Reviewerly/exHarmony huggingface dataset:
$$\pi $$ with Leftovers: A Mechanisation in Agda

Linear type systems need to keep track of how programs use their resources. The standard approach is to use context splits specifying how resources are (disjointly) split across subterms. In this approach, context splits redundantly echo information which is already present within subterms. An alternative approach is to use leftover typing [2, 23], where in addition to the usual (input) usage context, typing judgments have also an output usage context: the leftovers. In this approach, the leftovers of one typing derivation are fed as input to the next, threading through linear resources while avoiding context splits. We use leftover typing to define a type system for a resource-aware $$\pi $$ -calculus [26, 27], a process algebra used to model concurrent systems. Our type system is parametrised over a set of usage algebras [20, 34] that are general enough to encompass shared types (free to reuse and discard), graded types (use exactly n number of times) and linear types (use exactly once). Linear types are important in the $$\pi $$ -calculus: they ensure privacy and safety of communication and avoid race conditions, while graded and shared types allow for more flexible programming. We provide a framing theorem for our type system, generalise the weakening and strengthening theorems to include linear types, and prove subject reduction. Our formalisation is fully mechanised in about 1850 lines of Agda [37].
PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation

Large pre-trained language generation models such as GPT-2 have demonstrated their effectiveness as language priors by reaching stateof-the-art results in various language generation tasks.However, the performance of pretrained models on task-oriented dialog tasks is still under-explored.We propose a Pre-trained Role Alternating Language model (PRAL), explicitly designed for task-oriented conversational systems.We design several techniques: start position randomization, knowledge distillation, and history discount to improve pretraining performance.In addition, we introduce a high quality large-scale task-oriented dialog pre-training dataset.We effectively adapt PRAL on three downstream tasks.With much less training data, PRAL outperforms or is on par with state-of-the-art models.
Developing students’ soft skills in the context of teaching foreign languages at higher education institutions

The article deals with the methodological issue of building soft skills in the context of teaching modern foreign languages to students of higher institutions. The analysis of the available scientific and methodological research works devoted to the problem of developing students’ soft skills, especially in the process of teaching foreign languages, reveals that even though soft skills are crucial for securing employment of graduates they are still underdeveloped in the system of higher education, which results in a growing need for institutions of higher education to address the defined gap. The paper suggests some ways capable of bridging the gap and fostering the development of personal and interpersonal XXI century skills in the process of teaching foreign languages. It focuses on intra-curricular approaches to classroom management (e.g., pairing or grouping students) and methods and content of classroom activities (case study, debates, discussions, project-based learning, etc.), which have the potential to equip students with the core skills through real-life scenarios and different learning experiences. The author emphasizes that speaking and listening activities, being undoubtedly highly beneficial for soft skills development, are not the only ones that can be used to enhance learners’ soft skills. Students’ soft skills can be significantly improved while reading or writing, so the key point is to organically integrate diverse activities and teaching strategies. Therefore, before planning a class, teachers have to understand how soft skills are developed, choose what soft skills they want their students to learn, and select corresponding instructional methods and tools to model a situation in which soft skills could be naturally acquired. Particular emphasis is also placed on the question of how and what soft skills can be developed during self-assessment or peer-assessment practices. The article finishes with a few recommendations regarding possible aspects of further research.
Quantifying Quantum Correlation of Quasi‐Werner State and Probing Its Suitability for Quantum Teleportation

Abstract The significance of photon addition in engineering the single‐ and two‐mode (bipartite correlations) nonclassical properties of a quantum state is investigated. Specifically, the behavior of the Wigner function of two quasi‐Werner states constructed by superposing two normalized bipartite m ‐photon added coherent states are analyzed. This allows the authors' to quantify the nonclassicality present in the quantum states using Wigner logarithmic negativity (WLN), while quantum correlations are measured using concurrence, entanglement of formation, and quantum discord. The WLN for a two‐mode state corresponds to the sum of the single‐mode nonclassicality and quantum correlations, and both of these are observed to enhance with photon addition manifesting the efficacy of photon addition in the entanglement distillation. Usefulness of photon addition is further established by showing that the performance of the quasi‐Werner states as quantum channel for the teleportation of single‐mode coherent and squeezed states, as quantified via teleportation fidelity, improves with the photon addition. The non‐Gaussian operation is shown effective in stalling the detrimental effects of transmission through noisy channel and/or inefficient detection under realistic scenario. Further, in contrast to a set of existing results, it is established that the negative values of two‐mode Wigner function is not a witness of quantum correlation.
A comparative study of keyword extraction algorithms for English texts

