Reviewerly/exHarmony huggingface dataset:
A lightweight convolutional neural network model optimization method for ARM platform

With the optimization and upgrading of the ARM architecture and the popularization of mobile smart devices, the application requirements of deep learning technology in real life are increasing. It is a big problem to run the integrated neural network model on the ARM platform. In this paper, aiming at the problems existing in the deployment of the target detection lightweight network on the ARM mobile terminal, the network model is improved and the convolution parallel computing is optimized, and an effective inference acceleration scheme is proposed. In this paper, the YOLOv3 target detection algorithm based on one-stage is used for multi-scale target feature detection, and the YOLOv3 network model is deeply studied and improved. The theoretical acceleration effect of the depthwise separable convolution structure adopted in the classic lightweight network Mobile Net V1 is analyzed. In order to reduce the amount of network parameters, an improved Mobile Net_YOLOv3 model is proposed, Mobile Net V1 is selected to replace the backbone network Darknet53 in the original algorithm, and this model is accelerated for forward reasoning and model quantization deployment.
Unparalleled sarcasm: a framework of parallel deep LSTMs with cross activation functions towards detection and generation of sarcastic statements

Sarcasm is a modest kind of mockingly expressing one’s own thoughts. With the advent of social networking communication, new routes of sociability have proliferated. It may also be stated that the four chariots of being socially hilarious nowadays are humour, irony, sarcasm, and wit. Sarcasm is a clever means of encapsulating any intrinsic truth, message, or even satire in a humorous way. In this paper, we manually extract the features of a benchmark pop culture sarcasm corpus encompassing sarcastic conversations and monologues in order to build padding sequences from the vector representations’ matrices. We also suggest a hybrid of four Parallel Long Short Term Networks, each with its own activation classifier. Consecutively it achieves 98.31% accuracy among the test cases on open-source English literature. Our approach transcends several previous state-of-the-art works and results in sophisticated sarcastic statement generation. We also culture the probable prospects for producing even better refined automated sarcasm generation.
Classification of H&amp;E images exploring ensemble learning with two-stage feature selection

In this work, an investigation based on ensemble learning is presented for the recognition of patterns in histological tissues stained with Hematoxylin and Eosin, representative of breast cancer, colorectal cancer, liver tissues and oral dysplasia. The strategy considered compositions with multiple descriptors, such as deep learned and handcrafted, and multiple classifiers. The deep learned descriptors were calculated by exploring different architectures of convolutional neural networks. The handcrafted descriptors were representative of the multidimensional and multiscale fractal categories, Haralick and local binary pattern. The main combinations were obtained through two-stage feature selection (ranking with wrapper selection) and classified via an ensemble composed of five classifiers. The accuracy rates were values between 93.10% and 100%, with some highlights involving the main combinations of approaches.
Using Schema.org and Solid for Linked Data-based Machine-to-Machine Sales Contract Conclusion

We present a demo in which two robotic arms, controlled by rule-based Linked Data agents, trade a good in Virtual Reality. The agents follow the necessary steps to conclude a sales contract under German law. To conclude the contract, the agents exchange messages between their Solid Pods. The data in the messages is modelled using suitable terms from Schema.org.
Hybrid Learning Spaces with Spatial Audio

The two largest universities in Norway started a new joint international master’s program called “Music, communication and technology” (MCT) in 2018. MCT constitutes the framework for developing a new shared learning space (The Portal) for physical-virtual interaction, musical communication, and collaboration.The COVID-19 pandemic has illuminated several challenges within hybrid teaching concerning the design of learning spaces and related pedagogical and technical issues. In our case, we identify challenges in delivering a hybrid learning space with spatial audio and discuss a method to implement such a solution in the context of spatial audio and ambisonics workshops developed for the MCT master’s program. We propose a stable but flexible technical solution for decoding higher-order ambisonics audio in two remote physical locations and into virtual environments in real-time, creating a continuum between the physical, hybrid, and online learning spaces mediated by technology.
An Attention-Based Multimodal Siamese Architecture for Tweet-User Verification

