Reviewerly/exHarmony huggingface dataset:
Keyphrases Frequency Analysis From Research Articles: A Region-Based Unsupervised Novel Approach

Due to the advancement of technology and the exponential proliferation of digital sources and textual data, the extraction of high-quality keyphrases and the summarizing of content at a high standard has become increasingly difficult in current research. Extracting high-quality keyphrases and summing texts at a high level demands the use of keyphrase frequency as a feature for keyword extraction, which is becoming more popular. This article proposed a novel unsupervised keyphrase frequency analysis (KFA) technique for feature extraction of keyphrases that is corpus-independent, domain-independent, language-agnostic, and length-free documents, and can be used by supervised and unsupervised algorithms. This proposed technique has five essential phases: data acquisition; data pre-processing; statistical methodologies; curve plotting analysis; and curve fitting technique. First, the technique begins by collecting five different datasets from various sources and then feeding those datasets into the data pre-processing phase using text pre-processing techniques. The preprocessed data is then transmitted to the region-based statistical process, followed by the curve plotting phase, and finally, the curve fitting approach. Afterward, the proposed technique is tested and assessed using five (5) standard datasets. Then, the proposed technique is compared with our recommended systems to prove its efficacy, benefits, and significance. Finally, the experimental findings indicate that the proposed technique effectively analyses the keyphrase frequency from articles and delivers the keyphrase frequency of 70.63% in 1st region and 10.74% in 2nd region of the total present keyphrase frequency.
Hardware-friendly compression and hardware acceleration for transformer: A survey

&lt;abstract&gt; &lt;p&gt;The transformer model has recently been a milestone in artificial intelligence. The algorithm has enhanced the performance of tasks such as Machine Translation and Computer Vision to a level previously unattainable. However, the transformer model has a strong performance but also requires a high amount of memory overhead and enormous computing power. This significantly hinders the deployment of an energy-efficient transformer system. Due to the high parallelism, low latency, and low power consumption of field-programmable gate arrays (FPGAs) and application specific integrated circuits (ASICs), they demonstrate higher energy efficiency than Graphics Processing Units (GPUs) and Central Processing Units (CPUs). Therefore, FPGA and ASIC are widely used to accelerate deep learning algorithms. Several papers have addressed the issue of deploying the Transformer on dedicated hardware for acceleration, but there is a lack of comprehensive studies in this area. Therefore, we summarize the transformer model compression algorithm based on the hardware accelerator and its implementation to provide a comprehensive overview of this research domain. This paper first introduces the transformer model framework and computation process. Secondly, a discussion of hardware-friendly compression algorithms based on self-attention and Transformer is provided, along with a review of a state-of-the-art hardware accelerator framework. Finally, we considered some promising topics in transformer hardware acceleration, such as a high-level design framework and selecting the optimum device using reinforcement learning.&lt;/p&gt; &lt;/abstract&gt;
Research on mobile traffic data augmentation methods based on SA-ACGAN-GN

With the rapid development and application of the mobile Internet, it is necessary to analyze and classify mobile traffic to meet the needs of users. Due to the difficulty in collecting some application data, the mobile traffic data presents a long-tailed distribution, resulting in a decrease in classification accuracy. In addition, the original GAN is difficult to train, and it is prone to "mode collapse". Therefore, this paper introduces the self-attention mechanism and gradient normalization into the auxiliary classifier generative adversarial network to form SA-ACGAN-GN model to solve the long-tailed distribution and training stability problems of mobile traffic data. This method firstly converts the traffic into images; secondly, to improve the quality of the generated images, the self-attention mechanism is introduced into the ACGAN model to obtain the global geometric features of the images; finally, the gradient normalization strategy is added to SA-ACGAN to further improve the data augmentation effect and improve the training stability. It can be seen from the cross-validation experimental data that, on the basis of using the same classifier, the SA-ACGAN-GN algorithm proposed in this paper, compared with other comparison algorithms, has the best precision reaching 93.8%; after adding gradient normalization, during the training process of the model, the classification loss decreases rapidly and the loss curve fluctuates less, indicating that the method proposed in this paper can not only effectively improve the long-tail problem of the dataset, but also enhance the stability of the model training.
A method for identifying references between projects in GitHub

