Reviewerly/exHarmony huggingface dataset:
Challenges of Adopting SAFe in the Banking Industry – A Study Two Years After Its Introduction

Abstract The Scaled Agile Framework (SAFe) is a framework for scaling agile methods in large organizations. We have found several experience reports and white papers describing SAFe adoptions in different banks, which indicates that SAFe is being used in the banking industry. However, there is a lack of academic publications on the topic, the banking industry is missing in the scientific reports analyzing SAFe transformations. To fill this gap, we present a study on the main challenges with a SAFe transformation at a large full-service bank. We identify the challenges in the bank under study and compare the findings with experience reports from other banks, as well as with research on SAFe transformations in other domains. Many of the challenges reported in this paper overlap with the generic SAFe challenges, including management and organization, education and training, culture and mindset, requirements engineering, quality assurance, and systems architecture. However, we also report some novel challenges specific to the banking domain, e.g., the risk of jeopardizing customer relations, stability, and trust of external stakeholders. This study validates several SAFe-related challenges reported in previous work in the banking context. It also brings up some novel challenges specific to the banking industry. Therefore, we believe our results are particularly useful to practitioners responsible for SAFe transformations at other banks.
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences

Commonsense reasoning is intuitive for humans but has been a long-term challenge for artificial intelligence (AI).Recent advancements in pretrained language models have shown promising results on several commonsense benchmark datasets.However, the reliability and comprehensiveness of these benchmarks towards assessing model's commonsense reasoning ability remains unclear.To this end, we introduce a new commonsense reasoning benchmark dataset comprising natural language true/false statements, with each sample paired with its complementary counterpart, resulting in 4k sentence pairs.We propose a pairwise accuracy metric to reliably measure an agent's ability to perform commonsense reasoning over a given situation.The dataset is crowdsourced and enhanced with an adversarial model-in-the-loop setup to incentivize challenging samples.To facilitate a systematic analysis of commonsense capabilities, we design our dataset along the dimensions of knowledge domains, reasoning scenarios and numeracy.Experimental results demonstrate that our strongest baseline (UnifiedQA-3B), after fine-tuning, achieves ~71% standard accuracy and ~51% pairwise accuracy, well below human performance (~95% for both metrics).
Minimizing Labeling Cost for Nuclei Instance Segmentation and Classification with Cross-domain Images and Weak Labels

Nucleus instance segmentation and classification in histopathological images is an essential prerequisite in pathology diagnosis/prognosis. However, nucleus annotations (e.g., segmentation and labeling) require domain experts, and annotating nuclei at pixel-level is time-consuming and labor-intensive. Moreover, nuclei from different cancer types vary in shapes and appearances. These inter-cancer variations require careful annotations for specific cancer types. Therefore, to minimize the labeling cost, we propose a novel application that considers each cancer type as an individual domain and apply domain adaptation techniques to improve the segmentation/classification performance among different cancer types. Unlike the previous studies that focus on unsupervised or weakly-supervised domain adaptation independently, we would like to discover what kinds of labeling can achieve the most cost-effective domain adaptation performance in nucleus instance segmentation and classification. Specifically, we propose a unified framework that is applicable to different level annotations: no annotations, image-level, and point-level annotations. Cyclic adaptation with pseudo labels and adversarial discriminator are utilized for unsupervised domain alignment. Image-level or point-level annotations are additionally adopted to supervise the nucleus classification and refine the pseudo labels. Experiments demonstrate the effectiveness and efficacy of the proposed framework (jointly using unsupervised and weakly supervised learning) on adapting the segmentation and classification model from one cancer type to 18 other cancer types.
Generating Adversarial yet Inconspicuous Patches with a Single Image (Student Abstract)

Deep neural networks have been shown vulnerable to adversarial patches, where exotic patterns can result in model’s wrong prediction. Nevertheless, existing approaches to adversarial patch generation hardly consider the contextual consistency between patches and the image background, causing such patches to be easily detected by human observation. Additionally, these methods require a large amount of data for training, which is computationally expensive. To overcome these challenges, we propose an approach to generate adversarial yet inconspicuous patches with one single image. In our approach, adversarial patches are produced in a coarse-to-fine way with multiple scales of generators and discriminators. The selection of patch location is based on the perceptual sensitivity of victim models. Contextual information is encoded during the Min-Max training to make patches consistent with surroundings.
Leveraging Argumentation Knowledge Graph for Interactive Argument Pair Identification

