Reviewerly/exHarmony huggingface dataset:
Face and Voice Authentication System

Abstract: The issue of design and security is very predominant in any financial and business organization, especially such organization as a bank. Therefore, we intend to aid in security of the bank by bringing in an Artificial intelligence system that involves an individual to get an access to some items using face and voice recognition security system. This AI system is not just a normal password lock system that require a user to insert password and gain access to some items, it is a system that has an administrative authentication. In addition, with this kind of security authentication system we intend to implement, a highly secured AI feature, which enables the user with assured and highly secured transactions using their personal frame. Here an individual have to provide the face and voice authentication, which uses different algorithms, and is read by the AI server for clarification and verification. From this project, we hope to build an alternative and highly verified security for banks. Keywords: Artificial intelligence, administrative authentication, secured transactions, financial, business, organization.
Diversity Enhanced Table-to-Text Generation via Type Control

Generating natural language statements to convey logical inferences from tabular data (i.e., Logical NLG) is a process with one input and a variety of valid outputs. This characteristic underscores the need for a method to produce a diverse set of valid outputs, presenting different perspectives of the input data. We propose a simple yet effective diversity-enhancing scheme that builds upon an inherent property of the statements, their logic-types, by using a type-controlled table-to-text generation model. We demonstrate, through extensive automatic and human evaluations over the two publicly available Logical NLG datasets, that our proposed method both facilitates the ability to effectively control the generated statement type, and produces results superior to the strongest baselines in terms of quality and factuality-diversity trade-off.
GraB: Finding Provably Better Data Permutations than Random Reshuffling

Random reshuffling, which randomly permutes the dataset each epoch, is widely adopted in model training because it yields faster convergence than with-replacement sampling. Recent studies indicate greedily chosen data orderings can further speed up convergence empirically, at the cost of using more computation and memory. However, greedy ordering lacks theoretical justification and has limited utility due to its non-trivial memory and computation overhead. In this paper, we first formulate an example-ordering framework named herding and answer affirmatively that SGD with herding converges at the rate $O(T^{-2/3})$ on smooth, non-convex objectives, faster than the $O(n^{1/3}T^{-2/3})$ obtained by random reshuffling, where $n$ denotes the number of data points and $T$ denotes the total number of iterations. To reduce the memory overhead, we leverage discrepancy minimization theory to propose an online Gradient Balancing algorithm (GraB) that enjoys the same rate as herding, while reducing the memory usage from $O(nd)$ to just $O(d)$ and computation from $O(n^2)$ to $O(n)$, where $d$ denotes the model dimension. We show empirically on applications including MNIST, CIFAR10, WikiText and GLUE that GraB can outperform random reshuffling in terms of both training and validation performance, and even outperform state-of-the-art greedy ordering while reducing memory usage over $100\times$.
An Analysis of Texts Written by Third Semester of Undergraduate Students of English Education Study Program

This writing aims at finding out the kinds of texts written by the students, analyzing students’ texts’ constructions, and finding out their problems in writing English texts. This study employs descriptive qualitative method. It describes deeply the finding that related to aims of study above. The data of this result is taken from the written forms of English texts by the third semester of the undergraduate students of English Education Study Program of Widya Mandira Catholic University. The research shows that there are six kinds of texts written by the third semester of the undergraduate students of English Study Program of Widya Mandira Catholic University. Those kinds of texts are narrative text, descriptive text, recount text, comparison/contrast text, cause-effect text, and expository text. Some of Students’ text writings are cohesively, coherently and grammatically correct. However, some other students present incohesive, incoherent, and grammatically incorrect in writing English texts. Furthermore, there are several problems faced by the students in writing English texts, namely: transferring ideas in writing, lack of vocabulary, problem in using grammar, difficult to start writing, lack of reading sources, problem in organization of writing, and inconsistent in developing idea.
Probabilistic Structured Grammatical Evolution

The grammars used in grammar-based Genetic Programming (GP) methods have a significant impact on the quality of the solutions generated since they define the search space by restricting the solutions to its syntax. In this work, we propose Probabilistic Structured Grammatical Evolution (PSGE), a new approach that combines the Structured Grammatical Evolution (SGE) and Probabilistic Grammatical Evolution (PGE) representation variants and mapping mechanisms. The genotype is a set of dynamic lists, one for each non-terminal in the grammar, with each element of the list representing a probability used to select the next Probabilistic Context-Free Grammar (PCFG) derivation rule. PSGE statistically outperformed Grammatical Evolution (GE) on all six benchmark problems studied. In comparison to PGE, PSGE outperformed 4 of the 6 problems analyzed.
A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization

Parameter quantization in convolutional neural networks (CNNs) can help generate efficient models with lower memory footprint and computational complexity. But, homogeneous quantization can result in significant degradation of CNN model accuracy. In contrast, heterogeneous quantization represents a promising approach to realize compact, quantized models with higher inference accuracies. In this paper, we propose HQNNA, a CNN accelerator based on non-coherent silicon photonics that can accelerate both homogeneously quantized and heterogeneously quantized CNN models. Our analyses show that HQNNA achieves up to 73.8x better energy-per-bit and 159.5x better throughput-energy efficiency than state-of-the-art photonic CNN accelerators.
Improvements to Self-Supervised Representation Learning for Masked Image Modeling

This paper explores improvements to the masked image modeling (MIM) paradigm. The MIM paradigm enables the model to learn the main object features of the image by masking the input image and predicting the masked part by the unmasked part. We found the following three main directions for MIM to be improved. First, since both encoders and decoders contribute to representation learning, MIM uses only encoders for downstream tasks, which ignores the impact of decoders on representation learning. Although the MIM paradigm already employs small decoders with asymmetric structures, we believe that continued reduction of decoder parameters is beneficial to improve the representational learning capability of the encoder . Second, MIM solves the image prediction task by training the encoder and decoder together , and does not design a separate task for the encoder . To further enhance the performance of the encoder when performing downstream tasks, we designed the encoder for the tasks of comparative learning and token position prediction. Third, since the input image may contain background and other objects, and the proportion of each object in the image varies, reconstructing the tokens related to the background or to other objects is not meaningful for MIM to understand the main object representations. Therefore we use ContrastiveCrop to crop the input image so that the input image contains as much as possible only the main objects. Based on the above three improvements to MIM, we propose a new model, Contrastive Masked AutoEncoders (CMAE). We achieved a Top-1 accuracy of 65.84% on tinyimagenet using the ViT-B backbone, which is +2.89 outperforming the MAE of competing methods when all conditions are equal. Code will be made available.
Quantum state tomography of qudits via Hong-Ou-Mandel interference

We propose a method to perform the quantum state tomography (QST) of an $\mathit{n}$-partite qudit state embedded in single photons using the Hong-Ou-Mandel (HOM) interference between the target state and probe state. This method requires only passive beam splitters for the HOM interference and removes all active optical devices in the target modes to control the measurement bases needed in conventional QST. Hence, it is applicable to various degree of freedom of the target state without altering the measurement setup. Moreover, a faithful estimation is realized even with classical probe light such as laser and thermal light. As a proof-of-principle, we performed the experimental demonstration using a polarization qubit. Regardless of the photon statistics of the probe light, the estimated results of state reconstruction are as accurate as those verified by conventional QST.
All You Need Is Logs: Improving Code Completion by Learning from Anonymous IDE Usage Logs

In this work, we propose an approach for collecting completion usage logs from the users in an IDE and using them to train a machine learning based model for ranking completion candidates. We developed a set of features that describe completion candidates and their context, and deployed their anonymized collection in the Early Access Program of IntelliJ-based IDEs. We used the logs to collect a dataset of code completions from users, and employed it to train a ranking CatBoost model. Then, we evaluated it in two settings: on a held-out set of the collected completions and in a separate A/B test on two different groups of users in the IDE. Our evaluation shows that using a simple ranking model trained on the past user behavior logs significantly improved code completion experience. Compared to the default heuristics-based ranking, our model demonstrated a decrease in the number of typing actions necessary to perform the completion in the IDE from 2.073 to 1.832. The approach adheres to privacy requirements and legal constraints, since it does not require collecting personal information, performing all the necessary anonymization on the client's side. Importantly, it can be improved continuously: implementing new features, collecting new data, and evaluating new models - this way, we have been using it in production since the end of 2020.
Lithuanian Discourse Markers in Parallel Corpus for Teaching Translation Awareness

The contrastive analysis of discourse markers in Lithuanian and English provides data on the functions of discourse markers to language teachers and translators. The study reveals that there is a tentative tendency for translators to rarely choose to run an explicit discourse marker and leave it only in the implied translation. It is essential that philology students are taught in detail about discourse markers, and TED-MBD is an excellent medium for teaching text coherence.
Metadata Definition in Registries: What Is a Data Element?