In open source software platforms, software projects do not usually develop in isolation, and they depend on each other and develop together. It is important to identify references between projects in software development activities, which may help projects identify cross-project bugs or attract new contributors from related projects. In this paper, we propose a method IREL to Identify References between projects by Extracting Links. We first extract links from descriptions and comments on issues, pull requests, and commits with three matching patterns. Then we identify changes in project names and replace the original project names with their new project names. Finally, we identify references between projects by selecting links with different source projects and target projects. We evaluate the performance based on datasets with 20,347,228 projects. Our method IREL obtains 934,322 references, 26.461 times as many as the method Reference Coupling and 16.483 times as many as the method Issue Units. Project PageRank scores based on references identified by our method IREL are more correlated with the number of stars of projects. Our method supports researchers to identify references better.
Improving Security and Scalability in Smart Grids using Blockchain Technologies

In the current industrial century, smart grid is one of the technologies that has been proposed for efficient and quality distribution of electricity. However, this technology is exposed to many security threats and vulnerabilities. These challenges have led to the development of advanced technologies and sustainable solutions to make smart grids more secure and reliable. Blockchain is one of the recent technologies that has attracted a lot of attention in various applications, including smart grids. SealedGRID is a project designed, analyzed and implemented with the aim of providing a scalable and reliable Smart Grid security platform based on blockchain. In this paper, we present a scalable and secure solution for smart grids using Hyperledger Fabric and MQTT.
Niederreiter-Rosenbloom-Tsfasman LCD codes

<p style='text-indent:20px;'>Linear complementary dual (LCD) codes have wide applications in data storage, communications systems and cryptography. In this paper, we introduce a concept of LCD codes in the metric space <inline-formula><tex-math id="M1">\begin{document}$ Mat_{n, s}(\mathbb{F}_{q}) $\end{document}</tex-math></inline-formula> endowed with the Niederreiter-Rosenbloom-Tsfasman metric (NRT metric), which are called Niederreiter-Rosenbloom-Tsfasman LCD (NRT LCD) codes. NRT LCD codes introduced in this paper are actually a generalization of Euclidean LCD codes. For <inline-formula><tex-math id="M2">\begin{document}$ s = 1 $\end{document}</tex-math></inline-formula>, an NRT LCD code is just an Euclidean LCD code. A necessary and sufficient condition for an NRT linear code to be an NRT LCD code is provided. Also, the existence of NRT LCD codes is investigated and some constructions of NRT LCD codes and NRT LCD MDS codes are discussed.
ADMM-Based Differential Privacy Learning for Penalized Quantile Regression on Distributed Functional Data

Alternating Direction Method of Multipliers (ADMM) is a widely used machine learning tool in distributed environments. In the paper, we propose an ADMM-based differential privacy learning algorithm (FDP-ADMM) on penalized quantile regression for distributed functional data. The FDP-ADMM algorithm can resist adversary attacks to avoid the possible privacy leakage in distributed networks, which is designed by functional principal analysis, an approximate augmented Lagrange function, ADMM algorithm, and privacy policy via Gaussian mechanism with time-varying variance. It is also a noise-resilient, convergent, and computationally effective distributed learning algorithm, even if for high privacy protection. The theoretical analysis on privacy and convergence guarantees is derived and offers a privacy–utility trade-off: a weaker privacy guarantee would result in better utility. The evaluations on simulation-distributed functional datasets have demonstrated the effectiveness of the FDP-ADMM algorithm even if under high privacy guarantee.
Divide to Attend: A Multiple Receptive Field Attention Module for Object Detection in Remote Sensing Images

The study of remote sensing image object detection has excellent research value in environmental protection and public safety.However, the performance of the detectors is unsatisfactory due to the large variability of object size and complex background noise in remote sensing images.Therefore, it is essential to improve the detection performance of the detectors.Inspired by the idea of "divide and conquer", we proposed a Multiple Receptive Field Attention(MRFA) to solve these problems and which is a plug-and-play attention method.First, we use the method of multiple receptive field feature map generation to convert the input feature map into four feature maps with different receptive fields.In this way, the small, medium, large, and immense objects in the input feature maps are "seen" in these feature maps, respectively.Then, we used the multiple attention map fusion method to focus objects of different sizes separately, which can effectively suppress noise in the background of remote sensing images.Experiments on remote sensing object detection datasets DIOR and HRRSD demonstrate that the performance of our method is better than other state-of-the-art attention modules.At the same time, the experiments on remote sensing image semantic segmentation dataset WHDLD and classification dataset AID prove the generalization and superiority of our method.
Online Examination System

