Reviewerly/exHarmony huggingface dataset:
A joint model for entity boundary detection and entity span recognition

Named entity recognition is a task to extract named entities with predefined entity types. Span classification is a popular method to support this task. It has the advantage to solve nested structures and make full use of token features in a span. The problem is that exhaustively enumerating and verifying all entity spans suffer from high computational complexity and data imbalance. Furthermore, spans with a high overlapping ratio share the same contextual features in a sentence, which is easy to lead to false positive errors caused by inaccurate entity boundaries. In this paper, we present a model to detect the entity boundaries and predict entity candidates jointly. Instead of labeling tokens, our model makes the prediction based on gap representations between words, which avoids the ambiguity when a token has several labels. We also propose a neighborhood span proposal strategy to generate reasonable negative samples for training, which effectively reduces the data imbalance problem. Our model is evaluated on the ACE2005 and GENIA corpora. It achieves performance close to the state-of-the-art in F1 scores of 88.55% and 79.81%, respectively.
Research on the data driven intelligent employment information service system for college students

At present, the total employment of college graduates is under great pressure and the structural contradictions are very prominent. The State Council of China clearly proposes to strengthen the employment service and career guidance for college graduates. It is of great significance to fully integrate the information of campus teaching management and student ability evaluation system and improve the level of campus data governance for realizing personalized employment and career planning services. This paper comprehensively uses the methods of educational and psychological measurement, multivariate statistics and web technology, and establishes a multi-dimensional student information database and all-round talent portrait through the construction of data analysis model, which lays the foundation of quantitative analysis for the realization of two-way accurate matching of talent supply and demand.
Role of Transactional Blockchain in Facilitating Procurement in International Construction Projects

Blockchain is an evolving technological innovation that offers a distributed and secure method to record and share data, such as financial transactions, using cryptographic techniques that guarantee immutability. Due to its many benefits, blockchain has evolved into many sectors including finance and supply chain, among others. However, there is much speculation and ongoing research concerning its integration into the construction sector. Accordingly, the goal of this paper is to investigate the role of blockchain in facilitating procurement in construction projects, with a focus on international projects. This goal is achieved by: (1) studying the applications of blockchain in the construction industry; (2) analyzing case studies implementing blockchain in construction projects; and (3) performing a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis for adopting blockchain in the construction industry. The SWOT analysis is based on the thorough interrelated analysis of the case studies and surveyed literature, and is intended to provide guidelines on how to capitalize on the advantages of transactional blockchain in construction projects. Ultimately, this research aims to provide a holistic understanding of the applications of blockchain in facilitating procurement in international construction projects.
Enabling Agricultural Sector through Blockchain Technology – Farmers Perspective

The agricultural sectors in India and abroad have been affected extensively due to the Covid-19 pandemic.It is necessary to provide solutions for the availability of resources, controlling the cost, quality in production, transparent food supply, fulfilling demand, and removing intermediaries.The structural reforms in the agricultural sector by adopting emerging technologies, especially blockchain technology (BCT) and the robotics automation process, are inevitable during the pandemic and future development.To study the impact of blockchain on the Agriculture sector, the farmer's level of awareness of the blockchain technology, its methodological influence, the inclination of farmers to adopt the technology in their farming, and agri-related activity are vital.This paper aims to explore the opportunities of BCT in expanding the agriculture sector, ascertain the awareness and intensity of farmers' knowledge of the effect of BCT, and develop the mean difference in the opinion of the farmers towards the utilization of BCT in the relevant field of agriculture.A structured interview schedule was administered with 360 sample farmers from the Delta regions of three states located in the southern part of India, such as Andhra Pradesh, Karnataka, and Tamilnadu, using a purposive sampling technique intending.Irrespective of the age, gender, land capacity, possession, education level, learned procedures, and abundant experience helped the farmers demand a new technology interface to improve their income level and register their sustainability.
Distance learning quality assessment of universities based on interval monotonic decision tree algorithm

