Reviewerly/exHarmony huggingface dataset:
Decentralized nonconvex optimization with guaranteed privacy and accuracy

Privacy protection and nonconvexity are two challenging problems in decentralized optimization and learning involving sensitive data. Despite some recent advances addressing each of the two problems separately, no results have been reported that have theoretical guarantees on both privacy protection and saddle/maximum avoidance in decentralized nonconvex optimization. We propose a new algorithm for decentralized nonconvex optimization that can enable both rigorous differential privacy and saddle/maximum avoiding performance. The new algorithm allows the incorporation of persistent additive noise to enable rigorous differential privacy for data samples, gradients, and intermediate optimization variables without losing provable convergence, and thus circumventing the dilemma of trading accuracy for privacy in differential privacy design. More interestingly, the algorithm is theoretically proven to be able to efficiently guarantee accuracy by avoiding convergence to local maxima and saddle points, which has not been reported before in the literature on decentralized nonconvex optimization. The algorithm is efficient in both communication (it only shares one variable in each iteration) and computation (it is encryption-free), and hence is promising for large-scale nonconvex optimization and learning involving high-dimensional optimization parameters. Numerical experiments for both a decentralized estimation problem and an Independent Component Analysis (ICA) problem confirm the effectiveness of the proposed approach.
A Technical Assessment of Blockchain in Healthcare with a Focus on Big Data

New healthcare record management (HRM) systems have been introduced as technology has evolved to provide more efficient care. Since medical data is usually sensitive and must be protected from unauthorized access, attention must be paid to data integrity, patient privacy, and storage. Blockchain technology has been proposed in the literature to integrate healthcare information systems through a decentralized and unified network. However, the literature on blockchain in healthcare is full of promises that may not be true under certain conditions. In our paper, we evaluate the veracity and sophistication of some of the claims made in the literature. We go beyond performing a literature review and shed light on the weak technical aspects claimed about blockchain. In addition, we benefit from our technical assessment and suggest some future research directions to improve healthcare systems that use blockchain and big data solutions.
Improved Security of a Deep Learning-Based Steganography System with Imperceptibility Preservation

Since its inception, the steganography system (SS) has continuously evolved and is routinely used for concealing various sensitive data in an imperceptible manner. To attain high performance and a better hiding capacity of the traditional SS, it has become essential to integrate them with diverse modern algorithms, especially those related to artificial intelligence (AI) and deep learning (DL). Based on this fact, we proposed a DL-based SS (DLSS) to extract some significant features (like pixel locations, importance, and proximity to the imperceptibility) from the cover image using a neural network (NN) in a hierarchical form, thus selecting the candidate pixels for embedding afterwards. The pixel weight was expressed in terms of the position, imperceptibility, and its relationship with adjacent pixels to be a stego image. Performance evaluation revealed that the proposed DLSS achieved imperceptibility of 84 dB for images in training mode of a standard dataset.
AFEBRILE PLASMODIUM KNOWLESI SEVERE MALARIA WITH A FATAL OUTCOME

I read with great interest the article by Shah et al. on the causes of fever in adults in Thall and surrounding areas. Malaria was found to be the most common cause of fever in the study. However, clinicians need to be aware that not all malaria infection present with fever. I would like to share a fatal case of severe Plasmodium knowlesi malaria with no fever.
Evaluating geophysical logs as proxies for cyclostratigraphy in lacustrine deposits using power ratio accumulation

Using a single insensitive proxy in cyclostratigraphy may lead to misleading conclusions regarding timescale, paleoenvironment, paleoclimate and orbital behavior. How to obtain the most reliable one from multiple solutions of geophysical log proxies without time constraints is still an outstanding problem. Here, we develop a new method (power ratio accumulation, or PRA) combining power decomposition and null hypothesis of no astronomical forcing with the Monte Carlo simulation. This method obtains both optimal sedimentation rate and optimal proxy from multiple untuned proxy datasets. The sensitivity of multiple geophysical log proxies in lacustrine deposits is first revealed from member 1 of Nenjiang Fm of Cretaceous Continental Scientific Drilling in the Songliao Basin using this method. The results show that the optimal proxy is thorium content, followed by potassium content, uranium content and natural gamma-ray intensity, while total organic carbon content, resistivity, interval transit time and density are the worst. Evidence of phase reversal of proxies caused by the joint influence of astronomically forced weathering, water level and burial of biogenic materials is found. This supports a new mechanism that explains the sensitivity of geophysical log proxies from the perspective of phase stability. Our research not only provides a powerful tool (PRA method) for astrochronologic testing and sensitivity evaluation of multiple untuned proxy datasets, but also builds a bridge (new mechanism) between geophysical parameters and astronomically forced cycles. This will promote the application of geophysical data in the field of cyclostratigraphy.
High-Capacity Adaptive Steganography Based on Transform Coefficient for HEVC

