Reviewerly/exHarmony huggingface dataset:
Transcreation as a Creative Tool of Translation

The article touches upon the necessity of using the creative approach during translating the texts of the advertising discourse. The authors of the study emphasize the creative component and the knowledge of national and cultural peculiarities of the countries which are necessary to be considered on a par with the linguistic training. TV-commercials are used as the material of the research as they allow to perform and analyze the translating and creative skills of specialists. The authors consider the transcreation strategy to be an efficient creative means of delivering the authentic message based on the national and cultural peculiarities of another country to the Russian target audience. The transcreation strategy includes not only linguistic transformations but also presupposes the localization of foreign elements while the process of translation. Thus, the article shows that the process of translation of the TV-commercials should be of an interdisciplinary nature as it contributes to the full realization of both linguistic and cultural components of the original text.
Is BERT the New Silver Bullet? - An Empirical Investigation of Requirements Dependency Classification

Bidirectional Encoder Representations from Transformers (BERT) is a successful transformer-based Machine Learning technique for Natural Language Processing (NLP) based tasks developed by Google. It has taken various domains by storm, and Software Engineering is one among them. But does this mean that BERT is the new Silver Bullet? It is certainly not. We demonstrate it through an empirical investigation of the Requirements Dependency Classification (RDC). In general, based on various criteria used for evaluation, decisions on classification method preference may vary. For RDC, we go beyond traditional metrics such as the F1 score and consider Return-on-Investment (ROI) to evaluate two techniques for such decision making. We study RDC-BERT (fine-tuned BERT using data specific to requirements dependency classification) and compare with Random Forest, our baseline. For RDC and data from FOSS system Redmine, we demonstrate how decisions on method preference vary based on (i) accuracy, (ii) ROI, and (iii) sensitivity analysis. Results show that for all the three scenarios, method preference decisions depend on learning and evaluation parameters. Although these results are with respected to the chosen data sets, we argue that the proposed methodology is a prospective approach to study similar questions for data analytics, in general.
Spying through Virtual Backgrounds of Video Calls

Video calls have become an essential part of today's business life, especially due to the Corona pandemic. Several industry branches enable their employees to work from home and collaborate via video conferencing services. While remote work offers benefits for health safety and personal mobility, it also poses privacy risks. Visual content is directly transmitted from the private living environment of employees to third parties, potentially exposing sensitive information. To counter this threat, video conferencing services support replacing the visible environment of a video call with a virtual background. This replacement, however, is imperfect, leaking tiny regions of the real background in video frames. In this paper, we explore how these leaks in virtual backgrounds can be exploited to reconstruct regions of the real environment. To this end, we build on recent techniques of computer vision and derive an approach capable of extracting and aggregating leaked pixels in a video call. In an empirical study with the services Zoom, Webex, and Google Meet, we can demonstrate that the exposed fragments of the reconstructed background are sufficient to spot different objects. From 114 video calls with virtual backgrounds, 35% enable to correctly identify objects in the environment. We conclude that virtual backgrounds provide only limited protection, and alternative defenses are needed.
Content Based News Recommendation Engine using Hybrid BiLSTM-ANN Feature Modelling

Recommendation systems are widespread with the big data hosted on the internet and the users who actively access them. The recommendation model implemented here uses the weights of a trained Bi-LSTM ANN model that was used for multiclass news categorization and feeds these weights to a k-NN clustering algorithm. The k-NN based recommendation engine uses cosine similarity between the gradient activation maps for identifying the articles with highest similarity to the others to feed them as suggestions to the user on the platform. The implemented recommendation system is evaluated using coverage and cosine similarity analysis performed on the testing dataset. It has been observed from the coverage analysis that on an average 74.26% of news articles in the testing data have been covered for the chosen ‘n’ recommendations. The cosine similarity between different categories in the dataset is calculated and it is found that the ‘Science’, the highest score of similarity with other categories at 0.772.
Collaborative Overtaking of Multi-Vehicle Systems in Dynamic Environments: A Distributed Artificial Potential Field Approach

Multi-vehicle autonomous driving has attracted significant attention from academia and industries due to its ability to improve traffic efficiency and enhance the safety of drivers and passengers. In this paper, a distributed motion planning strategy based on the artificial potential field method is proposed to achieve overtaking of the autonomous vehicle fleet in dynamic environments. Firstly, a dynamic target tracking control protocol is constructed for an autonomous vehicle fleet using a modified artificial potential field. The target tracking control protocol overcomes the disadvantage of the traditional artificial potential field method (i.e., the dynamic target cannot be reached). Besides, a distributed obstacle avoidance control protocol is also designed to avoid potential collisions during the overtaking process. Finally, simulation experiments are performed to verify the feasibility and effectiveness of the proposed algorithm.
Deep Learning Based Colorectal Cancer (CRC) Tumors Prediction

