Reviewerly/exHarmony huggingface dataset:
(B)LOCKBOX -- Secure Software Architecture with Blockchain Verification

According to experts, one third of all IT vulnerabilities today are due to inadequate software verification. Internal program processes are not sufficiently secured against manipulation by attackers, especially if access has been gained. There is a lack of internal control instances that can monitor and control program flows. Especially when a software vulnerability becomes known, quick action is required, whereby the consequences for an individual application are often not foreseeable. With our approach (B)LOCKBOX, software building blocks act as verified entities within a transaction-based blockchain network. Source Code, binaries and application execution become supervised. Unwanted interference and manipulation are prevented by the integrity of the distributed system.
Efficient Generalization Improvement Guided by Random Weight Perturbation

To fully uncover the great potential of deep neural networks (DNNs), various learning algorithms have been developed to improve the model's generalization ability. Recently, sharpness-aware minimization (SAM) establishes a generic scheme for generalization improvements by minimizing the sharpness measure within a small neighborhood and achieves state-of-the-art performance. However, SAM requires two consecutive gradient evaluations for solving the min-max problem and inevitably doubles the training time. In this paper, we resort to filter-wise random weight perturbations (RWP) to decouple the nested gradients in SAM. Different from the small adversarial perturbations in SAM, RWP is softer and allows a much larger magnitude of perturbations. Specifically, we jointly optimize the loss function with random perturbations and the original loss function: the former guides the network towards a wider flat region while the latter helps recover the necessary local information. These two loss terms are complementary to each other and mutually independent. Hence, the corresponding gradients can be efficiently computed in parallel, enabling nearly the same training speed as regular training. As a result, we achieve very competitive performance on CIFAR and remarkably better performance on ImageNet (e.g. $\mathbf{ +1.1\%}$) compared with SAM, but always require half of the training time. The code is released at https://github.com/nblt/RWP.
Self-Adaptive, Dynamic, Integrated Statistical and Information Theory Learning

The paper analyses and serves with a positioning of various error measures applied in neural network training and identifies that there is no best of measure, although there is a set of measures with changing superiorities in different learning situations. An outstanding, remarkable measure called $E_{Exp}$ published by Silva and his research partners represents a research direction to combine more measures successfully with fixed importance weighting during learning. The main idea of the paper is to go far beyond and to integrate this relative importance into the neural network training algorithm(s) realized through a novel error measure called $E_{ExpAbs}$. This approach is included into the Levenberg-Marquardt training algorithm, so, a novel version of it is also introduced, resulting a self-adaptive, dynamic learning algorithm. This dynamism does not has positive effects on the resulted model accuracy only, but also on the training process itself. The described comprehensive algorithm tests proved that the proposed, novel algorithm integrates dynamically the two big worlds of statistics and information theory that is the key novelty of the paper.
2CET-GAN: Pixel-Level GAN Model for Human Facial Expression Transfer

Recent studies have used GAN to transfer expressions between human faces. However, existing models have many flaws: relying on emotion labels, lacking continuous expressions, and failing to capture the expression details. To address these limitations, we propose a novel CycleGAN- and InfoGAN-based network called 2 Cycles Expression Transfer GAN (2CET-GAN), which can learn continuous expression transfer without using emotion labels. The experiment shows our network can generate diverse and high-quality expressions and can generalize to unknown identities. To the best of our knowledge, we are among the first to successfully use an unsupervised approach to disentangle expression representation from identities at the pixel level.
IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning

Smart sensors, devices and systems deployed in smart cities have brought improved physical protections to their citizens. Enhanced crime prevention, and fire and life safety protection are achieved through these technologies that perform motion detection, threat and actors profiling, and real-time alerts. However, an important requirement in these increasingly prevalent deployments is the preservation of privacy and enforcement of protection of personal identifiable information. Thus, strong encryption and anonymization techniques should be applied to the collected data. In this IEEE Big Data Cup 2022 challenge, different masking, encoding and homomorphic encryption techniques were applied to the images to protect the privacy of their contents. Participants are required to develop detection solutions to perform privacy preserving matching of these images. In this paper, we describe our solution which is based on state-of-the-art deep convolutional neural networks and various data augmentation techniques. Our solution achieved 1st place at the IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images Challenge.
Title Pending 86

