Reviewerly/exHarmony huggingface dataset:
A biometric‐based verification system for handwritten image‐based signatures using audio to image matching

Signing a document or a cheque by hand or using a stored image-based signature is known to be a valid method for authentication and authorisation by the signer. However, signature forging has advanced to replicate exactly how a signature looks, which can be done by skilfully, unskilfully or randomly forging a signature. Such a dilemma presents a challenge to accurately authenticate and authorise using signatures. In this study, a verification system is proposed for handwritten image-based signatures for validating whether the image-based signature is authentic rather than forged. The system maps the live stream of an audio-based signature with the investigated image-based signature and returns the match results. Matching is done by classification and/or by correlation between the two signatures. If matching shows a similar class or a score above a pre-defined threshold, the image-based signature is verified to be authentic, otherwise it is flagged as forged. A total of 20 participated in the experiment, where each participant provided a legitimate signature and forged four other signatures in different settings. In a double-blind setting, the system reported 95% accuracy using a one-class SVM and 100% accuracy using a correlation coefficient for detecting forged versus legitimate signatures.
Breast Cancer Risk Assessment using Gabor Filter Banks and Curvelet Transform

The goal of breast cancer risk assessment is to establish the risk that a healthy person has of developing breast cancer in the future. Recently, mammographic images have shown great potential for the extraction of imaging biomarkers for the construction of breast cancer risk models. This work aims to compare Gabor filters and the Curvelet transform for the extraction of texture features from mammographic images, evaluating their performance for the computerized assessment of breast cancer risk. Gabor filters and the Curvelet transform decompose texture information at different scales and orientations in the spatial frequency domain and they can be utilized for the analysis of mammographic texture features. We compared different configurations of both Gabor and Curvelet filter banks by analyzing the effect of the number of bands and architecture of the filters. Results in a pilot case-control study with digital mammography images from 54 women (27 cases and 27 controls) indicate that Gabor filter banks yield a superior performance than Curvelet analysis, with an AUC of 0.722 in the classification between high-risk and low-risk women.
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains

Since late 2019, COVID-19 has quickly emerged as the newest biomedical domain, resulting in a surge of new information. As with other emergent domains, the discussion surrounding the topic has been rapidly changing, leading to the spread of misinformation. This has created the need for a public space for users to ask questions and receive credible, scientific answers. To fulfill this need, we turn to the task of open-domain question-answering, which we can use to efficiently find answers to free-text questions from a large set of documents. In this work, we present such a system for the emergent domain of COVID-19. Despite the small data size available, we are able to successfully train the system to retrieve answers from a large-scale corpus of published COVID-19 scientific papers. Furthermore, we incorporate effective re-ranking and question-answering techniques, such as document diversity and multiple answer spans. Our open-domain question-answering system can further act as a model for the quick development of similar systems that can be adapted and modified for other developing emergent domains.
Split-and-Rephrase in a Cross-Lingual Manner: a Complete Pipeline

Split-and-rephrase is a challenging task that promotes the transformation of a given complex input sentence into multiple shorter sentences retaining equivalent meaning.This rewriting approach conceptualizes that shorter sentences benefit human readers and improve NLP downstream tasks attending as a preprocessing step.This work presents a complete pipeline capable of performing the split-andrephrase method in a cross-lingual manner.We trained sequence-to-sequence neural models as from English corpora and applied them to predict the transformations in English and Brazilian Portuguese sentences jointly with BERT's masked language modeling.Contrary to traditional approaches that seek training models with extensive vocabularies, we present a non-trivial way to construct symbolic ones generalized solely by grammatical classes (POS tags) and their respective recurrences, reducing the amount of necessary training data.This pipeline contribution showed competitive results encouraging the expansion of the method to languages other than English.
Circle Object Detection Of A Moveable Vision Robot Using Hough Circle Method

The object detection system is very important in an automatic mobile robot system. The functions of this system for a navigation robot are to find objects and also at the same time to avoid obstacle. Detecting objects here including the shape, size, and color of object. Various implementations of robot detection have been made in various fields, especially in industrial field. In this study, a circular object detection application system was developed using the Hough Circle Transformation method. Circle object detection system developed in the following stages: Image processing (capturing video from the camera, BRG color conversion to Gray scale, and Gaussian Blur); the application of Hough Circle Transformation; and calculation of diameter and distance of circle objects. The test results show that the system works well to detect circle objects in various variations of the object given. The system can also detect the distance of circle objects with an average error of 8.17%. By contrast, upon measuring circle object diameter, the system can measure with an average error of 5.55%.
Configuring the VUI

