Reviewerly/exHarmony huggingface dataset:
Video analysis for the detection of animals using convolutional neural networks and consumer-grade drones

Determining animal distribution and density is important in conservation. The process is both time-consuming and labour-intensive. Drones have been used to help mitigate human-intensive tasks by covering large geographical areas over a much shorter timescale. In this paper we investigate this idea further using a proof of concept to detect rhinos and cars from drone footage. The proof of concept utilises off-the-shelf technology and consumer-grade drone hardware. The study demonstrates the feasibility of using machine learning (ML) to automate routine conservation tasks, such as animal detection and tracking. The prototype has been developed using a DJI Mavic Pro 2 and tested over a global system for mobile communications (GSM) network. The Faster-RCNN Resnet 101 architecture is used for transfer learning. Inference is performed with a frame sampling technique to address the required trade-off between precision, processing speed, and live video feed synchronisation. Inference models are hosted on a web platform and video streams from the drone (using OcuSync) are transmitted to a real-time messaging protocol (RTMP) server for subsequent classification. During training, the best model achieves a mean average precision (mAP) of 0.83 intersection over union (@IOU) 0.50 and 0.69 @IOU 0.75, respectively. On testing the system in Knowsley Safari our prototype was able to achieve the following: sensitivity (Sen), 0.91 (0.869, 0.94); specificity (Spec), 0.78 (0.74, 0.82); and an accuracy (ACC), 0.84 (0.81, 0.87) when detecting rhinos, and Sen, 1.00 (1.00, 1.00); Spec, 1.00 (1.00, 1.00); and an ACC, 1.00 (1.00, 1.00) when detecting cars.
Sentiment Analysis on User-generated Video, Audio and Text

Social Media prevails in today's world for voicing an opinion. That makes it crucial for businesses, artists, content creators, and pretty much anyone else on the internet to analyse what people are saying about them and what they offer. It provides vital information on what they can change and improve upon. Hence, Sentiment Analysis is going to be a significant aspect to help organizations and individuals grow. Contrary to previous work, a broader viewpoint is taken into consideration and any kind of discrepancies is eliminated since the sentiment classified by any one modality is confirmed by the analysis of the other two modalities as well. In all the previous work done on multimodal sentiment analysis, the weightage given to all the modules was the same for fusion but to get perfect results, a trial-and-error logging method is used with many different weightages for fusion. This helped us in knowing which module is best suited for sentimental analysis and how each of them can alter the results obtained. With this type of the development in the sentiment analysis domain, a system which can extract the information in terms of the emotions of the people regarding a specific product from any of the three mediums (text, audio or video) can lead to the better technological aspects in the market. The objective was to build a system which can identify the sentiment categorized into six types: anger, joy, disgust, sadness, fear, and surprise of a video when the data is fed into it. The system developed depicts how much of each of these sentiments are present in a particular input.
Adaptive LASSO logistic regression based on particle swarm optimization for Alzheimer's disease early diagnosis

Accurate classification of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI) play key roles in computer-assisted intervention for the diagnosis of AD. However, not all features of AD data will lead to a good classification result, because there are always some unrelated and redundant features. To solve this problem, an adaptive LASSO logistic regression model based on particle swarm optimization(PSO-ALLR)is proposed. This algorithm consists of two stages. In the first stage, the particle swarm optimization (PSO) algorithm is used for global search to remove redundant features and reduces the computational time for the later stage. In the second stage, the adaptive LASSO serves as a local search to select the most relevant features for AD classification.We evaluate the performance of the proposed method on 197 subjects from the baseline MRI data of ADNI database. The proposed method achieves a classification accuracy of 96.27%, 84.81%, and 76.13%, for AD vs. HC, MCI vs. HC, and cMCI vs. sMCI, respectively.
Development of a Lifeguard assist Drone for coastal search and rescue

Several lives are lost every year due to drowning on the beaches and other water bodies all over the world, representing a tragic loss to the family members as well as the nation. To save the lives of drowning victims, we have developed a low-cost drone for assisting the lifeguard, in search and rescue operations, for the coastal beaches of the state of Goa, India. The lifeguard on the control tower of a beach will deploy the drone towards the victim in distress in the water. A floatation device is deployed near the drowning victim. The person in distress can hold onto the flotation device till the lifeguards can approach the spot and perform a rescue operation. The use of the drone can also be extended to rivers, lakes and popular picnic as well as religious pilgrimage sites, where several members of the public congregate, to monitor and perform rescue operations. A proof-of-concept prototype of the lifeguard assist drone has been developed. A floatation device deployment mechanism has been designed and deployed on the lifeguard assist drone. Initial trials have provided excellent results and the cost of the system is much lesser than similar systems in use, as reported in the literature.
Arabic Diacritic Recovery Using a Feature-rich biLSTM Model

