Quantum computation received a steadily growing attention in recent years, especially supported by the emergence of publicly available quantum computers like the popular IBM QX series. In order to execute a reversible or quantum circuit on those devices, a mapping is required that replaces each reversible or quantum gate by an equivalent cascade of elementary, i.e. directly executable, gates-a task which tends to induce a significant mapping overhead. Several approaches have been proposed for this task which either rely on the swapping of physically adjacent qubits or the use of precomputed templates, so-called remote CNOT gates. In this paper, we show that combining both, swapping and remote gates, at the reversible circuit level has the prospect of significantly reducing the mapping overhead. We propose a methodology to compute the optimal combination of swaps and templates for Multiple-Controlled Toffoli gates. By using a formulation as a single-source shortest-path problem, a complete database of optimal combinations can be computed efficiently. Experimental results indicate that the mapping overhead can be significantly reduced.
A Data-Driven Method Using BRB With Data Reliability and Expert Knowledge for Complex Systems Modeling

The reliability of data is important for complex systems modeling. However, it remains to be a great challenge mainly due to the lack of effective measurement of data reliability. On the condition that there exist the actual result and gold standard in a piece of data simultaneously, a data-driven method is proposed using the belief rule base (BRB) with data reliability and expert knowledge. In the proposed method, gold standards are used to indicate the degree to which the actual results are correct and, thus, the reliability of each piece of data is defined as the similarity between its actual result and the corresponding gold standard. According to the reliability of each piece of data, the training dataset is divided into multiple clusters. Each cluster of data is separately used to construct and optimize a BRB model. The weight and reliability of each model are then calculated using the data in the respective cluster with the consideration of the reliabilities of the data. Finally, the estimated results of the models are integrated using the evidential reasoning rule to generate the integrated results. The proposed method is applied to help radiologists diagnose thyroid nodules, in which historical examination reports are collected from a tertiary hospital located in Hefei, Anhui, China. Its superior performance is verified by experiments.
Unified SVM algorithm based on LS-DC loss

Over the past two decades, support vector machine (SVM) has become a popular supervised machine learning model, and plenty of distinct algorithms are designed separately based on different KKT conditions of the SVM model for classification/regression with different losses, including the convex loss or nonconvex loss. In this paper, we propose an algorithm that can train different SVM models in a \emph{unified} scheme. First, we introduce a definition of the \emph{LS-DC} (\textbf{l}east \textbf{s}quares type of \textbf{d}ifference of \textbf{c}onvex) loss and show that the most commonly used losses in the SVM community are LS-DC loss or can be approximated by LS-DC loss. Based on DCA (difference of convex algorithm), we then propose a unified algorithm, called \emph{UniSVM}, which can solve the SVM model with any convex or nonconvex LS-DC loss, in which only a vector is computed, especially by the specifically chosen loss. Particularly, for training robust SVM models with nonconvex losses, UniSVM has a dominant advantage over all existing algorithms because it has a closed-form solution per iteration, while the existing algorithms always need to solve an L1SVM/L2SVM per iteration. Furthermore, by the low-rank approximation of the kernel matrix, UniSVM can solve the large-scale nonlinear problems with efficiency. To verify the efficacy and feasibility of the proposed algorithm, we perform many experiments on some small artificial problems and some large benchmark tasks with/without outliers for classification and regression for comparison with state-of-the-art algorithms. The experimental results demonstrate that UniSVM can achieve comparable performance in less training time. The foremost advantage of UniSVM is that its core code in Matlab is less than 10 lines; hence, it can be easily grasped by users or researchers.
Chaos teaching learning based algorithm for large‐scale global optimization problem and its application