In Chapter 8 , we created a diagram of the dialog flow for our 90-year calendar skill in Botmock. Now we will convert that diagram into a Voice User Interface in an Alexa skill. In this chapter, we will create intents, slots, and responses based on the diagram.
On Language Models for Creoles

Creole languages such as Nigerian Pidgin English and Haitian Creole are under-resourced and largely ignored in the NLP literature. Creoles typically result from the fusion of a foreign language with multiple local languages, and what grammatical and lexical features are transferred to the creole is a complex process. While creoles are generally stable, the prominence of some features may be much stronger with certain demographics or in some linguistic situations. This paper makes several contributions: We collect existing corpora and release models for Haitian Creole, Nigerian Pidgin English, and Singaporean Colloquial English. We evaluate these models on intrinsic and extrinsic tasks. Motivated by the above literature, we compare standard language models with distributionally robust ones and find that, somewhat surprisingly, the standard language models are superior to the distributionally robust ones. We investigate whether this is an effect of over-parameterization or relative distributional stability, and find that the difference persists in the absence of over-parameterization, and that drift is limited, confirming the relative stability of creole languages.
TabPert: An Effective Platform for Tabular Perturbation

To grasp the true reasoning ability, the Natural Language Inference model should be evaluated on counterfactual data. TabPert facilitates this by generation of such counterfactual data for assessing model tabular reasoning issues. TabPert allows the user to update a table, change the hypothesis, change the labels, and highlight rows that are important for hypothesis classification. TabPert also details the technique used to automatically produce the table, as well as the strategies employed to generate the challenging hypothesis. These counterfactual tables and hypotheses, as well as the metadata, is then used to explore the existing model’s shortcomings methodically and quantitatively.
Sub-Linear Memory: How to Make Performers SLiM

The Transformer architecture has revolutionized deep learning on sequential data, becoming ubiquitous in state-of-the-art solutions for a wide variety of applications. Yet vanilla Transformers are notoriously resource-expensive, requiring $O(L^2)$ in serial time and memory as functions of input length $L$. Recent works proposed various linear self-attention mechanisms, scaling only as $O(L)$ for serial computation. We perform a thorough analysis of recent Transformer mechanisms with linear self-attention, Performers, in terms of overall computational complexity. We observe a remarkable computational flexibility: forward and backward propagation can be performed with no approximations using sublinear memory as a function of $L$ (in addition to negligible storage for the input sequence), at a cost of greater time complexity in the parallel setting. In the extreme case, a Performer consumes only $O(1)$ memory during training, and still requires $O(L)$ time. This discovered time-memory tradeoff can be used for training or, due to complete backward-compatibility, for fine-tuning on a low-memory device, e.g. a smartphone or an earlier-generation GPU, thus contributing towards decentralized and democratized deep learning.
Knowledge-Guided Paraphrase Identification.

Paraphrase identification (PI), a fundamental task in natural language processing, is to identify whether two sentences express the same or similar meaning, which is a binary classification problem. Recently, BERT-like pre-trained language models have been a popular choice for the frameworks of various PI models, but almost all existing methods consider general domain text. When these approaches are applied to a specific domain, existing models cannot make accurate predictions due to the lack of professional knowledge. In light of this challenge, we propose a novel framework, namely , which can leverage the external unstructured Wikipedia knowledge to accurately identify paraphrases. We propose to mine outline knowledge of concepts related to given sentences from Wikipedia via BM25 model. After retrieving related outline knowledge, makes predictions based on both the semantic information of two sentences and the outline knowledge. Besides, we propose a gating mechanism to aggregate the semantic information-based prediction and the knowledge-based prediction. Extensive experiments are conducted on two public datasets: PARADE (a computer science domain dataset) and clinicalSTS2019 (a biomedical domain dataset). The results show that the proposed outperforms state-of-the-art methods.
IMPLEMENTATION OF RSA ENCRYPTION ALGORITHM ON INSTANT MESSAGING APPLICATION

At this time the use of instant messaging applications is increasingly used compared to the use of SMS or other media because of its use which is more practical and faster. From the other side, the message information sent certainly requires confidentiality so that the message is not spread and known by others. For this reason mechanisms are needed, one of which is encryption to maintain message security. This research will implement the RSA (Rivest Shamir Adleman) encryption algorithm in the instant messaging application. This study uses a key length scenario of RSA 1024, 2048, 4096, and 6144 bits and a message length of 125, 250, 500, and 1000 characters implemented on 3 different devices. The results of testing on time and speed are the shorter the key used, the process will be shorter and faster.&#x0D; Keywords : RSA, Processing Time, Processing Speed
Latent Low-Rank Sparse Multi-view Subspace Clustering