HEVC video is one of the most popular carriers for steganography. The existing transform coefficient-based HEVC steganography algorithms usually modify the coefficients of the candidate blocks to prevent distortion drift. Nevertheless, the embedding capacity is relatively small due to the strict candidate block selection rule, and embedding distortion is accumulated within group of pictures (GOP). In this paper, a novel transform coefficient-based steganography for HEVC is proposed to enlarge embedding capacity and reduce visual degradation. First, the visual distortion and GOP distortion are analyzed to elaborate the embedding influence of different cover coefficients. Next, different cover coefficients are assigned different costs. Besides, the modification in non-zero coefficients of $$4\times 4$$ TUs in P-frames is explored to enhance embedding capacity. Moreover, by introducing a new evaluation indicator, it is verified the proposed algorithm can preserve less visual degradation while embedding more secret messages. Experimental results show that the proposed algorithm outperforms the competing methods in terms of visual quality, embedding capacity and anti-steganalysis performance.
Simulating the photon statistics of multimode Gaussian states by automatic differentiation of generating functions

Advances in photonics require photon-number resolved simulations of quantum optical experiments with Gaussian states. We demonstrate a simple and versatile method to simulate the photon statistics of general multimode Gaussian states. The derived generating functions enable simulations of the photon number distribution, cumulative probabilities, moments, and factorial moments of the photon statistics of Gaussian states as well as of multimode photon-added and photon-subtracted Gaussian states. Numerical results are obtained by the automatic differentiation of these generating functions by employing the software framework PyTorch. Our approach is particularly well suited for practical simulations of the photon statistics of quantum optical experiments in realistic scenarios with low photon numbers, in which various sources of imperfections have to be taken into account. As an example, we calculate the detection probabilities for a recent multipartite time-bin coding quantum key distribution setup and compare them with the corresponding experimental values.
Corruption, Environmental Sustainability and Economic Performance in Emerging Economies: Evidence from Nigeria

This study was aimed at examining the linkages between corruption, environmental sustainability, and economic performance.It was motivated by the quest to test the validity of the Environmental Kuznets Curve (EKC) hypothesis in Nigerian.The EKC hypothesis explains the nexus between economic activities and environmental degradation.Therefore, this current study investigated the interconnections between corruption, environmental sustainability and economic performance in Nigeria using the modern Autoregressive Distributed Lag Model (ARDL) approach for the period from 1981 to 2020.The study revealed that corruption has negative and statistically significant effects on environmental quality and Nigeria's economic performance.In addition, the study showed that corruption has direct and indirect effects on environmental quality and asserted that corruption raises the level of C02 emissions and worsens the overall quality of environment, these empirical finding are in tandem with the postulation of the Environment Kuznets Curve (EKC) hypothesis.This shows that economic performance responds to the changes in corruption and carbon emission in the long-run in a negative and statistically significant manner.Therefore, the concerned regulatory agencies must be strengthened to firmly address violators of environmental regulations and enhance energy efficiency.
Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture

Classification on corn plants is used to classify leaf of corn plants that are healthy and have diseases consisting of Northern Leaf Blight, Common Rust and Gray Leaf Spot. Convolutional Neural Network (CNN) is one of algorithms from the branch of deep learning that utilizes artificial neural networks to produce accurate results in classifying an image. In this study, ResNet-9 architecture implemented to build the best model CNN for classification corn plant diseases. After that we doing comparisons of epochs have been carried out to obtain the best model, including comparisons of epochs of 5, 25, 55, 75 and 100. After the epoch comparison, the highest accuracy value was obtained in the 100 epoch experiment so that in this study 100 epochs were used in model formation. The number of datasets used is 9145 data which is divided into two, there are training data (80%) and testing data (20%). In this study, three hyperparameter tuning experiments were carried out and the results of hyperparameter tuning experiments where num_workers is 4 and batch_size is 32. This classification obtained an accuracy rate of 99% and the model is implemented into a web interface.
Approaching Taboos and Controversial Issues in Foreign Language Education

