Reviewerly/exHarmony huggingface dataset:
Queuing Theory of Improved Practical Byzantine Fault Tolerant Consensus

In recent years, the use of consensus mechanism to maintain the security of blockchain system has become a considerable concern of the community. Delegated proof of stake (DPoS) and practical Byzantine fault tolerant (PBFT) consensus mechanisms are key technologies in maintaining the security of blockchain system. First, this study proposes a consensus mechanism combining DPoS and PBFT, which can rapidly deal with malicious witness nodes and shorten the time of block verification. Second, the M/PH/1 queuing model is used to analyze the performance of the proposed consensus mechanism, and the performance of the improved practical Byzantine fault tolerant consensus mechanism is evaluated from steady-state conditions and key performance measure of the system. Third, the current study uses the theoretical method of open (Jackson) queuing network, combined with the blockchain consensus process, and provides theoretical analysis with special cases. Lastly, this research utilizes numerical examples to verify the computability of the theoretical results. The analytic method is expected to open a series of potentially promising research in queueing theory of blockchain systems.
Tackling Challenges of Robustness Measures for Agent Collaboration in Open Multi-Agent Systems

Open multi-agent systems (OMASs) allow autonomous agents (AAs) to collaborate in coalitions to accomplish complex tasks (e.g., swarm robots exploring new terrain).In OMASs, AAs can arbitrarily join and leave the network.Thus, AAs must often collaborate with unknown AAs that may corrupt coalitions, leading to less robust systems.However, measures to improve robustness of OMASs are subject to challenges, decreasing their effectiveness.To understand how to improve coalition robustness in OMASs and address challenges of existing robustness measures, we carried out a literature review and revealed three types of robustness measures (i.e., collaboration coordination, normative control, and reliability prediction).Moreover, we found 21 challenges for the identified robustness measures and 24 corresponding solutions.By carrying out this literature review, we forge new connections between existing measures and identify challenges and measures that apply to multiple existing measures.Hereby, our work supports more robust collaborations between AAs in open systems.
Enhancing the Takhrij Al-Hadith based on Contextual Similarity using BERT Embeddings

Muslims are required to conduct Takhrij to validate the truth of Hadith text, especially when it is obtained from online media.Typically, the traditional Takhrij processes are conducted by experts and apply to Arabic Hadith text.This study introduces a contextual similarity model based on BERT Embedding to handle Takhrij on Indonesian Hadith Text.This study examines the effectiveness of BERT Fine-Tuning on the six pre-trained models to produce embedding models.The result shows that BERT Fine-Tuning improves the embedding model average accuracy by 47.67%, with a mean of 0.956845.The most high-grade accuracy was the BERT embedding built based on the indobenchmark/indobert-large-p2 pre-trained model on 1.00.In addition, the manual evaluation achieved 91.67% accuracy.
Whole Slide Imaging for Teleconsultation: The Mount Sinai Hospital, Labcorp Dianon, and Philips Collaborative Experience

With the emergence of whole slide imaging (WSI) and widespread access to high-speed Internet, pathology labs are now poised to implement digital pathology as a way to access diagnostic pathology expertise. This paper describes a collaborative partnership between a high-volume reference diagnostic laboratory (Labcorp) and an academic pathology department (Mount Sinai Hospital) in the transition from a traditional glass slide service to a digital platform. Using the standard framework of implementation science, we evaluate the consistency and quality of the Philips IntelliSite Pathology Solution (PIPS) in delivering save and efficient diagnostic services.Digital and glass slide diagnoses of all consult cases were documented over a 12-month period. The Proctor guideline was used to quantitatively and qualitatively measure (e.g., focus group studies, field notes, and administrative data) implementation success. Lean techniques (e.g., value stream mapping) were applied to measure changes in efficiency with the transition to a digital platform.Our study supports the acceptability, high adoption, appropriateness, feasibility, fidelity, and sustainability of the digital pathology platform. The digital portal also improved the quality of patient care by increasing efficiency, effectiveness, safety, and timeliness. The intraobserver concordance rate was 100%. The digital transition resulted in a reduction in turnaround time from 86 h to an average 35 min and a 20-fold increase in efficiency of the consultation process.As the pathology community contemplates digital pathology as a transformational tool in providing broad access to diagnostic expertise across time and space, our study provides an implementation strategy along with evidence that the digital platform is safe, effective, and efficient.
Speech to Indian Sign Language Translator