To solve the problem of multi-view clustering,a latent low-rank sparse multi-view subspace clustering(LLSMSC)algorithm is proposed.A latent space shared by all views is constructed to explore the complementary information of multi-view data.The global and local structure of multi-view data can be captured to attain promising clustering results by imposing low-rank constraint and sparse constraint on the implicit latent subspace representation simultaneously.An algorithm based on augmented Lagrangian multiplier with alternating direction minimization strategy is employed to solve the optimization problem.Experiments on six benchmark datasets verify the effectiveness and superiority of LLSMSC.
A Speech-Based Data Collection Interface for Contact Tracing

In this paper we present the design of an innovative contact tracing application, with an interface that intends to meet several goals: automatic capture of speech and its transcription into text, enabled playback capability, entity recognition and entity visualization on a timeline. We present the overall design as well as a review of the implemented components of the interface, discuss challenges and future work.
What makes a good teacher?

Being a teacher in contemporary society is becoming more and more challenging. Social expectations of teachers are constantly growing. Therefore, teachers strive to improve their characteristics and competencies. In that respect, it is important to know which personality traits and competencies society expects from a good teacher. Guided by that, this paper aims to comprehensively present a good teacher, considering his or her desirable personality traits, professional competencies and perceptions of participants in the educational process. The results of the review of relevant literature and previous research indicate that a good teacher shows a combination of personality traits such as humanity, wisdom, prudence, perseverance, etc. and competencies such as achieving positive relationships with students, successful classroom management, recognizing the importance of motivation and others.Finally, a good teacher is a combination of desirable personality traits and permanent professional growth. Therefore, this research can contribute to the practice of creating professional development and teacher education programs.
A Framework for Detecting Cervical Cancer Based on UD-MHDC Segmentation and MBD-RCNN Classification Techniques

Cervical cancer is a cancer of the entrance to the uterus. Pap test has made cervical cancer quite possibly one of the most preventable types of cancer, which can be utilized for its initial identification. Be that as it may, the whole interaction is tedious, expensive and involves observer biases. In order to conquer existing challenges the work has developed an automated cervical cancer detection framework using the UDMHDC segmentation and MBD-RCNN classification algorithm. The proposed segmentation technique and MBDRCNN model provides accurate classification of cervical cancer along with low computational time.
A Preliminary Integrated Network Security Framework for Tertiary Institutions

The prevalence of network security threats at tertiary institutions has increasingly become a matter of grave concern. Tertiary institutions need greater network services to enhance sharing of sensitive information/data across the whole campus. As such, it is more difficult to secure the networks of tertiary institutions as compared to networks of other organisations. In this light, we propose a Preliminary Integrated Network Security Framework for Tertiary Institutions to address the concerns of the stakeholders and comprehend the state of tertiary institutions’ information technology security. The framework will help institutions to attain their strategic goals and market gains by effectively and innovatively employing Information Technology techniques to secure the entire network infrastructure. To develop a preliminary integrated network security framework, a review of literature on security frameworks was conducted and the findings of these reviews contributed to the realisation of the main objective of this paper. Basic components or elements that form part of an integrated network security framework were identified, thereby providing a foundation for the proposed framework which should be represented architecturally in the future for validation and implementation.
Factors Affecting the Psychology of Students in the Process of Studying at a University

The article is devoted to the success of education at the university. The essence of the concepts learning activity, learning success, academic performance is revealed, the structure of educational success in the process of education at a university is presented. The analysis of scientific research devoted to the identification of psychological factors influencing the success of students' education in higher educational institutions is carried out. The role of motivational-personal and self-regulatory qualities of students in achieving success in university studies is shown.
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints

Back-translation (BT) of target monolingual corpora is a widely used data augmentation strategy for neural machine translation (NMT), especially for low-resource language pairs. To improve effectiveness of the available BT data, we introduce HintedBT—a family of techniques which provides hints (through tags) to the encoder and decoder. First, we propose a novel method of using both high and low quality BT data by providing hints (as source tags on the encoder) to the model about the quality of each source-target pair. We don’t filter out low quality data but instead show that these hints enable the model to learn effectively from noisy data. Second, we address the problem of predicting whether a source token needs to be translated or transliterated to the target language, which is common in cross-script translation tasks (i.e., where source and target do not share the written script). For such cases, we propose training the model with additional hints (as target tags on the decoder) that provide information about the operation required on the source (translation or both translation and transliteration). We conduct experiments and detailed analyses on standard WMT benchmarks for three cross-script low/medium-resource language pairs: Hindi,Gujarati,Tamil-to-English. Our methods compare favorably with five strong and well established baselines. We show that using these hints, both separately and together, significantly improves translation quality and leads to state-of-the-art performance in all three language pairs in corresponding bilingual settings.
Enhancing the Reading for Pleasure Habit of the Students of Samtse College of Education