Taboos are not a new phenomenon. Yet, taboos change over time as social customs change, discard old taboos, and create new ones. What does not change, however, is how taboos regulate the way in which we live together in different communities and how they influence our behaviours. Notwithstanding the ubiquity of all sorts of taboos in daily life, many of them do not seem to find their way into foreign language classrooms easily. This is particularly surprising if we consider the growing importance of critical language pedagogy approaches to foreign language learning. Despite relevant efforts there, teachers, pre-service teachers, and learners often appear to feel discomfort as conversations about controversial issues such as mental illnesses, disabilities, racism, conspiracy theories, violence, and gender unfold. Consequently, classroom materials as well as suitable approaches to these issues in foreign language learning are needed. Taking this as a starting point, this introductory chapter argues for the integration of taboos and controversial topics into the foreign language classroom. It first investigates the concept of critical pedagogy in general and critical (foreign) language pedagogy more specifically. It then discusses taboos in the context of critical language pedagogy, arguing that despite numerous challenges, the foreign language classroom provides the perfect space for discussing some controversial topics with students—primarily by exploring a variety of different texts dealing with taboos instead of personalising a topic, which would be more common in communicative approaches. The chapter closes with a practical framework that provides suggestions for handling taboos in foreign language education.
Artificial intelligence in breast pathology – dawn of a new era

Artificial intelligence methods are been increasingly used for analysis of pathology slides. In this issue of the Journal, Sandbank et al. describe the validation and utility of a robust second reader system that can distinguish in situ and standfirst invasive carcinomas from non-neoplastic lesions of the breast.
On the Fitness Functions Involved in Genetic Algorithms and the Cryptanalysis of Block Ciphers

There are many algorithms used with different purposes in the area of cryptography. Amongst these, Genetic Algorithms have been used, particularly in the cryptanalysis of block ciphers. Interest in the use of and research on such algorithms has increased lately, with a special focus on the analysis and improvement of the properties and characteristics of these algorithms. In this way, the present work focuses on studying the fitness functions involved in Genetic Algorithms. First, a methodology was proposed to verify that the closeness to 1 of some fitness functions’ values that use decimal distance implies decimal closeness to the key. On the other hand, the foundation of a theory is developed in order to characterize such fitness functions and determine, a priori, if one method is more effective than another in the attack to block ciphers using Genetic Algorithms.
Point Cloud Deep Learning Solution for Hand Gesture Recognition

In the last couple of years, there has been an increasing need for Human-Computer Interaction (HCI) systems that do not require touching the devices to control them, such as ATMs, self service kiosks in airports, terminals in public offices, among others.The use of hand gestures offers a natural alternative to achieve control without touching the devices.This paper presents a solution that allows the recognition of hand gestures by analyzing three-dimensional landmarks using deep learning.These landmarks are extracted by using a model created with machine learning techniques from a single standard RGB camera in order to define the skeleton of the hand with 21 landmarks distributed as follows: one on the wrist and four on each finger.This study proposes a deep neural network that was trained with 9 gestures receiving as input the 21 points of the hand.One of the main contributions, that considerably improves the performance, is a first layer of normalization and transformation of the landmarks.In our experimental analysis, we reach an accuracy of 99.87% recognizing of 9 hand gestures.
Feature relocation network for fine-grained image classification

In fine-grained image classification, there are only very subtle differences between classes. It is challenging to learn local discriminative features and remove distractive features in fine-grained image classification. Existing fine-grained image classification methods learn discriminative feature mainly via manual part annotation or attention mechanisms. However, due to the large intraclass variance and interclass similarity, the discriminative information and distractive information still are not distinguished effectively. To address this problem, we propose a feature relocation network (FRe-Net) which takes advantage of the different natures of features learned from different stages of the network. Our network consists of a distractive feature learning module and a relocated high-level feature learning module. In the distractive feature learning module, we propose to exploit the difference between low-level features and high-level features to design a distractive loss Ldistractive, which guides the attention to locate distractive regions more accurately. In the relocated high-level feature learning module, we enhance the representing capacity of the middle-level feature via the attention module and subtract the distractive feature learned from the distractive feature learning module in order to learn more local discriminative features. In end-to-end model training, the distractive feature learning module and the relocated high-level feature learning module are beneficial to each other via joint optimization. We conducted comprehensive experiments on three benchmark datasets widely used in fine-grained image classification. The experimental results show that FRe-Net achieves state-of-the-art performance, which validates the effectiveness of FRe-Net.
Towards Knowledge-based Generation of Synthetic Data by Taxonomizing Expert Knowledge in Production

Synthetic data is a promising approach for industrial computer vision because it can enable highly autonomous production processes. However, this potential is not fulfilled by current software for synthetic data generation, which usually requires a programmer to create new datasets. To overcome this, we are proposing a framework for more autonomous synthetic data generation, formalizing user roles relevant to such systems. A central aspect of our framework is that domain experts can easily influence the generation of synthetic data by entering knowledge via user interfaces. To get a better idea of what such knowledge could be, we have systematically collected examples of knowledge types for synthetic data generation in production and combined them into a taxonomy with almost 300 nodes. Using this taxonomy as the basis for analyses, we derive six implications for our framework, such as knowledge being not only passed on by domain experts but also by the designer of the user interfaces and generation algorithms. We plan to incorporate these findings to further refine and implement our framework in future research.
Quantum codes from constacyclic codes over $S_{k}$