Cancer detection and prediction using computer assisted systems has become the most leading research area in recent times. It has a big demand in the medical sector for identifying not only cancer but also any diseases detected and predicted from pathological data or images. Colorectal Cancer or Colon Cancer (CRC) detection is also one of them. Because CRC has become a global health issue day by day. In this paper we used a dataset of 10,000 histopathological images with the same dimension of colonic tissue. We used ensemble methods and classifiers for classifying images. We obtained the best accuracy 99% from XGBoost classifier and from others were 98%, 97%, 96%, 92%, 92% and 89% which exactly classifying 523 colon adenocarcinoma images and 477 benign colonic tissue images from 1,000 histopathological images.
Relation Prediction via Graph Neural Network in Heterogeneous Information Networks with Missing Type Information

Relation prediction is a fundamental task in network analysis which aims to predict the relationship between two nodes. Thus, this differes from the traditional link prediction problem predicting whether a link exists between a pair of nodes, which can be viewed as a binary classification task. However, in the heterogeneous information network (HIN) which contains multiple types of nodes and multiple relations between nodes, the relation prediction task is more challenging. In addition, the HIN might have missing relation types on some edges and missing node types on some nodes, which makes the problem even harder.
A Case Study of Knowledge Management and Organizational Culture in an Undergraduate Software Development Team Project

People work in software development projects to bundle the human resources and use the systematic approach to share system development knowledge. One can view knowledge as personalized related to facts, procedures, concepts, interpretations, ideas, and judgments. This way, knowledge is the outcome of the cognitive processing of information. Knowledge can be transferred from a source to a receiver. The collaborative knowledge-sharing mechanism is known as knowledge management (KM) in the software industry. The software developers can communicate with, learn from, and solve problems with other participating team members. The organizational culture is an essential factor in knowledge management success since it influences how team members learn and share knowledge. This chapter presents a case study that aimed to compare, in practice, the relationship between the KM cycle (SECI – socialization, externalization, combination, and internalization model) and the organizational culture through the competing values framework (CVF).
A Hierarchical Assessment of Adversarial Severity

Adversarial Robustness is a growing field that evidences the brittleness of neural networks. Although the literature on adversarial robustness is vast, a dimension is missing in these studies: assessing how severe the mistakes are. We call this notion "Adversarial Severity" since it quantifies the downstream impact of adversarial corruptions by computing the semantic error between the misclassification and the proper label. We propose to study the effects of adversarial noise by measuring the Robustness and Severity into a large-scale dataset: iNaturalist-H. Our contributions are: (i) we introduce novel Hierarchical Attacks that harness the rich structured space of labels to create adversarial examples. (ii) These attacks allow us to benchmark the Adversarial Robustness and Severity of classification models. (iii) We enhance the traditional adversarial training with a simple yet effective Hierarchical Curriculum Training to learn these nodes gradually within the hierarchical tree. We perform extensive experiments showing that hierarchical defenses allow deep models to boost the adversarial Robustness by 1.85% and reduce the severity of all attacks by 0.17, on average.
Optimal Bayesian smoothing of functional observations over a large graph

In modern contexts, some types of data are observed in high-resolution, essentially continuously in time. Such data units are best described as taking values in a space of functions. Subject units carrying the observations may have intrinsic relations among themselves, and are best described by the nodes of a large graph. It is often sensible to think that the underlying signals in these functional observations vary smoothly over the graph, in that neighboring nodes have similar underlying signals. This qualitative information allows borrowing of strength over neighboring nodes and consequently leads to more accurate inference. In this paper, we consider a model with Gaussian functional observations and adopt a Bayesian approach to smoothing over the nodes of the graph. We characterize the minimax rate of estimation in terms of the regularity of the signals and their variation across nodes quantified in terms of the graph Laplacian. We show that an appropriate prior constructed from the graph Laplacian can attain the minimax bound, while using a mixture prior, the minimax rate up to a logarithmic factor can be attained simultaneously for all possible values of functional and graphical smoothness. We also show that in the fixed smoothness setting, an optimal sized credible region has arbitrarily high frequentist coverage. A simulation experiment demonstrates that the method performs better than potential competing methods like the random forest. The method is also applied to a dataset on daily temperatures measured at several weather stations in the US state of North Carolina.
TEDT2 – Highly Secure Leakage-Resilient TBC-Based Authenticated Encryption

