Reviewerly/exHarmony huggingface dataset:
Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech.

Automatic Speech Recognition (ASR) systems are often optimized to work best for speakers with canonical speech patterns. Unfortunately, these systems perform poorly when tested on atypical speech and heavily accented speech. It has previously been shown that personalization through model fine-tuning substantially improves performance. However, maintaining such large models per speaker is costly and difficult to scale. We show that by adding a relatively small number of extra parameters to the encoder layers via so-called residual adapter, we can achieve similar adaptation gains compared to model fine-tuning, while only updating a tiny fraction (less than 0.5%) of the model parameters. We demonstrate this on two speech adaptation tasks (atypical and accented speech) and for two state-of-the-art ASR architectures.
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration

Despite Graph Neural Networks (GNNs) have achieved remarkable accuracy, whether the results are trustworthy is still unexplored. Previous studies suggest that many modern neural networks are over-confident on the predictions, however, surprisingly, we discover that GNNs are primarily in the opposite direction, i.e., GNNs are under-confident. Therefore, the confidence calibration for GNNs is highly desired. In this paper, we propose a novel trustworthy GNN model by designing a topology-aware post-hoc calibration function. Specifically, we first verify that the confidence distribution in a graph has homophily property, and this finding inspires us to design a calibration GNN model (CaGCN) to learn the calibration function. CaGCN is able to obtain a unique transformation from logits of GNNs to the calibrated confidence for each node, meanwhile, such transformation is able to preserve the order between classes, satisfying the accuracy-preserving property. Moreover, we apply the calibration GNN to self-training framework, showing that more trustworthy pseudo labels can be obtained with the calibrated confidence and further improve the performance. Extensive experiments demonstrate the effectiveness of our proposed model in terms of both calibration and accuracy.
Semantic Perturbations With Normalizing Flows for Improved Generalization

Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often limited to a fixed set of hard-coded transformations. Recently, several works proposed to use generative models for generating semantically meaningful perturbations to train a classifier. However, because accurate encoding and decoding are critical, these methods, which use architectures that approximate the latent-variable inference, remained limited to pilot studies on small datasets. Exploiting the exactly reversible encoder-decoder structure of normalizing flows, we perform on-manifold perturbations in the latent space to define fully unsupervised data augmentations. We demonstrate that such perturbations match the performance of advanced data augmentation techniques -- reaching 96.6% test accuracy for CIFAR-10 using ResNet-18 and outperform existing methods, particularly in low data regimes -- yielding 10--25% relative improvement of test accuracy from classical training. We find that our latent adversarial perturbations adaptive to the classifier throughout its training are most effective, yielding the first test accuracy improvement results on real-world datasets -- CIFAR-10/100 -- via latent-space perturbations.
Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks

Despite remarkable performance in producing realistic samples, Generative Adversarial Networks (GANs) often produce low-quality samples near low-density regions of the data manifold. Recently, many techniques have been developed to improve the quality of generated samples, either by rejecting low-quality samples after training or by pre-processing the empirical data distribution before training, but at the cost of reduced diversity. To guarantee both the quality and the diversity, we propose a simple yet effective method to diagnose and emphasize underrepresented samples during training of a GAN. The main idea is to use the statistics of the discrepancy between the data distribution and the model distribution at each data instance. Based on the observation that the underrepresented samples have a high average discrepancy or high variability in discrepancy, we propose a method to emphasize those samples during training of a GAN. Our experimental results demonstrate that the proposed method improves GAN performance on various datasets, and it is especially effective in improving the quality of generated samples with minor features.
한국과 일본의 언어권(言語権)의 관점에서 본 다문화주의 -캐나다의 사례와 비교하여-

Linguistic rights mean using and learning languages personally, publicly, and freely. And it means the right to exchange with them. Minority declared the right not to be discriminated based on languages in the Universal Declaration of Human Rights. After this, problems for linguistic minorities were raised in the two declarations. They are legally non-binding, but they are meaningful in that the problems about languages were raised. And they formed the foundation of the Universal Declaration of linguistic rights. Canada implemented pioneering multicultural policies. It evaluated multiculture with social resources and implemented active policies and heritage language education. However, it currently suspended heritage language education because dissent of it was strong in it. It is difficult to keep minorities’ linguistic rights. Discussion on linguistic rights has not begun in Korea. Heritage language education has been implemented focusing on the Korean language. it has been a little bit done in the Japanese language. Korea has little interest in heritage language education of various languages. The Linguistic rights Society was established in Japan in 1998 and discussion on linguistic rights has been actively done. Heritage language education is done focusing on the Japanese language, but in some areas, heritage language education of Chinese, Spanish, Korean, etc. Heritage language education is good to be implemented in the public education system.
Using Collaborative Filtering to Model Argument Selection