Abstract This study mainly analyzed the keyword extraction of English text. First, two commonly used algorithms, the term frequency–inverse document frequency (TF–IDF) algorithm and the keyphrase extraction algorithm (KEA), were introduced. Then, an improved TF–IDF algorithm was designed, which improved the calculation of word frequency, and it was combined with the position weight to improve the performance of keyword extraction. Finally, 100 English literature was selected from the British Academic Written English Corpus for the analysis experiment. The results showed that the improved TF–IDF algorithm had the shortest running time and took only 4.93 s in processing 100 texts; the precision of the algorithms decreased with the increase of the number of extracted keywords. The comparison between the two algorithms demonstrated that the improved TF–IDF algorithm had the best performance, with a precision rate of 71.2%, a recall rate of 52.98%, and an F 1 score of 60.75%, when five keywords were extracted from each article. The experimental results show that the improved TF–IDF algorithm is effective in extracting English text keywords, which can be further promoted and applied in practice.
The influence of Selection, Motivation and Utilization of Information System Academic for Lecturer (SIAD) on the Lecturer Performance

Lecturer Academic Information System (SIAD) is an academic service facility that lecturers can use to accommodate and manage teachers' activities, including lectures, assignments, communication, and discussions with students. However, lecturers' obstacles in supporting educational processes include the obstacles of leak of features and items/ service forums in the academic portal /SIAD online. This research aims to find the influence of selecting the system, motivation, and utilization of academic information system lecturers (SIAD) on the performance of lecturers at the Faculty of Economics, University of Prima Indonesia. The study will test the variables both partially and simultaneously. Samples were taken from lecturers recorded in the management study program of 60 people. The test result obtained a calculated value of F (17,866) &gt; F table (2.76) and a probability of significance of 0.000 &lt; 0.05, it was concluded that simultaneously H0 was rejected, which means the selection, motivation, and utilization of SIAD had a positive and significant effect on the performance of lecturers at the Faculty of Economics, Universitas Prima Indonesia Medan. The result of the calculation of the coefficient of determination obtained an adjusted R Square value of 0.462.
Friend or Foe: An Investigation into Recipient Identification of SMS-Based Phishing

Short Message Service (SMS) messaging plays a key role in many people's lives, allowing communication between friends, family and businesses through the convenient use of a mobile phone. At the same time, criminals are able to utilise this technology to their own benefit, such as by sending phishing messages that convince their victims into sharing sensitive information or installing dangerous software on their devices. Indeed, Proofpoint's State of the Phish report found 81% of surveyed US organisations had faced smishing attacks – which is a type phishing attack via SMS message in 2020. Although phishing is well studied, the amount of research in SMS-based phishing is somewhat limited. Therefore, this study addresses the lack of SMS-based phishing insight, investigating which techniques/tactics are used by malicious senders and honest recipients to disguise/identify SMS-based phishing. By using an online questionnaire, a total of 576 participants' options upon 20 text messages (10 genuine and 10 phishing) were gathered. The result shows 73.4% of the SMS messages were categorised correctly; also a number of factors such as shortened URLs, inconsistent metadata/content, urgency cue, and age play a positive role in identifying phishing attacks.
Code-switched automatic speech recognition in five South African languages

Most automatic speech recognition (ASR) systems are optimised for one specific language and their performance consequently deteriorates drastically when confronted with multilingual or code-switched speech. We describe our efforts to improve an ASR system that can process code-switched South African speech that contains English and four indigenous languages: isiZulu, isiXhosa, Sesotho and Setswana. We begin using a newly developed language-balanced corpus of code-switched speech compiled from South African soap operas, which are rich in spontaneous code-switching. The small size of the corpus makes this scenario under-resourced, and hence we explore several ways of addressing this sparsity of data. We consider augmenting the acoustic training sets with in-domain data at the expense of making it unbalanced and dominated by English. We further explore the inclusion of monolingual out-of-domain data in the constituent languages. For language modelling, we investigate the inclusion of out-of-domain text data sources and also the inclusion of synthetically-generated code-switch bigrams. In our experiments, we consider two system architectures. The first considers four bilingual speech recognisers, each allowing code-switching between English and one of the indigenous languages. The second considers a single pentalingual speech recogniser able to process switching between all five languages. We find that the additional inclusion of each acoustic and text data source leads to some improvements. While in-domain data is substantially more effective, performance gains were also achieved using out-of-domain data, which is often much easier to obtain. We also find that improvements are achieved in all five languages, even when the training set becomes unbalanced and heavily skewed in favour of English. Finally, we find the use of TDNN-F architectures for the acoustic model to consistently outperform TDNN–BLSTM models in our data-sparse scenario.
A Practitioner’s View on Distributed Storage Systems: Overview, Challenges and Potential Solutions

Introduction The technology now called “blockchain” was originally conceived in Bitcoin as a decentralized e-commerce alternative to “financial institutions serving as trusted third parties to process electronic payments” (Satoshi Nakmoto, 2008). Blockchain was meant to usher in a “trust-less” model, where mechanisms (such as cryptographic proof) could enable all parties in a
ANALYSING AUTO ML MODEL FOR CREDIT CARD FRAUD DETECTION

Fraud Detection is a major concern these days because of digitalization.We are totally dependent on online transactions these days for even very small needs.There is no doubt that online transactions have made our life very easy but it has increased risk on other hand.And this risk can be very harmful one day.Confidential data is being stolen by the different apps and it is sold in international market.Which later on comes to us in totally different and very harmful way.So why not to use technology again to stop these risks and flaws.Various ML techniques has been observed by researchers but Auto ML is yet not discovered on a wider platform.Therefore, this paper at first aims to explore the trending technology Auto ML.Then a model for evaluating Auto ML is suggested and analysed with different classification algorithms.The experimental results ascertained the accuracy of Auto ML followed by a comparative analysis of ML and Auto ML.
Adaptive Video Watermarking against Scaling Attacks Based on Quantization Index Modulation

Video watermarking plays a vital role in protecting the video copyright. The quantization-based methods are widely used in the existing watermarking algorithms, owing to their low computational complexity and completely blind extraction. However, most of them work poorly in resisting scaling attacks, by which the quantization value may fall outside the original quantization interval. For addressing this issue, an adaptive quantization index modulation method is proposed. The property that is associated with the ratio of the DC coefficient before and after scaling the video resolution motivates us to select the DC coefficient as the quantization value and set the size of the quantization interval by the video resolution to maintain the synchronization between them before and after scaling. Moreover, a strategy taking advantage of the high decoding reliability of the QRCode is proposed to terminate the extraction in advance, and both the embedding and the extracting process are performed in the spatial domain, which all contribute to further enhance the execution efficiency. The experimental results show that our algorithm outperforms the state-of-the-art method in terms of imperceptibility, robustness, and computational cost.
Online Template Attacks: Revisited

An online template attack (OTA) is a powerful technique previously used to attack elliptic curve scalar multiplication algorithms. This attack has only been analyzed in the realm of power consumption and EM side channels, where the signals leak related to the value being processed. However, microarchitecture signals have no such feature, invalidating some assumptions from previous OTA works.In this paper, we revisit previous OTA descriptions, proposing a generic framework and evaluation metrics for any side-channel signal. Our analysis reveals OTA features not previously considered, increasing its application scenarios and requiring a fresh countermeasure analysis to prevent it.In this regard, we demonstrate that OTAs can work in the backward direction, allowing to mount an augmented projective coordinates attack with respect to the proposal by Naccache, Smart and Stern (Eurocrypt 2004). This demonstrates that randomizing the initial targeted algorithm state does not prevent the attack as believed in previous works.We analyze three libraries libgcrypt, mbedTLS, and wolfSSL using two microarchitecture side channels. For the libgcrypt case, we target its EdDSA implementation using Curve25519 twist curve. We obtain similar results for mbedTLS and wolfSSL with curve secp256r1. For each library, we execute extensive attack instances that are able to recover the complete scalar in all cases using a single trace.This work demonstrates that microarchitecture online template attacks are also very powerful in this scenario, recovering secret information without knowing a leakage model. This highlights the importance of developing secure-by-default implementations, instead of fix-on-demand ones.
Wheelchair Control System based Eye Gaze

The inability to control the limbs is the main reason that affects the daily activities of the disabled which causes social restrictions and isolation.More studies were performed to help disabilities for easy communication with the outside world and others.Various techniques are designed to help the disabled in carrying out daily activities easily.Among these technologies is the Smart Wheelchair.This research aims to develop a smart eye-controlled wheelchair whose movement depends on eye movement tracking.The proposed Wheelchair is simple in design and easy to use with low cost compared with previous Wheelchairs.The eye movement was detected through a camera fixed on the chair.The user's gaze direction is obtained from the captured image after some processing and analysis.The order is sent to the Arduino Uno board which controls the wheelchair movement.The Wheelchair performance was checked using different volunteers and its accuracy reached 94.4% with a very short response time compared with the other existing chairs.
Learning Cross-Task Attribute - Attribute Similarity for Multi-task Attribute-Value Extraction

Automatic extraction of product attribute-value pairs from unstructured text like product descriptions is an important problem for e-commerce companies. The attribute schema typically varies from one category of products (which will be referred as vertical) to another. This leads to extreme annotation efforts for training of supervised deep sequence labeling models such as LSTM-CRF, and consequently not enough labeled data for some vertical-attribute pairs. In this work, we propose a technique for alleviating this problem by using annotated data from related verticals in a multi-task learning framework. Our approach relies on availability of similar attributes (labels) in another related vertical. Our model jointly learns the similarity between attributes of the two verticals along with the model parameters for the sequence tagging model. The main advantage of our approach is that it does not need any prior annotation of attribute similarity. Our system has been tested with datasets of size more than 10000 from a large e-commerce company in India. We perform detailed experiments to show that our method indeed increases the macro-F1 scores for attribute value extraction in general, and for labels with low training data in particular. We also report top labels from other verticals that contribute towards learning of particular labels.
Effect of COVID-19 on Education in Saudi Arabia and E-Learning Strategies

The overall purpose of this paper is to explore the adverse effect of COVID-19 on the learning environment in the kingdom of Saudi Arabia (KSA). Considering that the impact of the coronavirus on education was abrupt, there was no contingency plan to mitigate the effect other than to shift to remote learning. The research addresses the question whether online/distance learning classes were effective. To deal with the research questions, the authors elicited teachers’ and students’ perceptions of regarding the challenges they faced amidst COVID-19 in learning via virtual mode- the only alternative e-learning strategy available at the time of social distancing. Hence it is important to mention that King Abdulaziz University (KAU) has been using e-learning/blended learning for more than a decade under the umbrella of the deanship of distance learning of KAU, however, in a different scenario. Data was collected from teachers and students through two questionnaires (adapted). The results reported that the majority of the participants had positive attitudes towards E-learning. It was also found that students were prepared to shift to online mode to avoid any academic loss.
Toxic Comment Collection: Making More Than 30 Datasets Easily Accessible in One Unified Format

With the rise of research on toxic comment classification, more and more annotated datasets have been released.The wide variety of the task (different languages, different labeling processes and schemes) has led to a large amount of heterogeneous datasets that can be used for training and testing very specific settings.Despite recent efforts to create web pages that provide an overview, most publications still use only a single dataset.They are not stored in one central database, they come in many different data formats and it is difficult to interpret their class labels and how to reuse these labels in other projects.To overcome these issues, we present a collection of more than forty datasets in the form of a software tool that automatizes downloading and processing of the data and presents them in a unified data format that also offers a mapping of compatible class labels.Another advantage of that tool is that it gives an overview of properties of available datasets, such as different languages, platforms, and class labels to make it easier to select suitable training and test data.
The Detection System of Helipad for Unmanned Aerial Vehicle Landing Using YOLO Algorithm

The challenge with using the Unmanned Aerial Vehicle (UAV) is when the UAV makes a landing. This problem can be overcome by developing a landing vision through helipad detection. This helipad detection can make it easier for UAVs to land accurately and precisely by detecting the helipad using a camera. Furthermore, image processing technology is used on the image produced by the camera. You Only Look Once (YOLO) is an image processing algorithm developed to detect objects in real-time, and it is the result of the development of one of the Convolutional Neural Network (CNN) algorithm methods. Therefore, in this study the YOLO method was used to detect a helipad in real-time. The models used in the YOLO algorithm were Mean-Shift and Tiny YOLO VOC. The Tiny YOLO VOC model performed better than the Mean-Shift method in detecting helipads. The test results obtained a confidence value of 91.1%, and the system processing speed reached 35 frames per second (fps) in bright conditions and 37 fps in dark conditions at an altitude of up to 20 meters.
INNOVATORS at SemEval-2021 Task-11: A Dependency Parsing and BERT-based model for Extracting Contribution Knowledge from Scientific Papers

In this work, we describe our system submission to the SemEval 2021 Task 11: NLP Contribution Graph Challenge.We attempt all the three sub-tasks in the challenge and report our results.Subtask 1 aims to identify the contributing sentences in a given publication.Subtask 2 follows from Subtask 1 to extract the scientific term and predicate phrases from the identified contributing sentences.The final Subtask 3 entails extracting triples (subject, predicate, object) from the phrases and categorizing them under one or more defined information units.With the NLPContributionGraph Shared Task, the organizers formalized the building of a scholarly contributions-focused graph over NLP scholarly articles as an automated task.Our approaches include a BERT-based classification model for identifying the contributing sentences in a research publication, a rule-based dependency parsing for phrase extraction, followed by a CNN-based model for information units classification and a set of rules for triples extraction.The quantitative results show that we obtain the 5 th , 5 th , and 7 th rank respectively in three evaluation phases.We make our codes available at