Reviewerly/exHarmony huggingface dataset:
Quantum Annealing and Algebraic Attack on Speck Cipher

Algebraic attacks using quantum annealing are a new idea of cryptanalysis. This paper shows how to obtain a QUBO problem equivalent to the algebraic attack on the Speck cipher, using as small a number of logical variables as possible. The main idea of minimizing the number of variables in the algebraic attack on this ARX cipher was appropriate cipher partition and insertion of additional variables. Using such an idea, in the case of the most popular variants: Speck-128/128 and Speck-128/256, the equivalent QUBO problem has 19,311 and 33,721 logical variables, which is more efficient than the same attack on AES cipher, where for AES-128 and AES-256, an equivalent QUBO problem consist of 29,770 and 72,597 logical variables, respectively. It is an open question if this kind of attack may overtake, in some cases, brutal or Grover’s attack.
Learning Based Hardware-Centric Quantum Circuit Generation

In this paper we present an approach to find quantum circuits suitable to mimic probabilistic and search operations on a physical NISQ device. We present both a gradient based and a non-gradient based machine learning approach to optimize the created quantum circuits. In our optimization procedure we make use of a cost function that differentiates between the vector representing the probabilities of measurement of each basis state after applying our learned circuit and the desired probability vector. As such our quantum circuit generation (QCG) approach leads to thinner quantum circuits which behave better when executed on physical quantum computers. Our approach moreover ensures that the created quantum circuit obeys the restrictions of the chosen hardware. By catering to specific quantum hardware we can avoid unforeseen and potentially unnecessary circuit depth, and we return circuits that need no further transpilation. We present the results of running the created circuits on quantum computers by IBM, Rigetti and Quantum Inspire.
Bidirectional Quantum Secure Direct Communication Using Dense Coding of Four Qubit Cluster States

We are introducing an efficient bidirectional quantum secure direct communication protocol that employs the four qubit cluster states, creating a novel quantum channel based on dense coding between the transmitter and receiver. By dense coding, after a conventional announcement, two legitimate users can simultaneously exchange their messages. Two safety checking measures are taken to ensure safe transmission. Information leaking is not an issue in this protocol, and it is secure against some well-known eavesdropping assaults. It is also accessible with cutting-edge technology.
Realm

Abstract In this chapter,we explore some of the fundamentals of the Realm database (sometimes referred to as RealmDB or simply Realm). It is widely known within the Digital Forensics discipline that SQLite is the most commonly found database format within any mobile device application and even some desktop applications. Realm is a relatively new database format built as a potential replacement for SQLite, as technology and applications continue to develop and evolve. At the time of writing, it is clear that the database is not as commonly found as some might have expected, but that is not to say the database format will not eventually find its way into many modern apps over the coming years. To that end, we decided to research the database to try and provide some of the details of interest relating to the fundamentals behind the new format.We hope this chapter will help digital forensic examiners and investigators learn and grasp some of the basic concepts of Realm, hoping that any new knowledge and understanding might support and assist in future research into the topic.
Unsupervised Geometric and Topological Approaches for Cross-Lingual Sentence Representation and Comparison

We propose novel structural-based approaches for the generation and comparison of cross lingual sentence representations.We do so by applying geometric and topological methods to analyze the structure of sentences, as captured by their word embeddings.The key properties of our methods are: (a) They are designed to be isometric invariant, in order to provide language-agnostic representations.(b) They are fully unsupervised, and use no cross-lingual signal.The quality of our representations, and their preservation across languages, are evaluated in similarity comparison tasks, achieving competitive results.Furthermore, we show that our structural-based representations can be combined with existing methods for improved results.
Image Super Resolution using Enhanced Super Resolution Generative Adversarial Network

Aside from enhancing the accuracy and speed of single picture modification utilizing fast and in-depth convolutional emotional networks, one significant challenge remains mostly commonly unaddressed, namely how do we recover soft texture details when we concentrate too much on exceptional improvement features? The resultant evaluations offer greater transmission ratings, but the high frequency data is non-existent and unsatisfactory mostly in sense that now it fails to meet the consistency anticipated in high resolution. The resulting ratings have higher signal-to-audio ratings, but the high frequency data is non-existent and unsatisfactory in the sense that it fails to match the consistency expected in high resolution. Introducing ESRGAN, an Advanced Optical Genetically Modified (GAN) network of high-resolution image (SR). To our knowledge, it is a framework capable of identifying immature real-world images up to 4x points rising. To achieve this, we propose a function of loss of vision that combines the loss of content with the loss of content (Mean Squared Error Loss). Controversial Loss has our solution for many uncooked pictures utilizing a discriminatory network which is taught to distinguish between high resolution images and realistic images. We have built a structure that contains several RRDB blocks (Residual in Residual Dense Block) outside the Batch Normalization layers. Our deep residual network can find realistic image texture in very low sample images. Additionally, we used techniques that included residual measurement and a small implementation to train a deeper model. We also introduced the relativistic GAN as a racist, who learns to judge whether one image is more realistic than another, directing the generator to return a detailed texture. In addition, we have improved vision loss by using features before activation, which provides greater security and thus restoring more precise light and texture.
Towards Better Characterization of Paraphrases