This study evaluates whether model-based Collaborative Filtering (CF) algorithms, which have been extensively studied and widely used to build recommender systems, can be used to predict which common nouns a predicate can take as its complement.We find that, when trained on verb-noun co-occurrence data drawn from the Corpus of Contemporary American-English (COCA), two popular model-based CF algorithms, Singular Value Decomposition and Non-negative Matrix Factorization, perform well on this task, each achieving an AUROC of at least 0.89 and surpassing several different baselines.We then show that the embedding-vectors for verbs and nouns learned by the two CF models can be quantized (via application of k-means clustering) with minimal loss of performance on the prediction task while only using a small number of verb and noun clusters (relative to the number of distinct verbs and nouns).Finally we evaluate the alignment between the quantized embedding vectors for verbs and the Levin verb classes, finding that the alignment surpassed several randomized baselines.We conclude by discussing how model-based CF algorithms might be applied to learning restrictions on constituent selection between various lexical categories and how these (learned) models could then be used to augment a (rulebased) constituency grammar.
Efficient Security for Smart Grid Applications using Elliptic Curve Cryptography with Gaussian Integer Technique

In the security based smart applications, there is the need for authentication to maintain the confidentiality and safety of the user details. Elliptic Curve Cryptography is the cornerstone of the embedded applications. Elliptic Curve Cryptography (ECC) provides such security with low key size. The most time-consuming operation in ECC is point multiplication. After careful implementation on point multiplication, there are many possibilities for side channel attacks such as timing attack in the communication channels by measuring the time for one particular point multiplication. In order to reduce the vulnerability of side channel attack the point multiplication is implemented with Gaussian integer technique which is a complicated key expansion algorithm. Gaussian integers are the subset of complex numbers.  Gaussian integer expansion is beneficial to reduce the attacks and the memory requirements of a secure hardware implementation. This paper represents the implementations with Raspberry pi kit as client and server node. A secure one-time password for every transaction is encrypted with   ECC algorithm with reasonable time duration to minimize the side channel and brute force attacks. Even though, point multiplications require more time, gaussian integer multiplication requires less time, transactions by one-time password requires 1.09 seconds for its transaction from server to client. Keywords: Elliptic curve cryptography, security, Raspberry Pi, point multiplication
Devising an ‘Image Steganography’ Framework for an Enhanced Efficacy for Sequential Data Embedded System

The process of hiding information between images is called Steganography. The usage has been increased more in the current decade. Formed various methods and predictions to ad-lib the communication. It involves more with regards to the solid transmission of the information in the correspondence series. The data might be in any form like the text, picture, sound and even video records. Steganography all the more deeply utilizes the image as a medium to cover the first data. In the proposed strategy uses the adjusted type of Least Significant Bit (LSB) addition method. The first text record is first changed over into a parallel structure. The cover picture is changing over concerning the info information's comparing pixel game plan. The substitution happens so that one pixel worth will be altered and supplanted in each square. Therefore, the interloper can't discover the progressions that occurred inside the picture. This strategy utilizes the LSB procedure in a changed manner with pixel substitution. The trial results show that the proposed technique stays secure and is quick to process.
A Comparative Study on Abstractive and Extractive Approaches In Summarization of European Legislation Documents

Extracting the most important part of legislation documents has great business value because the texts are usually very long and hard to understand.The aim of this article is to evaluate different algorithms for text summarization on EU legislation documents.The content contains domain-specific words.We collected a text summarization dataset of EU legal documents consisting of 1563 documents, in which the mean length of summaries is 424 words.Experiments were conducted with different algorithms using the new dataset.A simple extractive algorithm was selected as a baseline.Advanced extractive algorithms, which use encoders show better results than baseline.The best result measured by ROUGE scores was achieved by a fine-tuned abstractive T5 model, which was adapted to work with long texts.
Cryptocurrencies, Wallet, and Token Economy

The following sections are included:Digital Currencies, Cryptocurrencies, and TokensWalletsToken EconomyBibliographySample QuestionsSolutions
A PSO Based Cloud Framework for Knowledge Extraction