Indian Sign Language is one of the most important and widely used forms of communication for people with speaking and hearing impairments. Many people or communities have attempted to create systems that read the sign language symbols and convert the same to text, but text or audio to sign language is still infrequent. This project mainly focuses on developing a translating system consisting of many modules that take English audio and convert the input to English text, which is further parsed to structure grammar representation on which grammar rules of Indian Sign Language are applied. Stop words are removed from the reordered sentence. Since the Indian Sign Language does not support conjugation in words, stemming and lemmatization will transform the provided word into its root or original word. Then all the individual words are checked in a dictionary holding videos of each word. If the system does not find words in the dictionary, then the most suitable synonym replaces them. The system proposed by us is inventive as the current systems are bound to direct conversion of words into Indian Sign Language on-the-other-hand our system aims to convert the sentences in Indian Sign Language grammar and effectively display it to the user.
A Quantum interpretation of separating conjunction for local reasoning of Quantum programs based on separation logic

It is well-known that quantum programs are not only complicated to design but also challenging to verify because the quantum states can have exponential size and require sophisticated mathematics to encode and manipulate. To tackle the state-space explosion problem for quantum reasoning, we propose a Hoare-style inference framework that supports local reasoning for quantum programs. By providing a quantum interpretation of the separating conjunction, we are able to infuse separation logic into our framework and apply local reasoning using a quantum frame rule that is similar to the classical frame rule. For evaluation, we apply our framework to verify various quantum programs including Deutsch–Jozsa’s algorithm and Grover's algorithm.
BART for Post-Correction of OCR Newspaper Text

Optical character recognition (OCR) from newspaper page images is susceptible to noise due to degradation of old documents and variation in typesetting.In this report, we present a novel approach to OCR postcorrection.We cast error correction as a translation task, and fine-tune BART, a transformerbased sequence-to-sequence language model pretrained to denoise corrupted text.We are the first to use sentence-level transformer models for OCR post-correction, and our best model achieves a 29.4% improvement in character accuracy over the original noisy OCR text.Our results demonstrate the utility of pretrained language models for dealing with noisy text.
A Framework for Secure Healthcare Data Management using Blockchain Technology

In the current era of smart cities and smart homes, the patient's data like name, personal details and disease description are highly insecure and violated most often.These details are stored digitally in a network called Electronic Health Record (EHR).The EHR can be useful for future medical researches to enhance patients' healthcare and the performance of clinical practices.These data cannot be accessible for the patients and their caretakers, but they are readily available for unauthorized external agencies and are easily breached by hackers.This creates an imbalance in data accessibility and security.This can be resolved by using blockchain technology.The blockchain creates an immutable ledger and makes the transaction to be decentralized.The blockchain has three key features namely Security, Transparency, and Decentralization.These key features make the system to be highly secured, prevent data manipulation, and can only be accessible by authorized persons.In this paper, a blockchain-based security framework has been proposed to secure the EHR and provide a safe way of accessing the clinical data of the patients for the patients and their caretakers, doctors, and insurance agents using cryptography and decentralization.The proposed system also maintains the balance between data accessibility and security.This paper also establishes how the proposed framework helps doctors, patients, caretakers, and external authorities to securely store and access patients' medical data in EHR.
LTST: Long-term segmentation tracker with memory attention network