The standards and procedures of teaching quality evaluation systems improved with the continuous advancement of Video Internet of Things (VIoT) in the distance learning reform across colleges and universities. The old teaching quality rating approach is no longer adequate. We explore the idea of developing and designing the quality evaluation system for distance learning in this paper using an efficient interval monotonic decision tree algorithm. We use relational database technology to centralize the management of various knowledge elements in the VIoT system. It enables the VIoT system to perform operations like browse, delete, add, and modify knowledge that enhances knowledge transparency for designers and introduces intervals. We designed the interval monotonic decision tree algorithm with an evaluation domain partitioned. The proposed method creates the decision evaluation matrix, summarizes it, and obtains the evaluation conclusion using the complete weighting technique. Aspects of the system logic are tested using object-oriented software engineering, object-oriented design and analysis of requirements, and black-box testing.
Comparing Deep Learning and Human Crafted Features for Recognising Hand Activities of Daily Living from Wearables

This work presents a comparative analysis of human-crafted and automated feature extraction approaches for the discrimination of hand-based activities among eating, drinking and smoking. In this scheme, accelerometer and gyroscope sensors were utilised to capture activity signals. For this reason, wearable devices that embed the aforementioned sensors were employed to collect activity data from 12 office workers. The two approaches that were developed for feature mapping were evaluated equally on the collected dataset. Both the proposed schemes achieved to classify the hand-based activities. However, based on the experimental process, this study shows that the human-crafted features that extracted valuable information from the time and frequency domain of the raw signal measurements outperformed the automated feature mapping that utilised deep learning advances. The relevant results prove that the human-crafted features can recognise hand-based activities with 0.9109 and, on the other hand, automated features with a 0.907 F1 weighted score over the dataset.
Cell Based Raft Algorithm for Optimized Consensus Process on Blockchain in Smart Data Market

Due to the explosive increase in IoT devices and traffic, big data is developing into smart data that helps the data science experts understand human activities, through the relationship between mobility and resource application of the users in public spaces. For example, smart data markets help to predict crimes or understand the infection the cause of COVID-19 infections. For these smart services, the users agree to the privacy policy so that the personal and sensitive information can be collected by a third party. But the conditions of the privacy policy do not specify whether the information of the users can be tracked. To ensure data transparency, many systems are applying consortium/private blockchains with raft algorithm. The raft algorithm requires nodes to check countless messages for a single transaction. Eventually, as the number of nodes increases, the overall system degradation is derived from the burden of the leader node. This paper proposes a method to process the collected transactions by dividing a certain amount of transactions into cells, without any extra protocol. The proposed scheme also uses the federated learning model with high accuracy and data privacy, in order to determine the optimized cell size in a blockchain system that should lead to consensus on multiple servers. Therefore, the proposed CBR(Cell-based Raft) consensus algorithm proposes a protocol that reduces the number of messages, without interfering with the concept of the existing raft algorithm, in order to maintain stable throughput in the smart data market where massive transactions occur.
Derived Multi-population Genetic Algorithm for Adaptive Fuzzy C-Means Clustering

Fuzzy C-Means (FCM) is a common data analysis method, but the clustering effect of this algorithm is easily affected by the initial clustering centers. Currently, scholars often use the multiple population genetic algorithm (MPGA) to optimize the clustering centers, but the MPGA has insufficient global search ability and lacks self-adaptability, is prone to premature convergence, and has poor initial clustering centers. Therefore, this paper proposes an adaptive FCM clustering algorithm DMGA-FCM based on a derivative multiple population genetic algorithm (DMGA). In DMGA-FCM algorithm, firstly, the derivative operator, which is proposed for the first time in this paper, performs derivative operations on initialized populations to improve the algorithm's searchability and deal with the lack of inter-population search ability. Secondly, the adaptive probability fuzzy control operator is used to dynamically adjust the genetic probability to improve the adaptability of the algorithm, which in turn enhances the global merit-seeking ability of the DMGA algorithm and avoids premature convergence. Finally, the initial clustering center of FCM algorithm is optimized with DMGA to enhance the clustering effect of the algorithm. The analysis of simulation experiments and MRI brain map application results show that the DMGA-FCM algorithm can obtain a better clustering effect of medical data and image clustering segmentation effect compared with other related FCM algorithms.
Path Planning of Multirotor UAV Based on the Improved Ant Colony Algorithm

Path planning is an important part of the unmanned aerial vehicle (UAV) to realize its autonomous capabilities. Aiming at the shortcomings of the traditional ant colony algorithm-based trajectory planning method, which has slow convergence speed and easy to fall into the local optimum, a path planning method based on the improved ant colony algorithm is proposed. First, a dynamic adjusting factor is added into the heuristic function to improve the directivity of path selection and search speed. Then, the state transition strategy is improved to solve the problem of slow convergence in the initial stage and easy to fall into local optimum in the later stage. Finally, the path inflection point is smoothly optimized through the cubic B-spline curve. Simulation results show that the improved ant colony algorithm can quickly converge to the optimal path and well adapt to the flight requirements of multirotor UAV.
Evaluating performance variations cross cloud data centres using multiview comparative workload traces analysis

