Reviewerly/exHarmony huggingface dataset:
Graph loss function for unsupervised learning-based deformable medical image registration

Establishing accurate spatial correspondences is the main purpose for deformable medical image registration. Although many unsupervised learning-based methods have been proposed in this field and achieved fairly good results, because of their strong feature extraction ability and no need of the ground truth, they are often limited by relying on similarity of the spatially adjacent pixels which could not fully utilize geometrical feature for robust registration. To address this limitation, we propose a new graph loss function to represent the non-adjacent geometrical similarity. We divide the algorithm into two branches. The first branch takes pairs of medical images as input directly and obtains the loss term <sup>L</sup>CNN by typical convolution operation. In the second branch, we convert the images to forms of graph represents, and then obtain the loss term <sup>L</sup>GCN by graph convolution operation. Finally, the sum of the two loss terms constitute the total loss function. We verify our method on two datasets including LPBA40 and ADNI, and the experimental results demonstrate a marked improvement, with higher average Dice and lower registration errors of MSE compared with state-of-the-art methods.
Wireless Communications in the New Decade

For many of us, 2020 has been a lost year in some ways. On top of an enormous loss of human lives, the pandemic paused much of our progress on long-planned family and career goals. It forced international conferences and holiday trips to either be changed to virtual events or to be cancelled. It warped many people's sense of time, causing months-long stretches to seem interminable in the moment but as if they passed in a blip in retrospect. Nonetheless, IEEE Wireless Communications has made tremendous achievements again thanks to the great contributions of readers, authors, reviewers, editors, and the publications staff.
THE NEED OF CHANGES IN BULGARIA’S HIGHER EDUCATION

This work is an attempt for a brief analysis of the new ―Strategy for the development of higher education in the Republic of Bulgaria for 2021 - 2030 and is searching for an answer to the new challenges facing the education system. Searching and finding the right solutions in this direction does not only require specific skills and competencies, but also sufficient experience, information and knowledge in this direction. This challenge cannot and should not be the action of just one person, but of a group of scientists, researchers and politicians with a sufficiently high level of expertise that would contribute to the development of an objective and accurate action plan.
Identifikasi Kebutuhan Masyarakat Nusa Tenggara Barat pada Pandemi Covid-19 di Media Sosial dengan Metode Crawling

People can write to the government about what they want through social media in the freedom of information era. Especially in the Covid-19 pandemic, the public's desire for government attention is getting higher. The purpose of this activity is to collect community needs at the beginning of the pandemic. The period of information retrieval is from March 30 to April 5, 2020. The activity uses the crawling method, which is gathering information with browser tools. The implementation stage starts from taking information with selenium, identifying posts and comments with Html tags, calculating the emergence of issues with N-gram NLP, and analyzing sentiment with Naive Bayes and Support-Vector Machine. The results showed that the community did not have panic to need foods with a total reaction of 172. The community was more worried about the spread of outbreaks from outside NTB with a reaction of 1,421.
Effect of negative campaign strategy of election algorithm in solving optimization problem

Election algorithm (EA) is an optimization technique based on minimization and coalitionoperations to solve competition among neurons. The Election algorithm gives the best individualof the population by enhancing both minimization and coalition operations while local searchgives the best local solutions by testing all neighbouring solutions. Negative campaignmechanism is one of the most important mechanism in EA for its impact on the diversificationand overcoming premature convergence of the entire search space towards optimal searching.The challenging task lies in selecting the appropriate negative campaigning operator that leadsto optimal searching in a reasonable amount of time. The decision then becomes more difficultand needs more trial and error to find the best negative campaigning operator. This paperinvestigates the effect of negative campaign operators in enhancing the performance of EAbased on the Travelling Salesman Problem (TSP). New negative campaign operator has beenproposed based on selecting the best voter to be replaced. Experiments were conducted on theTSP to evaluate the proposed methods. The proposed mechanism was compared with othernegative campaign operators. The result reveals the significant enhancement of the EAperformance based on the proposed method in TSP problem.
EFFICACY OF STATE INTERVENTION AND PUBLIC RESPONSE IN THE CONTAINMENT OF COVID-19 : A NOTE ON AAROGYA SETU