Recent interest in the combination of visual object tracking (VOT) and video object segmentation (VOS) has yielded rapid progress. However, existing segmentation methods are still restricted to the target model created in the first frame, which leads to the lack of long-term adaptability. To overcome this limitation, we propose a novel long-term segmentation tracker LTST leveraging memory attention network to achieve the effect of online learning without additional training. Specifically, we first combine a discriminative correlation filter with a matching-based paradigm for the segmentation task, then we develop a memory attention network based on partial cost volume to extract relevant historical information and dynamically reform the segmentation template. Moreover, we extend our LTST for long-term tracking by introducing a multi-scale verification network to identify tracking failures, and a global detector to re-locate the missing target. Experimental results on VOT-LT2018, VOT-LT2019, LaSOT and TLP benchmarks show that our proposed tracker achieves comparable performance to the state-of-the-art long-term tracking algorithms.
Sports Achievement Prediction and Influencing Factors Analysis Combined with Deep Learning Model

Scientific sports training plans are only possible if you can accurately predict a player's performance. Accurate prediction of sporting performance not only is useful for athletes, but also helps to guide the development of sports. Research methods used in traditional forecasting include the time series method, analogy method, regression analysis, and other methods of analysis. Most of the data used to make these projections are derived from a relatively small set of static problems. A sports performance prediction model based on deep learning is proposed to address the current model's low prediction accuracy. Deep learning models are more accurate at predicting sports performance than traditional methods, and the difference between the two is greater in this study. Also, it performs well when it comes to both convergence and robustness.
The University of Students: A place for joint self-education

In this article, we explain, explore, and problematize the formation, organization, leadership, and daily educational life of the first (to our knowledge) international democratic university of students (UniS) in the 21st century. UniS is run by the students, for the students, and with the students for their diverse purposes, desires, interests, and needs. A student is anyone who freely chooses to study something for whatever reason. Everyone can become a student at any time without any high school credits, fees, bureaucracy, tests, or any other form of human suffering. But what exactly is UniS? Why students? What if…? How can one visualize UniS, which is “so vague, so bizarre, so unnecessary to me!” What are its philosophical principles? Who are we? What does the University of Students look like? In the spirit of curiosity, wonder, leisure, fun, freedom, and love for learning, we invite the reader to attend and connect with two working edu-clubs of UniS: a movie club “Schooling Around the World and Time” and an “Educationalist Club.” In addition, we discuss some of the main issues, limitations, and challenges, including the civilization of the necessities, colonization of the human spirit by the economy, a lack of genuine leisure, and toxification of the human by foisted education. The open-ended, poetic conclusion lets the readers form their own interpretations, ideas, questions, and answers about UniS. What is the future of UniS? And only time will tell, 10, 100 years later or 100 light-years from now.
Sometimes We Want Ungrammatical Translations

Rapid progress in Neural Machine Translation (NMT) systems over the last few years has focused primarily on improving translation quality, and as a secondary focus, improving robustness to perturbations (e.g.spelling).While performance and robustness are important objectives, by over-focusing on these, we risk overlooking other important properties.In this paper, we draw attention to the fact that for some applications, faithfulness to the original (input) text is important to preserve, even if it means introducing unusual language patterns in the (output) translation.We propose a simple, novel way to quantify whether an NMT system exhibits robustness or faithfulness, by focusing on the case of word-order perturbations.We explore a suite of functions to perturb the word order of source sentences without deleting or injecting tokens, and measure their effects on the target side.Across several experimental conditions, we observe a strong tendency towards robustness rather than faithfulness.These results allow us to better understand the trade-off between faithfulness and robustness in NMT, and opens up the possibility of developing systems where users have more autonomy and control in selecting which property is best suited for their use case.
Systematic segmentation method based on PCA of image hue features for white blood cell counting

Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works.
Trajectory tracking for non-Markovian quantum systems

We propose a systematic scheme to engineer quantum states of a quantum system governed by a time-convolutionless non-Markovian master equation. According to the idea of reverse engineering, the general algebraic equation to determine the control parameters, such as coherent and incoherent control fields, is presented. Without artificially engineering the time-dependent decay rates and retaining the environment-induced Lamb shifts, the quantum state can still be transferred into the target state in a finite period of time along an arbitrary designed trajectory strictly in Hilbert space. As an application, we apply our scheme to a driven two-level non-Markovian system and realize instantaneous-steady-state tracking and a complete population inversion with control parameters which are available in experimental settings.
Integration of Information Technology into Music Education to Cultivate Creative Thinking Among Middle School Students