Reading is an essential skill for everybody especially for the students of Samtse College of Education who are future teachers. They need to read to enhance their communication skills, upgrade their knowledge and skills and be role models in reading for their students. However, we have found that the reading for pleasure habit among the students is poor. Students generally use social media during their leisure time instead of reading which is a concern both for their lecturers and the institution. An action research study was carried out to improve students’ reading for pleasure habit. Data for the study were gathered using a survey and interview. A total of 325 students completed the survey and 57 students participated in the interview. The data were collected twice; before (baseline) and after the intervention. Comparison of means was used to analyse the quantitative data and the qualitative data were analysed thematically. Following the analysis of the baseline data, three intervention strategies were employed; Our Time To Enjoy Reading (OTTER), Self-indulgence Reading, and Raffle Draw. These strategies were implemented in all the 16 classes for a period of 8 weeks. The comparison of the analysis of the pre-and post-data showed that the interventions have been effective in promoting the reading for pleasure habit of the students.
Low-Rank Subspaces for Unsupervised Entity Linking.

Entity linking is an important problem with many applications. Most previous solutions were designed for settings where annotated training data is available, which is, however, not the case in numerous domains. We propose a light-weight and scalable entity linking method, Eigenthemes, that relies solely on the availability of entity names and a referent knowledge base. Eigenthemes exploits the fact that the entities that are truly mentioned in a document (the “gold entities”) tend to form a semantically dense subset of the set of all candidate entities in the document. Geometrically speaking, when representing entities as vectors via some given embedding, the gold entities tend to lie in a low-rank subspace of the full embedding space. Eigenthemes identifies this subspace using the singular value decomposition and scores candidate entities according to their proximity to the subspace. On the empirical front, we introduce multiple strong baselines that compare favorably to (and sometimes even outperform) the existing state of the art. Extensive experiments on benchmark datasets from a variety of real-world domains showcase the effectiveness of our approach.
Implementation of Multifunction Residue Architectures on FPGA for Cryptography Applications: A Recent Study

Confidentiality and encryption of data are critical features of modern computing. Elliptical curve cryptography (ECC) is the ultimate cryptographic method due to its high limited size and thus its reliability equal to that of other traditional public-key algorithms. Furthermore, hardware acceleration of cryptographic algorithms is required to meet the increasing speed demand for contemporary implementations. The forward converters, modulo decimal units, and reverse converters are the core components of the residue number method. In the current network of residue numbers, the reverse converter is based on standard and compact adders.&nbsp; It has a high power consumption and a low speed. By analysing the input/output conversion to/from residue representation, it proposed a residue Montgomery method for multiplying data pathways among converters and between the two residue representations. The dual field modulator is used for GF(p) and GF(2)n Montgomery multiplication, input/output conversions, mixed-radius conversions (MRC) for inputs and polynomials, and exponentiation and inversion in the same hardware. &nbsp
IMPLEMENTASI KOMBINASI KRIPTOGRAFI TEKNIK SUBSTITUSI DAN TEKNIK TRANSPOSISI DALAM PENGAMANAN PESAN TEKS

Cryptography is one of the right solutions to overcome wiretapping, piracy or theft of data or information from people who do not have access rights to such data. So that the data is still guaranteed its integrity, confidentiality, and accuracy. Therefore, the aim of this research is to build a text message security application using a combination of classical cryptography substitution techniques and algorithms reverse cipher. In terms of data security techniques, many cryptographic methods can be used. These cryptographic methods have their own techniques and methods. One of the cryptographic methods that can be used is the substitution technique method. But if you only use the substitution technique method, the security of text data is very weak. So to achieve a higher level of security this method is combined with the transposition technique method. In this study, the cryptography used is a combination of classical cryptography substitution techniques and classical cryptography transposition techniques, namely the Caesar cipher algorithm and the algorithm reverse cipher. In testing the algorithm caecar cipher, the process of encryption and decryption of messages is only carried out on uppercase (A-Z) and lowercase (a-z) characters. Based on the security analysis, it is found that the encryption or decryption process uses a substitution 468,235,319, 2,147,483,622, and 2,147,483,623 The encryption process and the decryption process of the algorithm caecar cipher can be done if the result of the calculation of the key input is not more than the capacity of the data type used, namely the data type integer.