Reviewerly/exHarmony huggingface dataset:
Distributed Education System for Deaf and Dumb Children and Educator: A Today’s Need

World today is facing the substantial public health risk in the form of COVID-19. The epidemic has spread in over 185 countries resulting in closure of above 90% of all educational institutions, which resulted in immediate updating to teaching–learning system and the whole world turn up to use online learning methods. Looking toward the health hazards of using mobile phones and laptops for learning by children for longer hours, government has ordered restrictions on online learning for children’s on the parameter of age groups and contact hours. Online education system becomes a challenge for some new and old age teachers also in addition to students who are not well equipped with resources and technology. The mostly effected student group is children with disability. Here in paper we are concentrating on effect of online education for deaf and dumb community students. Paper includes challenges, technological solutions, and comparison of new learning method for deaf and dumb students as compared to normal students. We used distributed environment to reduce the system work overhead of doing sign recognition and interpretation. This separation results in increasing the accuracy and efficiency of online learning system for deaf and dumb. The system further can also be used by deaf and dumb students in rural areas for distance learning.
Research on Distributed Data Sharing System based on Internet of Things and Blockchain

Abstract Aiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.
PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context Disambiguation

This paper presents the PALI team's winning system for SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation.We fine-tune XLM-RoBERTa model to solve the task of word in context disambiguation, i.e., to determine whether the target word in the two contexts contains the same meaning or not.In implementation, we first specifically design an input tag to emphasize the target word in the contexts.Second, we construct a new vector on the fine-tuned embeddings from XLM-RoBERTa and feed it to a fully-connected network to output the probability of whether the target word in the context has the same meaning or not.The new vector is attained by concatenating the embedding of the [CLS] token and the embeddings of the target word in the contexts.In training, we explore several tricks, such as the Ranger optimizer, data augmentation, and adversarial training, to improve the model prediction.Consequently, we attain the first place in all four cross-lingual tasks.
Diagnostic Pitfalls of Digital Microscopy Versus Light Microscopy in Gastrointestinal Pathology: A Systematic Review

Digital microscopy (DM) is one of the cutting-edge advances in pathology, which entails improved efficiency, diagnostic advantages, and potential application in virtual diagnosis, particularly in the current era of the coronavirus disease (COVID-19) pandemic.However, the diagnostic challenges are the remaining concerns for its wider adoption by pathologists, and these concerns should be addressed in a specific subspecialty.We aim to identify the common diagnostic pitfalls of whole slide imaging (WSI), one modality of DM, in gastrointestinal (GI) pathology.From validating studies of primary diagnosis performance, we included 16 records with features on GI cases involved, at least two weeks wash-out periods, and more than 60 case study designs.A tailored quality appraisal assessment was utilized to evaluate the risks of bias for these diagnostic accuracy studies.Furthermore, due to the highly heterogeneous studies and unstandardized definition of discordance, we extract the discordant cases in GI pathology and calculate the discrepant rate, resulting from 0.5% to 64.28%.Targeting discrepancy cases between digital microscopy and light microscopy, we demonstrate five main diagnostic pitfalls regarding WSI as follows: additional time to review slides in WSI, hard to identify dysplasia nucleus, missed organisms like Helicobacter pylori (H.pylori), specific cell recognitions, and technical issues.After detailed reviews and analysis, we generate two essential suggestions for further GI cases signing out by DM.One is to use systematized 20x scans for diagnostic workouts and requesting 40x or even 60x scans for challenging cases; another is that a high-volume slides training should be set before the real clinical application of WSI for primary diagnosis, particularly in GI pathology.
Estimating Global Solar Radiation from Empirical Models: An Application