Observational research benefits from a rich methodological foundation of registry development and operation published in international and national guidelines. Metadata management is an essential part of registry implementation based on concepts of data elements and value sets. The metadata from six German registries revealed vastly divergent interpretations of the concept of data elements. The different perspectives of research questions, data acquisition and data storage were all represented in the registries' catalogs of data elements. Consequently, the whole life cycle of a registry needs to be accompanied by a catalog of data elements, which has to be continuously adapted to the changing perspectives. A standard for the representation of those metadata is still missing. The FAIR Guiding Principles introduce important methodological requirements, but the tools for their fulfillment in respect to the management of metadata are still in its infancy.
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning

The goal of Unsupervised Reinforcement Learning (URL) is to find a reward-agnostic prior policy on a task domain, such that the sample-efficiency on supervised downstream tasks is improved. Although agents initialized with such a prior policy can achieve a significantly higher reward with fewer samples when finetuned on the downstream task, it is still an open question how an optimal pretrained prior policy can be achieved in practice. In this work, we present POLTER (Policy Trajectory Ensemble Regularization) - a general method to regularize the pretraining that can be applied to any URL algorithm and is especially useful on data- and knowledge-based URL algorithms. It utilizes an ensemble of policies that are discovered during pretraining and moves the policy of the URL algorithm closer to its optimal prior. Our method is based on a theoretical framework, and we analyze its practical effects on a white-box benchmark, allowing us to study POLTER with full control. In our main experiments, we evaluate POLTER on the Unsupervised Reinforcement Learning Benchmark (URLB), which consists of 12 tasks in 3 domains. We demonstrate the generality of our approach by improving the performance of a diverse set of data- and knowledge-based URL algorithms by 19% on average and up to 40% in the best case. Under a fair comparison with tuned baselines and tuned POLTER, we establish a new state-of-the-art for model-free methods on the URLB.
The MST-fan of a regular subdivision

The dual graph $\Gamma(h)$ of a regular triangulation $\Sigma(h)$ carries a natural metric structure. The minimum spanning trees of $\Gamma(h)$ recently proved to be conclusive for detecting significant data signal in the context of population genetics. In this paper we prove that the parameter space of such minimum spanning trees is organized as a polyhedral fan, called the MST-fan of $\Sigma(h)$, which subdivides the secondary cone of $\Sigma(h)$ into parameter cones. We partially describe its local face structure and examine the connection to tropical geometry in virtue of matroids and Bergman fans.
Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization

In this paper, we introduce a probabilistic model for learning nonnegative matrix factorization (NMF) that is commonly used for predicting missing values and finding hidden patterns in the data, in which the matrix factors are latent variables associated with each data dimension. The nonnegativity constraint for the latent factors is handled by choosing priors with support on the nonnegative subspace. Bayesian inference procedure based on Gibbs sampling is employed. We evaluate the model on several real-world datasets including MovieLens 100K and MovieLens 1M with different sizes and dimensions and show that the proposed Bayesian NMF GRRN model leads to better predictions and avoids overfitting compared to existing Bayesian NMF approaches.
Amine Bendahmane1* and Redouane Tlemsani1

Abstract Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we adapt this metaheuristic to robotics for solving the Unknown Area Exploration problem with energy constraints in both single and multi-robot scenarios. We conducted several experiments to validate the approach and compare its performance to well-known metaheuristics used in the literature using 5 different comparison criteria. We also proposed a new version of the algorithm (xBOA) based on the crossover operator. We compared its results to the original BOA and 3 other variants recently introduced in the literature. Although BOA and xBOA are not optimal in all evaluation criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima; has better fitness convergence; and achieves better exploration rates than the original BOA and its other variants.
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities

Recent advances in Neural Architecture Search (NAS) such as one-shot NAS offer the ability to extract specialized hardware-aware sub-network configurations from a task-specific super-network. While considerable effort has been employed towards improving the first stage, namely, the training of the super-network, the search for derivative high-performing sub-networks is still under-explored. Popular methods decouple the super-network training from the sub-network search and use performance predictors to reduce the computational burden of searching on different hardware platforms. We propose a flexible search framework that automatically and efficiently finds optimal sub-networks that are optimized for different performance metrics and hardware configurations. Specifically, we show how evolutionary algorithms can be paired with lightly trained objective predictors in an iterative cycle to accelerate architecture search in a multi-objective setting for various modalities including machine translation and image classification.
Automatic Prediction of Semantic Labels for French Medical Terms