Identity and Access is a complex topic covering a wide range of topics and sub-areas. Getting a grasp on this is difficult for anyone; understanding the subject well enough to explain it to others and collaborate is even more challenging. A natural starting point for understanding a complex topic or area is to seek to learn the native language, such as the professional jargon of the area. And many of us start precisely that way, by searching out dictionaries containing words and terms of “IAM” and its sub-components.There are many such dictionaries to be found. And the subtle—and not-so-subtle—differences in the definitions found in these illustrate the challenge this article aims to alleviate. We may have a de facto “language of Identity and Access,” but this language has no formal structure to its semantics.Our area of technology and business is characterized by an abundance of words having multiple meanings. These are confusing or frustrating for beginners and experienced practitioners alike. This article is intended as a guide for keeping track of ambiguous words and terms often encountered in our industry, as well as to show that this deficiency in understanding may be even more significant in Identity and Access than in other places.The article offers examples of where terms are ambiguous and definitions seem to vary across the industry. These examples serve as an aid both to lessen confusion and encourage better and clearer usage of the terms. This article also discusses reasons for the differences and offers some suggestions on countering this challenge in the line of work.
Recommending Related Products Using Graph Neural Networks in Directed Graphs

Related product recommendation (RPR) is pivotal to the success of any e-commerce service. In this paper, we deal with the problem of recommending related products i.e., given a query product, we would like to suggest top-k products that have high likelihood to be bought together with it. Our problem implicitly assumes asymmetry i.e., for a phone, we would like to recommend a suitable phone case, but for a phone case, it may not be apt to recommend a phone because customers typically would purchase a phone case only while owning a phone. We also do not limit ourselves to complementary or substitute product recommendation. For example, for a specific night wear t-shirt, we can suggest similar t-shirts as well as track pants. So, the notion of relatedness is subjective to the query product and dependent on customer preferences. Further, various factors such as product price, availability lead to presence of selection bias in the historical purchase data, that needs to be controlled for while training related product recommendations model. These challenges are orthogonal to each other deeming our problem nontrivial. To address these, we propose DAEMON, a novel Graph Neural Network (GNN) based framework for related product recommendation, wherein the problem is formulated as a node recommendation task on a directed product graph. In order to capture product asymmetry, we employ an asymmetric loss function and learn dual embeddings for each product, by appropriately aggregating features from its neighborhood. DAEMON leverages multi-modal data sources such as catalog metadata, browse behavioral logs to mitigate selection bias and generate recommendations for cold-start products. Extensive offline experiments show that DAEMON outperforms state-of-the-art baselines by 30-160% in terms of HitRate and MRR for the node recommendation task.
Dialogs Re-enacted Across Languages

To support machine learning of cross-language prosodic mappings and other ways to improve speech-to-speech translation, we present a protocol for collecting closely matched pairs of utterances across languages, a description of the resulting data collection and its public release, and some observations and musings. This report is intended for: people using this corpus, people extending this corpus, and people designing similar collections of bilingual dialog data.
DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder

Federated learning (FL) is getting increased attention for processing sensitive, distributed datasets common to domains such as healthcare. Instead of directly training classification models on these datasets, recent works have considered training data generators capable of synthesising a new dataset which is not protected by any privacy restrictions. Thus, the synthetic data can be made available to anyone, which enables further evaluation of machine learning architectures and research questions off-site. As an additional layer of privacy-preservation, differential privacy can be introduced into the training process. We propose DPD-fVAE, a federated Variational Autoencoder with Differentially-Private Decoder, to synthesise a new, labelled dataset for subsequent machine learning tasks. By synchronising only the decoder component with FL, we can reduce the privacy cost per epoch and thus enable better data generators. In our evaluation on MNIST, Fashion-MNIST and CelebA, we show the benefits of DPD-fVAE and report competitive performance to related work in terms of Fr\'echet Inception Distance and accuracy of classifiers trained on the synthesised dataset.
Probing the Non-exponential Decay Regime in Open Quantum Systems

The most important law of radioactivity is that of the exponential decay. In the realm of quantum mechanics, however, this decay law is neither rigorous nor fundamental. The deviations from the exponential decay have been observed experimentally at the early stage of a decay process, but there is little evidence for non-exponential behavior at long times. Yet such long-term non-exponentiality is expected theoretically to probe the non-resonant background components of the initial wave function which preserve the structural interference and the memory of how the state was created. In this paper, we propose new observables that can be used for experimental investigations of the post-exponential decay regime, including the decay of threshold resonances, particle correlations in three-body decays, and interference between near-lying resonances. While the specific examples presented in this work pertain to atomic nuclei, the properties of non-exponential decay are generic, i.e., they apply to other many-body open quantum systems, such as hadrons, atoms, molecules, and nanostructures.
Moment Propagation

