Reviewerly/exHarmony huggingface dataset:
An optimal nuclei segmentation method based on enhanced multi-objective GWO

Abstract In breast cancer image analysis, reliable segmentation of the nuclei is still an open-ended research problem. In this paper, a new clustering-based nuclei segmentation method is presented. First, the proposed method pre-processes the histopathology image through SLIC method. Then, a novel variant of multi-objective grey wolf optimizer is employed to group the obtained super-pixels into optimal clusters. Lastly, the optimal cluster with minimum value is segmented as the nuclei region. The experimental results demonstrates that the proposed variant of multi-objective grey wolf algorithm surpasses the existing multi-objective algorithms over ten standard multi-objective benchmark functions belonging to different categories. Particularly, the proposed variant has achieved best fitness value of more than 0.90 on 90% of the considered functions. Further, the nuclei segmentation accuracy of the proposed method is validated on H&amp;E-stained estrogen receptor positive (ER+) breast cancer images. Experimental results illustrates that the proposed method has attained dice-coefficient value of more than 0.52 on 80% of the images. This illustrates that the proposed method is efficient in producing efficacious segmenting over histology images of Breast cancer.
Security of Deep Learning Models in 5G Networks: Proposition of Security Assessment Process

5G networks bring a new design paradigm that will revolutionize telecommunications and other sectors, such as industry 4.0, smart cities, and autonomous vehicles. However, with the inherent advantages, many challenges can emerge. Today, the community is increasingly motivated to address these challenges by leveraging deep learning models to improve the 5G end-user quality of experience (QoE). However, this alternative approach would exhibit network assets to a series of security threats that could compromise the availability, integrity, and privacy of 5G architectures. This paper will extensively examine the vulnerabilities in the 5G models to draw the community's attention to the threats they may involve when integrated without sufficient prevention. Its main contribution lies in the comprehensive and adaptive approach it proposes to appraise deep learning models’ security by assessing and managing the vulnerabilities they may present before their implementation in 5G network architectures.
A Variance Controlled Stochastic Method with Biased Estimation for Faster Non-convex Optimization

This paper proposed a new technique Variance Controlled Stochastic Gradient (VCSG) to improve the performance of the stochastic variance reduced gradient (SVRG) algorithm. To avoid over-reducing the variance of gradient by SVRG, a hyper-parameter \(\lambda \) is introduced in VCSG that is able to control the reduced variance of SVRG. Theory shows that the optimization method can converge by using an unbiased gradient estimator, but in practice, biased gradient estimation can allow more efficient convergence to the vicinity since an unbiased approach is computationally more expensive. \(\lambda \) also has the effect of balancing the trade-off between unbiased and biased estimations. Secondly, to minimize the number of full gradient calculations in SVRG, a variance-bounded batch is introduced to reduce the number of gradient calculations required in each iteration. For smooth non-convex functions, the proposed algorithm converges to an approximate first-order stationary point (i.e. \(\mathbb {E}\Vert \nabla {f}(x)\Vert ^{2}\le \epsilon \)) within \(\mathcal {O}(min\{1/\epsilon ^{3/2},n^{1/4}/\epsilon \})\) number of stochastic gradient evaluations, which improves the leading gradient complexity of stochastic gradient-based method SCSG \((\mathcal {O}(min\{1/\epsilon ^{5/3},n^{2/3}/\epsilon \})\) []. It is shown theoretically and experimentally that VCSG can be deployed to improve convergence.
A Novel Approach for Speaker Recognition in Degraded Conditions

Human voice is an ideal data source for identifying people in many applications. Because of the increasing need for security in different public places, voice biometrics may be a good solution, as we can easily take voice records. This chapter provides a brief overview of the approaches utilized in recognizing speakers, and then presents a novel approach for recognizing speakers in degraded smart-home conditions. The suggested approach includes a pre-processing phase, a feature extraction phase, and a classification phase, where the feature extraction phase consists of formant extraction to get the spectrum energy maxima of speech audio, dynamic time warping (DTW) to find an optimal alignment between two provided temporal sequences under definite restrictions, and refinement process to improve the results of the DTW system output. The experiments are carried out on a database containing 1,248 samples in order to validate the suggested approach. The latter has good results as regards the state of the art with 94.5% accuracy.
High-speed batch processing of semidefinite programs with feedforward neural networks

