Reviewerly/exHarmony huggingface dataset:
Abstract PS3-20: Rapid beside diagnosis of breast core needle biopsies using stimulated raman histology

Abstract Image-guided core needle biopsy plays a central role in the diagnosis and management of a wide range of breast lesions. After collection, tissue cylinders are transported from a biopsy suite to a pathology laboratory for analysis. The existing workflow for microscopic diagnosis of tissue specimens precludes the option of using histologic data to inform clinical decision-making at the time of biopsy. Here, we introduce stimulated Raman histology (SRH), a rapid label-free optical imaging method that enables microscopic imaging of unprocessed breast biopsy specimens in the biopsy suite within minutes of sample collection. SRH has been thoroughly validated for use in the detection and diagnosis of brain tumors but its use has not been validated in breast lesions. In our study, we collected core needle biopsy specimens and allocated a 2mm portion of the tissue sample for SRH imaging. Each specimen was imaged with SRH and subsequently fixed in formalin, embedded, stained and sectioned in a conventional manner. A board-certified breast pathologist first analyzed the diagnostic features of the imaged specimens with SRH and then with conventional H&amp;E-stained sections. In a pilot series of six patients, we found that SRH could differentiate between lesions that can mimic cancer such as a radial scar from neoplastic ones like invasive ductal carcinoma. SRH also demonstrated histologic differences between a benign tumor like a fibroadenoma and a malignant tumor like an invasive ductal carcinoma. We also observed diagnostic differences amongst benign processes such as radial scar and pseudoangiomatous stromal hyperplasia. Importantly, we showed that SRH is non-consumptive and the imaged tissue can be used for downstream histologic and immunohistochemical analysis. Our data provide proof of concept that SRH can reveal the diagnostic features of tissue collected in breast core needle biopsies, supporting further validation of this technology. Ultimately, we envision the use of SRH to create new, efficient workflows for managing patients with breast lesions, especially those in which fine needle aspiration is of limited value, and where rapid diagnosis of tissue biopsies could streamline care and increase patient satisfaction. Citation Format: Daniel Avram Orringer, Thomas Chen, Steve Pastore, Farbod Darvishian, Hildegard Toth, Freya Schnabel. Rapid beside diagnosis of breast core needle biopsies using stimulated raman histology [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-20.
SGF (Semantic Graphs Fusion): A Knowledge-based Representation of Textual Resources for Text Mining Applications

The proper representation of textual documents has been the greatest challenge in text mining applications. In this paper, a knowledge-based representation model for text documents is introduced. The system works by integrating structured knowledge in the core components of the system. Semantic, lexical, syntactical and structural features are identified by the pre-processing module. The enrichment module is introduced to identify contextually similar concepts and concept maps for improving the representation. The information content of documents and the enriched contents are fused (merged) into the graphical structure of semantic network to form a unified and comprehensive representation of documents. The 20Newsgroup and Reuters-21578 dataset are used for evaluation. The evaluation results suggest that the proposed method exhibits a high level of accuracy, recall and precision. The results also indicate that even when a small portion of information content is available, the proposed method performs well in standard text mining applications.
TestGraphia, Document Analysis-Based Diagnosis of Dysgraphia

Dysgraphia is a disease related to handwriting and affects the size and distance of the characters and orthography. Patients with dysgraphia may have difficulties in motor skills and can hardly write down what they think. Dysgraphia symptoms are not rare but often are temporary. There are no specific causes known that can lead to dysgraphia, and then it is impossible to prevent it. For these reasons, it is crucial to find out dysgraphia; traditional methods are based on specific paper forms and rules to diagnose a suspect of dysgraphia. In this paper, we present a document analysis-based diagnosis process to support doctors to formulate a diagnosis. The medical examination can be completed in a short time. This system allows also large screening activities reducing any effort and permits a remote doctor-patient relationship: while it is being developed as a web-application, and due to its ease of use, it could be used at home, eventually revealing early symptoms.
Ricci-GNN: Defending Against Structural Attacks Through a Geometric Approach

Graph neural networks (GNNs) rely heavily on the underlying graph topology and thus can be vulnerable to malicious attacks targeting at graph structures. We propose a novel GNN defense algorithm against structural attacks that maliciously modify graph topology. In particular, we discover a robust representation of the input graph based on the advanced theory of graph Ricci flow, which captures the intrinsic geometry of graphs and is robust to structural perturbation. We propose an algorithm to train GNNs using re-sampled graphs based on such geometric representation. We show that this method can be effective to protect against adversarial structural attacks. Our method achieves state-of-the-art performance on synthetic and real datasets against different types of graph poisoning attacks.
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A Study on the Communication of literature genres through the ways to enjoy the Cailian Song: Focused on the period from the Southern Dynasties to the Tang and Five Dynasties