Leakage-resilient authenticated encryption (AE) schemes received considerable attention during the previous decade. Two core security models of bounded and unbounded leakage have evolved, where the latter has been motivated in a very detailed and practice-oriented manner. In that setting, designers often build schemes based on (tweakable) block ciphers due to the small state size, such as the recent two-pass AE scheme TEDT from TCHES 1/2020. TEDT is interesting due to its high security guarantees of \(O(n - \log (n^2))\)-bit integrity under leakage and similar AE security in the black-box setting. Though, a detail limited it to provide only n/2-bit privacy under leakage.
Few-Shot Learning with Improved Local Representations via Bias Rectify Module

Recent approaches based on metric learning have achieved great progress in few-shot learning. However, most of them are limited to image-level representation manners, which fail to properly deal with the intra-class variations and spatial knowledge and thus produce undesirable performance. In this paper, we propose a Deep Bias Rectify Network (DBRN) to fully exploit the spatial information that exists in the structure of the feature representations. We first employ a bias rectify module, which is able to focus on the features that are more discriminative for classification by giving different weights, to alleviate the adverse impact caused by the intra-class variations. To make full use of the training data, we design a prototype augment mechanism that can make the prototypes generated from the support set to be more representative. To validate the effectiveness of our method, we conducted extensive experiments on various popular few-shot classification benchmarks and our methods can outperform state-of-the-art methods.
Artificial Intelligence, Surveillance, and Big Data

The most important resource to improve technologies in the field of artificial intelligence is data. Two types of policies are crucial in this respect: privacy and data-sharing regulations, and the use of surveillance technologies for policing. Both types of policies vary substantially across countries and political regimes. This chapter examines how authoritarian and democratic political institutions can influence the quality of research in artificial intelligence, and the availability of large-scale datasets to improve and train deep learning algorithms. We focus mainly on the case of China, and find that—ceteris paribus—authoritarian political institutions continue to have a negative effect on innovation. They can, however, have a positive effect on research in deep learning, via the availability of large-scale datasets that have been obtained through government surveillance. We propose a research agenda to study which of the two effects might dominate in a race for leadership in artificial intelligence between countries with different political institutions, such as the USA and China.
Sentiment Analysis of Bengali Text using CountVectorizer with Logistic Regression

Sentiment analysis refers to find the polarity of a text. Sentiment analysis or opinion mining on social media data is an emerging topic in research. In Bangladesh, there remains a few researches on Bengali text. In Natural Language Processing, text document can be represented by vector which is a very efficient way to find the similarities among the text. Word2vec is a technique to represent each word as a vector and Doc2vec represent a whole sentence or document as a vector. CountVectorizer is another technique to transform a corpora of text to a vector form along with the token counts. There remains a few researches using Word2vec and Doc2vec. And recent studies showed that CountVectorizer outperforms both of the Doc2vec and Word2vec techniques. In this study, we have used a dataset of 7,000 Bengali text to apply sentiment classification into two classes: positive and negative. We have extracted the feature using CountVectorizer and then have applied the machine learning method: Logistic Regression for the classification task. The result shows higher accuracy than the previous works.
Fuzzy Classification of Multi-intent Utterances

Current intent classification approaches assign binary intent class memberships to natural language utterances while disregarding the inherent vagueness in language and the corresponding vagueness in intent class boundaries. In this work, we propose a scheme to address the ambiguity in single-intent as well as multi-intent natural language utterances by creating degree memberships over fuzzified intent classes. To our knowledge, this is the first work to address and quantify the impact of the fuzzy nature of natural language utterances over intent category memberships. Additionally, our approach overcomes the sparsity of multi-intent utterance data to train classification models by using a small database of single intent utterances to generate class memberships over multi-intent utterances. We evaluate our approach over two task-oriented dialog datasets, across different fuzzy membership generation techniques and approximate string similarity measures. Our results reveal the impact of lexical overlap between utterances of different intents, and the underlying data distributions, on the fuzzification of intent memberships. Moreover, we evaluate the accuracy of our approach by comparing the defuzzified memberships to their binary counterparts, across different combinations of membership functions and string similarity measures.
Edge Detection of Human Face