Abstract Teaching learning‐based optimization (TLBO) is a popular stochastic algorithm that has recently been widely applied in a variety of optimization problems since its start. In TLBO algorithm, the concept of chaos not only shows a vital effect in its convergence but also plays a substantial role to balance of exploration and exploitation through evolution. However, TLBO is quickly trapped in local optima and premature convergence seems when applied to sophisticated complex functions. To handle these problems, we introduced an improved TLBO algorithm using chaotic concept. To achieve ability to search for exploration and exploitation, new phase called chaotic phase is added in original TLBO algorithm. The proposed method is thoroughly evaluated on benchmark test suites. The numerical result show that proposed method is relatively effective in adapting the chaotic value regarding original TLBO in terms of solution quality and convergence rate. In addition, performance of proposed method is evaluated on benchmark KDD Cup 99 intrusion dataset. The experimental results demonstrate that proposed method achieves higher predictive accuracy, detection rate, false alarm rate, and provided more significant features as compared with other wrapper techniques.
Cloud Model for Security State Recognition Based on Factor Space

With the integration of informatization and industrialization, the oil-gas industry control system is currently confronting huge security risks. To more effectively assess and predict the security state of industry control systems, this study proposed a cloud model based on factor space for security state recognition. First, the malicious behavior factors of oil-gas industry control system were obtained and were described in the factor state space. Next, the expectation, entropy and hyper entropy of each factor were constructed to convert the fuzzy concepts in the cloud model into quantitative value. Finally, based on the forward and reverse generators, single-condition, multi-condition and multi-rule cloud generators were applied to rule-based reasoning model to realize the transformation from expected value to qualitative value. Experimental simulation demonstrates that cloud-reasoning model based on the factor space can predict the impact of unknown malicious program behavior on the security state and achieve a better evaluation effect. Moreover, this study can provide a new approach to the security state recognition of industrial control systems.
Rank-Based Verification for Long-Term Face Tracking in Crowded Scenes

Most current multi-object trackers focus on short-term tracking, and are based on deep and complex systems that often cannot operate in real-time, making them impractical for video-surveillance. In this paper we present a long-term, multi-face tracking architecture conceived for working in crowded contexts where faces are often the only visible part of a person. Our system benefits from advances in the fields of face detection and face recognition to achieve long-term tracking, and is particularly unconstrained to the motion and occlusions of people. It follows a tracking-by-detection approach, combining a fast short-term visual tracker with a novel online tracklet reconnection strategy grounded on rank-based face verification. The proposed rank-based constraint favours higher inter-class distance among tracklets, and reduces the propagation of errors due to wrong reconnections. Additionally, a correction module is included to correct past assignments with no extra computational cost. We present a series of experiments introducing novel specialized metrics for the evaluation of long-term tracking capabilities, and publicly release a video dataset with 10 manually annotated videos and a total length of 8' 54". Our findings validate the robustness of each of the proposed modules, and demonstrate that, in these challenging contexts, our approach yields up to 50% longer tracks than state-of-the-art deep learning trackers.
Comprehensive Knowledge Archive Network harvester improvement for efficient open‐data collection and management

With the recent increase in data disclosure, the Comprehensive Knowledge Archive Network (CKAN), which is an open-source data distribution platform, is drawing much attention. CKAN is used together with additional extensions, such as Datastore and Datapusher for data management and Harvest and DCAT for data collection. This study derives the problems of CKAN itself and Harvest Extension. First, CKAN causes two problems of data inconsistency and storage space waste for data deletion. Second, Harvest Extension causes three additional problems, namely source deletion that deletes only sources without deleting data themselves, job stop that cannot delete job during data collection, and service interruption that cannot provide service, even if data exist. Based on these observations, we propose herein an improved CKAN that provides a new deletion function solving data inconsistency and storage space waste problems. In addition, we present an improved Harvest Extension solving three problems of the legacy Harvest Extension. We verify the correctness and the usefulness of the improved CKAN and Harvest Extension functions through actual implementation and extensive experiments.
Shot in the Dark: Few-Shot Learning with No Base-Class Labels