Solar radiation data are required for many applications and many areas of research. In order to achieve this, several empirical models have been suggested to predict the global solar radiation in Turkey and other countries. The different meteorological data as the global solar radiation, the sunshine duration, the temperature, the atmospheric pressure, the wind speed and the relative humidity were measured by Eskisehir Osmangazi University during the period between 01 January 2011 and 31 December 2014. These data were used to develop the empirical models in order to estimate the monthly average daily global solar radiation on the horizontal surface over Eskisehir City of Turkey. The developed empirical models were analyzed with the widely used nine statistical methods, namely; the relative percentage error (E), the mean percentage error (MPE), the mean absolute percentage error (MAPE), the sum of squares of relative errors (SSRE), the relative standard error (RSE), the mean bias error (MBE), the root mean square error (RMSE), the t-statistic method (t-stat) and coefficient of determination (R2). It is expected that the new model will be beneficial to everyone who is the solar engineers, architects, agriculturists, and hydrologists involved or interested in the design and study of solar energy applications such as solar furnaces, wood drying, stoves, concentrating collectors, interior illumination and thermal load analyzing of buildings, and photovoltaics, agricultural and meteorological forecasting.
Development of domain thesaurus as a set of ontology concepts with use of semantic similarity and elements of combinatorial optimization

We consider use of ontological background knowledge in intelligent information systems and analyze directions of their reduction in compliance with specifics of particular user task. Such reduction is aimed at simplification of knowledge processing without loss of significant information. We propose methods of generation of task thesauri based on domain ontology that contain such subset of ontological concepts and relations that can be used in task solving. Combinatorial optimization is used for minimization of task thesaurus. In this approach, semantic similarity estimates are used for determination of concept significance for user task. Some practical examples of optimized thesauri application for semantic retrieval and competence analysis demonstrate efficiency of proposed approach.
Path Planning based on Multi-objective Topological Map

Recently, intelligent robot technology has developed rapidly. As an important topic, path planning has attracted more and more attention. However, the performance of multi-objective path planning is limited by the scale of the problem, and the results of path planning are generally not optimal. In this work, based on 12 problems defined in multimodal multiobjective path planning optimization contest for CEC 2021 [1], we present a multi-objective path planning optimization algorithm, which includes data preprocessing, multi-objective genetic evolution path planning algorithm, and non-dominated sorting algorithm with elite strategy. This algorithm flow can realize multi-objective path planning and generate the optimal solution set (i.e. Pareto solution set). We implement our algorithm flow to calculate the time complexity and space complexity indicators. Experimental results on problems indicate the proposed multi-objective path planning algorithm can solve the optimal solution set in time. Due to the constraints of the problem, the number of optimal solutions is different for different problems. We show the validity of our method with experiments for path planning. Finally, we visually present some experimental results intuitively. The code is released at https://github.com/zhangruihao/pathPlanning.
Guided Attention Network for Concept Extraction

Concept extraction aims to find words or phrases describing a concept from massive texts. Recently, researchers propose many neural network-based methods to automatically extract concepts. Although these methods for this task show promising results, they ignore structured information in the raw textual data (e.g., title, topic, and clue words). In this paper, we propose a novel model, named Guided Attention Concept Extraction Network (GACEN), which uses title, topic, and clue words as additional supervision to provide guidance directly. Specifically, GACEN comprises two attention networks, one of them is to gather the relevant title and topic information for each context word in the document. The other one aims to model the implicit connection between informative words (clue words) and concepts. Finally, we aggregate information from two networks as input to Conditional Random Field (CRF) to model dependencies in the output. We collected clue words for three well-studied datasets. Extensive experiments demonstrate that our model outperforms the baseline models with a large margin, especially when the labeled data is insufficient.
A system for relation-oriented faceted search over knowledge bases

Purpose The purpose of this paper is to propose a scheme that allows users to interactively explore relations between entities in knowledge bases (KBs). KBs store a wide range of knowledge about real-world entities in a structured form as (subject, predicate, object). Although it is possible to query entities and relations among entities by specifying appropriate query expressions of SPARQL or keyword queries, the structure and the vocabulary are complicated, and it is hard for non-expert users to get the desired information. For this reason, many researchers have proposed faceted search interfaces for KBs. Nevertheless, existing ones are designed for finding entities and are insufficient for finding relations. Design/methodology/approach To this problem, the authors propose a novel “relation facet” to find relations between entities. To generate it, they applied clustering on predicates for grouping those predicates that are connected to common objects. Having generated clusters of predicates, the authors generated a facet according to the result. Specifically, they proposed to use a couple of clustering algorithms, namely, agglomerative hierarchical clustering (AHC) and CANDECOMP/PARAFAC (CP) tensor decomposition which is one of the tensor decomposition methods. Findings The authors experimentally show test the performance of clustering methods and found that AHC performs better than tensor decomposition. Besides, the authors conducted a user study and show that their proposed scheme performs better than existing ones in the task of searching relations. Originality/value The authors propose a relation-oriented faceted search method for KBs that allows users to explore relations between entities. As far as the authors know, this is the first method to focus on the exploration of relations between entities.
Application of BIM + VR Technology in Immersed Tunnel Construction