We address the problem of semantic labeling of terms in two French medical corpora with the subset of the UMLS. We perform two experiments relying on the structure of words and terms, and on their context: 1) the semantic label of already identified terms is predicted; 2) the terms are detected in raw texts and their semantic label is predicted. Our results show over 0.90 F-measure.
Diverse Perspective of the Shared History

This paper discusses how the interactive documentary A Global Guide to the First World War (Francesca Panetta, 2014) effectively integrates interactive technology with historical narratives of the war through a three-way collaboration between news organizations, historians and digital materials from the Imperial War Museum from the perspective of transnational shared history. The paper argues that the different images, videos, news reports and audio clips reflect diverse aspects of common historical themes in the documentary. First, in its seven video chapters, the museum's audiovisual archive covers many countries, and audiences can watch videos related to the WWI alongside historians' commentaries that explain how different nations fought in the war; second, the interactive part is based on an elaborate digital world map, and users can view audio clips, photocopied newspapers and other materials of the war under various web interaction options, and all of the above can be accessed in multiple languages. The natural and direct feeling brought to audiences is the understanding of the WWI experienced by different peoples and countries with a global perspective, instead of the traditional single narrative perspective. Thus, the documentary is a testimony to the shared history of the war. I suggest that the shared historical events and interactive technology make it easier for viewers from different cultural backgrounds to understand this work, and trigger a wide range of historical resonance. Visual media has always led people to leave events in their memories that they did not experience. Although most viewers did not personally experience WWI, historians' commentaries, museums' audiovisual histories and the Guardian's authentic old stories give us credible evidence of the past. The traumatic memories that the images bring to the audiences create an effect of sympathy for the pain of war and the responsibility to fight against it, which transcends racial and national identification. However, this transnational empathy is quite limited. The reason is that the documentary employs an official authoritative narrative strategy rather than the perspective of the individual. It somehow creates a sense of alienation for the viewers, who realize that these are collages of archival records rather than a kind of historical memory closely related to themselves.
Named Entity Linking with Entity Representation by Multiple Embeddings

We propose a simple and practical method for named entity linking (NEL), based on entity representation by multiple embeddings. To explore this method, and to review its dependency on parameters, we measure its performance on Namesakes, a highly challenging dataset of ambiguously named entities. Our observations suggest that the minimal number of mentions required to create a knowledge base (KB) entity is very important for NEL performance. The number of embeddings is less important and can be kept small, within as few as 10 or less. We show that our representations of KB entities can be adjusted using only KB data, and the adjustment can improve NEL performance. We also compare NEL performance of embeddings obtained from tuning language model on diverse news texts as opposed to tuning on more uniform texts from public datasets XSum, CNN / Daily Mail. We found that tuning on diverse news provides better embeddings.
Wasserstein Generative Adversarial Networks for Online Test Generation for Cyber Physical Systems

We propose a novel online test generation algorithm WOGAN based on Wasserstein Generative Adversarial Networks. WOGAN is a general-purpose black-box test generator applicable to any system under test having a fitness function for determining failing tests. As a proof of concept, we evaluate WOGAN by generating roads such that a lane assistance system of a car fails to stay on the designated lane. We find that our algorithm has a competitive performance respect to previously published algorithms.
Covid 19 Monitoring System Using Social Distancing and Face Mask Detection Using YOLOv5 Algorithm

The World Health Organization (WHO) recommends maintaining social distance and using face masks to prevent the spread of COVID 19 in public places.Government and national health authorities have made physical distances of 2 meters and wearing face masks a mandatory safety measure in crowded or public areas [1].In this study, a popular CCTV surveillance camera was used to develop a collaborative deep learning network-based neural model for automatically detecting, tracking, and estimating distances between people in a crowd.increase.The proposed model includes social distance monitoring and a YOLOv5based facial recognition framework under difficult conditions such as human disability, restricted visibility, and lighting changes [8].Determine if the location is at high risk of virus infection and spread.This will help authorities redesign public spaces and take precautions to escalate high-risk areas. I-INTRODUCTIONIn late December 2019, the first case of coronavirus disease (COVID19) was reported in Wuhan, China.Only a few months later, the 2020 virus was hit by global outbreaks.In May 2020, the World Health Organization (WHO) declared a pandemic.According to WHO estimates released on August 26, 2020, there are 23.8 million people vitiate in 200 countries.The death toll from the virus is, a staggering 815,000.