This essay has a view that various Cailian song lyric genres may be similar to each other, this can be seen from the perspective of literary genre communication. From the Southern Dynasties to Tang Dynasties, various song lyric genres were used with the change of cailian song, in this process, I considered whether there was a similarity between the song lyric genres through the way of enjoying the Cailian culture. First, I compared the Cailian Fu with Yue-fu Poems written by the literati, so I found three facts. One is the fact that man and woman are presented in the poems, the other is that describe in detail the lotus and the picking lotus woman. Another is that place the singing of the cailian song at the end of poems. Next, compared Yue-fu Poems written by the literati and Cailian Ci, also found three facts. one is that the man exist in different ways in the poems, the other is that express a picking lotus woman’s emotion. Another is that use a long and short verse and one paragraph consisting of three verses. This similarities of Cailian song lyrics can be understood as communication of literary genres. In addition, through this essay, I came to know the man(character) of the Cailian Song is very important, and found out Tang literati pursued the artistry of Cailian Fu on top of the folk song tradition.
Test-Time Adaptation and Adversarial Robustness

This paper studies test-time adaptation in the context of adversarial robustness. We formulate an adversarial threat model for test-time adaptation, where the defender may have a unique advantage as the adversarial game becomes a maximin game, instead of a minimax game as in the classic adversarial robustness threat model. We then study whether the maximin threat model admits more ``good solutions'' than the minimax threat model, and is thus \emph{strictly weaker}. For this purpose, we first present a provable separation between the two threat models in a natural Gaussian data model. For deep learning, while we do not have a proof, we propose a candidate, Domain Adversarial Neural Networks (DANN), an algorithm designed for unsupervised domain adaptation, by showing that it provides nontrivial robustness in the test-time maximin threat model against strong transfer attacks and adaptive attacks. This is somewhat surprising since DANN is not designed specifically for adversarial robustness (e.g., against norm-based attacks), and provides no robustness in the minimax model. Complementing these results, we show that recent data-oblivious test-time adaptations can be easily attacked even with simple transfer attacks. We conclude the paper with various future directions of studying adversarially robust test-time adaptation.
The Global Flood of COVID-19 Contact Tracing Apps: Sailing with Human Rights and Data Protection Standards against the Wind of Mass Surveillance

Research background: Countries all around the world are rapidly introducing contact tracing apps and other surveillance technologies to tackle the spread of COVID-19 raising serious concerns about human rights and democratic principles.Purpose of the article: The article aims to analyse how human rights and data protection law regulate the COVID-19 contact tracing apps and reveal the biggest challenges that countries face in applying the essential requirements.Methods: The article will analyse the legal framework and compare many guidance documents issued by the international organisations, including the Council of Europe, the OECD and many EU institutions on the data protection requirements for contact tracing tools and the main challenges the governments face in different countries.Findings & Value added: The article will reveal that the existing human rights and data protection standards already impose significant requirements for contact tracing apps requiring to comply with such principles as legality, necessity, proportionality, transparency, purpose limitation, temporariness. Although countries tend to deviate from some of these standards a choice between effective response to crises and fundamental rights should not be made. The article argues that the global flood of digital surveillance technologies requires new regulatory framework and governance mechanisms to enable impact assessment, oversight and monitoring of these technologies both during and after the crises not only to ensure that they are lawful and ethical, but also to limit the dependency of governments on large technology companies as well as to prevent mass surveillance becoming the new normal on a global scale.
تقييم تكيف طلبة الجامعة مع الدعم البيداغوجي عن طريق التعليم عن بعد في ظل جائحة كوفيد 19:, دراسة حالة بجامعة غرادية

This Study Aims To Assess The Extent Of Student Adaptation After The Transition To Distance Learning In Light Of University Closure, Due To Covid-19, Using The Descriptive Analytical Method, And Exploring Different Statistical Tools. The Study Started From The Understanding Of Instructions Made Up By The Ministry Of Higher Education To Cope With The Emergency Situation, And Then Conducted A Study Of A Sample Of Pedagogical Skills. The Applied Study Was Conducted On The Faculty Of Economics Students At Ghardaia University, Data Gathered From A Sample Of 100 Individuals Using An E-Questionnaire. The Results Showed That There Is A Willingness To Distance Learning, However Access To The Moodle Platform Requires A Huge Support. It Also Shows That There Are Material And Human Obstacles That Limit Student’s Interaction With The Activities Available On Different Platforms. Students Prefer Props That Have An Asynchronous Reaction. However, The Level Of Interaction Was Very Low, Varying Between Different Grades And Specialties. Key Words : Covid- 19 , Remote Teaching, University Students, Evaluation, Moodle.
Single Color Image De-Hazing Using Dark Channel Prior