Interactive argument pair identification is essential in the context of dialogical argumentation mining.Existing research treats it as a problem of sentence matching and largely relies on textual information to compute the similarities.However, the interaction of opinions usually involves the background of the topic and requires reasoning of knowledge, which is beyond textual information.In this paper, we propose to leverage external knowledge to enhance the identification of interactive argument pairs.We construct the argumentation knowledge graph from the discussion thread of the target topic in the online forum.The interaction between the original argument and the reply is then represented as the path of concepts in the knowledge graph.In practice, we utilize Graph Convolutional Network (GCN) to learn the concept representation in the knowledge graph and use a Transformerbased encoder to learn the representation of paths.Finally, an information alignment network is employed to capture the interaction of textual information of conceptual information (both entity-level and path-level).Experiment results indicate that our model achieves state-of-the-art performance in the benchmark dataset.Further analysis demonstrates the effectiveness of our model for enforcing knowledge reasoning through paths in the knowledge graph.
Finding Phish in a Haystack: A Pipeline for Phishing Classification on Certificate Transparency Logs

Current popular phishing prevention techniques mainly utilize reactive blocklists, which leave a "window of opportunity" for attackers during which victims are unprotected. One possible approach to shorten this window aims to detect phishing attacks earlier, during website preparation, by monitoring Certificate Transparency (CT) logs. Previous attempts to work with CT log data for phishing classification exist, however they lack evaluations on actual CT log data. In this paper, we present a pipeline that facilitates such evaluations by addressing a number of problems when working with CT log data. The pipeline includes dataset creation, training, and past or live classification of CT logs. Its modular structure makes it possible to easily exchange classifiers or verification sources to support ground truth labeling efforts and classifier comparisons. We test the pipeline on a number of new and existing classifiers, and find a general potential to improve classifiers for this scenario in the future. We publish the source code of the pipeline and the used datasets along with this paper [12], thus making future research in this direction more accessible.
Platform and App Histories: Assessing Source Availability in Web Archives and App Repositories

In this chapter, we discuss the research opportunities for historical studies of apps and platforms by focusing on their distinctive characteristics and material traces. We demonstrate the value and explore the utility and breadth of web archives and software repositories for building corpora of archived platform and app sources. Platforms and apps notoriously resist archiving due to their ephemerality and continuous updates. As a consequence, their histories are being overwritten with each update rather than written and preserved. We present a method to assess the availability of archived web sources for social media platforms and apps across the leading web archives and app repositories. Additionally, we conduct a comparative source set availability analysis to establish how, and how well, various source sets are represented across web archives. Our preliminary results indicate that despite the challenges of social media and app archiving, many material traces of platforms and apps are in fact well preserved. We understand these contextual materials as important primary sources through which digital objects such as platforms and apps co-author their own "biographies" with web archives and software repositories.
Refiner: Refining Self-attention for Vision Transformers

Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs. Yet, they generally require much more data for model pre-training. Most of recent works thus are dedicated to designing more complex architectures or training methods to address the data-efficiency issue of ViTs. However, few of them explore improving the self-attention mechanism, a key factor distinguishing ViTs from CNNs. Different from existing works, we introduce a conceptually simple scheme, called refiner, to directly refine the self-attention maps of ViTs. Specifically, refiner explores attention expansion that projects the multi-head attention maps to a higher-dimensional space to promote their diversity. Further, refiner applies convolutions to augment local patterns of the attention maps, which we show is equivalent to a distributed local attention features are aggregated locally with learnable kernels and then globally aggregated with self-attention. Extensive experiments demonstrate that refiner works surprisingly well. Significantly, it enables ViTs to achieve 86% top-1 classification accuracy on ImageNet with only 81M parameters.
Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

In this study, we propose the gesture recognition algorithm using support vector machines (SVM) and histogram of oriented gradient (HOG). Besides, we also use the CNN model to classify gestures. We approach and select techniques of applying problem controlling for the robotic system. The goal of the algorithm is to detect gestures with real-time processing speed, minimize interference, and reduce the ability to capture unintentional gestures. Static gesture controls are used in this study including on, off, increasing, and decreasing. Besides, it uses motion gestures including turning on the status switch and increasing and decreasing the volume. Results show that the algorithm is up to 99% accuracy with a 70-millisecond execution time per frame that is suitable for industrial applications.
A Survey of Quantum Error Correction

