Reviewerly/exHarmony huggingface dataset:
Quantum Cryptography: The Future of Internet and Security Management

In today’s world, cryptography techniques are used and implemented on the elementary method of finding the prime factor of large integer, which is said to be “Inevitable to Track”. But living in an era where nothing is impossible to achieve, so cryptographic techniques are exposed to both technologies’ advancement in computational power of machines and advancement in the fields of mathematics to break the notion that factoring of large integers into their prime is impossible. To cope up with the threat that cryptography will face is handled by fusion of physics into cryptography, leading to the evolution of Quantum Cryptography. It is one of the fast-growing fields in computer technology. In this paper, I am going to brief the concepts of Quantum Cryptography and how this technology has led to the development of the strategy of complete secure key distribution. The paper covers the loophole present in the modern cryptography techniques, the fundamental principle of quantum cryptography, its implementation in the real world along with the limitation faced in this field, and the possible future of quantum cryptography.
A Novel Methodology for Providing Security in Electronic Health Record Using Fuzzy Based Multi Agent System

&lt;p class="0abstract"&gt;Privacy and security are crucial when it comes to the implementation of Electronic Healthcare Records (HER). Whenever a patient visits a doctor, their health records are updated since it consists of vital information on the patient's health and well-being. It also comprises of description about the previous and current treatment quality.Multi-Agent Systems can support E-Healthcare applications for improving quality of life. The availability and use of extensive systems for EHR has increased dramatically. The EHR technology currently available consists of health information of various patients which helps those people to monitor their health. These information should be protected from being altered or mishandled. Various studies have been conducted in the past literature to improve the protection and privacy requirements in E-health services. Despite this, it hasn't been entirely improved. This article proposes a Fuzzy based multi-agent system architecture that uses agents and fuzzy logic to monitor the flow and access of the EHR record using fuzzy logic and multi agents. Architecture comprises of multi agent and fuzzy systems are proposed. In this system, we have employed Action/ Computation (Interface) agent, communication interface agent and verification agent and Communication Module Agent. These agents collaboratively worked one another in order to obtain the desired output. These agent based e-health system can increase the accuracy of data transfer by providing additional security in selecting the user who handles the data. It also monitors the communication between different medical units in the health system.&lt;/p&gt;
Semantic Alignment with Calibrated Similarity for Multilingual Sentence Embedding

Measuring the similarity score between a pair of sentences in different languages is the essential requisite for multilingual sentence embedding methods.Predicting the similarity score consists of two sub-tasks, which are monolingual similarity evaluation and multilingual sentence retrieval.However, conventional methods have mainly tackled only one of the sub-tasks and therefore showed biased performances.In this paper, we suggest a novel and strong method for multilingual sentence embedding, which shows performance improvement on both sub-tasks, consequently resulting in robust predictions of multilingual similarity scores.The suggested method consists of two parts: to learn semantic similarity of sentences in the pivot language and then to extend the learned semantic structure to different languages.To align semantic structures across different languages, we introduce a teacher-student network.The teacher network distills the knowledge of the pivot language to different languages of the student network.During the distillation, the parameters of the teacher network are updated with the slow-moving average.Together with the distillation and the parameter updating, the semantic structure of the student network can be directly aligned across different languages while preserving the ability to measure the semantic similarity.Thus, the multilingual training method drives performance improvement on multilingual similarity evaluation.The suggested model achieves the state-of-the-art performance on extended STS 2017 multilingual similarity evaluation as well as two sub-tasks, which are extended STS 2017 monolingual similarity evaluation and Tatoeba multilingual retrieval in 14 languages.
The Korean Morphologically Tight-Fitting Tokenizer for Noisy User-Generated Texts

User-generated texts include various types of stylistic properties, or noises.Such texts are not properly processed by existing morpheme analyzers or language models based on formal texts such as encyclopedias or news articles.In this paper, we propose a simple morphologically tight-fitting tokenizer (K-MT) that can better process proper nouns, coinages, and internet slang among other types of noise in Korean user-generated texts.We tested our tokenizer by performing classification tasks on Korean user-generated movie reviews and hate speech datasets, and the Korean Named Entity Recognition dataset.Through our tests, we found that K-MT is better fit to process internet slangs, proper nouns, and coinages, compared to a morpheme analyzer and a characterlevel WordPiece tokenizer.
Interacting Knowledge Sources, Inspection and Analysis: Case-studies on Biomedical text processing

