Reviewerly/exHarmony huggingface dataset:
Extracting Tourist Attraction Entities from Text using Conditional Random Fields

With the abundance of data available on the internet today, extracting information from texts has become critical. As tourism becomes more popular, individuals need to search for tourism-related information. To deal with this issue, named entity recognition (NER) can be applied to assist people in getting more relevant information and extracting entities from articles on the websites. This paper aims to identify entities associated with tourist destinations, such as natural, heritage, and purpose tourist attractions. We used the open dataset containing 92 articles in English. Conditional Random Fields (CRF) with various features were proposed to extract tourist attractions. Four scenarios with 13 different features were conducted to find the best NER model. Among the four scenarios, scenario 4 performed best with 97.9 precision, 94.65 recall, and 95.75 F1 scores. We found four features contribute the most to identifying the correct tag: lowercase, n-gram, previous tag, and next tag features. Based on the experiments, adding previous and next tag information could improve model performance.
MMG/EMG Mapping with Reservoir Computing

Magnetomyography(MMG) is the method that measures the magnetic field around the human muscle as an informative bio-signal that has received considerable attention in recent years. However, the noise compositions of MMG signals are complex and difficult to be removed, thus hindering the application of MMG. To extract muscle movement information for MMG and attenuate the effect of noise, this paper proposes a method to convert noisy MMG to clean electromyography (EMG) that also stems from muscle activities. The conversion is done by using a recently proposed electronic Rotating Neuron Reservoir (eRNR) model with high efficiency and strong system approximation ability. This model is trained with our self-collected MMG data as input and the corresponding EMG as target output. After training, the model can successfully map the MMG signal to EMG with acceptable normalised root mean square error (0.3894), offering a new pathway for extracting desirable information from the noisy bio-signal.
Research on Remote Sensing Image Registration Algorithm Based on Saliency Mechanism

In order to solve the problem that remote sensing image registration is easily affected by resolution, viewing angle, weather and other problems, resulting in low real-time and poor accuracy of registration results, a remote sensing image registration algorithm based on improved network in network is proposed. Use channel attention to find important channels and filter out useful feature information; design multi-scale matching at the same time, keep fewer parameters, speed up convergence, and improve robustness; improve the loss function, and use the four parameters obtained by regression to design a new loss The function iteratively optimizes the network model to improve the registration accuracy. The experimental results show that the algorithm has higher registration accuracy and faster running efficiency.
C-Mobile: A Lightweight Composite MobileNetV2 Model for Intrusive Object Detection under Power Grid Surveillance

Intrusive object detection is a key task in real-time power grid surveillance, as the national smart grid is developing rapidly. It turns out to be time-consuming and inaccurate if the surveillance is manually performed by workers. Thus, with the booming of deep learning, we proposed an intrusive object detection algorithm, named C-Mobile, based on lightweight backbone MobileNetV2. To promote the interaction among features and ensure the real-time detection, we designed the composite MobileNetV2 backbone with an SE layer, where one of the MobileNetV2 can enhance the features of the other with a small increase in model complexity. To further utilize the extracted features, we proposed a top-down-bottom-up feature pyramid network (FPN) in which the bottom-up fusion with downsampling is applied after the traditional FPN and a cascaded region proposal network. Our dataset was collected through surveillance camera with 8,177 images and 17,883 object instances in five categories including trucks, cranes, lifts, excavators and pile drivers. Our C-Mobile reaches the highest mAP and the lowest model complexity on our dataset among state-of-the-art object detection algorithms, proving the efficacy of C-Mobile in real-time power grid surveillance.
Opposition as “A Mould on the Fatherland”: Hate Speech and Grassroots Telegram Propaganda in Belarus

Abstract After the August 2020 protests, citizens of Belarus saw unprecedented repression against government opponents, which has persisted for two years. One of the authorities’ methods has been aggressive hate propaganda in the official pro-government media and grassroots anonymous telegram channels. The anonymous Zheltye Slivy / Yellow Leaks telegram channel became the symbol of this propaganda. The channel’s main feature was its aggressive hate speech, which bordered on the incitement to genocide in its extreme versions. This article focuses on this channel’s rhetoric and semantic and discursive features. For this purpose, the article uses the methods of qualitative content analysis and natural language processing (word2vec). The article results reveal the existence of “hate vocabularies” in the channel’s rhetoric—sets of similar words to describe the opponents and enemies of the authorities in Belarus. The article’s main conclusion is that propaganda constructs the image of “anti-society”—a multidimensional imagined social formation with its hierarchy and institutions, which wages war with the authorities and citizens of Belarus.
An Improved SAR Image Speckle Reduction Algorithm Based on 3D Block Matching