Abstract Let $S_{k}={\mathbb{F}}_{q}[u_{1},u_{2},\ldots ,u_{k}]/\langle u^{3}_{i}=u_{i},u_{i}u_{j}=u_{j}u_{i}=0 \rangle $ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>S</mml:mi> <mml:mi>k</mml:mi> </mml:msub> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>q</mml:mi> </mml:msub> <mml:mo>[</mml:mo> <mml:msub> <mml:mi>u</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>u</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>u</mml:mi> <mml:mi>k</mml:mi> </mml:msub> <mml:mo>]</mml:mo> <mml:mo>/</mml:mo> <mml:mo>〈</mml:mo> <mml:msubsup> <mml:mi>u</mml:mi> <mml:mi>i</mml:mi> <mml:mn>3</mml:mn> </mml:msubsup> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>u</mml:mi> <mml:mi>i</mml:mi> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>u</mml:mi> <mml:mi>i</mml:mi> </mml:msub> <mml:msub> <mml:mi>u</mml:mi> <mml:mi>j</mml:mi> </mml:msub> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>u</mml:mi> <mml:mi>j</mml:mi> </mml:msub> <mml:msub> <mml:mi>u</mml:mi> <mml:mi>i</mml:mi> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> <mml:mo>〉</mml:mo> </mml:math> , where $1\leq i,j\leq k$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mn>1</mml:mn> <mml:mo>≤</mml:mo> <mml:mi>i</mml:mi> <mml:mo>,</mml:mo> <mml:mi>j</mml:mi> <mml:mo>≤</mml:mo> <mml:mi>k</mml:mi> </mml:math> , $q=p^{m}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>q</mml:mi> <mml:mo>=</mml:mo> <mml:msup> <mml:mi>p</mml:mi> <mml:mi>m</mml:mi> </mml:msup> </mml:math> , p is an odd prime. First, we define two new Gray maps $\phi _{k}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ϕ</mml:mi> <mml:mi>k</mml:mi> </mml:msub> </mml:math> and $\varphi _{k}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>φ</mml:mi> <mml:mi>k</mml:mi> </mml:msub> </mml:math> , and study their Gray images. Further, we determine the structure of constacyclic codes and their dual codes, and give a necessary and sufficient conditions of constacyclic codes to contain their duals. Finally, we obtain some new quantum codes over $\mathbb{F}_{q}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>q</mml:mi> </mml:msub> </mml:math> by using CSS construction, and compare the constructed codes better than the existing literature.
Learning Attention Propagation for Compositional Zero-Shot Learning

Compositional zero-shot learning aims to recognize unseen compositions of seen visual primitives of object classes and their states. While all primitives (states and objects) are observable during training in some combination, their complex interaction makes this task especially hard. For example, wet changes the visual appearance of a dog very differently from a bicycle. Furthermore, we argue that relationships between compositions go beyond shared states or objects. A cluttered office can contain a busy table; even though these compositions don’t share a state or object, the presence of a busy table can guide the presence of a cluttered office. We propose a novel method called Compositional Attention Propagated Embedding (CAPE) as a solution. The key intuition to our method is that a rich dependency structure exists between compositions arising from complex interactions of primitives in addition to other dependencies between compositions. CAPE learns to identify this structure and propagates knowledge between them to learn class embedding for all seen and unseen compositions. In the challenging generalized compositional zero-shot setting, we show that our method outperforms previous baselines to set a new state-of-the-art on three publicly available benchmarks.
CameraPose: Weakly-Supervised Monocular 3D Human Pose Estimation by Leveraging In-the-wild 2D Annotations

To improve the generalization of 3D human pose estimators, many existing deep learning based models focus on adding different augmentations to training poses. However, data augmentation techniques are limited to the "seen" pose combinations and hard to infer poses with rare "unseen" joint positions. To address this problem, we present CameraPose, a weakly-supervised framework for 3D human pose estimation from a single image, which can not only be applied on 2D-3D pose pairs but also on 2D alone annotations. By adding a camera parameter branch, any in-the-wild 2D annotations can be fed into our pipeline to boost the training diversity and the 3D poses can be implicitly learned by reprojecting back to 2D. Moreover, CameraPose introduces a refinement network module with confidence-guided loss to further improve the quality of noisy 2D keypoints extracted by 2D pose estimators. Experimental results demonstrate that the CameraPose brings in clear improvements on cross-scenario datasets. Notably, it outperforms the baseline method by 3mm on the most challenging dataset 3DPW. In addition, by combining our proposed refinement network module with existing 3D pose estimators, their performance can be improved in cross-scenario evaluation.