Reviewerly/exHarmony huggingface dataset:
Quantum Election Protocol Based on Quantum Public Key Cryptosystem

There is no quantum election protocol that can fulfil the eight requirements of an electronic election protocol, i.e., completeness, robustness, privacy, legality, unreusability, fairness, verifiability, and receipt-freeness. To address this issue, we employ the general construction of quantum digital signature and quantum public key encryption, in conjunction with classic public key encryption, to develop and instantiate a general construction of quantum election protocol. The proposed protocol exhibits the following advantages: (i) no pre-shared key between any two participants is required, and no trusted third party or anonymous channels are required. The protocol is suitable for large-scale elections with numerous candidates and voters and accommodates the situation in which multiple voters vote simultaneously. (ii) It is the first protocol that dismantles the contradiction between verifiability and receipt-freeness in a quantum election protocol. It satisfies all eight requirements stated earlier under the physical assumptions that there exists a one-way untappable channel from the administrator to the voter and that there is no collusion between any of the three parties in the protocol. Compared with current election protocols with verifiability and receipt-freeness, this protocol relies upon fewer physical assumptions. (iii) This construction is flexible and can be instantiated into an election scheme having post-quantum security by applying cryptographic algorithms conveying post-quantum security. Moreover, utilizing quantum digital signature and public key encryption yields a good result: the transmitted ballots are in quantum states, so owing to the no-cloning theorem, ballot privacy is less likely to be compromised, even if private keys of the signature and public key encryption are leaked after the election. However, in existing election protocols employing classic digital signatures and public key encryption, ballot privacy can be easily violated if attackers obtain private keys. Thus, our construction enhances privacy.
Naïve Bayes Twitter Sentiment Analysis In Visualizing The Reputation Of Communication Service Providers: During Covid-19 Pandemic

We present the real-world public sentiment expressed on Twitter using the proposed conceptual model (CM) to visualize the communication service providers (CSP) reputation during the Covid-19 pandemic in Malaysia from March 18 until August 18, 2020. The CM is a guideline that entails public tweets directly or indirectly mentioned to the three biggest CSP in Malaysia: Celcom, Maxis, and Digi. A text classifier model optimized for short snippets like tweets is developed to make bilingual sentiment analysis possible. The two languages explored are Bahasa Malaysia and English since they are the two most spoken languages in Malaysia. The classifier model is trained and tested on a huge multidomain dataset pre-labeled with the labels “0” and “1”, which resemble “positive” and “negative”, respectively. We used the Naïve Bayes (NB) technique as the core of the classifier model. Functionality testing has done to ensure no significant error that will render the application useless, and the accuracy testing score of 89% is considered quite impressive. We came out with the visualization through the word clouds and presented -56%, -42%, and -43% of Net Brand Reputation for Celcom, Maxis, and Digi.
Mathematical models for calculating the quantitative characteristics of the optimal quantization of information

Various additional mathematical aspects related to solving the problem of optimal information quantization in the sense of filling are considered, such as control of quantum elements, accounting for errors of quantum elements, determining the amount of information during quantization, and determining the numerical values of fractals of distributions represented as a sequential fractal distribution. The purpose of the article is to consider additional questions based on a specific "heavy" probability distribution the normal distribution. The considered questions are made in order to facilitate the solution of applied problems for researchers dealing with the problem of information quantization.
Dynamic fractional chaotic biometric isomorphic elliptic curve for partial image encryption

In this paper, a Modular Fractional Chaotic Sine Map (MFC-SM) has been introduced to achieve high Lyapunov exponent values and completely chaotic behavior of the bifurcation diagram for high level security. The proposed MFC-SM is compared with the conventional non MFC-SM and it has an excellent chaotic analysis. In addition, the randomness test results indicate that the proposed MFC-SM shows better performance and satisfy all randomness tests. Due to the excellent chaotic properties and good randomization results for the proposed MFC-SM, it is used to be cooperated with the biometric digital identity to achieve dynamic chaotic biometric digital identity. Also, for real time image encryption, both Discrete Wavelet Transform (DWT)partial image encryption and Isomorphic Elliptic Curve (IEC)key exchange are used. In addition, the biometric digital identity is extracted from the user fingerprint image as fingerprint minutia data incorporated with the proposed MFC-SM and hence, a new Dynamic Fractional Chaotic Biometric Digital IdentityIEC (DFC-BID-IEC) has been introduced. Dynamic Fractional Chaotic Key Generator (DFC-KG) is used to control the key schedule for all encryption and decryption processing. The encryption process consists of the confusion and diffusion steps. In the confusion step, the 2D Arnold Cat Map (ACM) is used with secret parameters taken from DFC-KG. Also, the diffusion step is based on the dynamic chaotic self-invertible secret key matrix which can be generated from the proposed MFC-SM. The IEC key exchange secret parameters are generated based on Elliptic Curve Diffie?Hellman(ECDH) key exchange and the isomorphism parametre. Statistical analysis, differential analysis and key sensitivity tests are performed to estimate the security strengths of the proposed DFC-BID-IEC system. The experimental results show that the proposed algorithm is robust against common signal processing attacks and provides a high security level and high speed for image encryption application.
Compact Implementation of ARIA on 16-Bit MSP430 and 32-Bit ARM Cortex-M3 Microcontrollers