Semidefinite programming is an important optimization task, often used in time-sensitive applications. Though they are solvable in polynomial time, in practice they can be too slow to be used in online, i.e. real-time applications. Here we propose to solve feasibility semidefinite programs using artificial neural networks (NNs). Given the optimization constraints as an input, a NN outputs values for the optimization parameters such that the constraints are satisfied, both for the primal and the dual formulations of the task. We train the network without having to exactly solve the semidefinite program even once, thus avoiding the possibly time-consuming task of having to generate many training samples with conventional solvers. The NN method is only inconclusive if both the primal and dual models fail to provide feasible solutions. Otherwise we always obtain a certificate, which guarantees false positives to be excluded. As a proof-of-principle demonstration, we examine the performance of the method on a hierarchy of quantum information tasks, the Navascués–Pironio–Acín hierarchy applied to the Bell scenario. We demonstrate that the trained NN gives decent accuracy, while showing orders of magnitude increase in speed compared to a traditional solver, once trained. The network does not have to be retrained for similarly structured problems, giving the promise for being a fast solver for these types of problems.
School society and pandemic in Mexico; online education: from temporary to permanent shelter

As a result of the global pandemic of covid-19, distance education went from being a little considered training option to the status of the main operating alternative for the school system. Educational institutions by adapting their work to the use of digital platforms have managed to keep their programs active despite reducing their training expectations. School activities at home face complex social situations that contravene the teaching precepts, cause poor learning and many cases of school dropout. Educational systems in the medium term will promote remote teaching modalities in digital and telematic versions, whose didactics will dominate the 21st century.
Asymmetric Loss for Multi-Label Classification

In a typical multi-label setting, a picture contains on average few positive labels, and many negative ones. This positive-negative imbalance dominates the optimization process, and can lead to under-emphasizing gradients from positive labels during training, resulting in poor accuracy. In this paper, we introduce a novel asymmetric loss (ASL), which operates differently on positive and negative samples. The loss enables to dynamically down-weights and hard-thresholds easy negative samples, while also discarding possibly mislabeled samples. We demonstrate how ASL can balance the probabilities of different samples, and how this balancing is translated to better mAP scores. With ASL, we reach state-of-the-art results on multiple popular multi-label datasets: MS-COCO, Pascal-VOC, NUS-WIDE and Open Images. We also demonstrate ASL applicability for other tasks, such as single-label classification and object detection. ASL is effective, easy to implement, and does not increase the training time or complexity. Implementation is available at: this https URL.
Quantum Illumination with Multiple Entangled Photons

Abstract In this work, a theoretical generalization of Lloyd's quantum illumination model for signal beams described by two entangled photon states is developed. It is shown that the new protocol is more sensitive than Lloyd's model with respect to signal‐to‐noise ratio (SNR) and reduces the required time‐bandwidth product to have the same enhancement in the SNR. The protocol is resilient against noise, potentially resilient against losses, and offers a method to find the range of the target. However, the generation of the required three photon states for the protocol remains a technical problem for its practical implementation.
Partitioning dysprosium's electronic spin to reveal entanglement in nonclassical states

Quantum spins of mesoscopic size are a well-studied playground for engineering non-classical states. If the spin represents the collective state of an ensemble of qubits, its non-classical behavior is linked to entanglement between the qubits. In this work, we report on an experimental study of entanglement in dysprosium's electronic spin. Its ground state, of angular momentum $J=8$, can formally be viewed as a set of $2J$ qubits symmetric upon exchange. To access entanglement properties, we partition the spin by optically coupling it to an excited state $J'=J-1$, which removes a pair of qubits in a state defined by the light polarization. Starting with the well-known W and squeezed states, we extract the concurrence of qubit pairs, which quantifies their non-classical character. We also directly demonstrate entanglement between the 14- and 2-qubit subsystems via an increase in entropy upon partition. In a complementary set of experiments, we probe decoherence of a state prepared in the excited level $J'=J+1$ and interpret spontaneous emission as a loss of a qubit pair in a random state. This allows us to contrast the robustness of pairwise entanglement of the W state with the fragility of the coherence involved in a Schr\"odinger cat state. Our findings open up the possibility to engineer novel types of entangled atomic ensembles, in which entanglement occurs within each atom's electronic spin as well as between different atoms.
Don’t Touch Me! A Comparison of Usability on Touch and Non-touch Inputs