Creative thinking is an important thinking skill for primary and secondary school students in the learning stage. In music education, through the integration of information technology into classroom teaching, a series of measures have been taken: (1) changing the way of music creation; (2) guiding students to participate in classroom practice; (3) exercising students’ reverse thinking for analysis. The full use of information technology in music education promotes the improvement of students’ imagination, provides more space for students to create, changes students’ inherent way of thinking, enables students to look at problems from a multi-level and multi-angle perspective, as well as trains students to improvise. Teaching practice has proven that the development of creative thinking reflects the overall quality of students. The integration of information technology into music education is of great significance to cultivate creative thinking among middle school students.
The application of neural network algorithm and embedded system in computer distance teach system

Abstract The computer distance teaching system teaches through the network, and there is no entrance threshold. Any student who is willing to study can log in to the network computer distance teaching system for study at any free time. Neural network has a strong self-learning ability and is an important part of artificial intelligence research. Based on this study, a neural network-embedded architecture based on shared memory and bus structure is proposed. By looking for an alternative method of exp function to improve the speed of radial basis function algorithm, and then by analyzing the judgment conditions in the main loop during the algorithm process, these judgment conditions are modified conditionally to reduce the calculation scale, which can double the speed of the algorithm. Finally, this article verifies the function, performance, and interface of the computer distance education system.
Long-Term Person Tracking for Unmanned Aerial Vehicle Based on Human-Machine Collaboration

Unmanned Aerial Vehicle (UAV) has been widely used in military reconnaissance, smart transportation, public security and other fields.UAV-based person tracking is attracting incremental attention for its wide application requirements.Currently, some state-of-the-art visual tracking methods have achieved promising performance in common scenarios.However, in the scene of UAV-based person tracking, there will be long-term target disappearance and unpredictable dramatic target appearance changes, which still pose a huge challenge to UAV-based person tracking.In this work, a human-machine hybrid augmented tracking system based on eye tracking is proposed to cope with the challenge.During tracking, through the interaction between humans and machines, humans can provide real-time guidance and corrections to the tracker, and the tracker can also learn interesting targets from humans to enhance itself.The experimental results show that human-in-the-loop can remarkable improve the success rate and robustness of the tracking and our tracking system outperforms the state-of-the-art tracker in complex environments.
The Effectiveness of Crossword Puzzle in Improving Mufradat Skills

Arabic mufradat is an element of various language elements, where the richness of vocabulary is part of the indicator of language proficiency. In reality, vocabulary learning often encounters obstacles, therefore choosing the right media is part of an effort to overcome the existing difficulties. This study aimed to test the effectiveness of the puzzle in improving Arabic vocabulary skills. This study was conducted with a quasi-experimental approach where data were obtained through pre-test and post-test. Based on the results, the use of puzzle games as learning media was effective to improve mufradat skills.
A Comparative Analysis of Search Engine Ranking Algorithms

Ranking Algorithm is the most proper way of positioning on a scale. As the information and knowledge on the internet are increasing every day.The search engine's ability to deliver the most appropriate material to the customer. It is more and more challenging without even any assistance in filtering through all of it. However, searching what user requires is extremely difficult. In this research, an effort has been made to compare and analyze the most popular and effective search engines. The keywords were used in uniform resource locator like, title tag, header, or even the keyword's resembles to the actual text. The page rank algorithm computes a perfect judgment of how relevant a webpage is by analyzing the quality and calculating the number of links connected to it. In this study the keyword relevancy and time response were used for search engines and observed the results. It is observed that the google search engine is faster than the bing and youtube, and after all, bing is the best search engine after google. Moreover, youtube is the fastest search engine in terms of video content search. The google results were found more accurate. However, it is better than all of the search engine