In this paper we investigate the recently proposed multi-input RIM for inspectability.This framework follows an encapsulation paradigm, where external knowledge sources are encoded as largely independent modules, enabling transparency for model inspection.
SpanAlign: Efficient Sequence Tagging Annotation Projection into Translated Data applied to Cross-Lingual Opinion Mining

Following the increasing performance of neural machine translation systems, the paradigm of using automatically translated data for cross-lingual adaptation is now studied in several applicative domains.The capacity to accurately project annotations remains however an issue for sequence tagging tasks where annotation must be projected with correct spans.Additionally, when the task implies noisy usergenerated text, the quality of translation and annotation projection can be affected.In this paper we propose to tackle multilingual sequence tagging with a new span alignment method and apply it to opinion target extraction from customer reviews.We show that provided suitable heuristics, translated data with automatic span-level annotation projection can yield improvements both for crosslingual adaptation compared to zero-shot transfer, and data augmentation compared to a multilingual baseline.
SME’S Accounting Information in the Eyes of Bank Credit Analyst: Exploration with ZMET Method

This study aims to explore and gain an in-depth understanding of the views of bank credit analysts on SME’s accounting information in credit decision making. This study used the ZMET (Zaltman Metaphor Elicitation Technique) method to dig information from nine credit analysts as research participants with a qualitative approach. With ZMET's projective techniques through image metaphors, the study has identified 16 important constructs that describe bank credit analysts' mental models when facing the vagueness of SME’s accounting information in their credit decision-making process. The connection between constructs is described in the consensus map, which explains the four main themes of the study's findings; SME accounting information blur, the use of alternative information, the dual role of credit analysts, and information needs in credit decision-making.&#x0D; &#x0D; Received: 3 July 2021 / Accepted: 23 August 2021 / Published: 5 November 2021
Analysis of S, Se and Ag in volcanic glasses by ICP-QQQ-MS

Many elements, such as S, Se and Ag, are challenging to analyse using traditional quadrupole ICP-MS instruments, because of high backgrounds and/or unresolvable interferences.The Agilent 8800 ICP-QQQ-MS has an octopole reaction system, which separates two quadrupole mass filters.This configuration allows targeted reactions within the reaction cell, and effective removal of most interferences.This major advance in ICP-MS technology reduces the need for complex chemistry prior to sample analysis and the analysis of 'problem' elements using high-resolution instruments.A growing number of laboratories are now able to produce high-quality datasets for elements that traditionally were only possible with highresolution instruments and the necessary wet chemistry facilities.We developed protocols for analysis of S, Se, and Ag using the ICP-QQQ-MS, because analyses of these elements are invaluable for studying magmatic, volcanic and ore-forming processes.As an initial step we performed standard addition on a range of USGS reference materials for S and Ag to provide calibration values for our measurements.Both S and Se were analysed with O 2 as the reaction gas.For S this reduces the background by five orders of magnitude and gives detection limits of <2 ppb in solution (2 ppm in the rock).Selenium concentrations in volcanic glasses are typically <0.8 ppm.Our long-term Se BHVO-2 data give a precision of 9 % and an accuracy of 3 %, which are comparable to those achieved using ID-HG-ICPMS techniques.Silver presents a particular analytical challenge, because the Ag content of volcanic glasses is typically <50 ppb and is usually at least 200 times less than the content of the interfering element (Zr).Even with minimal oxide generation the ZrO interference on Ag is impossible to correct for using normal solution ICP-MS analysis.Using NH 3 as a reaction cell gas reduces oxide interferences to a sufficient level such that offline corrections for measured ZrO can be applied.Values for our in-house standards are within 4-10% of ID-HG-ICPMS measurements and analyses of BHVO-2 over a 2-year period give a precision of 10 %.
Competing Independent Modules for Knowledge Integration and Optimization

This paper presents a neural framework of untied independent modules, used here for integrating off the shelf knowledge sources such as language models, lexica, POS information, and dependency relations.Each knowledge source is implemented as an independent component that can interact and share information with other knowledge sources.We report proof of concept experiments for several standard sentiment analysis tasks and show that the knowledge sources interoperate effectively without interference.As a second use-case, we show that the proposed framework is suitable for optimizing BERT-like language models even without the help of external knowledge sources.We cast each Transformer layer as a separate module and demonstrate performance improvements from this explicit integration of the different information encoded at the different Transformer layers .
Compositional Data and Task Augmentation for Instruction Following