Diacritics (short vowels) are typically omitted when writing Arabic text, and readers have to reintroduce them to correctly pronounce words. There are two types of Arabic diacritics: The first are core-word diacritics (CW), which specify the lexical selection, and the second are case endings (CE), which typically appear at the end of word stems and generally specify their syntactic roles. Recovering CEs is relatively harder than recovering core-word diacritics due to inter-word dependencies, which are often distant. In this article, we use feature-rich recurrent neural network model that use a variety of linguistic and surface-level features to recover both core word diacritics and case endings. Our model surpasses all previous state-of-the-art systems with a CW error rate (CWER) of 2.9% and a CE error rate (CEER) of 3.7% for Modern Standard Arabic (MSA) and CWER of 2.2% and CEER of 2.5% for Classical Arabic (CA). When combining diacritized word cores with case endings, the resultant word error rates are 6.0% and 4.3% for MSA and CA, respectively. This highlights the effectiveness of feature engineering for such deep neural models.
AWA: Adversarial Website Adaptation

One of the most important obligations of privacy-enhancing technologies is to bring confidentiality and privacy to users’ browsing activities on the Internet. The website fingerprinting attack enables a local passive eavesdropper to predict the target user’s browsing activities even she uses anonymous technologies, such as VPNs, IPsec, and Tor. Recently, the growth of deep learning empowers adversaries to conduct the website fingerprinting attack with higher accuracy. In this paper, we propose a new defense against website fingerprinting attack using adversarial deep learning approaches called Adversarial Website Adaptation (AWA). AWA creates a transformer set in each run so that each website has a unique transformer. Each transformer generates adversarial traces to evade the adversary’s classifier. AWA has two versions, including Universal AWA (UAWA) and Non-Universal AWA (NUAWA). Unlike NUAWA, there is no need to access the entire trace of a website in order to generate an adversarial trace in UAWA. We accommodate secret random elements in the training phase of transformers in order for AWA to generate various sets of transformers in each run. We run AWA several times and create multiple sets of transformers. If an adversary and a target user select different sets of transformers, the accuracy of adversary’s classifier is almost 19.52% and 31.94% with almost 22.28% and 26.28% bandwidth overhead in UAWA and NUAWA, respectively. If a more powerful adversary generates adversarial traces through multiple sets of transformers and trains a classifier on them, the accuracy of adversary’s classifier is almost 49.10% and 25.93% with almost 62.52% and 64.33% bandwidth overhead in UAWA and NUAW, respectively.
Movie Recommender system using Sentiment Analysis

In Todays era, Recommendation systems are the most important intelligent systems that plays in giving the information to the users. Previously approaches in recommendation systems (RS) include Content-based-filtering and collaborative filtering. Thus, these approaches has certain limitations as like the necessity of the user history as they visit. So as to make back the effect of such dependencies, this research paper provides a hybrid RS are those which mixes both Collaborative filtering, Content based filtering with sentiment analysis of movies. In this research paper, we developed a recommender system based on the sentiment of the user to suggest the movie to the user based on their view history.
Progressive Self-Supervised Clustering With Novel Category Discovery

These days, clustering is one of the most classical themes to analyze data structures in machine learning and pattern recognition. Recently, the anchor-based graph has been widely adopted to promote the clustering accuracy of plentiful graph-based clustering techniques. In order to achieve more satisfying clustering performance, we propose a novel clustering approach referred to as the progressive self-supervised clustering method with novel category discovery (PSSCNCD), which consists of three separate procedures specifically. First, we propose a new semisupervised framework with novel category discovery to guide label propagation processing, which is reinforced by the parameter-insensitive anchor-based graph obtained from balanced K -means and hierarchical K -means (BKHK). Second, we design a novel representative point selected strategy based on our semisupervised framework to discover each representative point and endow pseudolabel progressively, where every pseudolabel hypothetically corresponds to a real category in each self-supervised label propagation. Third, when sufficient representative points have been found, the labels of all samples will be finally predicted to obtain terminal clustering results. In addition, the experimental results on several toy examples and benchmark data sets comprehensively demonstrate that our method outperforms other clustering approaches.
Twitter &amp; bitcoin: are the most influential accounts really influential?