How to evaluate the performance variations of large-scale cloud data centres is challenging due to diverse nature of cloud platforms. Classic methods such as profiling-based evaluating methods tend to only provide global statistics for a system compared with cloud tracing based approaches. However, existing tracing based research lacks a systematic comparative multiview analysis from architecure-view to job-view and task-view, etc.to evaluate cloud performance variations, together with a detailed case study. We introduce MuCoTrAna, a multiview comparative workload traces analysis approach to evaluate the performance variations of large-scale cloud data centres which assists the cloud platform performance managers and big trace analysts. The efficiency of the proposed approach is demonstrated via case studies in Alibaba 2018 trace and Google trace. The multifaceted analysis results of traces reveals the qualitative insights, performance bottlenecks, inferences and adequate suggestions from global view, machine view, job-task view, etc.
Hierarchical Vision Transformer with Channel Attention for RGB-D Image Segmentation

Although convolutional neural networks (CNNs) have become the mainstream for image processing and achieved great success in the past decade, due to the local characteristics, CNN is difficult to obtain global and long-range semantical information. Moreover, in some scenes, the pure RGB image-based model is difficult to accurately identify the pixel classification and finely segment the edge of objects. This study presents a hierarchical vision Transformer model named Swin-RGB-D to incorporate and exploit the depth information in depth images to supplement and enhance the ambiguous and obscure features in RGB images. In this design, RGB and depth images are used as the two inputs of the two-branch network. The upstream branch applies the Swin Transform which is capable of learning global continuous information from RGB images for segmentation; the other branch performs channel attention on depth image to abstract the feature correlation and dependency between channels and generates a weight matrix. Then matrix multiplication on the feature maps in each stage of the down-sampling process is performed for weighted multi-modal feature extraction. Then this study adds the fused maps to the up-sampled feature maps of the corresponding size, which sufficiently compensates for the distortion of feature in the sampling process. The experiment results on the two benchmark datasets show that the proposed model makes the network more sensitive to edge information.
PDRSD-A systematic review on plan-driven SDLC models for software development

Software has become an integral component of society these days. Various kinds of software are being used in our daily lives. This paper presents a systematic literature review to identify the merits and demerits associated with seven plan-driven Software Development Life Cycle (SDLC) models, namely Waterfall Model, V-Model, Iterative Model, Incremental Prototyping Model, Spiral Model, and Rapid Application Development (RAD) Model. This paper also describes the usability of each model with some real-world examples.
Automatic Breast Tumor Cancer Diagnosis Based on a Hybrid Densenet

The breast cancer based image classification and division is proposed by utilizing a Deep learning (DL) technique. A few DL models is used to classify Mammographic information to forecast and analyze breast cancer. In this work, the mixture of two DL is named as Hybrid Auto encoder and DenseNet CNN (HAE-DNN). The Auto-encoder (AE) is likewise a NN that is utilized for a compacted portrayal of crude information. This AE has both encoder and a decoder for pressure and extraction process. Therefore DL of DenseNet is a sort of CNN that has thick associations among layers, through Dense Blocks. This organization can associate each layer with relating feature-map measures straightforwardly with each other. Therefore the proposed strategy is very well based on foreseeing performance in conditions of accuracy, Recall, F1 score, RMSE and MAE individually. The proposed technique is contrasted and the earlier DL strategy and accomplished a preferred outcome over existing.
Haar Cascade Face Detection and Local Binary Pattern Histogram Face Recognition based Drone

Face recognition is a popular technique that uses image processing to identify people&#x0027;s faces. Face recognition is becoming momentous due to the growing populace, which necessitates high security and monitoring systems, as well as the need for self confirmation in the current era, fight in rustic regions, disaster relief, and so on. This study proposes a way to assist the task force to track illicit, missing people and for surveillance employing a face recognition based drone. Haar Cascade is a face detection algorithm that is used to make out faces in images and real-time videos. Local Binary Pattern Histogram (LBPH) is used to recognize a person&#x0027;s face. The drone yielded an accuracy of 98 percent in the identification of individuals there by finds significant applications.
A Survey on IoT-based Hand Hygiene Dispenser with Temperature and Level Monitoring Systems