With the advent of internet technologies, it has created different ways of writing anonymously, which has lead to criminal and malicious activities over social media platforms. Thus, the automatic authentication checking of the available contents is the need of the hour. Social media sites, such as Facebook, Twitter, and so on, are used heavily by the users for sharing of information about their day-to-day activities. The identity of the suspect user is matched against tweets written by the specific user in tweet-user verification process. Writing styles of different users differ from each other, due to unique word choices, emoji selection, sentence formation, and punctuation usage. We have developed a multimodal Siamese-based architecture, which uses attention between the text and emoji parts of the tweet for generating a combined representation for the tweet. Attention helps in selecting the relevant information from different modalities. Modality attention is used for fusing the two modalities (text and emoji). We have used a newly developed multimodal Twitter dataset for evaluating the performance of the proposed model. We achieved an average accuracy, precision, recall, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> -measure values of 68.50%, 78.52%, 69.47%, and 67.05%, respectively. The results show an increase of 2.14% in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> -measure in comparison with the current state-of-the-art (SOTA) models for this dataset.
A Password Secure Mechanism using Reformation-based Honey Encryption and Decryption

With the emerging technological advances, different upgradation are deployed on the internet to receive and process huge amounts of data. However data storage and analysis process require some security mechanisms. The most common method used for achieving data security is password protection. Passwords serve as the entry point to the accessible environment. The majority of the data is maintained on a common storage system called servers. A password with a weak pattern will be more susceptible to data attacks. Due to the user-generated simple passwords, the complexity of password cracking becomes simple for hackers. The proposed framework includes a strong password initiated security mechanism based on the Honey encryption and decryption method. A dynamic keypad altering system based on reformation is also implemented. The passwords will be generated each time and also the alignment of keypad letters will be changed. Weak passwords will not be authorized to access the system. The Honey encryption and decryption algorithm is used here to encrypt both the user ID and password. For password generation, the proposed framework considers the text, alpha numeric characters, special characters, as well as capital and small letters. The proposed method ensures the intricacy of the password system. The user ID will be checked each time when the user accesses the proposed secured system. Unauthorized user IDs are not permitted to enter the password. The proposed approach is efficient compared to state-of-the-art methods discussed in existing implementations in terms of security.
A Comparison of Deep Learning and Traditional Machine Learning Approaches in Detecting Cognitive Impairment Using MRI Scans

Deep learning has attracted a great amount of interest in recent years and has become a rapidly emerging field in artificial intelligence. In medical image analysis, deep learning methods have produced promising results comparable to and, in some cases, superior to human experts. Nevertheless, researchers have also noted the limitations and challenges of the deep learning approaches, especially in model selection and interpretability. This paper compares the efficacy of deep learning and traditional machine learning techniques in detecting cognitive impairment (CI) associated with Alzheimer's disease (AD) using brain MRI scans. We base our study on 894 brain MRI scans provided by the open access OASIS platform. In particular, we explore two deep learning approaches: 1) a 3D convolutional neural network (3D-CNN) and 2) a hybrid model with a CNN plus LSTM (CNN-LSTM) architecture. We further examine the performance of five traditional machine learning algorithms based on features extracted from the MRI images using the FreeSurfer software. Our experimental results demonstrate that the deep learning models achieve higher Precision and Recall, while the traditional machine learning methods deliver more stability and better performance in Specificity and overall accuracy. Our findings could serve as a case study to highlight the challenges in adopting deep learning-based approaches.
A Multi-Task Multiple Instance Learning Algorithm to Analyze Large Whole Slide Images from Bright Challenge 2022

Malignant lesions in breast tissue specimen whole slide images (WSIs), may lead to a dangerous diagnosis, such as cancer. However, WSIs analysis is time-consuming and expensive, requiring the work of expert pathologists. This paper aims to present a method for the 2022 BRIGHT Challenge, that involves the analysis of breast WSIs. The organizers provided over 550 breast WSIs and over 3900 regions of interest (ROIs) to develop and validate methods for breast cancer images. The method presented in this work is based on a Multiple Instance Learning instance-based Convolutional Neural Network (CNN), allowing the combination of strongly-annotated data (from ROIs) and weakly-annotated data (from WSIs) via the optimization of a multi-task loss function. Furthermore, during the CNN training, the input patches are clustered and filtered according to their entropy, to reduce the non-informative content used to train the model. The CNN reaches an averaged <tex>$\text{F1-score}=0.63\pm 0.02$</tex> on the 3-class classification task and averaged <tex>$\text{F1-score}=0.39 \pm 0.08$</tex> on the 6-class classification task, considering the val-idation partition; an averaged <tex>$\text{F1-score}=0.65$</tex> on the cancer risk classification task and averaged <tex>$\text{F1-score}=0.45$</tex> on the sub-typing cancer risk classification task, considering the best result achieved on the test partition. These results show that Multiple Instance Learning instance-based CNNs may represent a good resource to tackle this kind of problem.
Image Encryption using Reverse Data Hiding Algorithm with Triple DES

