Reviewerly/exHarmony huggingface dataset:
Multimodal-Boost: Multimodal Medical Image Super-Resolution Using Multi-Attention Network With Wavelet Transform

Multimodal medical images are widely used by clinicians and physicians to analyze and retrieve complementary information from high-resolution images in a non-invasive manner. Loss of corresponding image resolution adversely affects the overall performance of medical image interpretation. Deep learning-based single image super resolution (SISR) algorithms have revolutionized the overall diagnosis framework by continually improving the architectural components and training strategies associated with convolutional neural networks (CNN) on low-resolution images. However, existing work lacks in two ways: i) the SR output produced exhibits poor texture details, and often produce blurred edges, ii) most of the models have been developed for a single modality, hence, require modification to adapt to a new one. This work addresses (i) by proposing generative adversarial network (GAN) with deep multi-attention modules to learn high-frequency information from low-frequency data. Existing approaches based on the GAN have yielded good SR results; however, the texture details of their SR output have been experimentally confirmed to be deficient for medical images particularly. The integration of wavelet transform (WT) and GANs in our proposed SR model addresses the aforementioned limitation concerning textons. While the WT divides the LR image into multiple frequency bands, the transferred GAN uses multi-attention and upsample blocks to predict high-frequency components. Additionally, we present a learning method for training domain-specific classifiers as perceptual loss functions. Using a combination of multi-attention GAN loss and a perceptual loss function results in an efficient and reliable performance. Applying the same model for medical images from diverse modalities is challenging, our work addresses (ii) by training and performing on several modalities via transfer learning. Using two medical datasets, we validate our proposed SR network against existing state-of-the-art approaches and achieve promising results in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR).
Real Time Pedestrian Detection Using Robust Enhanced Tiny-YOLOv3

One of the key components in developing smart cities in countries like India, autonomous pedestrian detection plays a very crucial role in Computer Vision tasks such as smart video surveillance and intelligent traffic monitoring system. In self-driving cars, real time performance is required without scarifying the detection accuracy, while detecting smaller pedestrians. In the proposed paper, a robust Enhanced Tiny-Yolov3 Network is designed by introducing an anti-residual module which helps to improve network’s feature extraction ability. Second, the loss function is improved which reduces bounding box loss error and optimizes the network. Third one prediction scale of size 26 x 26 is removed from the Tiny-Yolov3 network so the computational complexity of the network is reduced. The proposed network is trained on the extracted pedestrian images from Pascal Voc-2007 dataset. Experimental results show this network improves the detection accuracy while detecting smaller pedestrians and it still meets the real-time requirements.
A cognitive approach for blockchain-based cryptographic curve hash signature (BC-CCHS) technique to secure healthcare data in Data Lake

In today's digital world, information is exchanged among various sources, and it is expected that each interaction or transaction among the sources must be reliable and secure. In these circumstances, blockchain technology can be applied to ensure healthcare data security in an efficient way. Blockchain is an ordered list of records linked together through a chain of blocks in a distributed ledger. It is a decentralized and tamper-proof database system. It can be used to store the medical records of patients and play a vital role in healthcare to maintain and share medical data securely. At present, many scholars are focusing on the privacy and security in electronic health record (EHR) sharing based on blockchain technology. But still, the security of health data plays a significant challenge. A Cognitive Approach blockchain-based cryptographic curve hash signature (BC-CCHS) technique is proposed to secure patients' medical records and share their personal health data safely and conveniently. The proposed approach is carried out in the Hyperledger framework. Here, several phases like registration, authentication, uploading, and requesting are involved in enhancing the security mechanism. The proposed methodology is experimentally tested and validated with the existing techniques regarding encryption time, decryption time, throughput, delay, and overall processing time.
Colored 3D Path Extraction Based on Depth-RGB Sensor for Welding Robot Trajectory Generation

The necessity for intelligent welding robots that meet the demand in real industrial production, according to the objectives of Industry 4.0, has been supported owing to the rapid development of computer vision and the use of new technologies. To improve the efficiency in weld location for industrial robots, this work focuses on trajectory extraction based on color features identification on three-dimensional surfaces acquired with a depth-RGB sensor. The system is planned to be used with a low-cost Intel RealSense D435 sensor for the reconstruction of 3D models based on stereo vision and the built-in color sensor to quickly identify the objective trajectory, since the parts to be welded are previously marked with different colors, indicating the locations of the welding trajectories to be followed. This work focuses on 3D color segmentation with which the points of the target trajectory are segmented by color thresholds in HSV color space and a spline cubic interpolation algorithm is implemented to obtain a smooth trajectory. Experimental results have shown that the RMSE error for V-type butt joint path extraction was under 1.1 mm and below 0.6 mm for a straight butt joint; in addition, the system seems to be suitable for welding beads of various shapes.
Optimized relinearization algorithm of the multikey homomorphic encryption scheme