Coronavirus flare-up chances human existence and it challenges the general wellbeing. Hand cleanliness, an extremely basic activity, is very much acknowledged to be one of the essential methods of decreasing medical care-related disease and of improving patient security. These days wearing masks, utilizing hand sanitizers, checking temperatures are being done in all places, and so forth yet at the same time in many spots individuals utilize conventional sanitizer gadgets which build the spread of microorganisms. Normally an individual is in charge of observing the body temperature and sanitizer level by taking a chance with his life. This survey Internet of Things (IoT) based hand sanitizer dispenser with temperature and level monitoring distinguishes the presence of the people and gives out alerts in form of the human voice to sanitize their hands if the dispenser goes unrecognized. Furthermore, we have a contactless sanitizer dispenser to stay away from contact among humans and dispensers which will keep us away from the further spread of infection and microorganisms.
An Enhanced Cryptosystem Based on Modified Classical Ciphers

With the advancement of technologies, the amount of data shared each day keeps increasing. Thus, it becomes imperative to protect the contents of these data from any external influence. This is where the importance of Cryptography comes in. Cryptography is a process of protecting data by transforming it into a secure format, such that it can only be accessed by the sender and the recipient. The process of cryptography utilises a set of encryption algorithms to serve its purpose. Although it is one of the most secure ways of encrypting data, many such algorithms are now breakable. Cryptography has been in practice since ancient Rome, where ciphers like the Caesar Cipher were used to encrypt texts. Due to the simple nature of the classical ciphers, they are now considered weak and vulnerable to outside attacks. This paper sheds light on a new encryption technique, using two classical ciphers- The Vigen&#x00E8;re Cipher and the Polybius Cipher, in a more modified and secure form.
SLAV Mitigation and Energy-Efficient VM Allocation Technique Using Improvised Grey Wolf Optimization Algorithm for Cloud Computing

A cloud computing environment is an attractive platform to facilitate large-scale sales processes for customers from various industries. However, extensive research has been carried out on the cloud data center to reduce power consumption due to operating costs and CO2 emissions. To provide a prominent solution for reducing power consumption in the cloud center, we proposed a novel VM allocation technique that overcomes the Service Level Agreement (SLA) violation and allocates the VM to the best host through Improvised Grey Wolf Optimization (IGWO) Algorithm. The proposed VM allocation technique takes into account host characteristics such as power consumption and CPU utilization to evaluate the best fit. After VM allocation, we evaluate the remaining capacity of CPU and RAM of the host, which enhances the resource utilization in the cloud center. The experiment was carried out on random datasets with different VMs. The performance of the proposed VM allocation technique is compared to the existing VM migration algorithm like Ant Colony Optimization (ACO) and PABFD (Power-Aware Best Fit Decreasing), the result shows that the proposed method provides a significant performance in terms of energy consumption, SLA violation, and several VM migrations. The proposed VM allocation method consumes low power and violates fewer SLAs, thus providing a green computing environment.
Anisotropy of geochemical distributions characterized by irregular window-based local singularity analysis

Supporting the discovery of new resources to maintain the continuous development of human society is a long-term goal in mineral exploration. Geochemical data as an important geo-information carrier played significant roles in geographical information system (GIS)-based mineral prospectivity. Among the various analytical methodologies, local singularity analysis has been broadly discussed due to its high efficiency in the characterization of non-linear mineralization. Focusing on this topic, the current study first reviews local singularity analysis from its original proposition to recent progresses. Meanwhile, limitations in square and rectangular window-based singularity index <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mi>α</mml:mi></mml:math> estimation algorithms are discussed. Based on this, analytical window parameters in U -statistics and algorithms to quantify the power-law relationship between the physical quantity and the measuring scale in local singularity are integrated. At each scale, optical elliptical windows along all azimuth angles are determined and further merged, which defines an irregular analytical window. Irregular windows at all scales are further applied to local singularity analysis that can consequently propose an irregular window-based singularity algorithm. Practical application in the Duolong mineral district, northern Tibet, China demonstrates well the comparable efficiency of weak anomaly identification with that of the original algorithm. More importantly, multiscale irregular windows can supplement delicate patterns to characterize anisotropic natures associated with mineralization. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis