Reviewerly/exHarmony huggingface dataset:
Optimal concurrency on FPGA for lightweight medical image encryption

Digitized forms of images do widely used for medical diagnostics. To maintain the privacy of an individual in e-health care applications, securing the medical image becomes essential. Hence exclusive encryption algorithms have been developed to protect the confidentiality of medical images. As an alternative to software implementations, the realization of image encryption architectures on hardware platforms such as FPGA offers significant benefit with its reconfigurable feature. This paper presents a lightweight image encryption scheme for medical image security feasible to realize as concurrent architectural blocks on reconfigurable hardware like FPGA to achieve higher throughput. In the proposed encryption scheme, Lorentz attractor’s chaotic keys perform the diffusion process. Simultaneously, the pseudo-random memory addresses obtained from a Linear Feedback Shift Register (LFSR) circuit accomplishes the confusion process. The proposed algorithm implemented on Intel Cyclone IV FPGA (EP4CE115F29C7) analyzed the optimal number of concurrent blocks to achieve a tradeoff among throughput and resource utilization. Security analyses such as information entropy, histogram, correlation, and PSNR confirms the algorithm’s encryption quality. The strength of diffusion keys was ensured by randomness verification through the standard test suite from the National Institute of Standards and Technology (NIST). The proposed scheme has a larger keyspace of 2384 that guarantees good confusion through near-zero correlation, and successful diffusion with a PSNR of &lt;5 dB towards the statistical attacks. Based on the hardware analysis, the optimal number of concurrent architectural blocks (2 N) on the chosen FPGA to achieve higher throughput (639.37 Mbps), low power dissipation (138.85 mW), minimal resource utilization (1268 Logic Elements) and better encryption quality for the proposed algorithm is recommended as 4 (with N = 2).
An Oracle Bone Inscription Detector Based on Multi-Scale Gaussian Kernels

The detection of Oracle Bone Inscriptions (OBIs) is one of the most fundamental tasks in the study of Oracle Bone, which aims to locate the positions of OBIs on rubbing images.The existing methods are based on the scheme of anchor boxes, involving complex network design and a great number of anchor boxes.In order to overcome the problem, this paper proposes a simpler but more effective OBIs detector by using an anchor-free scheme, where shapeadaptive Gaussian kernels are employed to represent the spatial regions of different OBIs.More specifically, to address the problem of misdetection caused by regional overlapping between some tightly distributed OBIs, the character regions are simultaneously represented by multiscale Gaussian kernels to obtain regions with sharp edges.Besides, based on the kernel predictions of different scales, a novel post-processing pipeline is used to obtain accurate predictions of bounding boxes.Experiments show that our OBIs detector has achieved significant results on the OBIs dataset, which greatly outperforms several mainstream object detectors in both speed and efficiency.
Towards an Evolution of E-Learning Recommendation Systems: From 2000 to Nowadays

In e-learning, recommendation systems have proven to be highly efficient for improving learners' performance and knowledge. They can manage the different pedagogical resources and simplify the workload for the instructor and learners as well. Throughout the years, recommendation systems in e-learning have wit-nessed a major evolution since the 2000s. Several aspects have been developed, including techniques involved, test data (...). In this respect, this paper analyses the evolution of recommendation systems in e-learning since 2000 with a focus on the evolution sides. It furthermore addresses areas not fully addressed to date. A set of recommendation systems is identified and then analysed in order to define techniques used, as well as algorithms deployed.
Turkish sign language recognition based on multistream data fusion

Sign languages are nonverbal, visual languages that hearing- or speech-impaired people use for communication.Aside from hands, other communication channels such as body posture and facial expressions are also valuable insign languages. As a result of the fact that the gestures in sign languages vary across countries, the significance ofcommunication channels in each sign language also differs. In this study, representing the communication channels usedin Turkish sign language, a total of 8 different data streams-4 RGB, 3 pose, 1 optical flow-were analyzed. Inception3D was used for RGB and optical flow; and LSTM-RNN was used for pose data streams. Experiments were conductedby merging the data streams in different combinations, and then a sign language recognition system that merged themost suitable streams with the help of a multistream late fusion mechanism was proposed. Considering each data streamindividually, the accuracies of the RGB streams were between 28% and 79%; pose stream accuracies were between9% and 50%; and optical flow data accuracy was 78.5%. When these data streams were used in combination, the signlanguage recognition performance was higher in comparison to any of the data streams alone. The proposed sign languagerecognition system uses a multistream data fusion mechanism and gives an accuracy of 89.3% on BosphorusSign Generaldataset. The multistream data fusion mechanisms have a great potential for improving sign language recognition results.
Non-Linear Consumption of Videos Using a Sequence of Personalized Multimodal Fragments