Public touchscreens are filthy and, regardless of how often they are cleaned, they pose a considerable risk in the transmission of bacteria and viruses. While we rely on their use, we should find a feasible alternative to touch devices. Non-touch (touchless) interaction, via the use of mid-air gestures, has been previously labelled as not user friendly and unsuitable. However, previous works have extensively compared such interaction to precise mouse movements. In this paper, we investigate and compare the usability of an interface controlled via a touchscreen and a non-touch device. Participants (N=22) using a touchscreen and the Leap Motion Controller, performed tasks on a mock-up ticketing machine, later evaluating their experience using the System Usability and Gesture Usability scales. Results show that, in contrast to the previous works, the non-touch method was usable and quickly learnable. We conclude with recommendations for future work on making a non-touch interface more user-friendly.
Public Software Development Activity During the Pandemic

Background The emergence of the COVID-19 pandemic has impacted all human activity, including software development. Early reports seem to indicate that the pandemic may have had a negative effect on software developers, socially and personally, but that their software development productivity may not have been negatively impacted. Aims: Early reports about the effects of the pandemic on software development focused on software developers' well-being and on their productivity as employees. We are interested in a different aspect of software development: the developers' public contributions, as seen in GitHub and Stack Overflow activities. Did the pandemic affect the developers' public contributions and, of so, in what way? Method: Considering the data from between 2017 and till 2020, we study the trends within GitHub's push, create, pull request, and release events, and within Stack Overflow's new users, posts, votes, and comments. We performed linear regressions, correlation analyses, outlier analyses, hypothesis testing, and we also contacted individual developers in order to gather qualitative insights about their unusual public contributions. Results: Our study shows that within GitHub and Stack Overflow, the onset of the pandemic (March/April 2020) is reflected in a set of outliers in developers' contributions that point to an increase in activity. The distributions of contributions during the entire year of 2020 were, in some aspects, different, but, in other aspects, similar from the recent past. Additionally, we found one noticeably disrupted pattern of contribution in Stack Overflow, namely the ratio Questions/Answers, which was much higher in 2020 than before. Testimonials from the developers we contacted were mixed: while some developers reported that their increase in activity was due to the pandemic, others reported that it was not. Conclusion: In Github, there was a noticeable increase in public software development activity in 2020, as well as more abrupt changes in daily activities; in Stack Overflow, there was a noticeable increase in new users and new questions at the onset of the pandemic, and in the ratio of Questions/Answers during 2020. The results may be attributed to the pandemic, but other factors could have come into play.
Drought Forecasting using Wavelet-GMDH Model with Standardized Precipitation Index

This paper proposes Wavelet-Group Methods of Data Handling (W-GMDH) model to explore its ability of drought forecasting. The W-GMDH model was developed by combining Discrete Wavelet Transform (DWT) and GMDH model using the Standardized Precipitation Index (SPI) drought data for forecasting to assess the effectiveness of the new (W-GMDH) model. These methods were used on four SPI data sets (SPI3, SPI6, SPI9 and SPI12). To achieve this, a 624 month of SPI data from January 1956 to December 2008 was used and divided into two parts (80% for training and 20% for testing). The results of the W-GMDH model were then compared with the conventional GMDH model using Root Mean Square Error (RMSE), Mean Average Error (MAE) and coefficient of correlation as the performance evaluation measures. Both results of the proposed W-GMDH model and the GMDH showed very clearly that the propose method can achieve the best forecasting performance in terms of accuracy for each of the SPI data series. The key role played by the DWT is to smooth the analysis of SPI data obtained after the wavelet decomposition which is also used to decompose the SPI data into different number of component series to minimize the forecasting error. In all the results computed, the proposed model has a minimum error indicating its superiority over the GMDH model. This indicates that W-GMDH model’s performance has outweighed that of the conventional GMDH model in SPI drought forecasting. The research contributes to the discovering of viable forecasting of drought and demonstrates that the established model is good and appropriate for drought. In all the analysis W-GMDH model has the minimum error. The overall results showed that SPI12 has the minimum error among all the SPI data considered.
Unpaired Image to Image Translation using Cycle Generative Adversarial Networks

In this burgeoning age and society where people are tending towards learning the benefits adversarial network we hereby benefiting the society tend to extend our research towards adversarial networks as a general-purpose solution to image-to-image translation problems. Image to image translation comes under the peripheral class of computer sciences extending our branch in the field of neural networks. We apprentice Generative adversarial networks as an optimum solution for generating Image to image translation where our motive is to learn a mapping between an input image(X) and an output image(Y) using a set of predefined pairs[4]. But it is not necessary that the paired dataset is provided to for our use and hence adversarial methods comes into existence. Further, we advance a method that is able to convert and recapture an image from a domain X to another domain Y in the absence of paired datasets. Our objective is to learn a mapping function G: A —B such that the mapping is able to distinguish the images of G(A) within the distribution of B using an adversarial loss.[1] Because this mapping is high biased, we introduce an inverse mapping function F B—A and introduce a cycle consistency loss[7]. Furthermore we wish to extend our research with various domains and involve them with neural style transfer, semantic image synthesis. Our essential commitment is to show that on a wide assortment of issues, conditional GANs produce sensible outcomes. This paper hence calls for the attention to the purpose of converting image X to image Y and we commit to the transfer learning of training dataset and optimising our code.You can find the source code for the same here.
Collaborative Local-Global Learning for Temporal Action Proposal