Executing natural language instructions in a physically grounded domain requires a model that understands both spatial concepts such as left_of and above, and the compositional language used to identify landmarks and articulate instructions relative to them.In this paper, we study instruction understanding in the blocks world domain.Given an initial arrangement of blocks and a natural language instruction, the system executes the instruction by manipulating selected blocks.The highly compositional instructions are composed of atomic components and understanding these components is a necessary step to executing the instruction.We show that while end-to-end training (supervised only by the correct block location) fails to address the challenges of this task and performs poorly on instructions involving a single atomic component, knowledge-free auxiliary signals can be used to significantly improve performance by providing supervision for the instruction's components.Specifically, we generate signals that aim at helping the model gradually understand components of the compositional instructions, as well as those that help it better understand spatial concepts, and show their benefit to the overall task for two datasets and two state-of-the-art (SOTA) models, especially when the training data is limited-which is usual in such tasks.
Employing a User-Centered Approach for Evaluating a VR-based Pilot Training System

Researchers took a user-centered approach to evaluate pilots’ preferences and perceptions of training with an innovative VR-based immersive training device (ITD). Over the course of one week, usability and user experience data were gathered from U.S. Air Force instructor pilots (IPs), unqualified instructor pilots (UIs),and student pilots (SP). Coming from various squadrons, these pilots provided feedback regarding their interactions with the ITDs. A think-aloud protocol, observations, and surveys were used to capture participants’ perceptions of the different hardware variants using the following usability metrics: fit and feel, function, and sustained and future use. At this stage of the development, various configurations of the ITDs were evaluated to determine which technological components should be included in the final design. The data presented here focused on one of those components, the aircraft control or center stick. The results for the stick component will be discussed as a use case as it illustrates the user-centered approach and data analysis strategy that captured and identified noteworthy differences in perceived training value.
Dagger linear logic for categorical quantum mechanics

Categorical quantum mechanics exploits the dagger compact closed structure of finite dimensional Hilbert spaces, and uses the graphical calculus of string diagrams to facilitate reasoning about finite dimensional processes. A significant portion of quantum physics, however, involves reasoning about infinite dimensional processes, and it is well-known that the category of all Hilbert spaces is not compact closed. Thus, a limitation of using dagger compact closed categories is that one cannot directly accommodate reasoning about infinite dimensional processes. A natural categorical generalization of compact closed categories, in which infinite dimensional spaces can be modelled, is *-autonomous categories and, more generally, linearly distributive categories. This article starts the development of this direction of generalizing categorical quantum mechanics. An important first step is to establish the behaviour of the dagger in these more general settings. Thus, these notes simultaneously develop the categorical semantics of multiplicative dagger linear logic. The notes end with the definition of a mixed unitary category. It is this structure which is subsequently used to extend the key features of categorical quantum mechanics.
Text classification of DGCNN model based on deep learning

This paper focuses on analyzing and studying the application of Recurrent Neural Network (RNN), a deep learning method, in news text classification. Aiming at the problem that the RCNN model cannot fully extract the text context feature information in text classification, and the training complexity of the LSTM method is high, a DGCNN model text classification method based on deep learning is proposed. The DGCNN model adopts dual-channel forward and backward bidirectional threshold recurrent unit (D-GRU) to fully obtain the text context feature information. Tests on the Sogou news corpus show that the improved model has a good classification effect, improving the accuracy, precision, recall and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> value of the classification.
The role of SMEs finance in economic growth, and will their interactions lead to stimulating economic growth in Jordan?

This study aims to determine the impact of small and medium-sized finance on Jordan's economic growth.In addition to determine the effect of interaction between small and medium size finance on economic growth according to the theory of endogenous growth.For this purpose, time series analysis (Autoregressive Distributed Lag model) is used .Due to the limited data available, quarterly data from small and medium-sized finance was used during this period (2016)(2017)(2018)(2019)(2020).Results are shown that some of variables are stationary at the level and some stationary at the first difference.The results also show that there are long-term relationship between economic growth and explanatory variables.The estimate according to the first model showed that there is a significant negative effect of small finance, while the medium finance have positive insignificant impact on economic growth.In the second model, it agreed with the endogenous growth theory, that if small and medium finance interact with each other, the impact of these finance on economic growth will be positive and significant .The study finished with several recommendations, including the need to provide support to small projects to help them overcome the challenges they face, as well as removing obstacles to small and medium finance.facilitating their access to sufficient and sustainable sources of funding, In addition to increasing government spending on research and development, to help these projects introduce innovative ideas that deepen the role of physical capital and increase its productivity.On the other hand, it is necessary to support the absorptive capacity (knowledge, skills and experience) of the workforce, and to encourage these companies to implement capital projects that would enhance education, training and skills acquisition for human capital
Theoretical Realization of a Two Qubit Quantum Controlled-NOT Logic Gate and a Single Qubit Quantum Hadamard Logic Gate in the Anti-Jaynes-Cummings Model