In recent decades, like computer science and digital image processing have become more mature, face recognition has attracted more attention. However, the human face is a very complex “picture”, and the computer will face a variety of problems when processing face patterns in different scenes. As one of the basic features of the image, the edge forms the outline of the object. Edge detection is one of the most important operations in image analysis and processing. In this paper, process the images by Matlab software, and compare the effects of different edge detectors. Take a picture of the face with a mobile phone, process it with Canny edge detector, Sobel edge detector, Roberts edge detector, and Prewitt edge detector, and compare the results. The experimental results demonstrated that Canny Edge Detector is the most suitable detector for detecting human faces' edges.
Employing chunk size adaptation to overcome concept drift

Modern analytical systems must process streaming data and correctly respond to data distribution changes. The phenomenon of changes in data distributions is called concept drift , and it may harm the quality of the used models. Additionally, the possibility of concept drift appearance causes that the used algorithms must be ready for the continuous adaptation of the model to the changing data distributions. This work focuses on non-stationary data stream classification, where a classifier ensemble is used. To keep the ensemble model up to date, the new base classifiers are trained on the incoming data blocks and added to the ensemble while, at the same time, outdated models are removed from the ensemble. One of the problems with this type of model is the fast reaction to changes in data distributions. We propose the new Chunk Adaptive Restoration framework that can be adapted to any block-based data stream classification algorithm. The proposed algorithm adjusts the data chunk size in the case of concept drift detection to minimize the impact of the change on the predictive performance of the used model. The experimental research, backed up with the statistical tests, has proven that Chunk Adaptive Restoration significantly reduces the model&amp;rsquo;s restoration time.
USING MOBILE AUGMENTED REALITY IN PERFORMANCE SUPPORT

This article describes the benefits of using mobile augmented reality (AR) as a performance support intervention in Human Performance Technology (HPT). It demonstrates examples of using mobile AR to support job performance from areas outside of HPT such as engineering, computer science, and business. Moreover, it explores the applicable approaches of adopting mobile AR to assist performance support for workers. The article also addresses three main challenges to adoptmobile AR and guides the implementation of mobile AR in the workplace.
VeriFly: <i>On-the-fly Assertion Checking via Incrementality</i>

Abstract Assertion checking is an invaluable programmer’s tool for finding many classes of errors or verifying their absence in dynamic languages such as Prolog. For Prolog programmers, this means being able to have relevant properties, such as modes, types, determinacy, nonfailure, sharing, constraints, and cost, checked and errors flagged without having to actually run the program. Such global static analysis tools are arguably most useful the earlier they are used in the software development cycle, and fast response times are essential for interactive use. Triggering a full and precise semantic analysis of a software project every time a change is made can be prohibitively expensive. This is specially the case when complex properties need to be inferred for large, realistic code bases. In our static analysis and verification framework, this challenge is addressed through a combination of modular and incremental (context- and path-sensitive) analysis that is responsive to program edits, at different levels of granularity. In this tool paper, we present how the combination of this framework within an integrated development environment (IDE) takes advantage of such incrementality to achieve a high level of reactivity when reflecting analysis and verification results back as colorings and tooltips directly on the program text – the tool’s VeriFly mode. The concrete implementation that we describe is Emacs-based and reuses in part off-the-shelf “on-the-fly” syntax checking facilities (flycheck). We believe that similar extensions are also reproducible with low effort in other mature development environments. Our initial experience with the tool shows quite promising results, with low latency times that provide early, continuous, and precise assertion checking and other semantic feedback to programmers during the development process. The tool supports Prolog natively, as well as other languages by semantic transformation into Horn clauses.
Public Key Encryption with Fuzzy Matching

The rise of cloud computing is driving the development in various fields and becoming one of the hot topics in recent years. To solve the problem of comparing ciphertexts between different users in cloud storage, Public Key Encryption with Equality Test (PKEET) was proposed. In PKEET, a tester can determine whether two ciphertexts encrypted with different public keys contain the same message without decrypting the ciphertexts. However, PKEET only supports exact matching, which may not be practical when two messages have misspellings, formatting differences, or differences in the data itself. Therefore, to support fuzzy matching, in this paper we propose the concept of Public Key Encryption with Fuzzy Matching (PKEFM), which allows to determine whether the edit distance between two encrypted messages is less than a threshold value. PKEFM can be well applied to support fuzzy data comparison in encrypted e-mail systems or encrypted gene testing. We then modify the scheme to provide the decryption function, and support (encrypted) wildcards in the matching, in order to further improve the generality of PKEFM at the cost of a small amount of extra computation.