Reviewerly/exHarmony huggingface dataset:
Automated Generation of Meeting Minutes using Deep Learning Techniques

Meeting minutes are important to keep track of key decisions and agreements that were made during a meeting.It is crucial to document the topics discussed and the decisions made so they can be reviewed at the beginning of the next meeting as well as for future reference.Many companies while conducting meetings, keep paid employees to note down meeting minutes taking up valued time and resources.We offer a solution to make better use of available tools and technological advancements to help employees conduct effective discussions to boost a company's productivity.Automated Minute Book Creation (AMBOC) uses machine learning to derive key information from important discussions.AMBOC is an automated system to create transcripts and minutes of a meeting with the added advantage of speaker recognition.The model we propose will be capable of transforming an audio file into plain-text using Deep Neural Networks (DNN), recognizing the speaker using Mel Frequency Cepstral Co-efficient (MFCC) as well as summarizing the meeting transcript into condensed minutes with the help of Transformers.
ANDI at SemEval-2021 Task 1: Predicting complexity in context using distributional models, behavioural norms, and lexical resources

In this paper we describe our participation in the Lexical Complexity Prediction (LCP) shared task of SemEval 2021, which involved predicting subjective ratings of complexity for English single words and multi-word expressions, presented in context. Our approach relies on a combination of distributional models, both context-dependent and context-independent, together with behavioural norms and lexical resources.
Multi-round encryption for COVID-19 data using the DNA key

The need for a reliable and fast encryption algorithm to encrypt medical data for patients is an extremely important topic to be considered especially during pandemic times such as the pandemic COVID-19. This pandemic forced governments and healthcare institutions to monitor COVID-19 patients. All the patient's data or records are also shared among healthcare researchers to be used to help them find vaccines or cures for this pandemic. Therefore, protecting such data (images, text) or records face an everincreasing number of risks. In this paper, a novel multi-round encryption algorithm based on deoxyribonucleic acid (DNA) is proposed. The significance of the proposed algorithm comes from using a different random key to perform simple and fast encryption operations on multiple rounds to achieve a high level of confusion and diffusion effects in encrypted data. Experiments were conducted using a set of datasets of various types such as Excel sheets, images, and database tables. The experiments were conducted to test the performance and security level of the proposed encryption algorithm against well-known algorithms such as data encryption standard (DES) and advanced encryption standard (AES). The experiments show an outstanding performance regarding the encryption time, key size, information entropy, and the avalanche effects.
Face Mask Detection System

COVID-19 pandemic has affected the world severely, according to the World Health Organization (WHO), coronavirus disease (COVID-19) has globally infected over 176 million people causing over 3.8 million deaths. Wearing a protective mask has become a norm. However, it is seen in most public places that people do not wear masks or don’t wear them properly. In this paper, we propose a high accuracy and efficient face mask detector based on MobileNet architecture. The proposed method detects the face in real-time with OpenCV and then identifies if it has a mask on it or not. As a surveillance task, it supports motion, and is trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context.
An Efficient Compartmented Secret Sharing Scheme Based on Linear Homogeneous Recurrence Relations

Multipartite secret sharing schemes are those that have multipartite access structures. The set of the participants in those schemes is divided into several parts, and all the participants in the same part play the equivalent role. One type of such access structure is the compartmented access structure, and the other is the hierarchical access structure. We propose an efficient compartmented multisecret sharing scheme based on the linear homogeneous recurrence (LHR) relations. In the construction phase, the shared secrets are hidden in some terms of the linear homogeneous recurrence sequence. In the recovery phase, the shared secrets are obtained by solving those terms in which the shared secrets are hidden. When the global threshold is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>t</mi> </math> , our scheme can reduce the computational complexity of the compartmented secret sharing schemes from the exponential time to polynomial time. The security of the proposed scheme is based on Shamir’s threshold scheme, i.e., our scheme is perfect and ideal. Moreover, it is efficient to share the multisecret and to change the shared secrets in the proposed scheme.
Wheelchair Movement Based on Convolution Neural Network

This paper intends to develop a methodology for helping amputees and crippled people old, by ongoing voice direction and association between patient and personal computer (PC) where these blends offer a promising response for helping the debilitated people. The major objective of this work is accurately detected audio orders via a microphone of an English language (go, stop, right and left) in a noisy environment by the proposed system. Thus, a patient that utilizes the proposed system can be controlling a wheelchair movement. The venture depends on preparing an off-line dataset of audio files are included 10000 orders and background noise. The proposed system has two important steps of preprocessing to get accurate of specific audio orders, accordingly, the accurate direction of wheelchair movement. Firstly, a dataset was preprocessed to reduce ambient noise by using Butterworth (cutoff 500-5000 Hz) and Wiener filter. Secondly, in the input (a microphone) of the proposed discriminative model put a procedure of infinite impulse response filter (Butterworth), passband filter for cutoff input microphone from 150-7000 Hz for back-off the loud and environment noise and local polynomial approximation (Savitzky-Golay) smoothing filter that plays out a polynomial regression on the signal values. Thus, a better for filtering from ambient noise and keeping on a waveform from distortion that makes the discriminative model accurate when voice orders were recognized. The proposed system can work with various situations and speeds for steering; forward, stop, left and right. All datasets are trained by using deep learning with specific parameters of a convolutional neural network (CNN). These capacities are dependent on code written in MATLAB. The prototype uses Arduino UNO and a microphone (MIC)
Interactive Reinforcement Learning for Table Balancing Robot

With the development of robotics, the use of robots in daily life is increasing, which has led to the need for anyone to easily train robots to improve robot use.Interactive reinforcement learning(IARL) is a method for robot training based on human-robot interaction; prior studies on IARL provide only limited types of feedback or require appropriately designed shaping rewards, which is known to be difficult and time consuming.Therefore, in this study, we propose interactive deep reinforcement learning models based on voice feedback.In the proposed system, a robot learns the task of cooperative table balancing through deep Q-network using voice feedback provided by humans in real time, with automatic speech recognition(ASR) and sentiment analysis to understand human voice feedback.As a result, an optimal policy convergence rate of up to 96% was realized, and performance was improved in all voice feedback-based models.
graphed: A Web-Based Concept Mapping Application for Instruction and Research

The ongoing worldwide pandemic has forced educational establishments to accelerate full-scale adoption of online learning at an accelerated pace, while the development of tools appropriate for remote instruction assessment is yet to catch up. Most of the time traditional assessment methods are still employed, but they are not always optimal for use in online environments; better tools are needed to help gain deeper insights into how students think and learn. graphed is a web application developed to support the assessment of learners’ understanding and knowledge acquisition and, simultaneously, provide researchers with data that can help in the development of dedicated processes for the automatic evaluation and comparison of concept maps. Our goal is to take a more practical approach by studying the capabilities offered by existing software, libraries, and computational avenues to advance the use of concept maps as assessment tools. Preliminary findings suggest that the concept mapping activity has achieved its purpose of promoting deep thinking, that the application is relatively usable, and clarified the path for future development and enhancement. Examples on the use of graphed in the classroom are provided.
One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets

Semantic parsers map natural language utterances to meaning representations. The lack of a single standard for meaning representations led to the creation of a plethora of semantic parsing datasets. To unify different datasets and train a single model for them, we investigate the use of Multi-Task Learning (MTL) architectures. We experiment with five datasets (Geoquery, NLMaps, TOP, Overnight, AMR). We find that an MTL architecture that shares the entire network across datasets yields competitive or better parsing accuracies than the single-task baselines, while reducing the total number of parameters by 68%. We further provide evidence that MTL has also better compositional generalization than single-task models. We also present a comparison of task sampling methods and propose a competitive alternative to widespread proportional sampling strategies.
Cascaded Span Extraction and Response Generation for Document-Grounded Dialog

This paper summarizes our entries to both subtasks of the first DialDoc shared task which focuses on the agent response prediction task in goal-oriented document-grounded dialogs.The task is split into two subtasks: predicting a span in a document that grounds an agent turn and generating an agent response based on a dialog and grounding document.In the first subtask, we restrict the set of valid spans to the ones defined in the dataset, use a biaffine classifier to model spans, and finally use an ensemble of different models.For the second subtask, we use a cascaded model which grounds the response prediction on the predicted span instead of the full document.With these approaches, we obtain significant improvements in both subtasks compared to the baseline.
Design Issues in e-Learning during the COVID-19 Pandemic