In real world scenario due to bad weather conditions the presence of fog and haze, the particles in the outdoor environment or atmosphere (e.g., droplets, smoke, sand, snow, mist, volcanic ash, liquid dust or solid dust) greatly reduces the visibility of the scene. As a consequence, the clarity of an image would be seriously degraded, which may decrease the performance of many image processing applications. Image Dehazing methods try to alleviate these problems by estimating a haze free version of the given hazy image. Traditionally the task of image dehazing can be processed as recovering the scene radiance from a noisy hazy image by estimating the atmospheric light and transmission properties. In those kinds of techniques, it additionally needs some more information regarding the image such as scene depth, weather condition parameters and so on. But this is not suitable for real world scenario. This research focus on proposing an approach to fully capture the intrinsic attributes of a hazy image and improves the performance of dehazing. Dark Channel Prior plays vital role in dehazing process. Hence this research focus on recovering dehaze version of the input image by CNN. So that all methods are comes under the categories image enhancement, image fusion image restoration based on statistical and structural features of the hazed image.
Scaling Symbolic Methods using Gradients for Neural Model Explanation

Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for analyzing and verifying neural network properties, but their usage has been fairly limited owing to their poor scalability with larger networks. In this work, we propose a technique for combining gradient-based methods with symbolic techniques to scale such analyses and demonstrate its application for model explanation. In particular, we apply this technique to identify minimal regions in an input that are most relevant for a neural network's prediction. Our approach uses gradient information (based on Integrated Gradients) to focus on a subset of neurons in the first layer, which allows our technique to scale to large networks. The corresponding SMT constraints encode the minimal input mask discovery problem such that after masking the input, the activations of the selected neurons are still above a threshold. After solving for the minimal masks, our approach scores the mask regions to generate a relative ordering of the features within the mask. This produces a saliency map which explains where a model is looking when making a prediction. We evaluate our technique on three datasets-MNIST, ImageNet, and Beer Reviews, and demonstrate both quantitatively and qualitatively that the regions generated by our approach are sparser and achieve higher saliency scores compared to the gradient-based methods alone.
Rancang Bangun Aplikasi Presensi Dengan Metode Local Binary Pattern Histograms dan Geofencing Berbasis Mobile Pada Universitas Dinamika

Dinamika university  is one of the private campuses in Surabaya that has implemented a attendance management system with attendance recording tool that can be used with biometric validation of faces and fingerprints. The review recording tools available today take a long time for data integration between the tool and the Dinamika university attendance management system, besides the intergration process is not done in real time. The number of tools used is not proportional to the number of employees, causing queues of employees when conducting a review. This final task provides a attendance application development solution that combines two methods namely local binary pattern histograms as biometric validation of face matching and geofencing as validation of restrictions on employee's perspective area, the two methods will be applied to mobile-based applications to overcome queues and record reviews in real time with the Dinamika university attendance management system. The results of the trial of all functional using black box testing showed 100% results. Based on the results of the test, it can be concluded that the local binary pattern histograms and geofencing based on the overall function has been running well.
Functional literacy in the educational system

The article explains the concepts of literacy and functional literacy. The concept of "literacy" is widely studied, its meaning is revealed. To explain the contemporary meaning of literacy, it is necessary to look at historical changes in the concept of literacy. However, it focuses not on the process of changing the concept of literacy, but on the modern meaning of literacy, based on the concept of literacy presented in various materials. The importance of functional literacy in the modern education system is explained. The article highlights the specifics of education and training on the formation of functional literacy. Functional literacy is a measure of a person's ability to interact with the external environment and the level of the ability to adapt and communicate with this environment as quickly as possible. Thus, functional literacy consists of a set of knowledge, skills and abilities that are considered necessary for a person to live in a particular cultural environment and ensure his social interaction. And in a broad sense, this is not just a way to get into the world of 4 skills and knowledge, it is an indicator of the cultural and social development of a nation, country or group of people. From the point of view of such a qualitative nature, functional literacy is used as a mechanism for personal development.
E-LEARNING: THE LEARNER’S PERCEPTION (OPPORTUNITIS AND CHALLENGES DURING COVID -19 PANDEMIC)