We introduce and develop moment propagation for approximate Bayesian inference. This method can be viewed as a variance correction for mean field variational Bayes which tends to underestimate posterior variances. Focusing on the case where the model is described by two sets of parameter vectors, we develop moment propagation algorithms for linear regression, multivariate normal, and probit regression models. We show for the probit regression model that moment propagation empirically performs reasonably well for several benchmark datasets. Finally, we discuss theoretical gaps and future extensions. In the supplementary material we show heuristically why moment propagation leads to appropriate posterior variance estimation, for the linear regression and multivariate normal models we show precisely why mean field variational Bayes underestimates certain moments, and prove that our moment propagation algorithm recovers the exact marginal posterior distributions for all parameters, and for probit regression we show that moment propagation provides asymptotically correct posterior means and covariance estimates.
SPARF: Neural Radiance Fields from Sparse and Noisy Poses

Neural Radiance Field (NeRF) has recently emerged as a powerful representation to synthesize photorealistic novel views. While showing impressive performance, it relies on the availability of dense input views with highly accurate camera poses, thus limiting its application in real-world scenarios. In this work, we introduce Sparse Pose Adjusting Radiance Field (SPARF), to address the challenge of novel-view synthesis given only few wide-baseline input images (as low as 3) with noisy camera poses. Our approach exploits multi-view geometry constraints in order to jointly learn the NeRF and refine the camera poses. By relying on pixel matches extracted between the input views, our multi-view correspondence objective enforces the optimized scene and camera poses to converge to a global and geometrically accurate solution. Our depth consistency loss further encourages the reconstructed scene to be consistent from any viewpoint. Our approach sets a new state of the art in the sparse-view regime on multiple challenging datasets.
Teaching Structured Vision&amp;Language Concepts to Vision&amp;Language Models

Vision and Language (VL) models have demonstrated remarkable zero-shot performance in a variety of tasks. However, some aspects of complex language understanding still remain a challenge. We introduce the collective notion of Structured Vision&Language Concepts (SVLC) which includes object attributes, relations, and states which are present in the text and visible in the image. Recent studies have shown that even the best VL models struggle with SVLC. A possible way of fixing this issue is by collecting dedicated datasets for teaching each SVLC type, yet this might be expensive and time-consuming. Instead, we propose a more elegant data-driven approach for enhancing VL models' understanding of SVLCs that makes more effective use of existing VL pre-training datasets and does not require any additional data. While automatic understanding of image structure still remains largely unsolved, language structure is much better modeled and understood, allowing for its effective utilization in teaching VL models. In this paper, we propose various techniques based on language structure understanding that can be used to manipulate the textual part of off-the-shelf paired VL datasets. VL models trained with the updated data exhibit a significant improvement of up to 15% in their SVLC understanding with only a mild degradation in their zero-shot capabilities both when training from scratch or fine-tuning a pre-trained model.
Towards continually learning new languages

Multilingual speech recognition with neural networks is often implemented with batch-learning, when all of the languages are available before training. An ability to add new languages after the prior training sessions can be economically beneficial, but the main challenge is catastrophic forgetting. In this work, we combine the qualities of weight factorization and elastic weight consolidation in order to counter catastrophic forgetting and facilitate learning new languages quickly. Such combination allowed us to eliminate catastrophic forgetting while still achieving performance for the new languages comparable with having all languages at once, in experiments of learning from an initial 10 languages to achieve 26 languages without catastrophic forgetting and a reasonable performance compared to training all languages from scratch.
Adaptive Stochastic Optimisation of Nonconvex Composite Objectives

In this paper, we propose and analyse a family of generalised stochastic composite mirror descent algorithms. With adaptive step sizes, the proposed algorithms converge without requiring prior knowledge of the problem. Combined with an entropy-like update-generating function, these algorithms perform gradient descent in the space equipped with the maximum norm, which allows us to exploit the low-dimensional structure of the decision sets for high-dimensional problems. Together with a sampling method based on the Rademacher distribution and variance reduction techniques, the proposed algorithms guarantee a logarithmic complexity dependence on dimensionality for zeroth-order optimisation problems.
Spanish Abstract Meaning Representation: Annotation of a General Corpus

&#x0D; &#x0D; &#x0D; Abstract Abstract Meaning Representation (AMR), originally designed for English, has been adapted to a number of languages to facilitate cross-lingual semantic representation and analysis. We build on previous work and present the first sizable, general annotation project for Spanish AMR. We release a detailed set of annotation guidelines and a corpus of 486 gold-annotated sentences spanning multiple genres from an existing, cross-lingual AMR corpus. Our work constitutes the second largest non-English gold AMR corpus to date. Fine-tuning an AMR to-Spanish generation model with our annotations results in a BERTScore improvement of 8.8%, demonstrating initial utility of our work.&#x0D; &#x0D; &#x0D;
The Role of Online Bible Readers in Biblical Concordance Making