For any Synthetic Aperture Radar (SAR) system, obtaining high-quality images is always a prerequisite for improving the performance of SAR applications. However, since the coherent imaging mechanism is affected by speckle noise, speckle suppression is required for SAR images. Aiming at the problem of block matching (BM3D) algorithm in SAR image denoising due to the problem of block matching grouping errors, this paper uses the structural similarity index to improve the block similarity measure, and when similar blocks are selected, according to the standard normal The distribution principle is used to select the threshold value of similar blocks. Compared with several other non-local mean filtering algorithms, the experimental results show that the proposed algorithm can not only effectively suppress the speckle noise in SAR images, but also maintain certain edge information details and obtain a good denoising effect.
Modeling the Intention to Use AI Healthcare Chabot’s in the Indian Context

Covid-19 has accelerated the need and use of artificial Intelligence-based healthcare Chabots. Penetration of the internet, smartphone, computational capability and machine learning technology brings healthcare services close to the patients. The penetration of AI healthcare Chatbot technology worldwide is on the rise. However, the healthcare ecosystem in India is unique and poses challenges in the adoption of healthcare chatbots. The demographic characteristics, economic conditions, diversity, belief systems on health-seeking, and alternative medical practices play a role in accepting and using chatbots. In this study, we attempt to model the factors influencing the intention and the purpose of using the chatbot. Through a literature review, we identify the variables related to the adoption of healthcare chatbots. We then focus on the more relevant concepts to the Indian context and develop a conceptual model. Through cases and literature, we frame the propositions of the study. We look at the awareness of chatbot features, perception towards the chatbot, trust and mistrust of the healthcare system, the doctors and the chatbots, health-seeking behavior, and the belief in traditional, complementary, and alternative medicine prevalent in India. This study contributes by developing an initial conceptual model for healthcare chatbots adoption in the Indian context. In the future, we plan to operationalize the study and test the propositions through an elaborate survey to validate the model empirically.
On Robustness and Sensitivity of a Neural Language Model: A Case Study on Italian L1 Learner Errors

In this paper, we propose a comprehensive linguistic study aimed at assessing the implicit behavior of one of the most prominent Neural Language Models (NLM) based on Transformer architectures, BERT Devlin et al., when dealing with a particular source of noisy data, namely essays written by L1 Italian learners containing a variety of errors targeting grammar, orthography and lexicon. Differently from previous works, we focus on the pre-training stage and we devise two complementary evaluation tasks aimed at assessing the impact of errors on sentence-level inner representations in terms of semantic robustness and linguistic sensitivity. While the first evaluation perspective is meant to probe the model's ability to encode the semantic similarity between sentences also in the presence of errors, the second type of probing task evaluates the influence of errors on BERT's implicit knowledge of a set of raw and morpho-syntactic properties of a sentence. Our experiments show that BERT's ability to compute sentence similarity and to correctly encode multi-leveled linguistic information of a sentence are differently modulated by the category of errors and that the error hierarchies in terms of robustness and sensitivity change across layer-wise representations.
Medical Application of Deep Learning-Based Detection on Malignant Melanoma

The application of machine learning and deep learning has over the years revealed a high level of image accuracy in medical diagnosis and classifications. This study was focused on the application of deep learning in the staging of Malignant Melanoma coupled with the use of VGG-16 and AlexNet architecture in the training over different datasets, for comparison purposes. The first objective of the study was to analyze the impact of deep learning in Melanoma identification. This was accompanied with the acquisition of datasets for analysis. The dataset used in this study consists of 5342 images that are classified as melanoma and non-melanoma. Moreover, the two models (AlexNet and VGG-16) were used for the classification 7 different classes using 7 datasets consisting of actinic keratosis; Basal cell carcinoma (bcc), benign keratosis-like lesions, dermatofibroma, Melanoma, Melanocytic nevi and Vascular lesions images. The result from this study showed a highly precise performance with an accuracy of 96.3%, recall (96.5%), specificity (96%), precision (96%), and a F1 score (96.2%). For the 7 datasets model, AlexNet had an accuracy of 90.2%. Experimentally, the result obtained from this study has revealed that the possible use of deep learning in Malignant Melanoma detection is a highly effective and accurate technique.
Upskilling Educators for Pandemic Teaching: Using Video Technology in Higher Education

The popularization of technology in the higher education environment has raised awareness about the importance of educators’ technological knowledge for teaching and learning. The Technological Pedagogy Content Knowledge (TPACK) framework explains the set of knowledge that educators need to be equipped with for enhanced pedagogies. This will help them to adapt to the expectations of their digital native students. From a training needs analysis conducted in a higher education institution in Malaysia, a two-day training was conducted for the institution’s lecturers to cultivate their use of video technology based on the TPACK framework. The training facilitators evaluated the lecturers’ performance, and the results of the evaluation were presented in this study. Results show that the training successfully closed the identified training gap, and enhanced the lecturers’ capabilities in using video technology for teaching and learning purposes across various disciplines.
Accessibility of Archival Reference Services