Projects play an important role in the education system.At the end of 2019, COVID 2019 came into existence which made people quarantined in their places.At that time learning and education did not stop.Learning through the video calls and watching videos made learning ease at that time.Conforming to the project title, introducing the project to the guide, project in-charge, HODs, and making proper communication was tedious.This generates the need for automating the process.An efficient Online Examination system not only ensures significant of let the students to give the online exams through this website, which made easy for students to learn as well as to practice test papers.The main object of this research is to give the exams online because of the conditions of COVID-19 Which were the tough years to give the exams offline by the afraid of offline exams.EXAM or Online Examination System a desktop app and which is for online exam registration.MySql used for database management and php used as back end scripting language, javascript as front end.The future scope of this project is to work on candidate, admin, superadmin panel functionalities with full determinations to deliver EXAM project well and good.
Expanding the attack surface: Robust profiling attacks threaten the privacy of sparse behavioral data

Behavioral data, collected from our daily interactions with technology, have driven scientific advances. Yet, the collection and sharing of this data raise legitimate privacy concerns, as individuals can often be reidentified. Current identification attacks, however, require auxiliary information to roughly match the information available in the dataset, limiting their applicability. We here propose an entropy-based profiling model to learn time-persistent profiles. Using auxiliary information about a single target collected over a nonoverlapping time period, we show that individuals are correctly identified 79% of the time in a large location dataset of 0.5 million individuals and 65.2% for a grocery shopping dataset of 85,000 individuals. We further show that accuracy only slowly decreases over time and that the model is robust to state-of-the-art noise addition. Our results show that much more auxiliary information than previously believed can be used to identify individuals, challenging deidentification practices and what currently constitutes legally anonymous data.
Cyber Security in the Age of Covid-19: A Timeline and Analysis of Cyber-Crime and Cyber-Attacks during the Pandemic

The entire world has been impacted by Covid-19, which has forced the nations to undergo lockdowns, which have opened the new horizons of virtual learning and work from home culture. Though virtual learning and online meetings were available prior to the lockdowns, use of these platforms have intensified. There is a lack of awareness among many in the public concerning the perils of using these platforms which makes them susceptible to cyber-crime attacks phishing, sexual or verbal abuse, eve teasing etc. Lockdowns have provided cyber criminals with new criminal opportunities and evidence shows that there has been a rampant increase in the number of cyber-crimes during this period. Public lack of awareness has led to innocent people falling prey in the hands of attackers. Since the pandemic and ensuing lockdowns came as a surprise, public and private authorities lacked the opportunity to make appropriate arrangements for training people and making these platforms secure. There is no question that if these platforms are made secure, then they will prove to be as an asset to the society but much work must be done to achieve the goal of complete cyber security.
Electromyography based Gesture Recognition: An Implementation of Hand Gesture Analysis Using Sensors

Motion sign-based language has an important role in the mute community, that is for data transmission. Usually, silent and dumb people counter very difficult situations to convey information to normal people. This paper proposes research that can ease the life of the deaf community. The work presented in this paper is a communication bridge between normal-hearing persons and persons with less hearing ability or impaired persons. The proposed research ‘Gesture–Talk’ can be used as an interpreter between any normal person and deaf and mute persons. The Gesture–Talk is based on the language used in Pakistan which is Pakistan Sign Language (PSL), which is a standard language used by deaf persons in Pakistan. Using Gesture–Talk, PSL can be translated into voice. The idea is to develop a small portable application that can be used as a middle layer application between normal and deaf people. The authors have used an electrical sensor that will collect the data by detecting electrical pulses that will be sent to the microcomputer (raspberry pi 3) where the data will be processed and sent to the speaker from which voice will be generated. A cost-effective and novel approach has been proposed in this research paper compared to the other existing approaches. Moreover, Artificial Intelligence (AI) based predictive or classification algorithms may be applied for the best optimal results.
A graph-based method for interactive mapping revision in DL-Lite