Idea of the cryptography was utilized in this paper for communicating the information subtly and securely starting with the first client then onto the next as it shrouds the information behind picture. In this sender encode information utilizing triple DES calculation, shrouds scrambled information in picture utilizing RDH method and then framework automatically produce a hided key. Now the Sender will send a document by assistance with current mail framework. The collector able to present activity dependent of the key in the event that he is having just information storing away, at that point he can possibly get the picture in unique structure and in the event that he has information covering up and information decoding key, at that point he will have the option to get the first information, it likewise gives insurance to the key. Each of this was finished by appropriate sign in measure. This framework produces counterfeit information supposing that client isn't validated during the time spent login. In such a manner just the approved client, who should get the keys, can get the first information
WasteDet: An Anchor Free Novel Detection Algorithm for improving Waste Management

Noteworthy steps are being taken towards improving the level of hygiene and cleanliness in cities. Even with these efforts littering and non-recyclable waste is still a serious major issue socially and ecologically. Locating, identifying, and picking up the waste by staff can be a tiresome and inefficient task requiring long hours of manpower. To address the problem of locating and identification of the waste objects we propose a cost-effective method which is based on application detection algorithms on low altitude imagery from streets, sidewalks, houses, and landfills taken by surveillance camera or Unmanned aerial vehicles or UAVs. The novel deep learning-based framework called WasteDet consists of deep convolutional neural networks which perform the localization and the classification task which can then be processed and put through into automated pickup planning system. A custom dataset was compiled and annotated to train and evaluate the model. The model achieved a mAP score of 87.82% when tested on 8 classes while testing on all classes a mAP score of 85.60% was obtained, outperforming the current state-of-art object detection algorithm by 4% AP. This model is also capable of detecting objects in real-time applications on live and recorded videos achieving 49.6 fps on our video dataset.
Survey on Fake Accounts Detection Algorithms on Online Social Networks

Online Social Networks (OSNs) are the most popular web services nowadays. They provide users with different kinds of services. Anyone can create his account on a certain OSN such as Facebook, using an email and password for registration. In addition, a single user can own one or more accounts. However, this feature has a lot of disadvantages and security drawbacks, such as creating fake accounts. A fake account is a profile that exists physically on OSN. Nonetheless, it is missing identity information such as names, last name, profile photo, and other profile attributes. Owners of fake accounts exploit them (accounts) for malicious internet activities like phishing, hacking, and more. Recently, this problem attracted considerably the research community. In this context, a lot of approaches have been emerged to solve fake account detection on OSNs. However, despite its importance, this field of research is still missing a systematic review. In this paper, we introduce a survey on sixteen kinds of research studies that have proposed solutions for solving the detection of fake accounts problem. We analyzed the following themes: Social networking platforms, evaluation metrics, machine learning algorithms, and models, data scale, features, and result accuracy were studied and analyzed in this survey.
Supporting Second Language Learners through SKANDIBOT: A Lexicographical Design Approach

Migrant professional language learners need to be supported in acquiring a second language effectively beyond the classroom. These learners frequently lack opportunities to participate in language classes due to full-time jobs. Yet, quick acquisition of the target language is a needed prerequisite for their successful integration into job settings and the host society. To assist these learners in their smooth acquisition of the target language, this study takes advantage of the recent developments in artificial intelligence and presents the design of a chatbot, called SKANDIBOT aimed at fostering second language learners’ conversational practice in professional work settings of Sweden and Denmark. The results of this work-in-progress study indicate that both healthcare and learning professionals perceive the overall interaction design of SKANDIBOT and the information offered in it to be useful for their second language acquisition.
Dementia Care: An Android Application for Assisting Dementia Patients

Dementia is a term used to depict a category of indications influencing memory, thinking and social capacities seriously enough to disrupt your everyday existence. Changes faced by dementia patient includes trouble in communicating, planning, problem-solving, memory loss etc. Due to lack of knowledge dementia patients are either lock up at home, sent to mental hospital or ignored. Caretakers and doctors can look after the patient once in a day or week but they can&#x2019;t be 24X7 with the patient. Technology is advancing and Mobile Health has turned into a quickly developing innovation for patients. Various solutions were developed for dementia patients like Application for mind games, find a Doctor, GPS tracker, analyze behavior etc. It has become vital to foster something that can help patients in tackling their everyday tasks. This paper describes an application which is developed to assist patients, caretakers as well as family members. Application can run on all android devices and include functionalities like track location of patients using GPS, contain medicine reminder so that patient can take medicine on time, to-do list so that patient can remember work they need to complete, provide patient way to their home, fall detection using accelerometer in phone and location sharing. This system includes features which can help to improve their health without losing their freedom as well as assist caretakers.
A New Lattice-Based Plane-Probing Algorithm