Abstract As an important way to relieve the traffic pressure across rivers, immersed tunnel engineering has been gradually promoted. This paper introduces building information modeling and virtual reality technology into immersed tunnel engineering, analyzes its technical characteristics and advantages, expounds the application of BIM + VR technology in immersed tunnel engineering, and carries out practice in immersed tunnel engineering of Shenzhen-Zhongshan Bridge, which verifies the superiority and efficiency of the method.
Efficient Solid Waste Management in Prai Industrial Area through GIS using Dijkstra and Travelling Salesman Problem Algorithms

The fourth industrial revolution (IR 4.0) supports new solid waste management and effective routing system for collection and transport of solid wastes, especially in achieving Penang 2030 vision to become a pollution free smart city. This study will enhance Seberang Perai Municipal Council (MBSP) solid waste routing system in Prai industrial area by implementing Dijkstra and Travelling Salesman Problem (TSP) algorithms using Geographic Information System version 10.1. The route optimization study involved 24 companies in Phase I, Phase II, and Phase IV of Prai industrial area. The authority is currently using only one route to transfer the waste-to-waste transfer station. The Dijkstra algorithm can optimize alternative route 1 distance by 19.74% whereby alternative route 2 ended up with extra distance by 3.73% compared to existing single route used by MBSP. The forward Dijkstra algorithm involves single direction route with cleaning depot (source) as starting point and waste transfer station (destination) as ending point. TSP algorithm is having advantage with return direction route. The alternative route 1 evaluated through TSP algorithm gave shorter distance by 6.61% compared to existing route. Alternative route 1 evaluated through Dijkstra algorithm is potential to save fuel cost by 19.75%. Existing route carries 9.2% per year of transportation carbon emission level. The alternative route 1 assessed through Dijkstra and TSP algorithms reported lower carbon emission level at 7.4% per year and 8.6% per year, respectively. Findings of this study can help in improving MBSP’s routing system and realize Penang 2030 vision.
Modulation Classification of MQAM Signals Based on Gradient Color Constellation and Deep Learning

Modulation classification is a key issue in non-cooperative communication systems, and signal constellation images can be used as input features of deep learning (DL) networks for classification. However, the conventional gray constellation image cannot exactly reflect density and location information of constellation points. To solve this problem, this paper proposes a gradient color constellation (GCC) algorithm based on the density of constellation points, which converts the density of constellation points into color data to realize its visualization, and uses two deep learning network models, i.e., the modified convolution neural network (M-CNN) and the residual network (ResNet), as classifiers. The experimental results show that, compared with the scheme based on gray constellation, the overall classification accuracy of the seven multilevel quadrature amplitude modulation (MQAM) signals under low signal-to-noise ratios (SNRs) is improved by 3%-4%.
Deep Descriptive Clustering

Recent work on explainable clustering allows describing clusters when the features are interpretable. However, much modern machine learning focuses on complex data such as images, text, and graphs where deep learning is used but the raw features of data are not interpretable. This paper explores a novel setting for performing clustering on complex data while simultaneously generating explanations using interpretable tags. We propose deep descriptive clustering that performs sub-symbolic representation learning on complex data while generating explanations based on symbolic data. We form good clusters by maximizing the mutual information between empirical distribution on the inputs and the induced clustering labels for clustering objectives. We generate explanations by solving an integer linear programming that generates concise and orthogonal descriptions for each cluster. Finally, we allow the explanation to inform better clustering by proposing a novel pairwise loss with self-generated constraints to maximize the clustering and explanation module's consistency. Experimental results on public data demonstrate that our model outperforms competitive baselines in clustering performance while offering high-quality cluster-level explanations.
Anti-crossings and spectral gap during quantum adiabatic evolution

We aim to give more insights on adiabatic evolution concerning the occurrence of anti-crossings and their link to the spectral minimum gap $\Delta_{min}$. We study in detail adiabatic quantum computation applied to a specific combinatorial problem called weighted max $k$-clique. A clear intuition of the parametrization introduced by V. Choi is given which explains why the characterization isn't general enough. We show that the instantaneous vectors involved in the anti-crossing vary brutally through it making the instantaneous ground-state hard to follow during the evolution. This result leads to a relaxation of the parametrization to be more general.
Can Federated Learning Save The Planet?

Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL, in particular, is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and the civil society for privacy protection. However, the potential environmental impact related to FL remains unclear and unexplored. This paper offers the first-ever systematic study of the carbon footprint of FL. First, we propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. Then, we compare the carbon footprint of FL to traditional centralized learning. Our findings show FL, despite being slower to converge, can be a greener technology than data center GPUs. Finally, we highlight and connect the reported results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency.
Knowledge Distillation via Channel Correlation Structure

Knowledge distillation (KD) has been one of the most popular techniques for model compression and acceleration, where a compact student model can be trained under the guidance of a large-capacity teacher model. The key of known KD methods is to explore multiple types of knowledge to direct the training of the student to mimic the teacher’s behaviour. To this end, we aims at the knowledge exploration on channel correlation structure in terms of intra-instance and inter-instance relationship among a mini-batch, that can be extracted and transferred from the teacher’s various outputs. Specifically, we propose a novel KD loss that derived from the Channel Correlation Structure (CCS) including feature-based and relation-based knowledge. With this novel KD loss, we can align the channel correlation of both feature maps between the teacher and student model by their channel correlation matrices. Extensive experimental results are performed to verify the effectiveness of our method compared with other KD methods on two benchmark datasets.
Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities.

In this paper, we examine and analyze the challenges associated with developing and introducing language technologies to low-resource language communities. While doing so, we bring to light the successes and failures of past work in this area, challenges being faced in doing so, and what they have achieved. Throughout this paper, we take a problem-facing approach and describe essential factors which the success of such technologies hinges upon. We present the various aspects in a manner which clarify and lay out the different tasks involved, which can aid organizations looking to make an impact in this area. We take the example of Gondi, an extremely-low resource Indian language, to reinforce and complement our discussion.
Reservoir Computing with Low-Power-Consumption All-Optical Nonlinear Activation Using Membrane SOA on Si

We demonstrate reservoir computing using a fiber delay line and membrane semiconductor optical amplifier on Si. Thanks to its small active volume and low fiber-coupling loss, the reservoir consumes only 43 mW for nonlinear activation.
A Triumvirate Approach of Blockchain MQTT and Edge Computing Toward Efficient and Secure IoT

Internet of Things (IoT) is an environment where a collection of heterogenous devices is interconnected to perform a specific function. IoT applications had gradually become an essential part in different fields due to the ascent of information-based society and the diversity of IoT domains and applications. Therefore, we need to guarantee a certain level of security for this system. In this paper, we propose a new solution that takes the advantage of blockchain decentralized and secure nature and combine it in IoT system that work with MQTT protocol. Furthermore, whole environment utilizes edge computing layer that would act as the backbone for this system to enhance the overall performance.
A study on time models in graph databases for security log analysis

Purpose For aiding computer security experts in their study, log files are a crucial piece of information. Especially the time domain is very important for us because in most cases, timestamps are the only linking points between events caused by attackers, faulty systems or simple errors and their corresponding entries in log files. With the idea of storing and analyzing this log information in graph databases, we need a suitable model to store and connect timestamps and their events. This paper aims to find and evaluate different approaches how to store timestamps in graph databases and their individual benefits and drawbacks. Design/methodology/approach We analyse three different approaches, how timestamp information can be represented and stored in graph databases. For checking the models, we set up four typical questions that are important for log file analysis and tested them for each of the models. During the evaluation, we used the performance and other properties as metrics, how suitable each of the models is for representing the log files’ timestamp information. In the last part, we try to improve one promising looking model. Findings We come to the conclusion, that the simplest model with the least graph database-specific concepts in use is also the one yielding the simplest and fastest queries. Research limitations/implications Limitations to this research are that only one graph database was studied and also improvements to the query engine might change future results. Originality/value In the study, we addressed the issue of storing timestamps in graph databases in a meaningful, practical and efficient way. The results can be used as a pattern for similar scenarios and applications.