Discovering the semantic relationships among heterogeneous ontologies has been one of the core research topics in Semantic Web. As ontology matching systems inevitably adopt heuristic strategies, wrong mappings are often contained in final alignments. Most methods for mapping revision depend on dealing with logical incoherence. However, erroneous mappings that do not cause incoherence may be left out. Hence, manual validations with domain expertise are needed. Nevertheless, existing interactive methods for mapping revision still suffer from two limitations. Firstly, revision methods designed to present friendly graphical interfaces and contextual information related to mappings pay little attention to reducing manual decisions, leading to the efficiency problem. Secondly, revision methods focusing on automated reasoning to reduce manual decisions employ models of high complexity, leading to the practicality problem when dealing with alignments across large-scale ontologies. To address these problems, we propose a novel graph-based method for interactive mapping revision, aiming to reduce the manual efforts as much as possible. DL-Lite ontologies and their mappings are encoded into an integrated graph, where the mapping arcs will be judged by the experts. We define the decision space tailored for mapping revision, which can be used to improve the efficiency of manual making decisions. After a manual decision is made in each interaction, the mapping arcs will be automatically updated in the integrated graph. The whole update process modeled in our defined graph-based decision space could be accomplished in polynomial time. We further design an impact function based on the integrated graph and weights of mappings, which can display the most influential mappings to experts. In this way, the number of manual decisions can be reduced further. To cope with the practicality of our method for alignments of large-scale ontologies, we introduce the notion of “reliable” mappings as an attempt to alleviate the burden of experts for making decisions, and propose two soft principles to ensure the reliability of selected mappings. Moreover, we define influence relation and design a corresponding algorithm to enhance the method for detecting incoherence of the integrated graph, which is transformed from the ontologies beyond DL-Lite and their mappings. We implement our method and evaluate its efficiency by 16 alignments generated across real-world ontologies. Experimental results show that our proposed method can improve the efficiency by 19% on average and save more manual decisions than other interactive revision methods in most cases.
Task reformulation and data-centric approach for Twitter medication name extraction

Abstract Automatically extracting medication names from tweets is challenging in the real world. There are many tweets; however, only a small proportion mentions medications. Thus, datasets are usually highly imbalanced. Moreover, the length of tweets is very short, which makes it hard to recognize medication names from the limited context. This paper proposes a data-centric approach for extracting medications in the BioCreative VII Track 3 (Automatic Extraction of Medication Names in Tweets). Our approach formulates the sequence labeling problem as text entailment and question–answer tasks. As a result, without using the dictionary and ensemble method, our single model achieved a Strict F1 of 0.77 (the official baseline system is 0.758, and the average performance of participants is 0.696). Moreover, combining the dictionary filtering and ensemble method achieved a Strict F1 of 0.804 and had the highest performance for all participants. Furthermore, domain-specific and task-specific pretrained language models, as well as data-centric approaches, are proposed for further improvements. Database URL https://competitions.codalab.org/competitions/23925 and https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-3/
CAMION: Cascade Multi-input Multi-output Network for Skeleton Extraction

Skeletonization is an important process of extracting the medial axis of the object shape while maintaining the original geometric and topological properties. Some recent studies have demonstrated that deep learning-based segmentation models can extract the main skeleton from objects more robustly. However, we find that the skeleton extracted by a vanilla segmentation process is always discontinuous and not accurate enough. In this paper, we propose a general cascade deep learning pipeline that achieves competitive performance only using a simple U-shape network. The semantic information contained in the shapes is limited, so we introduce a ConvNet with multi-source input and multi-task output, CAMION for short, on top of the basic shape-to-skeleton network. With the multi-source inputs, CAMION can converge faster than using only bi-nary shapes; and with the introduction of multi-task learning, relevant and suitable auxiliary tasks (e.g., feature point detection and contour extraction) bring considerable gains for the extraction of skeleton. Our code used in Pixel Skel-NetOn - CVPR 2022 challenge will be released at https://github.com/likyoo/CAMION-CVPRW2022.
CENet: Consolidation-and-Exploration Network for Continuous Domain Adaptation

Unsupervised Domain Adaptation (UDA) deals with transferring knowledge from labeled source domains to an unlabeled target domain under domain shift. However, this does not reflect the breadth of scenarios that arise in real-world applications since source domains could increase. A plausible conjecture is: can we train a life-long learning model learned on continuous source domains given the target without the presence of labels? We formalize this task as the Continuous Domain Adaptation (CDA) and empirically show that conventional domain adaptation methods may suffer severe generalization deterioration due to the limited incremental transferability and negative transfer. To tackle this issue, we propose a novel sample-to-sample framework—Consolidation-and-Exploration Network (CENet) to facilitate incremental transferring. This method underscores both the qualitative and quantitative relationship between samples. Moreover, we conduct comprehensive experiments to evaluate the effectiveness of each component in our pair-based method. Extensive experiments show that our approach achieves significant improvement over related state-of-the-art methods. Our source code will be publicly available at https://github.com/GekFreeman/continuous_da.