The COVID-19 pandemic led to fast and unexpected changes in social life, including education. Institutions and social actors were forced to rethink school participation in order to respect social distance, but also to ensure quality in education and social order. Digital and collaborative platforms replaced the physical classrooms and mediated the virtual interaction between students and teacher. The paper contributes to the scientific debate on collaborative learning, by analyzing the challenges and opportunities encountered by Romanian students, teachers, and parents during the COVID-19 pandemic. The analysis is based on a national survey that elicits perspectives on quality and strategies of online education from 9401 students, 3265 teachers and 4269 parents. Online education has proven both an opportunity for systemic reform and a risk for social exclusion, rising concerns on how to leverage technology for extending, rather than diminishing, learning experiences.
Generative Pretraining for Paraphrase Evaluation

We introduce ParaBLEU, a paraphrase representation learning model and evaluation metric for text generation. Unlike previous approaches, ParaBLEU learns to understand paraphrasis using generative conditioning as a pretraining objective. ParaBLEU correlates more strongly with human judgements than existing metrics, obtaining new state-of-the-art results on the 2017 WMT Metrics Shared Task. We show that our model is robust to data scarcity, exceeding previous state-of-the-art performance using only 50% of the available training data and surpassing BLEU, ROUGE and METEOR with only 40 labelled examples. Finally, we demonstrate that ParaBLEU can be used to conditionally generate novel paraphrases from a single demonstration, which we use to confirm our hypothesis that it learns abstract, generalized paraphrase representations.
CS-UM6P at SemEval-2021 Task 1: A Deep Learning Model-based Pre-trained Transformer Encoder for Lexical Complexity

Lexical Complexity Prediction (LCP) involves assigning a difficulty score to a particular word or expression, in a text intended for a target audience.In this paper, we introduce a new deep learning-based system for this challenging task.The proposed system consists of a deep learning model, based on a pre-trained transformer encoder, for word and Multi-Word Expression (MWE) complexity prediction.First, on top of the encoder's contextualized word embedding, our model employs an attention layer on the input context and the complex word or MWE.Then, the attention output is concatenated with the pooled output of the encoder and passed to a regression module.We investigate both single-task and joint training on both Sub-Tasks data using multiple pre-trained transformer-based encoders.The obtained results are very promising and show the effectiveness of fine-tuning pre-trained transformers for LCP tasks.
Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation

Question answering (QA) models for reading comprehension have achieved human-level accuracy on in-distribution test sets.However, they have been demonstrated to lack robustness to challenge sets, whose distribution is different from that of training sets.Existing data augmentation methods mitigate this problem by simply augmenting training sets with synthetic examples sampled from the same distribution as the challenge sets.However, these methods assume that the distribution of a challenge set is known a priori, making them less applicable to unseen challenge sets.In this study, we focus on question-answer pair generation (QAG) to mitigate this problem.While most existing QAG methods aim to improve the quality of synthetic examples, we conjecture that diversity-promoting QAG can mitigate the sparsity of training sets and lead to better robustness.We present a variational QAG model that generates multiple diverse QA pairs from a paragraph.Our experiments show that our method can improve the accuracy of 12 challenge sets, as well as the in-distribution accuracy. 1
Enabling Role-Based Orchestration for Cloud Applications

With the rapidly growing popularity of cloud services, the cloud computing faces critical challenges to orchestrate the deployment and operation of cloud applications on heterogenous cloud platforms. Cloud applications are built on a platform model that abstracts away underlying platform-specific details, so that their orchestration can benefit from the abstract view and flexibility of the underlying platform configuration. However, considerable efforts are still required to properly manage complicated cloud applications. This paper proposes a model-driven approach to cloud application orchestration which promotes the concerns of distinct roles for cloud system provisioning and operation. By establishing a set of capabilities as modeling constructs, our approach allows TOSCA-based application topology itself and its orchestration needs to be specified in a way to provide a more targeted support for different needs and concerns of application developers and operators. With novel orchestration features like application topology description, platform capability modeling, and role-awareness for cloud application orchestration, it can significantly reduce the complexity of application orchestration in diverse cloud environments. To show the feasibility and effectiveness of our proposal for cloud application orchestration, we present a proof-of-concept orchestration system implementation and evaluate its deployment and orchestration results in a Kubernetes cluster.
Joint Detection and Coreference Resolution of Entities and Events with Document-level Context Aggregation