It is indeed well established that, the smartphone holds great potential to play a role in personalized healthcare and also aid in the following factors: good decision making, accurate diagnosis, patient compliance, effective monitoring of the condition and accurate interpretation of clinical results. It is all the most important in the present health crisis of a magnitude which is beyond our imagination. A host of measures are being taken by the local, regional, state and global level institutions of all types to find out an escape route from the Corona Virus and its unyielding spread. Among the several types of communication between the public and state: between the healthcare service providers and the corona patients : between the persons who are affected (both symptomatic and asymptomatic) and those who are having potential to be affected : and between the state machinery of all types and the target population, one effective tool is the mobile apps. Notwithstanding the popularity and rapidly increasing number of mobile health applications for the self-care of consumers / patients there is a proper need for research into consumer engagement with electronic self-monitoring. The introduction of Arogya Setu‟ by the Central Government in India has become the increasingly powerful smart phone and assumed as a useful aid in preventing the spread of the corona virus among the general public in India. To examine its efficacy, at the micro level a study has been done by a combination of online interview as well as personal interview with the two sets of people selected : one in a cosmopolitan city of Chennai (State Capital) and a remote village (Odayagoundenpalayam in Erode district). This helped to present a qualitative exploration of how the general public in Chennai and in the remote village perceived the uses of Arogya Setu‟ app for preventing corona virus spreading in homes and communities. The contrasting perception of the benefits from the use of Arogya Setu app, has been found as highly visible. On the basis of the findings a few suggestions for the improvement of this health app are put forward. Further, a brief discussion has been made on how the mobile health (mHealth) apps could be distributed in the near future, as well as, the impact of government regulations with respect to mHealth apps in the years to come. The conclusion highlights the challenges and opportunities for app developers in the mHealth industry in India.
Anti-Noise Relation Network for Few-shot Learning

Few-shot classification has received great attention in the field of machine learning and computer vision. It aims is to achieve the learning ability close to human recognition by training from a few labelled samples. The existing few-shot classification methods have attempted to alleviate the impact of insufficient samples in a variety of ways, such as meta-learning and metric learning, but they ignore the noise robustness. This work proposes a new Anti-Noise Relation Network by embedding an autoencoder network into a classical neural network of fewshot classification, Relation Network. Experimental results on the Stanford Car and CUB-200-2011 datasets demonstrate the superiority of the proposed method in both classification accuracy and robustness against different noises.
Implementation of Boolean Functions

The main task of classical computations is the computation of functions defined in analytical or tabular form. Let us start with Boolean functions that depend on several arguments. The implementation of Boolean functions with a quantum computer is based on the construction of a quantum circuit that depends on the kind \(f(\mathbf {x})\). We represent the given function using only operations of conjunctions and addition modulo two. This representation is called algebraic normal form (ANF), or Reed–Muller expansion, or Zhegalkin polynomial. The algebraic normal form of the function \(f(\mathbf {x})\) is the sum modulo two of several elementary conjunctions of the form $$G=K_1\oplus K_2\oplus \ldots \oplus K_s,$$where \(K_i\), \(i=1,2,\dots ,s\) are pairwise different monotone elementary conjunctions over some set of variables \(\{x_1\), ..., \(x_n\}\), where \(n=1,2,\dots \). One of the \(K_i\) can be a constant one. The greatest of ranks of elementary conjunctions included in the polynomial G is called a degree of the function. It is known that any Boolean function is uniquely represented as a Reed–Muller decomposition accurate to the order of summands in the sum and the order of cofactors in the conjunctions [].
#FakeNews

&#x0D; &#x0D; &#x0D; Misinformation in the form of “fake news” can potentially be weaponized by malicious actors to undermine Canada’s national security and government infrastructure. Developing a comprehensive database to track and understand potential threat actors and their use of fake news can potentially provide actionable intel, thereby exposing and publicly challenging fake news items. Fake news has been used to negatively influence the reputation of government officials and to incite violence between ethnic groups. Fake news utilizes confirmation bias (the tendency to interpret new evidence as confirmation of one's existing beliefs or theories) through disseminating meticulously crafted messages to targeted audiences, who are selected based on their online activities.&#x0D; &#x0D; &#x0D;
Reunited? On the Aesthetics and Rhetoric of Meeting the Dead Through Virtual Reality

Even though death creates irremediable distance between the living and the departed, human beings have been struggling since ages to bring the absent once again present through any form of available media. In all of the multifarious attempts at making the dead seem as if alive, a distance of the living from the departed was nevertheless retained in that the deceased was not really present, but only ‘presentified’ through a clearly perceivable medium. Yet nowadays, virtual reality promises to finally bridge the gap and make the living cross the border of the afterworld. By focusing on the paradigmatic case study of a South Korean mother who in February 2020 ‘met’ her dead daughter through a VR simulation, this essay takes into account both the visual and the linguistic strategies used to convey the idea of a direct, non-mediated ‘encounter’ or ‘reunion’ between the two. The overall objective of the article is to show that in immersive virtual environments, despite all rhetoric, the dialectic between proximity and distance which is common to the traditional notions of both the dead and the image is not only still present, but also greater than ever before.
Facial Expression Recognition and Gender Detection Using CNN