Temporal action proposal generation is an essential and challenging task in video understanding, which aims to locate the temporal intervals that likely contain the actions of interest. Although great progress has been made, the problem is still far from being well solved. In particular, prevalent methods can handle well only the local dependencies (i.e., short-term dependencies) among adjacent frames but are generally powerless in dealing with the global dependencies (i.e., long-term dependencies) between distant frames. To tackle this issue, we propose CLGNet, a novel Collaborative Local-Global Learning Network for temporal action proposal. The majority of CLGNet is an integration of Temporal Convolution Network and Bidirectional Long Short-Term Memory, in which Temporal Convolution Network is responsible for local dependencies while Bidirectional Long Short-Term Memory takes charge of handling the global dependencies. Furthermore, an attention mechanism called the background suppression module is designed to guide our model to focus more on the actions. Extensive experiments on two benchmark datasets, THUMOS’14 and ActivityNet-1.3, show that the proposed method can outperform state-of-the-art methods, demonstrating the strong capability of modeling the actions with varying temporal durations.
A training on the development of adaptive digital learning materials to improve teachers’ competence during the COVID-19 pandemic

The COVID-19 pandemic has altered the education order towards a virtual experience. Also, the use of digital media appears convenient for conveying learning materials and engaging students effectively. Under this circumstance, elementary school teachers commonly encounter certain difficulties using online platforms, due to poor knowledge of technology. Therefore, the objectives of this study are to: 1) strengthen educators' ability in mastering technology for distance learning, and 2) improve cooperation and reputation of the Maranatha Women's Family Association to the wider community. An asset-based community development (ABCD), using a mentoring and practical approach was adopted. This study was conducted online, with an attendance of 50 elementary school educators in Tangerang. The results showed that the participants gained significant insights and practical techniques for developing distance learning materials using power point slides to support the online learning process.
A Hybrid Ensemble Word Embedding based Classification Model for Multi-document Summarization Process on Large Multi-domain Document Sets

Contextual text feature extraction and classification play a vital role in the multi-document summarization process.Natural language processing (NLP) is one of the essential text mining tools which is used to preprocess and analyze the large document sets.Most of the conventional single document feature extraction measures are independent of contextual relationships among the different contextual feature sets for the document categorization process.Also, these conventional word embedding models such as TF-ID, ITF-ID and Glove are difficult to integrate into the multi-domain feature extraction and classification process due to a high misclassification rate and large candidate sets.To address these concerns, an advanced multi-document summarization framework was developed and tested on number of large training datasets.In this work, a hybrid multi-domain glove word embedding model, multi-document clustering and classification model were implemented to improve the multi-document summarization process for multi-domain document sets.Experimental results prove that the proposed multi-document summarization approach has improved efficiency in terms of accuracy, precision, recall, F-score and run time (ms) than the existing models.
Tensor decision trees for continual learning from drifting data streams

Data stream classification is one of the most vital areas of contemporary machine learning, as many real-life problems generate data continuously and in large volumes. However, most of research in this area focuses on vector-based representations, which are unsuitable for capturing properties of more complex multi-dimensional structures, such as images and video sequences. In this paper, we propose a novel methodology for learning adaptive decision trees from data streams of tensors. We introduce Chordal Kernel Decision Tree for continual learning from tensor data streams. In order to maintain the tensor characteristics, we propose to train and update classifiers in the kernel space designed to work with tensor representation. We use chordal distance to compute similarities between tensors and then apply it as a new feature space in which decision trees are trained. This allows for a direct decision tree induction on tensors. In order to accommodate the streaming and drifting nature of data, we propose a concept drift detection scheme based on tensor representation. It allows us to reconstruct the kernel feature space every time when change is detected. The proposed approach allows for fast and efficient induction of decision trees on streaming data with tensor representation. Experimental study, conducted on 4 real-world and 52 artificial large-scale tensor data streams, shows that using the native tensor feature space leads to more accurate classification than outperforms the vectorized representations.