To effectively characterize the nature of paraphrase pairs without expert human annotation, we propose two new metrics: word position deviation (WPD) and lexical deviation (LD).WPD measures the degree of structural alteration, while LD measures the difference in vocabulary used.We apply these metrics to better understand the commonly-used MRPC dataset and study how it differs from PAWS, another paraphrase identification dataset.We also perform a detailed study on MRPC and propose improvements to the dataset, showing that it improves generalizability of models trained on the dataset.Lastly, we apply our metrics to filter the output of a paraphrase generation model and show how it can be used to generate specific forms of paraphrases for data augmentation or robustness testing of NLP models.
A Natural Diet: Towards Improving Naturalness of Machine Translation Output

Machine translation (MT) evaluation often focuses on accuracy and fluency, without paying much attention to translation style.This means that, even when considered accurate and fluent, MT output can still sound less natural than high quality human translations or text originally written in the target language.Machine translation output notably exhibits lower lexical diversity, and employs constructs that mirror those in the source sentence.In this work we propose a method for training MT systems to achieve a more natural style, i.e. mirroring the style of text originally written in the target language.Our method tags parallel training data according to the naturalness of the target side by contrasting language models trained on natural and translated data.Tagging data allows us to put greater emphasis on target sentences originally written in the target language.Automatic metrics show that the resulting models achieve lexical richness on par with human translations, mimicking a style much closer to sentences originally written in the target language.Furthermore, we find that their output is preferred by human experts when compared to the baseline translations.
Retrieval Data Augmentation Informed by Downstream Question Answering Performance

Training retrieval models to fetch contexts for Question Answering (QA) over large corpora requires labeling relevant passages in those corpora.Since obtaining exhaustive manual annotations of all relevant passages is not feasible, prior work uses text overlap heuristics to find passages that are likely to contain the answer, but this is not feasible when the task requires deeper reasoning and answers are not extractable spans (e.g.: multi-hop, discrete reasoning).We address this issue by identifying relevant passages based on whether they are useful for a trained QA model to arrive at the correct answers, and develop a search process guided by the QA model's loss.Our experiments show that this approach enables identifying relevant context for unseen data greater than 90% of the time on the IIRC dataset and generalizes better to the end QA task than those trained on just the gold retrieval data on IIRC and QASC datasets.
Mitigating Contradictions in Dialogue Based on Contrastive Learning

Chatbot models have achieved remarkable progress in recent years but tend to yield contradictory responses.In this paper, we exploit the advantage of contrastive learning technique to mitigate this issue.To endow the model with the ability of discriminating contradictory patterns, we minimize the similarity between the target response and contradiction related negative example.The negative example is generated with learnable latent noise, which receives contradiction related feedback from the pretrained critic.Experimental results show that our method helps to avoid contradictions in response generation while preserving response fluency, outperforming existing methods on both automatic and human evaluation.
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages

Fine-grained entity typing (FGET) aims to classify named entity mentions into fine-grained entity types, which is meaningful for entityrelated NLP tasks.For FGET, a key challenge is the low-resource problem -the complex entity type hierarchy makes it difficult to manually label data.Especially for those languages other than English, human-labeled data is extremely scarce.In this paper, we propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages.Specifically, we use multi-lingual pre-trained language models (PLMs) as the backbone to transfer the typing knowledge from high-resource languages (such as English) to low-resource languages (such as Chinese).Furthermore, we introduce entity-pair-oriented heuristic rules as well as machine translation to obtain cross-lingual distantly-supervised data, and apply cross-lingual contrastive learning on the distantly-supervised data to enhance the backbone PLMs.Experimental results show that by applying our framework, we can easily learn effective FGET models for low-resource languages, even without any language-specific human-labeled data.Our code is also available at https://github.com/thunlp/CrossET.
SUH_ASR@LT-EDI-ACL2022: Transformer based Approach for Speech Recognition for Vulnerable Individuals in Tamil