Quantum gates are fundamental in Quantum computing for their role in manipulating elementary information carriers referred to as quantum bits.In this paper, a theoretical scheme for realizing a quantum Hadamard and a quantum controlled-NOT logic gates operations in the anti-Jaynes-Cummings interaction process is provided.Standard Hadamard operation for a specified initial atomic state is achieved by setting a specific sum frequency and photon number in the normalized anti-Jaynes-Cummings qubit state transition operation with the interaction component of the anti-Jaynes-Cummings Hamiltonian generating the state transitions.The quantum controlled-NOT logic gate is realized when a single atomic qubit defined in a two-dimensional Hilbert space is the control qubit and two non-degenerate and orthogonal polarized cavities defined in a two-dimensional Hilbert space make the target qubit.With precise choice of interaction time in the anti-Jaynes-Cummings qubit state transition operations defined in the anti-Jaynes-Cummings sub-space spanned by normalized but non-orthogonal basic qubit state vectors, ideal unit probabilities of success in the quantum controlled-NOT operations is determined.
Data Acquisition System and Pattern Image Generations for Hand Grip Device

Grip pattern is essential to understand how an object being held in hand. One of the solutions is to use the pressure sensing glove to capture the gripping pressure distributed on the surface of the palm. The objective of this project is to develop a data acquisition system for a gripping device that can capture the grip patterns when a person is gripping an object. The design comprises of Velostat sheet, rows, and columns of conductive threads, that are sandwiched and layered to form a glove with pressure sensor grids. Arduino is used to generate the signals for data acquisition and interface with the MATLAB program through serial communication. On the MATLAB, the sensor data are organized and represented in hand pattern color image. Voltage Divider Rule (VDR) was used in an experiment with different resistor values and the effect of the image patterns were observed. Another experiment has been designed to find out the grip consistency. The results show that resistor values 330ohm can cause the image pattern create noises. Meanwhile, 4.7kohm resistance value is sufficient to eliminate most of the noises made in the pattern images. In this paper, different grip images can be obtained from different grip activities, such as holding toothbrush, lifting dumbbell, and pressing syringe. Future works can be done in resolution improvement and grip pattern recognition.
Weakly Supervised Semantic Parsing by Learning from Mistakes

Weakly supervised semantic parsing (WSP) aims at training a parser via utterancedenotation pairs.This task is challenging because it requires (1) searching consistent logical forms in a huge space; and (2) dealing with spurious logical forms.In this work, we propose Learning from Mistakes (LFM), a simple yet effective learning framework for WSP.LFM utilizes the mistakes made by a parser during searching, i.e., generating logical forms that do not execute to correct denotations, for tackling the two challenges.In a nutshell, LFM additionally trains a parser using utterance-logical form pairs created from mistakes, which can quickly bootstrap the parser to search consistent logical forms.Also, it can motivate the parser to learn the correct mapping between utterances and logical forms, thus dealing with the spuriousness of logical forms.We evaluate LFM on WikiTableQuestions, WikiSQL, and TabFact in the WSP setting.The parser trained with LFM outperforms the previous state-of-the-art semantic parsing approaches on the three datasets.Also, we find that LFM can substantially reduce the need for labeled data.Using only 10% of utterancedenotation pairs, the parser achieves 84.2 denotation accuracy on WikiSQL, which is competitive with the previous state-of-the-art approaches using 100% labeled data.
Decoupled Transformer for Scalable Inference in Open-domain Question Answering

Large transformer models, such as BERT, achieve state-of-the-art results in machine reading comprehension (MRC) for open-domain question answering (QA).However, transformers have a high computational cost for inference which makes them hard to apply to online QA systems for applications like voice assistants.To reduce computational cost and latency, we propose decoupling the transformer MRC model into input-component and crosscomponent.The decoupling allows for part of the representation computation to be performed offline and cached for online use.To retain the decoupled transformer accuracy, we devised a knowledge distillation objective from a standard transformer model.Moreover, we introduce learned representation compression layers which help reduce by four times the storage requirement for the cache.In experiments on the SQUAD 2.0 dataset, a decoupled transformer reduces the computational cost and latency of open-domain MRC by 30-40% with only 1.2 points worse F1-score compared to a standard transformer.