This paper surveys development of quantum error correction. With the familiarity with conventional coding theory and tensor product in multi-linear algebra, this paper can be read in a self-contained manner.
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information

Recent pretraining models in Chinese neglect two important aspects specific to the Chinese language: glyph and pinyin, which carry significant syntax and semantic information for language understanding. In this work, we propose ChineseBERT, which incorporates both the {\it glyph} and {\it pinyin} information of Chinese characters into language model pretraining. The glyph embedding is obtained based on different fonts of a Chinese character, being able to capture character semantics from the visual features, and the pinyin embedding characterizes the pronunciation of Chinese characters, which handles the highly prevalent heteronym phenomenon in Chinese (the same character has different pronunciations with different meanings). Pretrained on large-scale unlabeled Chinese corpus, the proposed ChineseBERT model yields significant performance boost over baseline models with fewer training steps. The porpsoed model achieves new SOTA performances on a wide range of Chinese NLP tasks, including machine reading comprehension, natural language inference, text classification, sentence pair matching, and competitive performances in named entity recognition. Code and pretrained models are publicly available at https://github.com/ShannonAI/ChineseBert.
Design and Fabrication of Grating Couplers for the Optical Addressing of Trapped Ions

In this work, silicon photonics structures for the optical addressing of trapped ion is developed for quantum computing applications. Grating-waveguide-grating structures of various designs are designed, and fabricated for various radius curvatures of 12, 15, 25 and 30 µm, respectively. From the optical measurements, gratings with radius of curvature of 25 and 30 µm exhibit lower power loss of 36.5 and 33.9 dB with better-focused beam profiles as compared to those with radius of curvature of 12 and 15 µm. The beam width from these gratings ranges between 17.31 to 41.54 µm, which provides the feasibility to perform optical addressing on 2 to 4 Sr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ions trapped along the ground electrode of the ion trap.
A New Digital Image Steganography Based on Center Embedded Pixel Positioning

Abstract In this study we propose a new approach to tackle the cropping problem in steganography which is called Center Embedded Pixel Positioning (CEPP) which is based on Least Significant Bit (LSB) Matching by setting the secret image in the center of the cover image. The evaluation of the experiment indicated that the secret image can be retrieved by a maximum of total 40% sequential cropping on the left, right, up, and bottom of the cover image. The secret image also can be retrieved if the total asymmetric cropping area is 25% that covered two sides (either left-right, left-up or right-up). In addition, the secret image can also be retrieved if the total asymmetric cropping area is 70% if the bottom part is included. If asymmetric cropping area included three sides, then the algorithm fails to retrieve the secret image. For cropping in the botom the secret image can be extracted up to 70%.
AMP

Advances in graphics and machine learning have led to the general availability of easy-to-use tools for modifying and synthesizing media. The proliferation of these tools threatens to cast doubt on the veracity of all media. One approach to thwarting the flow of fake media is to detect modified or synthesized media through machine learning methods. While detection may help in the short term, we believe that it is destined to fail as the quality of fake media generation continues to improve. Soon, neither humans nor algorithms will be able to reliably distinguish fake versus real content. Thus, pipelines for assuring the source and integrity of media will be required---and increasingly relied upon. We present AMP, a system that ensures the authentication of media via certifying provenance. AMP creates one or more publisher-signed manifests for a media instance uploaded by a content provider. These manifests are stored in a database allowing fast lookup from applications such as browsers. For reference, the manifests are also registered and signed by a permissioned ledger, implemented using the Confidential Consortium Framework (CCF). CCF employs both software and hardware techniques to ensure the integrity and transparency of all registered manifests. AMP, through its use of CCF, enables a consortium of media providers to govern the service while making all its operations auditable. The authenticity of the media can be communicated to the user via visual elements in the browser, indicating that an AMP manifest has been successfully located and verified.
FFANet: Feature fusion attention network to medical image segmentation

Medical images are an important means to assist doctors in making judgments. For the problem that it is difficult to segment various complex tissue structures in medical images, we propose a segmentation method based on feature aggregation. Firstly, our method adopts VoVNet as the backbone and outputs multi-scale features. Secondly, we use the multi-scale features aggregation module to extract context information fully. Finally, we adopt an attention module to consider the relevance of each spatial and channel. Through the experiments conduct on two datasets, the proposed model scores a dice coefficient of 90.90% and 86.10%. Results show that our network can segment the target area well in the gray image and RGB image and outperforms the existing methodologies. We provide a download link to our method.
Soil Fertility Analysis and Fertilizer Recommendation System