Multikey homomorphic encryption (MKHE) supports arbitrary homomorphic evaluation on the ciphertext of different users and thus can be applied to scenarios involving multiusers (e.g., cloud computing and artificial intelligence) to protect user privacy. CDKS19 is the current most efficient MKHE scheme, and its relinearization process consumes most of the time of homomorphic evaluation. In this study, an optimized relinearization algorithm of CDKS19 is proposed. This algorithm reorganizes the evaluation key during the key generation process, decreases the complexity of relinearization, and reduces the error growth rate during homomorphic evaluation. First, we reduce the scale of the evaluation key by increasing its modulus instead of using a gadget vector to decompose the user's public key and extend the ciphertext of homomorphic multiplication. Second, we use rescaling technology to optimize the relinearization algorithm; thus, the error bound of the ciphertext is reduced, and the homomorphic operation efficiency is improved. Lastly, the average-case error estimation on the variances of polynomial coefficients and the upper bound of the canonical embedding map are provided. Results show that our scheme reduces the scale of the evaluation key, the error variance, and the computational cost of the relinearization process. Our scheme can effectively perform the homomorphic multiplication of ciphertexts.
User Needs of Smart Home Services

Technological advances have improved the functionality of smart home systems and services, enabling consumers to automate some of in-home tasks, enhance their safety and security, and save energy. Despite improved functionality, user adoption of many available products and services remains low. This phenomenon suggests that smart home technologies may not be meeting the needs of potential users. Furthermore, continued technological advancement combined with changes in people’s lifestyle and attitudes towards in-home technologies require updated investigations to gain a clear view of current user needs. This study presents findings from a set of semi-structured interviews and card-sorting activities with eleven participants from different socio-demographic backgrounds, with a focus on describing ten challenges commonly experienced in the home environment. Possible technological solutions are discussed from the existing unmet needs within the scope of smart and connected homes.
Refined Color Texture Classification Using CNN and Local Binary Pattern

Representation and classification of color texture generate considerable interest within the field of computer vision. Texture classification is a difficult task that assigns unlabeled images or textures to the correct labeled class. Some key factors such as scaling and viewpoint variations and illumination changes make this task challenging. In this paper, we present a new feature extraction technique for color texture classification and recognition. The presented approach aggregates the features extracted from local binary patterns (LBP) and convolution neural network (CNN) to provide discriminatory information, leading to better texture classification results. Almost all of the CNN model cases classify images based on global features that describe the image as a whole to generalize the entire object. LBP classifies images based on local features that describe the image’s key points (image patches). Our analysis shows that using LBP improves the classification task when compared to using CNN only. We test the proposed approach experimentally over three challenging color image datasets (ALOT, CBT, and Outex). The results demonstrated that our approach improved up to 25% in the classification accuracy over the traditional CNN models. We identify optimal combinations for each dataset and obtain high classification rates. The proposed approach is robust, stable, and discriminatory among the three datasets and has shown enhancement in classification and recognition compared to the state-of-the-art method.
Dynamic and Scalable Enforcement of Access Control Policies for Big Data

The conflict between the need of protecting and sharing data is hampering the spread of big data applications. Security and privacy assurance is required to protect data owners, while data access and sharing are fundamental to implement smart big data solutions. In this context, access control systems can assume a central role in balancing data protection and data sharing. However, existing access control solutions are not general and scalable enough to address the software and technological complexity of big data ecosystems, being unable to support such a dynamic and collaborative environment. In this paper, we propose an access control system that enforces access to data in a distributed, multi-party big data environment. It is based on data annotations and secure data transformations performed at ingestion time. We show the feasibility of our approach in the smart city domain using an Apache-based big data engine.
Scabbard: a suite of efficient learning with rounding key-encapsulation mechanisms