An Automatic Speech Recognition System is developed for addressing the Tamil conversational speech data of the elderly people and transgender.The speech corpus used in this system is collected from the people who adhere their communication in Tamil at some primary places like bank, hospital, vegetable markets.Our ASR system is designed with pre-trained model which is used to recognize the speech data.WER(Word Error Rate) calculation is used to analyse the performance of the ASR system.This evaluation could help to make a comparison of utterances between the elderly people and others.Similarly, the comparison between the transgender and other people is also done.Our proposed ASR system achieves the word error rate as 39.65%.
Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data

Multi-modal techniques offer significant untapped potential to unlock improved NLP technology for local languages.However, many advances in language model pre-training are focused on text, a fact that only increases systematic inequalities in the performance of NLP tasks across the world's languages.In this work, we propose a multi-modal approach to train language models using whatever text and/or audio data might be available in a language.Initial experiments using Swahili and Kinyarwanda data suggest the viability of the approach for downstream Named Entity Recognition (NER) tasks, with models pre-trained on phone data showing an improvement of up to 6% F1-score above models that are trained from scratch.Preprocessing and training code will be uploaded to https://github.com/sil-ai/phone-it-in.
Intelligent Courses Recommendation System of Collaborative Filtering Algorithm Based on K-means Clustering under Spark Platform

Students in Chinese universities face a variety of electives.Their choice of the course impacts their scope of knowledge and their grade point, which influences their career or further learning.But in most condition, the most appropriate option will not be chosen due to the lack of detail and reference.Intelligent recommendation system was applied in multiple fields, collaborative filtering algorithm is one of common recommendation algorithm and is widely used.However, a collaborative filtering algorithm has the drawback in accuracy and user similarity.In that case, an improved algorithm based on K-means clustering is applied.The improved algorithm uses Dichotomous K-means algorithm to deal with the long distance of cluster centroid.As original similarity algorithm leads to the result of low user similarity, a mixed algorithm is applied.Traditional methodology of data computing and data storing cannot handle the demand of that much data set.As a big data platform, Spark has become a popular solution to these problems.University management combined with big data is a tendency [4].
Out-of-Distribution Detection in High-Dimensional Data Using Mahalanobis Distance - Critical Analysis

Convolutional neural networks used in real-world recognition must be able to detect inputs that are Out-of-Distribution (OoD) with respect to the known or training data. A popular, simple method is to detect OoD inputs using confidence scores based on the Mahalanobis distance from known data. However, this procedure involves estimating the multivariate normal (MVN) density of high dimensional data using the insufficient number of observations (e.g., the dimensionality of features at the last two layers in the ResNet-101 model are 2048 and 1024, with ca. 1000–5000 examples per class for density estimation). In this work, we analyze the instability of parametric estimates of MVN density in high dimensionality and analyze the impact of this on the performance of Mahalanobis distance-based OoD detection. We show that this effect makes Mahalanobis distance-based methods ineffective for near OoD data. We show that the minimum distance from known data beyond which outliers are detectable depends on the dimensionality and number of training samples and decreases with the growing size of the training dataset. We also analyzed the performance of modifications of the Mahalanobis distance method used to minimize density fitting errors, such as using a common covariance matrix for all classes or diagonal covariance matrices. On OoD benchmarks (on CIFAR-10, CIFAR-100, SVHN, and Noise datasets), using representations from the DenseNet or ResNet models, we show that none of these methods should be considered universally superior.
Upside Down: Exploring the Ecosystem of Dark Web Data Markets

Large-scale dark web marketplaces have been around for more than a decade. So far, academic research has mainly focused on drug and hacking-related offers. However, data markets remain understudied, especially given their volatile nature and distinct characteristics based on shifting iterations. In this paper, we perform a large-scale study on dark web data markets. We first characterize data markets by using an innovative theoretical legal taxonomy based on the Council of Europe’s Cybercrime Convention and its implementation in Dutch law. The recent Covid-19 pandemic showed that cybercrime has become more prevalent with the increase of digitalization in society. In this context, important questions arise regarding how cybercrime harms are determined, measured, and prioritized. We propose a determination of harm based on criminal law qualifications and sanctions. We also address the empirical question of what the economic activity on data markets looks like nowadays by performing a comprehensive measurement of digital goods based on an original dataset scraped from twelve marketplaces consisting of approximately 28,000 offers from 642 vendors. The resulting analysis combines insights from the theoretical legal framework and the results of the measurement study. To our knowledge, this is the first study to combine these two elements systematically.
Human Action Recognition Based on Attention Mechanism and HRNet