There is a vast amount of information flow in social media about bitcoin, which may affect investors' decisions. This article investigates whether tweets may affect returns or trade volume changes of bitcoin and, more importantly, whether some Twitter accounts are more influential than other Twitter accounts. We conduct two separate analyses based first on all Twitter accounts and then on the most influential 50 Twitter accounts, which have been selected as such by Unitedtraders. We use the number of positive, negative and neutral tweets by Valence Aware Dictionary and Sentiment Reasoner (VADER) in a logistic model to analyse if tweets have any valuable information about the change in both return and trade volume of bitcoin. Our results indicate that tweets can be used to predict bitcoin returns. Notably, the most influential accounts are the drivers of returns, but all Twitter accounts simply introduce some noise in volatility. This result indicates that following only these 50 most influential accounts may provide the information needed for investors.
Self-attention guided representation learning for image-text matching

Image-text matching plays an important role in bridging vision and language. Most existing research works embed both images and sentences into a joint latent space to measure their similarities. However, these methods failed to either exploit the interactions between sentences and images, or distinguish the importance of different elements within each modality. In this paper, we propose a self-attention guided representation (SGR) learning model, which incorporates the guidance of self-attention mechanism into cross-attention representation learning module for image-text matching. Specifically, we introduce a self-attention mechanism to discriminate the importance of different words within a sentence, as well as that of different regions within an image. The representations associated with each modality are then fed into a cross-attention module to discover distinct alignments between words and regions. Moreover, we employ Term Frequency - Inverse Document Frequency (TF-IDF) to highlight the words that function prominently in textual descriptions, paving the way to accurate image-text matching results. Extensive experiments on datasets MSCOCO and Flickr30K demonstrate the effectiveness and superiority of our framework.
Digital competence in teacher education: comparing national policies in Norway, Ireland and Spain

This paper explores the development of policies dealing with teachers' digital competence in Norway, Ireland and Spain.Using a documentary research approach, the study analysed relevant policy documents from each country over a thirty-year period to the present day.Analysis of the documents highlights historical differences and similarities in how technology in education policies developed during that period and differences in how teacher education was addressed.Despite these differences, the analysis indicates a convergence in recent years towards a common understanding and the importance of teachers' digital competence influenced by supranational frameworks.The paper discusses the potential influence of these supranational frameworks and examines the opportunities and challenges of this policy convergence.
Evaluation of a Human–Machine Interface for Motion Sickness Mitigation Utilizing Anticipatory Ambient Light Cues in a Realistic Automated Driving Setting

Motion sickness (MS) is a syndrome associated with symptoms like nausea, dizziness, and other forms of physical discomfort. Automated vehicles (AVs) are potent at inducing MS because users are not adapted to this novel form of transportation, are provided with less information about the own vehicle’s trajectory, and are likely to engage in non-driving related tasks. Because individuals with an especially high MS susceptibility could be limited in their use of AVs, the demand for MS mitigation strategies is high. Passenger anticipation has been shown to have a modulating effect on symptoms, thus mitigating MS. To find an effective mitigation strategy, the prototype of a human–machine interface (HMI) that presents anticipatory ambient light cues for the AV’s next turn to the passenger was evaluated. In a realistic driving study with participants (N = 16) in an AV on a test track, an MS mitigation effect was evaluated based on the MS increase during the trial. An MS mitigation effect was found within a highly susceptible subsample through the presentation of anticipatory ambient light cues. The HMI prototype was proven to be effective regarding highly susceptible users. Future iterations could alleviate MS in field settings and improve the acceptance of AVs.
Sentiment Analysis of COVID-19 Nationwide Lockdown effect in India

Nowadays, Sentiment Analysis has become an active research area due to the availability of many opinionated data through increased activity in Blogging, Tagging, Podcasting, social networking sites, RSS feeds, and Social Bookmarking. In the present situation, the whole world is facing the crisis of the COVID-19 pandemic. Particularly, let's talk about nationwide lockdown in India to control the spread of COVID-19. The government relies on social media to observe people's aviews on their policies during the lockdown. In this paper, Twitter data has been used for Sentiment Analysis, which focus on people opinion during the COVID-19 nationwide Lockdown effect in India. Different keywords data was collected on various dates between March 25, 2020, to June 09, 2020. This research work is an application of the real-time TextBlob sentiment analyzer tool built based on the Natural Language Toolkit (NLTK). Relevant keyword tweets were extracted by tweeter API. Then a model was trained to classify the result on a specific opinion. This NLPbased sentiment analysis model is ideal for analyzing the emotions while tested with seven primary keywords: Lockdown1.0, Migrant Workers, Indian Economic, ICMR, Lockdown5.0, Medical Facilities, and Police. The result shows that Lockdown 1.0 got the most positive sentiments, followed by ICMR and Medical Facility.
Argument Mining on Twitter: A survey