In this paper, we introduce Scabbard, a suite of post-quantum keyencapsulation mechanisms. Our suite contains three different schemes Florete, Espada, and Sable based on the hardness of module- or ring-learning with rounding problem. In this work, we first show how the latest advancements on lattice-based cryptographycan be utilized to create new better schemes and even improve the state-of-the-art on post-quantum cryptography. We put particular focus on designing schemes that can optimally exploit the parallelism offered by certain hardware platforms and are also suitable for resource constrained devices. We show that this can be achieved without compromising the security of the schemes or penalizing their performance on other platforms.To substantiate our claims, we provide optimized implementations of our three new schemes on a wide range of platforms including general-purpose Intel processors using both portable C and vectorized instructions, embedded platforms such as Cortex-M4 microcontrollers, and hardware platforms such as FPGAs. We show that on each platform, our schemes can outperform the state-of-the-art in speed, memory footprint, or area requirements.
Application Value of Multimedia Artificial Intelligence Technology in English Teaching Practice

Since the 1980s, with the continuous deepening of penetration of reform and liberalization, there has been scientific and technological development. In such an environment, the traditional teaching model of colleges and universities posed new challenges and put forward higher requirements for English teaching practice. To enhance the performance of English instruction, this article applies a multiple media technique to start English education practice; firstly, through the study of the current English teaching mode, it analyzes the specific needs of teachers and students and designs the functions to meet their specific needs. Secondly, combined with multimedia technology and network technology, a multimedia English practice teaching platform was designed and implemented. In this way, the overall educational efficiency has been increased by nearly 30%, and the acceptance of the model by students has been increased by 35%. Finally, this paper constructs a small multimedia piano practice teaching platform for testing and proves the practicability and usefulness of multimedia synthesis technology in English education practice through comparison with traditional English teaching effects. Experiments show that the adoption of multimedia artificial smart technology in English education has a remarkable impact on enhancing the result of English teaching, can stimulate students’ learning enthusiasm, and realize the reform of English teaching.
Usability of the login authentication process: passphrases and passwords

Purpose The average employee spends a total of 18.6 h every two months on password-related activities, including password retries and resets. The problem is caused by the user forgetting or mistyping the password (usually because of character switching). The source of this issue is that while a password containing combinations of lowercase characters, uppercase characters, digits and special characters (LUDS) offers a reasonable level of security, it is complex to type and/or memorise, which prolongs the user authentication process. This results in much time being spent for no benefit (as perceived by users), as the user authentication process is merely a prerequisite for whatever a user intends to accomplish. This study aims to address this issue, passphrases that exclude the LUDS guidelines are proposed. Design/methodology/approach To discover constructs that create security and to investigate usability concerns relating to the memory and typing issues concerning passphrases, this study was guided by three theories as follows: Shannon’s entropy theory was used to assess security, chunking theory to analyse memory issues and the keystroke level model to assess typing issues. These three constructs were then evaluated against passwords and passphrases to determine whether passphrases better address the security and usability issues related to text-based user authentication. A content analysis was performed to identify common password compositions currently used. A login assessment experiment was used to collect data on user authentication and user – system interaction with passwords and passphrases in line with the constructs that have an impact on user authentication issues related to security, memory and typing. User–system interaction data was collected from a purposeful sample size of 112 participants, logging in at least once a day for 10 days. An expert review, which comprised usability and security experts with specific years of industry and/or academic experience, was also used to validate results and conclusions. All the experts were given questions and content to ensure sufficient context was provided and relevant feedback was obtained. A pilot study involving 10 participants (experts in security and/or usability) was performed on the login assessment website and the content was given to the experts beforehand. Both the website and the expert review content was refined after feedback was received from the pilot study. Findings It was concluded that, overall, passphrases better support the user during the user authentication process in terms of security, memory issues and typing issues. Originality/value This research aims at promoting the use of a specific type of passphrase instead of complex passwords. Three core aspects need to be assessed in conjunction with each other (security, memorisation and typing) to determine whether user-friendly passphrases can support user authentication better than passwords.
Performance Study of N-grams in the Analysis of Sentiments

In this work, a study investigation was carried out using n-grams to classify sentiments with different machine learning and deep learning methods. We used this approach, which combines existing techniques, with the problem of predicting sequence tags to understand the advantages and problems confronted with using unigrams, bigrams and trigrams to analyse economic texts. Our study aims to fill the gap by evaluating the performance of these n-grams features on different texts in the economic domain using nine sentiment analysis techniques and found more insights. We show that by comparing the performance of these features on different datasets and using multiple learning techniques, we extracted useful intelligence. The evaluation involves assessing the precision, recall, f1-score and accuracy of the function output of the several machine learning algorithms proposed. The methods were tested using Amazon, IMDB, Reuters, and Yelp economic review datasets and our comprehensive experiment shows the effectiveness of n-grams in the analysis of sentiments.
Artificial Intelligence-Based Load Balancing in Cloud Computing Environment: A Study