The very popular method for Facial Recognition and Classification is Deep Learning. There are different types of deep learning algorithms such as Deep Belief Network (DBN) and Convolutional Neural Network (CNN). In this paper we present a new architecture network based on CNN for building a facial recognizer model which detects the emotion and gender of a person in real-time and classify accordingly. We will be using Convolution Neural Network to implement this model which is the best algorithm for Facial Emotion Recognition (FER) and Gender Detection. To evaluate our network architecture we will test the model with large databases that are publicly available. Obtained results show that the CNN approach is an effective expression and gender detection model.
Intriguing class-wise properties of adversarial training

Adversarial training is one of the most effective approaches to improve model robustness against adversarial examples. However, previous works mainly focus on the overall robustness of the model, and the in-depth analysis on the role of each class involved in adversarial training is still missing. In this paper, we provide the first detailed class-wise diagnosis of adversarial training on six widely used datasets, i.e., MNIST, CIFAR-10, CIFAR-100, SVHN, STL-10 and ImageNet. Surprisingly, we find that there are remarkable robustness discrepancies among classes, demonstrating the following intriguing properties: 1) Many examples from a certain class could only be maliciously attacked to some specific semantic-similar classes, and these examples will not exist adversarial counterparts in bounded ϵ-ball if we re-train the model without those specific classes; 2) The robustness of each class is positively correlated with its norm of classifier weight in deep neural networks; 3) Stronger attacks are usually more powerful for vulnerable classes. Finally, we propose an attack to better understand the defense mechanism of some state-of-the-art models from the class-wise perspective. We believe these findings can contribute to a more comprehensive understanding of adversarial training as well as further improvement of adversarial robustness.
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models

Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO). In this setting, variational posteriors are often only partially conditioned. While the true posteriors depend, e.g., on the entire sequence of observations, approximate posteriors are only informed by past observations. This mimics the Bayesian filter---a mixture of smoothing posteriors. Yet, we show that the ELBO objective forces partially--conditioned amortised posteriors to approximate products of smoothing posteriors instead. Consequently, the learned generative model is compromised. We demonstrate these theoretical findings in three scenarios: traffic flow, handwritten digits, and aerial vehicle dynamics. Using fully-conditioned approximate posteriors, performance improves in terms of generative modelling and multi-step prediction.
Rhetorical Analysis and Classification of Poem Text

Poetry is one of the richest forms of literature, which in itself includes all components of language a human learns; by components here, the context is towards the rhetorical devices. The rhetorical devices constitute the witty use of words used in the reference to things. The work intends to identify the forms of creative references used by the poets to contrast their style of writing and categorize the text on the basis of the same. On the basis of each such prominent device such as rhymes or alliteration, one can derive the boundary or similarity percentage amongst the poems, which can be further extended to compare the writing style of the poets. The method of analysis holds a good value to study different poets of the modern and renaissance era and could be helpful in contrasting their way of putting things into words. Keywords NLP Analysis of Poem, Poem Analysis, Poem Classification, Poem Comparison, Poem Qualifiers, Poet Classification, Poetry Analysis, Poetry Recommendation System
Adversarial Post-Processing of Voice Conversion against Spoofing Detection

With the development of speech synthesis and voice conversion techniques, the anti-spoofing task that detects artificial speech signals has received more and more research attentions recently. State-of-the-art spoofing detectors can distinguish the utterances generated by voice conversion from natural ones with high accuracy. This paper proposes a method that improves the ability of voice conversion models against spoofing detection by post-processing the converted speech using a neural network. The network is built using long short-term memories (LSTM) and trained by reducing the distance between the linear frequency cepstrum coefficients (LFCC) of converted utterances and natural references. In our experiments, the SAS dataset was adopted to construct the anti-spoofing system, and the VCTK dataset was used to build voice conversion models. Experimental results show that our proposed method can reduce the detection rate of the anti-spoofing system significantly without losing subjective performance of converted speech.
R-LAtte: Attention Module for Visual Control via Reinforcement Learning

Attention mechanisms are generic inductive biases that have played a critical role in improving the state-of-the-art in supervised learning, unsupervised pre-training and generative modeling for multiple domains including vision, language and speech. However, they remain relatively under-explored for neural network architectures typically used in reinforcement learning (RL) from high dimensional inputs such as pixels. In this paper, we propose and study the effectiveness of augmenting a simple attention module in the convolutional encoder of an RL agent. Through experiments on the widely benchmarked DeepMind Control Suite environments, we demonstrate that our proposed module can (i) extract interpretable task-relevant information such as agent locations and movements without the need for data augmentations or contrastive losses; (ii) significantly improve the sample-efficiency and final performance of the agents. We hope our simple and effective approach will serve as a strong baseline for future research incorporating attention mechanisms in reinforcement learning and control.
Deep learning-based 3D image generation using a single 2D projection image