Abstract In the last decade, the field of argument mining has grown notably. However, only relatively few studies have investigated argumentation in social media and specifically on Twitter. Here, we provide the, to our knowledge, first critical in-depth survey of the state of the art in tweet-based argument mining. We discuss approaches to modelling the structure of arguments in the context of tweet corpus annotation, and we review current progress in the task of detecting argument components and their relations in tweets. We also survey the intersection of argument mining and stance detection, before we conclude with an outlook.
Improving measurement accuracy of laser triangulation sensor via integrating a diffraction grating

In this paper, a laser triangulation sensor (LTS) with improved measurement accuracy via integrating a diffraction grating was proved. Compared to conventional LTS that only has the information of one light spot, the diffraction grating generates several diffraction light spots on CMOS image sensor. This enables to obtain multiple results of the object displacement simultaneously during one sampling period. Thus, it is achievable to obtain a higher measurement accuracy by averaging operation, due to the averaging-error effect. The operating principle of the proposed method is explained in detail, and a prototype LTS is developed. Contrastive experiments are performed to evaluate the nonlinearity and repeatability of the developed LTS, which are generally combined to present the measurement accuracy of LTS. In the full-scale measurement range of 20 mm (F.S. = 20 mm), the results show that the measurement nonlinearity and repeatability of the developed LTS with diffraction grating are 0.113% of F.S. and 0.89 μm respectively, which are significantly improved compared to that without diffraction grating.
Prototype-Based Multisource Domain Adaptation

Unsupervised domain adaptation aims to transfer knowledge from labeled source domain to unlabeled target domain. Recently, multisource domain adaptation (MDA) has begun to attract attention. Its performance should go beyond simply mixing all source domains together for knowledge transfer. In this article, we propose a novel prototype-based method for MDA. Specifically, for solving the problem that the target domain has no label, we use the prototype to transfer the semantic category information from source domains to target domain. First, a feature extraction network is applied to both source and target domains to obtain the extracted features from which the domain-invariant features and domain-specific features will be disentangled. Then, based on these two kinds of features, the named inherent class prototypes and domain prototypes are estimated, respectively. Then a prototype mapping to the extracted feature space is learned in the feature reconstruction process. Thus, the class prototypes for all source and target domains can be constructed in the extracted feature space based on the previous domain prototypes and inherent class prototypes. By forcing the extracted features are close to the corresponding class prototypes for all domains, the feature extraction network is progressively adjusted. In the end, the inherent class prototypes are used as a classifier in the target domain. Our contribution is that through the inherent class prototypes and domain prototypes, the semantic category information from source domains is transformed into the target domain by constructing the corresponding class prototypes. In our method, all source and target domains are aligned twice at the feature level for better domain-invariant features and more closer features to the class prototypes, respectively. Several experiments on public data sets also prove the effectiveness of our method.
Face Sketch Synthesis Using Regularized Broad Learning System

There are two main categories of face sketch synthesis: data- and model-driven. The data-driven method synthesizes sketches from training photograph-sketch patches at the cost of detail loss. The model-driven method can preserve more details, but the mapping from photographs to sketches is a time-consuming training process, especially when the deep structures require to be refined. We propose a face sketch synthesis method via regularized broad learning system (RBLS). The broad learning-based system directly transforms photographs into sketches with rich details preserved. Also, the incremental learning scheme of broad learning system (BLS) ensures that our method easily increases feature mappings and remodels the network without retraining when the extracted feature mapping nodes are not sufficient. Besides, a Bayesian estimation-based regularization is introduced with the BLS to aid further feature selection and improve the generalization ability and robustness. Various experiments on the CUHK student data set and Aleix Robert (AR) data set demonstrated the effectiveness and efficiency of our RBLS method. Unlike existing methods, our method synthesizes high-quality face sketches much efficiently and greatly reduces computational complexity both in the training and test processes.
RETRACTED: Design of College Physical Education Teaching System Based on Artificial Intelligence Technology

Abstract In recent years, with the deepening reform of domestic education, the education mode of education mode is changing. The physical education teaching system based on artificial intelligence technology can not only improve the work efficiency of the management department, but also promote the construction of digital, information and intelligent School Park. In this paper, college physical education curriculum as the center, artificial intelligence technology as the research direction, put forward the introduction of artificial intelligence into physical education, through the construction of agent layer and data service layer to achieve personalized teaching. By comparing the results of the control group and the experimental group after the experiment, and through the investigation of the experimental group students, the system improved the students’ sports performance to a certain extent, the experimental group compared with the control group, the average score increased by 3%, improved the students’ interest in sports.