Abstract Bible concordances lead Bible readers with rendering words and phrases of the Holy Bible in alphabetical listings and show where the terms occur throughout all books of Scripture. With cross-references for verses, concordances make it easy to understand the meaning of terms and the context in which those words are used. In Hungarian Bible studies several translations of the Bible are available (also online) and some of the contemporary translations are provided with on-the-fly concordances. Online versions of the Bible translations can easily build KWIC-concordances but not in an equal way. The study shows different approaches to online Bible concordance of the Hungarian translations to be found online and also tries to illustrate bottlenecks of concordance making on the off-line concordance being built to the so called ÚRK Bible (Újonnan Revideált Biblia – Newly Revised Bible). The obstacles in this concordance are connected with terminological and lexicographic approaches as this concordance is based on translations of the keywords of the ESV Bible Concordance.
Pay Attention To The Global Component: An Effective Defense Approach against Adversarial Attacking

Abstract Deep neural networks (DNNs) approaches have been widely applied for real-world machine learning applications. However, they were shown to be vulnerable to adversarial attacks. Nature images blended with human indistinguishable perturbations, so-called adversarial samples, can cause mis-classification of DNNs. On the other hand, recent defense approaches still suffer from strong attacks. This paper proposes a Matrix-Completion and Component Attention (MCCA) framework, a novel defense framework that leverages matrix completion and attention mechanism to defend the DNNs against adversarial attacks. MCCA would process the input (perturbed) images by two steps: firstly, the matrix completion is applied on the randomly masked inputs to execute the “reconstruc-tion” process; then, the reconstructed images will go through a DNN attached with flexible “attention” modules to enhance its classification performance. We show that the mask-reconstruction process can clear the area poisoned by perturbation, and maintain the global component in the processed image; while the attention module will capture such global component during the feed-forward phase, further make the DNNs focus on the area of major component during the classification step. These two processes would make the DNNs consistent with the typical perception of human beings. We conduct comprehensive experiments on three image benchmarks, comparing MCCA with state-of-the-art defense approaches. The quantitative comparisons demonstrate that our defense approach consistently outperforms prior techniques, improving the robustness against both black-box and white-box attacks.
Upaya Edukasi Pentingnya Membaca Buku Sejak Usia Dini Di Desa Hamparan Perak Kecamatan Hamparan Perak

The book as a bridge of science implies that in the book there are so a lot of science. The book is a source of various information that can open vision universally. Many things can be known in life, when kids get used to reading books from an early age. Through reading, kids can explore their skills and potential, improve reasoning power, train concentration, and improve academic performance. Therefore, educational efforts to kids about the importance of reading books, cannot be separated from the interest in learning great for the kids themselves. Hence the importance of giving education and motivation for kids to always read books by providing education for kids to increase interest in learning of kids at Desa Hamparan Perak Kecamatan Hamparan Perak.
Upaya Meningkatkan Minat Belajar Saat New Normal Di Desa Mekar Tanjung

The Covid-19 (Corona Virus Disease-19) pandemic was first discovered in December 2019 in Wuhan City. All sectors are expected to comply with the protocols that have been set to limit the distance from each other (physical distancing) and avoid restrictions. With all the efforts that have been made by the government community, the spread of Covid-19 after completion until 2021. The government and the term New Normal which includes the living community must adapt to this virus, carry out all activities that are usually carried out by complying with health protocols, such as: using hand sanitizers, wash hands, fulfill capacity in one room, check temperature, and wear masks. In Teluk Dalam District, several schools have conducted interesting learning activities, this could be one of the reasons for the increase in activities outside the home for children. From these problems, the researcher brought a program for the children to continue to be enthusiastic to gain knowledge even though the situation had returned to normal. During the research process, there was an increase in learning and enthusiasm during the guidance process carried out.
Generational Challenges in National Security Education

In recent years, increasingly far-reaching findings and researches on different generations appear, and parallel to this, the challenges related to the topic keep emerging in almost every area of life. The latter is particularly strong in the field of employment and education, the main reason for which is that the completely different attitudes of the younger generations have catalysed serious changes in both segments. In addition, in recent years, the global technological and IT development have accelerated tremendously, and the generations of the digital age are socialising in a completely different environment than their predecessors. Thus, their vision on the world, their values and their future prospects are also fundamentally different.