Abstract A human action recognition network (AE-HRNet) based on high-resolution network (HRNet) and attention mechanism is proposed for the problem that the semantic and location information of human action features are not sufficiently extracted by convolutional networks. Firstly, the channel attention (ECA) module and spatial attention (ESA) module are introduced; on this basis, new base (EABasic) and bottleneck (EANeck) modules are constructed to reduce the computational complexity while obtaining more accurate semantic and location information on the feature map. Experimental results on the MPII and COCO validation sets in the same environment configuration show that AE-HRNet reduces the computational complexity and improves the action recognition accuracy compared to the high-resolution network.
IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention Mechanism

With the increase of users on social media platforms, manipulating or provoking masses of people has become a piece of cake.This spread of hatred among people, which has become a loophole for freedom of speech, must be minimized.Hence, it is essential to have a system that automatically classifies the hatred content, especially on social media, to take it down.This paper presents a simple modular pipeline classifier with BERT embeddings and attention mechanism to classify hope speech content in the Hope Speech Detection shared task for Equality, Diversity, and Inclusion-ACL 2022.Our system submission ranks fourth with an F1-score of 0.84.We release our code-base here https: //github.com/Deepanshu-beep/hope-speech-attention.
A Graph-oriented Framework for Online Analytical Processing

OLAP (Online Analytical Processing) is a triedand-tested technology and a core concept in Business Intelligence.With data flowing from different and countless sources, exploring data in order to deliver actionable insights has become a daunting task with current OLAP tools despite the cycle of improvement that has gone through it.In the last decade, with the emergence of the big data phenomenon, NoSQL databases are seeing a spike in popularity and become more used in industry and academia as their value in handling a huge and varied amount of data become increasingly evident.Graph oriented database is one of the four chief types of NoSQL oriented databases that represent a promising technology candidate for big data analytics.In this paper we bring forward our contribution to graph-oriented analytical processing, which is twofold.First, we provide a novel approach for modeling a graph-oriented data warehouse.Second, we propose a data cube materialization through the precomputation of aggregated nodes.We present how typical OLAP queries can be performed against data warehouses stored in NoSQL graph-oriented database management systems.An implementation is conducted on a fictional data warehouse using Neo4j and the Cypher declarative language.The same dataset is stored in a relational data warehouse in order to compare storage space and query performance.Thus, the obtained results shows that graph OLAP implementation outperform clearly the relational alternative in term of query response time.
Structured Extraction of Terms and Conditions from German and English Online Shops

The automated analysis of Terms and Conditions has gained attention in recent years, mainly due to its relevance to consumer protection.Well-structured data sets are the base for every analysis.While content extraction, in general, is a well-researched field and many open source libraries are available, our evaluation shows, that existing solutions cannot extract Terms and Conditions in sufficient quality, mainly because of their special structure.In this paper, we present an approach to extract the content and hierarchy of Terms and Conditions from German and English online shops.Our evaluation shows, that the approach outperforms the current state of the art.A python implementation of the approach is made available under an open license.
Image Style Transfering Based on StarGAN and Class Encoder

The image style transfer technology has been integrated into people's lives and is widely used in practical scenarios such as artistic images, photo to cartoon, image coloring, filter processing, and occlusion removal, which bears important research significance and application value.StarGAN is a generative adversarial network framework used in recent years for multidomain image style transfer, which extracts features through simple down-sampling and then generates images through up-sampling.However, the background color information and detailed features of characters' faces in the generated images are greatly different from those in the input images.In this paper, the network structure of StarGAN is improved, and a UE-StarGAN model for image style transfer is proposed by introducing U-Net and edge-promoting adversarial loss function.At the same time, the class encoder is introduced into the generator of the UE-StarGAN model, and an image style transfer model fusing class encoder based on a small sample size is designed to realize the image style transfer with a small sample size.The experimental results reveal that the model can extract more detailed features and has some advantages in the case of a small sample size.The images obtained by applying the image style transfer based on the proposed model are improved in both qualitative and quantitative analyses, which verifies the effectiveness of the proposed model.
Historical and pedagogical analysis of the national vocational education system development

The article analyzes the historical foundations of the creation and development of the vocational education system in Russia, the trends and risks of training middle-level specialists, and the institutional basis of the process. The theoretical foundation of the study of combining theoretical vocational training with practical training at the enterprise, the specificity of vocational education in the modern world, affecting the formation of the economy, production and the development of the structure of society are considered. Based on the historical analysis, the stages and periods of the formation of the professional training system are described. The results of the study are presented.