The discussion is based in the vicinity of load balance technique by using the artificial intelligence for cloud computing system. Cloud load balancing is a series of actions for distributing workloads to underutilized VMs for computing and sharing the resources in a more effective way for a cloud computing environment. The research is still finding for a more robust technique to distribute the workloads among the servers in this environment. The acceptable way of artificial neural network (ANN) model along with back propagation technique has been studied for an efficient proposed system. Objective of this article is for evaluation of load balancing algorithms in view of the proficiency of each virtual machine or VM, each of the requested task, and its interdependency on multiple jobs.
Flyables: Haptic Input Devices for Virtual Reality using Quadcopters

Virtual Reality (VR) has made its way into everyday life. While VR delivers an ever-increasing level of immersion, controls and their haptics are still limited. Current VR headsets come with dedicated controllers that are used to control every virtual interface element. However, the controller input mostly differs from the virtual interface. This reduces immersion. To provide a more realistic input, we present Flyables, a toolkit that provides matching haptics for virtual user interface elements using quadcopters. We took five common virtual UI elements and built their physical counterparts. We attached them to quadcopters to deliver on-demand haptic feedback. In a user study, we compared Flyables to controller-based VR input. While controllers still outperform Flyables in terms of precision and task completion time, we found that Flyables present a more natural and playful way to interact with VR environments. Based on the results from the study, we outline research challenges that could improve interaction with Flyables in the future.
Research on Classroom Online Teaching Model of “Learning” Wisdom Music on Wireless Network under the Background of Artificial Intelligence

This article uses a multimodal smart music online teaching method combined with artificial intelligence to address the problem of smart music online teaching and to compensate for the shortcomings of the single modal classification method that only uses audio features for smart music online teaching. The selection of music intelligence models and classification models, as well as the analysis and processing of music characteristics, is the subjects of this article. It mainly studies how to use lyrics and how to combine audio and lyrics to intelligently classify music and teach multimodal and monomodal smart music online. In the online teaching of smart music based on lyrics, on the basis of the traditional wireless network node feature selection method, three parameters of frequency, concentration, and dispersion are introduced to adjust the statistical value of wireless network nodes, and an improved wireless network is proposed. After feature selection, the TFIDF method is used to calculate the weights, and then artificial intelligence is used to perform secondary dimensionality reduction on the lyrics. Experimental data shows that in the process of intelligently classifying lyrics, the accuracy of the traditional wireless network node feature selection method is 58.20%, and the accuracy of the improved wireless network node feature selection method is 67.21%, combined with artificial intelligence and improved wireless, the accuracy of the network node feature selection method is 69.68%. It can be seen that the third method has higher accuracy and lower dimensionality. In the online teaching of multimodal smart music based on audio and lyrics, this article improves the traditional fusion method for the problem of multimodal fusion and compares various fusion methods through experiments. The experimental results show that the improved classification effect of the fusion method is the best, reaching 84.43%, which verifies the feasibility and effectiveness of the method.
Line structured light 3D measurement technology for pipeline microscratches based on telecentric lens

We propose a line structured light 3D measurement technology for pipeline microscratches based on a telecentric lens that realizes the 3D measurement of microscratches on the pipeline surface. A method for calibrating the light plane normal vector under the telecentric camera model is proposed. This method only needs to consider the orientation information in the light plane calibration without involving position information, which avoids the difficulty of calibrating the light plane due to the insensitivity of the telecentric camera to the depth changes of the object along the optical axis. The self-adaptive convolution-mass method is used to extract the centerline points of the light stripe with speckles. Finally, the 3D coordinates of the intersection of the light plane and the measured surface are reconstructed. A line structured light micromeasurement platform based on a telecentric lens was built. Experiments show that the maximum standard deviation of the measurement is 2.0 μm, and the maximum error of the average value of repeated measurements is 0.3 μm, indicating that the measurement platform has high accuracy that meets the requirements for the measurement of the microscratch depth of pipelines in industrial applications.