Background of the study: Archival services are the final stage of records management, if they are based on an archive lifecycle approach. Accessibility aspects need to be studied in an effort to optimize archiving services so that archives can be widely used by the community. Purpose: This paper describes the process of archiving services and the accessibility aspects of archiving services carried out by Dinas Perpustakaan dan Kearsipan Kabupaten Sleman. Method: This research was a descriptive qualitative approach. Data was collected through observations, interviews, and literature studies. The data then has been analyzed in the following stages: observing the data, developing general sense, coding, compiling a narrative framework for presenting the results of data interpretation, and interpreting the data using data triangulation techniques. Findings: The accessibility aspect of archiving services at the Sleman Regency Library and Archives Service is viewed from four indicators, namely policies, facilities and infrastructure, service personnel, and archive user satisfaction. In policy indicators, the agency has been guided by legal products that are applied to technical archiving services, both at local and national levels. In terms of facilities and infrastructure indicators, in general, the department has met the minimum standard of availability and condition of archival service facilities and infrastructure. Conclusion: There are limitations in this study, namely the limitations of the analysis of each indicator which does not describe in depth so that archival services in the office are declared affordable. In addition, it is necessary to carry out a critical analysis of the results of the user satisfaction survey, namely through in-depth interview techniques to comprehensively understand the archive service process at the office.
PENDAMPINGAN PENGELOLAAN PERPUSTAKAAN SEKOLAH DALAM MENYAMBUT PEMBELAJARAN TATAP MUKA TERBATAS

The school library is one of the essential factors in supporting limited face-to-face learning. The purpose of community service in this article is to carry out assistance in the preparation, implementation and service of the library in welcoming little face-to-face learning. The method used is an action approach method with a mentoring model so that direct service is carried out at the SMP Islam DDI Sangatta Utara Library. The result is that library management becomes more organized by creating book codes to make it easier for visitors to get the books they need. The affixing of book stamps as a sign of ownership and as a safeguard for library book collections. Making archives, visiting books and loans in an effort to control library services. It is hoped that community service can further assist technology-based library management and service systems so that management and services can be more effective and efficient.
Human Parsing for Image-Based Virtual Try-On Using Pix2Pix

Image-based virtual try-on is a method that can let people try on clothes virtually. One of the challenges in image-based virtual try-on is segmentation. The segmentation needed in the virtual try-on implementation is the one that can divide humans into several objects based on their body parts such as hair, face, neck, hands, upper body, and lower body. This type of segmentation is called human parsing. There are several human parsing methods and datasets that have achieved great results. Unfortunately, some limitations make the method unsuitable in an image-based virtual try-on model. We proposed human parsing using the Pix2Pix model with the VITON dataset. Our model yields an average accuracy of 89.76%, an average F1-score of 86.80%, and an average IoU of 76.79%. These satisfactory results allow our model to be used in upcoming image-based virtual try-on research.
The Development Off-Chain Blockchain Smart Contract Model on MSCWR SmartBox

A smart contract is a business process agreed upon to run. With a smart contract, all business processes can run automatically after the smart contract receives a trigger and cannot be canceled or changed. Previous research on smart boxes is generally only used for health storage of smart drugs. In this study, facilitated off-chain blockchain model MSCWR SmartBox will focus on shipping for logistics, such as for the use of shipping goods such as valuables. This research focuses on creating an off-chain blockchain smart contract model that is written in pseudocode to support the operations of the MSCWR SmartBox. The creation of off-chain blockchain smart contracts is very important in supporting the MSCWR business process. Making off-chain blockchain Smart Contracts is very important considering the business processes that create follow the Multichain process (Blockchain applications for cryptocurrencies without changing the multichain application). The research method used is user-centered design (UCD). In this study, we use an application prototype approach, use multichain as a blockchain platform and use the environment at BeeBlock Laboratories. The validation and testing of the equipment used with proof of logs from the server show that the smart contract created can be used to support the business processes of the MSCWR SmartBox.
Human Activity Recognition via Smartphone Embedded Sensor using Multi-Class SVM