Agriculture is one among the most important sector of Indian Economy. More than 50% of population depends on agriculture as their source of income. Growing crops over thousands of years without caring about replenishing has led to depletion and exhaustion of soil nutrients resulting in their low productivity. To improve the production, chemical fertilizers were added. But excessive use of these not only makes the plants dependent on artificial fertilizers but also erodes the natural quality of land. It is therefore important to make sure that only the sufficient amount of fertilizers is added so that yield can be increased and at the same time, the natural quality of soil remain intact. For this, it is essential to know the soil nutrient levels. Our project aims at finding the soil nutrient richness and predict the fertility of a given soil sample in real time. Based on the result obtained, the system will also be giving recommendation on the type of fertilizer and in what quantity it is to be used in the soil sample so that the yield can be maximized.
Sparse Bayesian mass mapping with uncertainties: hypothesis testing of structure

ABSTRACT A crucial aspect of mass mapping, via weak lensing, is quantification of the uncertainty introduced during the reconstruction process. Properly accounting for these errors has been largely ignored to date. We present a new method to reconstruct maximum a posteriori (MAP) convergence maps by formulating an unconstrained Bayesian inference problem with Laplace-type l1-norm sparsity-promoting priors, which we solve via convex optimization. Approaching mass mapping in this manner allows us to exploit recent developments in probability concentration theory to infer theoretically conservative uncertainties for our MAP reconstructions, without relying on assumptions of Gaussianity. For the first time, these methods allow us to perform hypothesis testing of structure, from which it is possible to distinguish between physical objects and artefacts of the reconstruction. Here, we present this new formalism, and demonstrate the method on simulations, before applying the developed formalism to two observational data sets of the Abell 520 cluster. Initial reconstructions of the Abell 520 catalogues reported the detection of an anomalous ‘dark core’ – an overdense region with no optical counterpart – which was taken to be evidence for self-interacting dark matter. In our Bayesian framework, it is found that neither Abell 520 data set can conclusively determine the physicality of such dark cores at $99{{\ \rm per\ cent}}$ confidence. However, in both cases the recovered MAP estimators are consistent with both sets of data.
High-resolution image multiplexing using amplitude grating for remote sensing applications

High-resolution images are essential in many remote sensing applications such as weather prediction, deforestation, crop monitoring, land mapping, urban growth, etc. In these applications, it is necessary to use multiple high-resolution images for proper analysis. These images carry a lot of information; therefore, multiplexing plays an important role for efficient storage of the images. Hence, we provide an idea of multiplexing high-resolution images using amplitude grating. Initially, images are modulated with variable spatial frequencies and orientation angles followed by the addition of modulated spectrums to form a single spectrum plane. The resultant spectrum plane is filtered before transmission to eliminate noise. To optimize storage space, only a part of the filtered spectrum is transmitted. A pixel intensity graph is plotted to identify the location of maximum information of the images, which is required during the retrieval process. The quality of the output images is analyzed by peak signal-to-noise ratio, structural similarity index measurement, and correlation coefficient methods. The entire work is done by simulation software, and we prove that our proposed multiplexing method is suitable for efficient storage of high-resolution images in remote sensing applications.
Attitudes of Czech College Students Toward Digital Literacy and Their Technical Aids in Times of COVID-19

At present, when the whole world is struggling with the Covid-19 pandemic, it is important to address both the issue of flexibility when educating the younger generation and the issue of further professional education, which contributes to the development of competences that are directly relevant to life and everyday activities in the 21st century. The common denominator of these competences is digital competence. It is the aspect of education that is of primary and crucial importance for digital literacy development. This re-search study aims at establishing the availability of digital media and atti-tudes towards one’s own technical aids and digital literacy among students of two higher education institutions in the Czech Republic. Empirical research involved 1233 college students. The set research hypotheses were tested with a questionnaire-based survey using the Likert scale. A cluster analysis was conducted in the SPSS program. In view of the ordinal nature of data, the nonparametric Mann–Whitney U test conducted at significance levels of 5% and 1% in SPSS was applied to test the dependence of competence self-assessment on the specialization of the higher education institutions/ study level/ study form. The research has unequivocally proven that digital literacy is influenced by education, in terms of the specific study programme special-ization, study level and study form.