Many industries, such as oil, construction, banking, and insurance, have substantial historical physical data. Companies store this data in physical warehouses that are geographically distributed and usually taken care of by record management companies. Storing large volumes of historical physical data poses many critical challenges, such as increased maintenance cost, high time for recovery, and unsearchable data. Many companies digitize this data and consolidate this data into cloud repositories as part of their Digital Transformation (DT) journey to address these challenges. This DT process introduces many other technical challenges while dealing with poor scans, huge file size, geographically distributed files, and confidential documents. Though there are options to resolve each of these limitations individually, there are no frameworks that deal with digitization and historical data storage in its entirety. Moreover, they cannot handle a large number of documents having variable file sizes. This paper presents a generic cloud-based high-performance computing framework for knowledge extraction, comprising document classification based on neural networks and particle swarm optimization (PSO), data extraction, metadata enrichment, image enhancement using image processing (IP) techniques, and high data availability to users using cloud-based search. The proposed framework is executed on two cloud providers, i.e., Azure and AWS, to test its efficacy.
The Ambitions and Anxieties of Ambience

What do we talk about when we talk about reading and how has this changed in a digital age? Taking an interdisciplinary approach, Ambient Literature: Towards a New Poetics of Situated Writing and Reading Practices (2021) addresses the complications of this question by providing accounts of technologically-assisted and embodied reading experiences that interact with ‘the visual, sonic, social and historical resources of places’. Ambient Literature shifts our attention to the under-researched sensory entanglements between the act of reading, embodiment, city life, and most importantly, their foregrounding and enablement by networking technologies.
Upaya Meningkatkan Kemampuan Mengenal Huruf Menggunakan Media Kartu Kata Murid Kelompok B RA Samanhudi Kecamatan Tanete Riattang Kabupaten Bone

This study aims to improve the process of learning activities and the ability to recognize the letters of group B children RA Saman Hudi through the learning process by applying word card media. The type of research conducted is classroom action research (Classroom Action Research) which will be carried out in 2 cycles. The subjects of this study were the children of group B RA Saman Hudi, Tanete Riattang District, Bone Regency in 2020 with a total of 15 children. Data collection techniques using observation and documentation techniques. The validation techniques used in this study are member check, triangulation, and audit trail. Observational data were analyzed descriptively quantitatively. The results showed that the use of word card media can improve the ability to recognize letters of RA Saman Hudi's students, Tanete Riattang District, Bone Regency. Based on the results of the study, it can be concluded that through the use of word card media it is proven to be able to improve the ability to recognize letters in group B children RA Saman Hudi, Tanete Riattang District, Bone Regency.
환율과 경상수지의 구조적 변화와 정책방향 (Structural Factors Behind Foreign Exchange Rate and Current Account Balances and Policy Directions)

본 연구의 주요 목적은 원화 약세 시 평가절상 압박에 대해서 실증적인 근거와 대응논리를 제공하는 것이다. 이를 위해서 외환시장 개입정책이 환율을 장기적으로 변화시킬 수 있는지 살펴보았으며, 경상수지와 환율에 구조적으로 영향을 주는 결정요인도 실증적으로 분석하였다. 한편 코로나19 이후 중국경제의 빠른 회복세와 미 달러화 약세에 대한 기대 등은 우리 원화의 강세 압력으로 이어질 가능성이 높다. 원화 강세가 지속될 것으로 예상되는 최근의 상황을 감안하여 환율이 우리 수출경쟁력에 미치는 영향력을 점검하고 수출경쟁력 강화를 위한 정책방향을 제시하였다. The goal of this study is twofold. On the one hand, when the Korean won is weak, we provide an empirical basis and response logic to the pressure of appreciation. On the other hand, when the won is strong, we analyze the effect of the exchange rate on the competitiveness of Korean exporting firms, thereby providing relevant policy directions. Korea’s current account has generally maintained a surplus since 2000. However, the current account surplus did not immediately lead to an increase in net foreign assets. Indeed, Korea has become a net foreign asset country only since 2014. Korea's continuous current account surplus and investment in safe but low-return foreign assets can be attributed to the rational choice of Korean investors who take into account that Korea has traditionally been a country with certain characteristics (e.g. the absence of a key currency, relatively less developed domestic financial market, and low accessibility to the international financial market). (the rest omitted)
Finding Similar Tweets in Health Related Topics

Social networks have become a very important means to facilitate the creation and sharing of information. They also provide real-time information on sales, marketing, politics, natural disasters, and crisis situations, among others. In this work, we investigate neural models for text similarity that can be used to: 1) determine if messages are related or not with a disease, 2) group similar messages to those that we have already captured, analyzed or stored, and 3) find similarity indices between messages using different learning algorithms. Our results show that we can achieve 90% accuracy on the task of classifying which of two tweets is more similar to a sample tweet.
Ethnic Propaganda, Hate Speech, and Mass Violence in Igbo-Hausa/Fulani Relations in Postcolonial Nigeria

Opinions are divided on the conduct and nature of the 1967–70 Nigerian civil war, occasioned partly by the Igbo secession and declaration of the Republic of Biafra. Some believe that the Nigerian government adopted a genocidal war strategy characterised by mass violence against civilians, aggressive blockade of the eastern region, artificial famine, and hateful/threatening utterances by many military commanders, accompanied by about one million civilian casualties. On the other hand, some are of the view that the actions of some Nigerian military officers and men were not in consonant with the position of the Nigerian government during the war, but were influenced by their animosity towards the Igbo. In post-civil war Nigeria, Igbo-Hausa/Fulani relations and political discourses are increasingly tense and indicative of a fearful resurgence of mass violence due to prevalent lies, propaganda, and misrepresentations –verbally, on paper, and online/on social media – particularly among the youths on both sides. This paper argues that the historical crisis-ridden relations between the Igbo and the Hausa/Fulani, the attendant 1966 mass killings of Igbos in the north, the nature of the war strategies of the federal government during the civil war, and the currently mounting tension are all direct results of contending ethnic propaganda including hate speech, lies, and name-calling in a bid to gain political and strategic advantages over other ethnic groups. Thus, this paper is a historical inquiry into the role of propaganda and hate speech in socio-political interactions, discourses, and incitements of mass violence among the heterogeneous Nigerian population, particularly the Igbo and the Hausa/Fulani. The paper proposes legislative, constitutional, and active citizenship advocacies to address the menace. The article utilises primary and secondary sources to analyse and interpret the subject-matter of the paper.
Factors affecting trace elements enrichment and its interaction with organic materials in Qalikouh oil shale, Lorestan, western Iran

Qalikouh anticline is located 35 km southwest of Aligudarz city, Lorestan province, SW of Iran. The area is located in the high structural zone of Zagros. The oil shales of the areas are located in the Middle Jurassic and Lower Triassic of Sargelu and Garu Formations. The Sargelu Formation contains a series of shale, carbonate, oil shale, and clays. Garau Formation includes oil shale horizons with different thicknesses, ammonite shales, and black cherty layer. The shales of these two formations in the high Zagros region have formed oil shale reserves due to their richness in organic matter (OM), kerogen type is I and II, and relatively low thermal maturity at the beginning of the oil window. This study focuses on the effect of geochemical processes on the enrichment of trace elements and their interaction with organic matter in Qalikouh oil shale. analysis was performed by XRF, XRD, ICP-Ms, and Rock-Eval methods and Statistical significance were analyzed. The results of this study indicated the high enrichment of elements Cd, Mo, Ag, U, Cs. The correlation between immobilized and mobile oxides, organic matter, formed minerals, and other factors were tested. These tests revealed that the enrichment of trace elements is affected by classical, biological, and autogenic materials. Elements U, V, Cd are related to the organic and autogenic matter. Elements Ca, Si, P is of biological origin, and elements Ag, Ti are of classical origin.
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning

Operating in the real-world often requires agents to learn about a complex environment and apply this understanding to achieve a breadth of goals. This problem, known as goal-conditioned reinforcement learning (GCRL), becomes especially challenging for long-horizon goals. Current methods have tackled this problem by augmenting goal-conditioned policies with graph-based planning algorithms. However, they struggle to scale to large, high-dimensional state spaces and assume access to exploration mechanisms for efficiently collecting training data. In this work, we introduce Successor Feature Landmarks (SFL), a framework for exploring large, high-dimensional environments so as to obtain a policy that is proficient for any goal. SFL leverages the ability of successor features (SF) to capture transition dynamics, using it to drive exploration by estimating state-novelty and to enable high-level planning by abstracting the state-space as a non-parametric landmark-based graph. We further exploit SF to directly compute a goal-conditioned policy for inter-landmark traversal, which we use to execute plans to frontier landmarks at the edge of the explored state space. We show in our experiments on MiniGrid and ViZDoom that SFL enables efficient exploration of large, high-dimensional state spaces and outperforms state-of-the-art baselines on long-horizon GCRL tasks.
MIPE: A Metric Independent Pipeline for Effective Code-Mixed NLG Evaluation

Code-mixing is a phenomenon of mixing words and phrases from two or more languages in a single utterance of speech and text. Due to the high linguistic diversity, code-mixing presents several challenges in evaluating standard natural language generation (NLG) tasks. Various widely popular metrics perform poorly with the code-mixed NLG tasks. To address this challenge, we present a metric independent evaluation pipeline MIPE that significantly improves the correlation between evaluation metrics and human judgments on the generated code-mixed text. As a use case, we demonstrate the performance of MIPE on the machine-generated Hinglish (code-mixing of Hindi and English languages) sentences from the HinGE corpus. We can extend the proposed evaluation strategy to other code-mixed language pairs, NLG tasks, and evaluation metrics with minimal to no effort.