Human Activity tracking is the process of detection and understanding of the human activity. It can be done by analyzing human motion behavior data extracted from different smartphone-embedded sensors. Recognizing human activity has become widely popular and particularly attracted many researchers in different industries. Activity recognition has become increasingly important in many areas, especially for the recognition of fitness, sports, and health monitoring. This paper propose a robust model that is trained and tested on remotely extracted data from the smartphone-embedded inertial sensor. Initially, the system clean the input data and then performs windowing and segmentation. After pre-processing, a number of features are extracted. Further, the Lukasiewicz similarity measure (LS) based features selection is used to reduce the features set by removing the least important features. In the next step, the Yeo-Johnson power transformation method is utilized to optimize the selected features. The optimized features set is then forwarded to the multi-class support vector machines (SVM) classifier. The system was designed and experimented with over a well-known dataset named WISDM. The presented model performed well by achieving a mean accuracy rate of 94%.
Leveraging BERT's Power to Classify TTP from Unstructured Text

Tactics, Techniques and Procedures (TTP) are valuable information to cyber-security analysts. However, they are mostly disseminated through unstructured text. This work presents a proposal for tackling this problem by using BERT models, a state-of-the-art approach in Natural Language Processing. We investigate the effect of some chosen hyperparameters on the fine-tuning of the models. MITRE's example sentences are used to train (fine-tuning step) eleven BERT models. The purpose is to find the best model and the finest combination of hyperparameters for the task of classifying TTPs according to the ATT&CK framework. As a result, we observed that the best models presented an accuracy of 82.64% and 78.75% on two datasets tested, demonstrating the potential of the application of BERT models in the complex task of TTP classification. At last, we gather some insights from the misclassified data that help better understand the dataset and how the models manage and classify the proposed data.
Class Incremental Learning for Visual Task using Knowledge Distillation

The Artificial Agent's ability to enhance knowledge incrementally for new data is challenging in class incremental learning because of catastrophic forgetting in which new classes make the trained model quickly forget old classes knowledge. Knowledge distilling techniques and keeping subset of data from the old classes have been proposed to revamp models to accommodate new classes. These techniques allow models to sustain their knowledge without forgetting everything they already know but somewhat alleviate the catastrophic forgetting problem. In this study we propose class incremental learning using bi-distillation (CILBD) method that effectively learn not only the classes of the new data but also previously learned classes. The proposed architecture uses knowledge distillation in such a way that the student model directly learns knowledge from two teacher model and thus alleviate the forgetting of the old class. Our experiments on the iCIFAR-100 dataset showed that the proposed method is more accurate at classifying, forgets less, and works better than state-of-the-art methods.
Enhanced deep learning network for emotion recognition from GIF

The Graphic Interchange Format (GIF) is a bitmap picture format that has a series of perpetually repeating images or silent movies that may be viewed without the user having to click and start them. GIFs are frequently used to visually represent emotions that are expressed through body language such as gestures, movements, and facial expressions. Computing may be used to recognise thoughts and other emotions like desire, interest, sentiments, etc. by using emotional expressions or movements as face markers or properties in GIFs. The ability to predict emotions in GIFs may make it easier to express oneself on social media and convey a person’s attitude or personality. Emotion detection in GIFs may be utilised for a range of purposes, e.g., developing a recommendation system, detecting inappropriate content, sentiment identification from GIF-induced sentiment as perceived by person and creating a GIF tag generating system. This study discusses the prior contributions made towards emotion identification in GIFs and describes a method for detecting seven different emotion classes (Happy, Anger, Sad, Surprise, Disgust, Fear, and Neutral) in GIFs by combining an activity recognition network with face emotional expression. The suggested deep neural network, RNN, LSTM approach produced an F1-score of 0.89 and an accuracy of 88 percent.
Numerical simulation of quantum key distribution network based on wavelength division multiplexing technology

Abstract Wavelength division multiplexing (WDM) technology can improve the networking level and overcome the shortcomings of low key rate under the existing technic background of measurement device independent quantum key distribution (MDI-QKD) protocol application. reference frame independent quantum key distribution (RFI-MDI-QKD) protocol cut down on the influence of the reference frame drift on the key distribution system. The decoy state protocol has important development prospects in the development of quantum communication networking. Combining the advantages of of the above protocols to realize RFI-MDI-QKD with WDM, the results show that the key distribution efficiency is improved with the increase in the number of WDM multiplexed channels, which promotes the further exploration and achievement of QKD networking application.
cosasi: Graph Diffusion Source Inference in Python

cosasi (COntagion Simulation And Source Inference) is, to the author's knowledge, the first extensible open-source framework for graph diffusion source inference that allows users to:• perform and evaluate source localization using standard techniques from literature,• contribute innovative algorithms to a growing core library, and• benchmark new techniques against a battery of comparable schemes.The software is currently used within the Logistics Management Institute.Additional development continues, and we welcome contribution from the broader academic and industrial communities.