In this paper, we propose the first ARIA block cipher on both MSP430 and Advanced RISC Machines (ARM) microcontrollers. To achieve the optimized ARIA implementation on target embedded processors, core operations of ARIA, such as substitute and diffusion layers, are carefully re-designed for both MSP430 (Texas Instruments, Dallas, TX, USA) and ARM Cortex-M3 microcontrollers (STMicroelectronics, Geneva, Switzerland). In particular, two bytes of input data in ARIA block cipher are concatenated to re-construct the 16-bit wise word. The 16-bit word-wise operation is executed at once with the 16-bit instruction to improve the performance for the 16-bit MSP430 microcontroller. This approach also optimizes the number of required registers, memory accesses, and operations to half numbers rather than 8-bit word wise implementations. For the ARM Cortex-M3 microcontroller, the 8×32 look-up table based ARIA block cipher implementation is further optimized with the novel memory access. The memory access is finely scheduled to fully utilize the 3-stage pipeline architecture of ARM Cortex-M3 microcontrollers. Furthermore, the counter (CTR) mode of operation is more optimized through pre-computation techniques than the electronic code book (ECB) mode of operation. Finally, proposed ARIA implementations on both low-end target microcontrollers (MSP430 and ARM Cortex-M3) achieved (209 and 96 for 128-bit security level, respectively), (241 and 111 for 192-bit security level, respectively), and (274 and 126 for 256-bit security level, respectively). Compared with previous works, the running timing on low-end target microcontrollers (MSP430 and ARM Cortex-M3) is improved by (92.20% and 10.09% for 128-bit security level, respectively), (92.26% and 10.87% for 192-bit security level, respectively), and (92.28% and 10.62% for 256-bit security level, respectively). The proposed ARIA–CTR implementation improved the performance by 6.6% and 4.0% compared to the proposed ARIA–ECB implementations for MSP430 and ARM Cortex-M3 microcontrollers, respectively.
New sufficient conditions on the degree sequences of uniform hypergraphs

The study of the degree sequences of k-uniform hypergraphs, usually called k-sequences, has been a longstanding open problem for the case of k>2, and the corresponding decision version was proved to be NP-complete recently in 2018 [15]. The problem can be formalized as follows: Given a non decreasing sequence of positive integers π=(d1,d2,…,dn), can π be the degree sequence of a k-uniform simple hypergraph? If the answer is positive, then the sequence π is said to be k-graphic. For k=2, that is for simple graphs, Erdös and Gallai [16] provided a characterization of the sequences that are 2-graphic (or simply, graphic). From this characterization, a polynomial time algorithm can be designed to reconstruct the incidence matrix of a graph having a given π as degree sequence (provided this graph exists). Due to the result of [15] and assuming P≠NP, an efficiently computable characterization like the one for k=2 does not even exist for the case of 3-uniform hypergraphs. Necessary or sufficient conditions for π to be k-graphic (k≥3) can be found in the literature. In this paper we prove some different new conditions: first we provide sufficient and also necessary conditions for the case of k-uniform and (almost) regular hypergraphs. Then, for k=3, we prove sufficient conditions in the case where π can be decomposed into π′ and π″, and π′ is graphic. Most of the results are obtained by borrowing tools from discrete tomography, a current research field on discrete mathematics.
TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction

Eye movement data during reading is a useful source of information for understanding language comprehension processes.In this paper, we describe our submission to the CMCL 2021 shared task on predicting human reading patterns.Our model uses RoBERTa with a regression layer to predict 5 eye-tracking features.We train the model in two stages: we first finetune on the Provo corpus (another eye-tracking dataset), then fine-tune on the task data.We compare different Transformer models and apply ensembling methods to improve the performance.Our final submission achieves a MAE score of 3.929, ranking 3rd place out of 13 teams that participated in this shared task.
How Fast can BERT Learn Simple Natural Language Inference?

This paper empirically studies whether BERT can really learn to conduct natural language inference (NLI) without utilizing hidden dataset bias; and how efficiently it can learn if it could.This is done via creating a simple entailment judgment case which involves only binary predicates in plain English.The results show that the learning process of BERT is very slow.However, the efficiency of learning can be greatly improved (data reduction by a factor of 1,500) if task-related features are added.This suggests that domain knowledge greatly helps when conducting NLI with neural networks.
Income Inequality and Socio-Political Instability in Sub-Saharan Africa

In this paper, we analyse the impact of income inequality on Socio-Political Instability (hereinafter SPI) in Sub-Saharan Africa from 1990 to 2018 with a sample of 47 countries. We first present the theoretical and empirical debate on income inequality and SPI. This literature review allows us tomeasure SPI using the principal component analysis method and hierarchical clustering and partitioning to analyse the similarities and differences between countries from a multidimensional perspective. We then estimate the SPI concerning income inequality and democracy. The findings are that assassinations are not linked to a regime’s duration, and the duration of a regime reduces if coups d’état (successful or not) are rampant. Between democracy and income inequality, the former has 34 times more impact on SPI. GDP growth increases SPI and education reduces SPI.
Non-Autoregressive Text Generation with Pre-trained Language Models

Non-autoregressive generation (NAG) has recently attracted great attention due to its fast inference speed.However, the generation quality of existing NAG models still lags behind their autoregressive counterparts.In this work, we show that BERT can be employed as the backbone of a NAG model to greatly improve performance.Additionally, we devise mechanisms to alleviate the two common problems of vanilla NAG models: the inflexibility of prefixed output length and the conditional independence of individual token predictions.Lastly, to further increase the speed advantage of the proposed model, we propose a new decoding strategy, ratio-first, for applications where the output lengths can be approximately estimated beforehand.For a comprehensive evaluation, we test the proposed model on three text generation tasks, including text summarization, sentence compression and machine translation.Experimental results show that our model significantly outperforms existing non-autoregressive baselines and achieves competitive performance with many strong autoregressive models.In addition, we also conduct extensive analysis experiments to reveal the effect of each proposed component. 1
Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems

Shubhi Tyagi, Antonio Bonafonte, Jaime Lorenzo-Trueba, Javier Latorre. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers. 2021.
Sentimental Analysis on Impact of COVID-19 Outbreak

COVID-19 a pandemic created a devastating impact throughout the world on the basis of social losses, economical loss and political loss. Opinion of individuals on the basis of feelings, thoughts, attitude and emotions should be expressed which is known as sentiments. The aim of analysis is to determine or reflect the impact of pandemic COVID-19 over the various countries. In this, we evaluate the impact of lockdown on various countries on the basis of various parameters like size of lockdown, death ratio, recovery ratio, etc. In this, reviews of individuals gathered from news reports, blogs, social media, survey forms and authenticated web pages. The sentiments are categorized into positive, negative and neutral using K-nearest neighbor algorithm of machine learning. Twitter being the more popular site for analyzing the sentiments is used. Tweepy and Node-RED tools are used to extract data from twitter, and Octoparse is used to scrap data from various news channel portals and blogs. Classification algorithm is used on the gathered data. Extraction of feature is achieved using N-gram modeling technique.
MTC: Minimizing Time and Cost of Cloud Task Scheduling based on Customers and Providers Needs using Genetic Algorithm

Cloud computing is considered a pattern for distributed and heterogeneous computing derived from many resources, and requests aim to share resources. Recently, cloud computing is graded among the top best technologies globally, which must be scheduled favorably to maximize providers’ profit and improve service quality for their customers. Scheduling specifies how users’ requests are assigned to virtual machines, and it plays a vital role in the efficiency and capability of the system. Its objective is to have a throughput or complete jobs in minimum time and the highest standard. Scheduling jobs in heterogeneous distributed systems is an NP-hard polynomial indecisive problem that is not solvable in polynomial time for real-time scheduling. The time complexity of jobs is growing exponentially, and this problem has a considerable effect on the quality of cloud services and providers’ efficiencies. The optimization of scheduling-related parameters using heuristic and meta-heuristic algorithms can reduce the search space complexity and execution time. This study intends to represent a fitness function to minimize time and cost parameters. The proposed method uses a multi-purposed weighted genetic algorithm that provides six basic parameters: utility, task execution cost, response time, wait time, Makespan, and throughput to provide comprehensive optimization. The proposed approach improved response and wait times, throughput, Makespan, and utility 16, 9, 7, 8 percentages, respectively, by only a one cost unit reduction, which is dispensable. As a result, both providers and users will experience better services. The statistical tests show that the achieved improvement is valid for 94% of experiments.
Webometric Analysis of Institutional Repositories of Malaysian Public Universities

An institutional repository (IR) is one of the resources available in most university libraries that have attracted external publishers, search engines and social media to link, share and index IR content. The traditional citation-based indicators of a publication may not reflect the IR quality and have led to the creation of new indicators such as webometrics or web metrics. This study aims to analyse and explore Malaysia’s public university IR visibility, the numbers of an external link, page count, PDF count and URL web mention. We utilised backlinks web crawler and web search engine to collect raw data. A visualisation was created using the force-directed graphing method to interpret the IR network in the webspace. This study revealed that two research universities, Universiti Malaya (UM) and Universiti Putra Malaysia (UPM), dominate web visibility based on webometrics indicators. All non-research universities are at the bottom of the rankings. This study shows institutional repositories from research universities are more visible in academic social networks and digital library sites. In contrast, non-research universities need to improve their visibility by mapping the universities’ IRs websites through hyperlink exchange and collaboration activities between each university and promoting the university publication to the academic social network sites.
Exploiting Emojis for Abusive Language Detection

We propose to use abusive emojis, such as the middle finger or face vomiting, as a proxy for learning a lexicon of abusive words.Since it represents extralinguistic information, a single emoji can co-occur with different forms of explicitly abusive utterances.We show that our approach generates a lexicon that offers the same performance in cross-domain classification of abusive microposts as the most advanced lexicon induction method.Such an approach, in contrast, is dependent on manually annotated seed words and expensive lexical resources for bootstrapping (e.g.WordNet).We demonstrate that the same emojis can also be effectively used in languages other than English.Finally, we also show that emojis can be exploited for classifying mentions of ambiguous words, such as fuck and bitch, into generally abusive and just profane usages.
Artificial Neural Network as a Universal Model of Nonlinear Dynamical Systems

We suggest a universal map capable of recovering the behavior of a wide range of dynamical systems given by ODEs. The map is built as an artificial neural network whose weights encode a modeled system. We assume that ODEs are known and prepare training datasets using the equations directly without computing numerical time series. Parameter variations are taken into account in the course of training so that the network model captures bifurcation scenarios of the modeled system. The theoretical benefit from this approach is that the universal model admits applying common mathematical methods without needing to develop a unique theory for each particular dynamical equations. From the practical point of view the developed method can be considered as an alternative numerical method for solving dynamical ODEs suitable for running on contemporary neural network specific hardware. We consider the Lorenz system, the Rössler system and also the Hindmarch – Rose model. For these three examples the network model is created and its dynamics is compared with ordinary numerical solutions. A high similarity is observed for visual images of attractors, power spectra, bifurcation diagrams and Lyapunov exponents.
WER-BERT: Automatic WER Estimation with BERT in a Balanced Ordinal Classification Paradigm

Automatic Speech Recognition (ASR) systems are evaluated using Word Error Rate (WER), which is calculated by comparing the number of errors between the ground truth and the transcription of the ASR system.This calculation, however, requires manual transcription of the speech signal to obtain the ground truth.Since transcribing audio signals is a costly process, Automatic WER Evaluation (e-WER) methods have been developed to automatically predict the WER of a speech system by only relying on the transcription and the speech signal features.While WER is a continuous variable, previous works have shown that positing e-WER as a classification problem is more effective than regression.However, while converting to a classification setting, these approaches suffer from heavy class imbalance.In this paper, we propose a new balanced paradigm for e-WER in a classification setting.Within this paradigm, we also propose WER-BERT, a BERT based architecture with speech features for e-WER.Furthermore, we introduce a distance loss function to tackle the ordinal nature of e-WER classification.The proposed approach and paradigm are evaluated on the Librispeech dataset and a commercial (black box) ASR system, Google Cloud's Speech-to-Text API.The results and experiments demonstrate that WER-BERT establishes a new state-of-the-art in automatic WER estimation.
Sensitivity of shortest distance search in the ant colony algorithm with varying normalized distance formulas

The ant colony algorithm is an algorithm adopted from the behavior of ants which naturally ants are able to find the shortest route on the way from the nest to places of food sources based on footprints on the track that has been passed. The ant colony algorithm helps a lot in solving several problems such as scheduling, traveling salesman problems (TSP) and vehicle routing problems (VRP). In addition, ant colony has been developed and has several variants. However, in its function to find the shortest distance is optimized by utilizing several normalized distance formulas with the data used in finding distances between merchants in the mercant ecosystem. Where in the test normalized distance is superior Hamming distance in finding the shortest distance of 0.2875, then followed by the same value, namely the normalized formula Manhattan distance and normalized Euclidean distance with a value of 0.4675 and without using the normalized distance formula or the original ant colony algorithm gets a value 0.6635. Given the sensitivity in distance search using merchant ecosystem data, the method works well on the ant colony Algorithm using normalized Hamming distance.
Adv-OLM: Generating Textual Adversaries via OLM

Deep learning models are susceptible to adversarial examples that have imperceptible perturbations in the original input, resulting in adversarial attacks against these models.Analysis of these attacks on the state of the art transformers in NLP can help improve the robustness of these models against such adversarial inputs.In this paper, we present Adv-OLM, a black-box attack method that adapts the idea of Occlusion and Language Models (OLM) to the current state of the art attack methods.OLM is used to rank words of a sentence, which are later substituted using word replacement strategies.We experimentally show that our approach outperforms other attack methods for several text classification tasks.