COVID-19 has impacted almost every sector of the economy especially service sector and the education sector are no exception. Schools, colleges, and institutes are all close and students are at home with the least contact with teachers and friends. Everything is virtual, with no physical movement. The interference in the education system due to the pandemic is a reminder that there is a need for transformation. Digital transformation is the need of the hour to battle against the twist and turns of undefined future. E-learning and teaching have become inevitable. It is now the new normal. In this context this paper tries to analyze the perception of students on e-learning methods. This research is a qualitative and exploratory research in which primary data is used which is collected through an online survey from 140 students who are undergoing e-learning experience at IBRA College of Technology, Oman. A structured questionnaire was used to collect the responses. Pilot study was conducted to identify the factors that influence effective learning. Statistical tools such as factor analysis and multiple regression was used to draw meaningful conclusion. SPPS and AMOS 21 statistical packages were used to analyze the data. This paper becomes significant because the results of this study will enable the policy makers and educators to understand the perception and expectation of the students and to formulate strategies accordingly. This understanding will help to improve the quality and experience of virtual learning process.
REFLECTIONS ON CIVIL LIABILITY ARISING FROM THE USE OF ARTIFICIAL INTELLIGENCE: THE "PRINCIPLES" OF THE EUROPEAN UNION

Abstract: The matter dedicated to civil liability arising from the use of artificial intelligence deserves to be rethought so that it effectively arrives to fulfil its role: to compensate victims. To carry out this exercise, we must analyse the most relevant documents issued by the European institutions or support agencies. After that, we will be able to establish a series of "principles" that should ultimately be reported by national legal systems. However, we understand that some issues are not addressed with the depth required and require further study. In this article, we will seek to establish a kind of basis which we believe should preside over-regulation in this sector. Key words: Artificial intelligence, principles, compensation, civil liability, European Union.
Subband Channel Selection using TEO for Replay Spoof Detection in Voice Assistants

Recently, there is an increase in the demand for Voice Assistants (VAs) due to their convenience in accessing and controlling the household devices. To make VAs user-friendly, less strict speaker verification constraints are imposed onto them which makes VAs highly vulnerable to spoofing attacks. In this paper, authors propose the design of front-end countermeasure system against replay spoofing attack for VAs that make use of microphone array to capture spatial diversity. We exploit this microphone array information by proposing a novel approach of the subband channel selection using mathematical structure of Teager Energy Operator (TEO). These selected subband channels are used to compute proposed Teager Energy Cepstral Coefficients (TECC max ) feature set. With this approach, we gain significant improvement in the performance of replay attack detection task on VAs against the baseline feature set, i.e., Constant-Q Cepstral Coefficient (CQCC). Results indicate an absolute reduction in Equal Error Rate (EER) of 4.11% and 8.66% on development and evaluation set, respectively, of ReMASC dataset. Authors also performed classffier-level fusion of GMM, and LCNN-based back end classifiers using proposed TECC max feature set and obtained absolute reduction of 5.98% and 10.67% on development and evaluation sets, respectively.
Review on CNN Based Image Denoising

In the last few years, convolutional neural networks (CNNs) have attracted good attention in the area of denoising. There have a lot of approaches to remove noise from an affected image and to reconstruct a clean image with high visual quality. Some of the non-CNN related approaches are much efficient to remove noise from affected images. But these methods also show some issues like complex optimization, Computational expenses, etc. while performing denoising. To beat the disadvantages of these denoising approaches, CNN related methodologies are created to enhance the denoising performance. These denoising strategies are drawing in acceptable considerations for their efficient performance in image rebuilding. They utilize some techniques like residual learning to enhance the denoising performance. So this paper provides a review on some CNN related image denoising approaches.
Competitive Environment in Banking Industry: Evidence from an Emerging Economy

The study investigates competitive conditions in commercial banking sector of Pakistan. The study employs a measure of estimating banking competition derived from modified Panzar and Rosse Model – The PR Model. The data was obtained from annual financial statements of 22 commercial banks listed on Pakistan Stock Exchange (PSX) for the period 2007-2016. The H-statistic for commercial banking sector is calculated through three different estimation methods (OLS, GLS fixed effects and GMM). The results depict monopolistic competition and significant role of bank size in determining the level of competition in commercial banking sector of Pakistan. The empirical evidence unveils that more competition exists between smaller banks as compared to larger banks. Further, speed of adjustment in profits with respect to factor input prices is better in smaller banks as compared to their larger counterparts. The findings imply that the speed adjustment process is slow, which allows commercial banks in Pakistan to accumulate abnormal profits.