Due to the inter-fraction and intra-fraction variation of respiratory motion, it is highly desired to provide real-time volumetric images during the treatment delivery of lung stereotactic body radiation therapy (SBRT) for accurate and active motion management. Motivated by this need, in this study we propose a novel generative adversarial network integrated with perceptual supervision to derive instantaneous 3D image from a single 2D kV projection. Our proposed network, named TransNet, consists of three modules, i.e., encoding, transformation, and decoding modules. Rather than only using image distance loss between the generated 3D image and the ground truth 3D CT image to supervise the network, perceptual loss in feature space is integrated into loss function to force the TransNet to yield accurate lung boundary. Adversarial loss is also used to improve the realism of the generated 3D image. We conducted a simulation study on 20 patient cases, who had undergone 4D-CT scan and received lung SBRT treatments in our institution, and evaluated the efficacy and consistency of our method at four different projection angles, i.e., 0&deg;, 30&deg;, 60&deg; and 90&deg;. For each 3D CT image of a breathing phase in the 4D CT image set, we simulated its 2D projections at these two angles. Then for each projection angle, a patient’s 3D CT images of 9 phases and the corresponding 2D projection data were used for training, with the remaining phase used for testing. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) achieved by our method are 99.5&plusmn;13.7HU, 23.4&plusmn;2.3dB and 0.949&plusmn;0.010, respectively. These results demonstrate the feasibility and efficacy of our method on generating a 3D image from single 2D projection, which provides a potential solution for in-treatment real-time on-board volumetric imaging to guide treatment delivery and ensure the effectiveness of lung SBRT treatment.
PGPS : Coupling Policy Gradient with Population-based Search

Gradient-based policy search algorithms (such as PPO, SAC or TD3) in deep reinforcement learning (DRL) have shown successful results on a range of challenging control tasks. However, they often suffer from flat or deceptive gradient problems. As an alternative to policy gradient methods, population-based evolutionary approaches have been applied to DRL. While population-based search algorithms show more robust learning in a broader range of tasks, they are usually inefficient in the use of samples. Recently, reported are a few attempts (such as CEMRL) to combine gradient with a population in searching optimal policy. This kind of hybrid algorithm takes advantage of both camps. In this paper, we propose yet another hybrid algorithm, which more tightly couples policy gradient with the population-based search. More specifically, we use the Cross-Entropy Method (CEM) for population-based search and Twin Delayed Deep Deterministic Policy Gradient (TD3) for policy gradient. In the proposed algorithm called Coupling Policy Gradient with Population-based Search (PGPS), a single TD3 agent, which learns by a gradient from all experiences generated by population, leads a population by providing its critic function Q as a surrogate to select better performing next-generation population from candidates. On the other hand, if the TD3 agent falls behind the CEM population, then the TD3 agent is updated toward the elite member of the CEM population using loss function augmented with the distance between the TD3 and the CEM elite. Experiments in a MuJoCo environment show that PGPS is robust to deceptive gradient and also outperforms the state-of-the-art algorithms.
Design and Performance of Solar Tracking System with Fuzzy Logic Controller

This work presents the design and construction of PV tracking system to improve the efficiency of solar output power. Here, single axis solar tracking technique is implemented to track the azimuth angle which enables the movement of solar panel in according to the sun’s illumination. FLC topology is utilised to control the position of a panel. Simulation has been made through Matlab/Simulink. The obtained results reveal that the proposed solar tracking improves the efficiency of PV array output power comparatively than a fixed position.Keywords- Solar tracking system, Fuzzy controller, Azimuth angle.
A Hybrid Lemmatiser For Old Church Slavonic

The article considers a lemmatiser that is developed specifically for Old Church Slavonic (OCS). The introduction underlines the problem of the lack of lemmatisers that might deal with different datasets of the OCS. The review gives a short description of previous attempts and current trends in lemmatisation. The lemmatiser is hybrid-based and uses the advantages of linguistic rules for specific cases (fragmentary tokens, punctuation, or digits), a dictionary for the most common tokens, and a sequence-to-sequence (seq2seq) neural network with an attention mechanism for the rest of material. The model achieves an 85% overall accuracy score, which is lower than one of the previous models for the Universal Dependencies(UD) dataset. However, when specific tokens are taken into consideration, the model outperforms the previous ones with the help of its rule-based part. Possible further directions of the research include the use of more sophisticated architectures, such as BART.