Plane-probing algorithms have become fundamental tools to locally capture arithmetical and geometrical properties of digital surfaces (boundaries of a connected set of voxels), and especially normal vector information. On a digital plane, the overall idea is to consider a local pattern, a triangle, that is expanded starting from a point of interest using simple probes of the digital plane with a predicate “Is a point x in the digital plane?”. Challenges in plane-probing methods are to design an algorithm that terminates on a triangle with several geometrical properties: its normal vector should match with the expected one for digital plane (correctness), the triangle should be as compact as possible (acute or right angles only), and probes should be as close as possible to the source point (locality property). In addition, we also wish to minimize the number of iterations or probes during the computations. Existing methods provide correct outputs but only experimental evidence for these properties. In this paper, we present a new plane-probing algorithm that is theoretically correct on digital planes, and with better experimental compactness and locality than existing solutions. Additional properties of this new approach also suggest that theoretical proofs of the aforementioned geometrical properties could be achieved.
Crowd Counting Model with Convolutional Neural Networks and Transformer

Crowd counting means calculating the crowd density of the current scene. In modern society, the study on crowd counting is of great significance. This paper focuses on multi-view crowd counting, using multi-view images to predict crowd density and crowd distribution of the current scene. This paper proposes a crowd counting model with convolutional neural networks and transformer, which uses the same view branch to extract features with images from various views. The feature maps will be projected and fused in the same world space, then the scene-level feature maps are regressed to a scene-level density map. This paper does experiments on the PETS2009 and CityStreet datasets. Numerous experiments have proven that our method has good accuracy, especially for dense small objects.
Knowledge-based Interactive Configuration Tool for Industrial Augmented Reality Systems

The innovative technology Augmented Reality (AR) provides enormous potentials for companies. But due to the high technological complexity and the lack of expertise and experience in companies, the potentials are only rarely exploited. One major challenge for companies is the demand-oriented configuration of the AR system for a specific application scenario. This includes the selection of a suitable AR device and the specification of the AR application software. Relevant knowledge is only implicitly available in a variety of individual distributed sources. Companies therefore heavily rely on technology providers or experts when using AR. This work presents a novel knowledge-based approach to support companies in the individual AR system configuration. Based on a digital knowledge base an interactive configuration tool is developed. The tool enables the targeted selection of a suitable AR device and required AR software functionalities for a custom application scenario considering individual circumstantial conditions and requirements. It thereby empowers companies to configure AR systems independently and without prior expertise, facilitating the exploitation of the potentials of AR.
On Performance Modeling and Prediction for Spark-HBase Applications in Big Data Systems

Many large-scale applications in various business and scientific domains require both parallel computing and distributed data management for big data processing. One typical scenario is the use of the Spark computing engine to process a large amount of data managed by HBase in Hadoop. Such computing workflows provide an opportunity to optimize application performance through strategic resource allocation with suitable parameter settings. As such, it necessitates accurate modeling and prediction of application performance to provide an effective recommendation of optimal system configurations to end users. However, this is a challenging problem for multiple reasons, mainly the large parameter space and the dynamic interactions between different technology layers of big data systems. In this paper, we propose a class of regression-based machine learning models to predict the execution performance of Spark-HBase applications in Hadoop. We first explore and identify an exhaustive set of system parameters across multiple layers including Spark and HBase, and then conduct in-depth exploratory analysis of their effects on the execution time of Spark-HBase applications. Based on these analysis results, we design a performance predictor using regression-based machine learning algorithms. Experimental results show that the resulted predictor achieves high accuracy with different algorithms in comparison. The proposed approach can facilitate automatic system configurations and has potential to be applied to other similar systems for big data processing.
Artificial Intelligence Fusion of Information for Aerospace (AIFIA) Systems

For intelligence and surveillance methods, information fusion techniques typically increase the accuracy and reliability of results through uncertainty reduction. Recently, artificial intelligence and machine learning (AI/ML) methods presented the engineering community with numerous opportunities to improve avionics controls, structural health monitoring, and platform design. Hence, alignment of traditional Fusion Integration of Sensor Harvesting (FISH) with that of AI/ML should advance aerospace performance. As such, Artificial Intelligence Fusion of Information for Aerospace (AIFIA) Systems relies on multiple sensors for systems and situation identification. This paper identifies some aerospace applications of AI enhancing information fusion methods; while outlining areas of future opportunity in aerospace systems design. The example utilizes the ESCAPE data using machine and deep learning (DL) to monitor an area, detect moving platforms, and meeting certifiable bounds.