Few-shot learning aims to build classifiers for new classes from a small number of labeled examples and is commonly facilitated by access to examples from a distinct set of ‘base classes’. The difference in data distribution between the test set (novel classes) and the base classes used to learn an inductive bias often results in poor generalization on the novel classes. To alleviate problems caused by the distribution shift, previous research has explored the use of unlabeled examples from the novel classes, in addition to labeled examples of the base classes, which is known as the transductive setting. In this work, we show that, surprisingly, off-the-shelf self-supervised learning outperforms transductive few-shot methods by 3.9% for 5-shot accuracy on miniImageNet without using any base class labels. This motivates us to examine more carefully the role of features learned through self-supervision in few-shot learning. Comprehensive experiments are conducted to compare the transferability, robustness, efficiency, and the complementarity of supervised and self-supervised features.
Towards Tight Random Probing Security

Proving the security of masked implementations in theoretical models that are relevant to practice and match the best known attacks of the side-channel literature is a notoriously hard problem. The random probing model is a promising candidate to contribute to this challenge, due to its ability to capture the continuous nature of physical leakage (contrary to the threshold probing model), while also being convenient to manipulate in proofs and to automate with verification tools. Yet, despite recent progress in the design of masked circuits with good asymptotic security guarantees in this model, existing results still fall short when it comes to analyze the security of concretely useful circuits under realistic noise levels and with low number of shares. In this paper, we contribute to this issue by introducing a new composability notion, the Probe Distribution Table (PDT), and a new tool (called STRAPS, for the Sampled Testing of the RAndom Probing Security). Their combination allows us to significantly improve the tightness of existing analyses in the most practical (low noise, low number of shares) region of the design space. We illustrate these improvements by quantifying the random probing security of an AES S-box circuit, masked with the popular multiplication gadget of Ishai, Sahai and Wagner from Crypto 2003, with up to six shares.
Pseudo-margin-based universal domain adaptation

As a more practical setting for unsupervised domain adaptation, Universal Domain Adaptation (UDA) is recently introduced, where the structure of the target label set is assumed to be unavailable. One of the significant challenges in UDA is identifying the common label set shared by source and target domains without labels in the target domain. Most previous works identify the common label set according to the intra-domain information. This article proposes a novel inter-domain approach to identify the common label set efficiently, where the pseudo-margins of target samples are defined to estimate each source class’s probability belonging to the common label set. Specifically, a pseudo-margin vector (PMV) is proposed to describe each source class’s reliability belonging to the common label set, equipped with a target pseudo-margin register (TPMR), which stores and updates the PMV during training. We propose an improved universal adaptation network (I-UAN) to perform domain alignment on the commonly-labeled samples by incorporating the PMV. In I-UAN, a class-wise weighting mechanism based on TPMR for source classes, combined with the sample-wise weighting for target samples, efficiently improves the UDA’s performance. Our approach is robust to outliers as it applies a class-wise weighting mechanism in the source domain. Moreover, we provide experimental analysis on the adaptability, universality, and robustness of I-UAN. Extensive experimental results demonstrate that I-UAN works well in various UDA settings and outperforms the state-of-the-art methods by large margins.
Topic Modeling Enhancement using Word Embeddings

Latent Dirichlet Allocation(LDA) is one of the powerful techniques in extracting topics from a document. The original LDA takes the Bag-of-Word representation as the input and produces topic distributions in documents as output. The drawback of Bag-of-Word is that it represents each word with a plain one-hot encoding which does not encode the word level information. Later research in Natural Language Processing(NLP) demonstrate that word embeddings technique such as Skipgram model can provide a good representation in capturing the relationship and semantic information between words. In recent studies, many NLP tasks could gain better performance by applying the word embedding as the representation of words. In this paper, we propose Deep Word-Topic Latent Dirichlet Allocation(DWT-LDA), a new process for training LDA with word embedding. A neural network with word embedding is applied to the Collapsed Gibbs Sampling process as another choice for word topic assignment. To quantitatively evaluate our model, the topic coherence framework and topic diversity are the metrics used to compare between our approach and the original LDA. The experimental result shows that our method generates more coherent and diverse topics.