Constructing knowledge graphs from unstructured text is an important task that is relevant to many domains.Most previous work focuses on extracting information from sentences or paragraphs, due to the difficulty of analyzing longer contexts.In this paper we propose a new jointly trained model that can be used for various information extraction tasks at the document level.The tasks performed in this paper are entity and event identification, typing, and coreference resolution.In order to improve entity and event extraction, we utilize contextaware representations aggregated from the detected mentions of the corresponding entities and event triggers across the entire document.By extending our system to documentlevel, we can improve our results by incorporating cross-sentence dependencies and additional contextual information that might not be available at the sentence level, which allows for more globally optimized predictions.We evaluate our system on documents from the ACE05-E + dataset and find significant improvement over the sentence-level state-of-theart on entity extraction and event detection. 1
Analytical Investigation on Torque of Three-Degree-of-Freedom Electromagnetic Actuator for Image Stabilization

In keeping with consumers’ preferences for electromagnetic motors of ever smaller power consumption, it is necessary to improve the power efficiency of the electromagnetic motors used in unmanned aerial vehicles and robots without sacrificing their performance. Three-degree-of-freedom (3-DOF) spherical motors have been developed for these applications. Accordingly, this study modifies the 3-DOF spherical motor proposed by Hirata’s group in a previous study (Heya, A.; Hirata, K.; Niguchi, N., Dynamic modeling and control of three-degree-of-freedom electromagnetic actuator for image stabilization, IEEE Transactions on Magnetics 2018, 54, 8207905.) to accomplish a 3-DOF spherical motor for camera module with higher torque output in the large rotation angle. The main contribution of this study is to improve the static torque in the X- and Y-axes with an improved electromagnetic structure and a particular controlling strategy. In the structural design, eight symmetrical coils with specific coil combination are used instead of conventional four symmetrical coils. In this study, the development of the proposed 3-DOF spherical motor was constructed and verified by using a 3D finite-element method (3D FEM). The simulation results show that the proposed 3-DOF spherical motor has higher torque output in the large rotation angle when compared to the original 3-DOF spherical motor.
Scattering in Algebraic Approach to Quantum Theory—Associative Algebras

The definitions of scattering matrix and inclusive scattering matrix in the framework of formulation of quantum field theory in terms of associative algebras with involution are presented. The scattering matrix is expressed in terms of Green functions on shell (LSZ formula), and the inclusive scattering matrix is expressed in terms of generalized Green functions on shell. The expression for inclusive scattering matrix can be used also for quasi-particles (for elementary excitations of any translation-invariant stationary state, for example, for elementary excitations of equilibrium state). An interesting novelty is the consideration of associative algebras over real numbers.
Design and implement a new secure prototype structure of e-commerce system

&lt;p&gt;&lt;br /&gt;The huge development of internet technologies and the widespread of modern and advanced devices lead to an increase in the size and diversity of e-commerce system development. These developments lead to an increase in the number of people that navigate these sites asking for their services and products. Which leads to increased competition in this field. Moreover, the expansion in the size of currency traded makes transaction protection an essential issue in this field. Providing security for each online client especially for a huge number of clients at the same time, causing an overload on the system server. This problem may lead to server deadlock, especially at rush time, which reduce system performance. To solve security and performance problems, this research suggests a prototype design for agent software. This agent will play the role of broker between the clients and the electronic marketplace. This is done by providing security inside the client device and converting the client’s order into a special form which is called a record form to be sent to the commercial website. Experimental results showed that this method increase system performance in terms of page loading time, transaction processing and improves the utilization of system resources.&lt;/p&gt;
Contextualized Filtering for Shared Cyber Threat Information

Cyber threat information sharing is an imperative process towards achieving collaborative security, but it poses several challenges. One crucial challenge is the plethora of shared threat information. Therefore, there is a need to advance filtering of such information. While the state-of-the-art in filtering relies primarily on keyword- and domain-based searching, these approaches require sizable human involvement and rarely available domain expertise. Recent research revealed the need for harvesting of business information to fill the gap in filtering, albeit it resulted in providing coarse-grained filtering based on the utilization of such information. This paper presents a novel contextualized filtering approach that exploits standardized and multi-level contextual information of business processes. The contextual information describes the conditions under which a given threat information is actionable from an organization perspective. Therefore, it can automate filtering by measuring the equivalence between the context of the shared threat information and the context of the consuming organization. The paper directly contributes to filtering challenge and indirectly to automated customized threat information sharing. Moreover, the paper proposes the architecture of a cyber threat information sharing ecosystem that operates according to the proposed filtering approach and defines the characteristics that are advantageous to filtering approaches. Implementation of the proposed approach can support compliance with the Special Publication 800-150 of the National Institute of Standards and Technology.