As videos progressively take a central role in conveying information on the Web, current linear-consumption methods that involve spending time proportional to the duration of the video need to be revisited. In this work, we present NoVoExp, a method that enables a Non-linear Video Consumption Experience by generating a sequence of multimodal fragments that represents the content in different segments of the videos in a succinct fashion. These fragments aid understanding the content of the video without watching it in entirely and serve as pointers to different segments of the video, enabling a new mechanism to consume videos. We design several baselines by building on top of video captioning and video summarization works to understand the relative advantages and disadvantages of NoVoExp, and compare the performances across video durations (short, medium, long) and categories (entertainment, lectures, tutorials). We observe that the sequences of multimodal fragments generated by NoVoExp have higher relevance to the video and are more diverse yet coherent. Our extensive evaluation using automated metrics and human studies show that our fragments are not only good at representing the contents of the video, but also align well with targeted viewer preferences.
Mind the App - Considerations on the Ethical Risks of COVID-19 Apps

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D-MOSG: Discrete multi-objective shuffled gray wolf optimizer for multi-level image thresholding

Segmentation is an important step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is a very popular approach. To apply the thresholding approach, many methods such as Otsu, Kapur, Renyi etc. have been proposed in order to produce the thresholds that will segment the image optimally. These suggested methods usually have their own characteristics and are successful for particular images. It can be thought that better results may be obtained by using objective functions with different characteristics together. In this study, the thresholding which is originally applied as a single-objective problem has been considered as a multi-objective problem by using the Otsu and Kapur methods. Therefore, the discrete multi-objective shuffled gray wolf optimizer (D-MOSG) algorithm has been proposed for multi-level thresholding segmentation. Experiments have clearly shown that the D-MOSG algorithm has achieved superior results than the compared algorithms.
Relation between dark-field images derived using X-Ray Talbot–Lau interferometer and carbon fiber distribution in CFRP

This study proposes a model that relates the carbon fiber arrangement and orientation in CFRP to the dark-field images captured by X-ray Talbot-Lau interferometer (TLI). X-ray TLI visualizes X-ray phase contrast, which is more sensitive to light elements such as carbon, than conventional X-ray absorption contrast. By using TLI, novel non-destructive inspection (NDI) technique for CFRP reinforced by distributed discontinuous carbon fibers, which precisely evaluates local orientation and distribution of fibers in large area, can be developed. The model proposed in this study forms the basis of such NDI technique. In this paper, the model is proposed first, and material parameters needed for the model are then identified from the TLI dark-field images of the multi-fiber composite (MFC) specimens. The proposed model is validated by comparing the dark-field images simulated by the model to those experimentally measured by TLI.
Protect privacy of deep classification networks by exploiting their generative power

Abstract Research showed that deep learning models are vulnerable to membership inference attacks, which aim to determine if an example is in the training set of the model. We propose a new framework to defend against this sort of attack. Our key insight is that if we retrain the original classifier with a new dataset that is independent of the original training set while their elements are sampled from the same distribution, the retrained classifier will leak no information that cannot be inferred from the distribution about the original training set. Our framework consists of three phases. First, we transferred the original classifier to a Joint Energy-based Model (JEM) to exploit the model’s implicit generative power. Then, we sampled from the JEM to create a new dataset. Finally, we used the new dataset to retrain or fine-tune the original classifier. We empirically studied different transfer learning schemes for the JEM and fine-tuning/retraining strategies for the classifier against shadow-model attacks. Our evaluation shows that our framework can suppress the attacker’s membership advantage to a negligible level while keeping the classifier’s accuracy acceptable. We compared it with other state-of-the-art defenses considering adaptive attackers and showed our defense is effective even under the worst-case scenario. Besides, we also found that combining other defenses with our framework often achieves better robustness. Our code will be made available at https://github.com/ChenJiyu/meminf-defense.git .
Comparing Approaches to Dravidian Language Identification

This paper describes the submissions by team HWR to the Dravidian Language Identification (DLI) shared task organized at VarDial 2021 workshop. The DLI training set includes 16,674 YouTube comments written in Roman script containing code-mixed text with English and one of the three South Dravidian languages: Kannada, Malayalam, and Tamil. We submitted results generated using two models, a Naive Bayes classifier with adaptive language models, which has shown to obtain competitive performance in many language and dialect identification tasks, and a transformer-based model which is widely regarded as the state-of-the-art in a number of NLP tasks. Our first submission was sent in the closed submission track using only the training set provided by the shared task organisers, whereas the second submission is considered to be open as it used a pretrained model trained with external data. Our team attained shared second position in the shared task with the submission based on Naive Bayes. Our results reinforce the idea that deep learning methods are not as competitive in language identification related tasks as they are in many other text classification tasks.
Organizational and Methodological Guidelines for Training Education Managers to Implement the Strategy of Corporate Social Responsibility

The article highlights the theoretical bases for professional training of future education managers to effectively implement the strategy of corporate social responsibility in educational institutions. There outlined prospects for introducing a cluster of elective courses for masters at faculties of education oriented towards acquiring knowledge on design tools and successfully applying strategies of corporate social responsibility in institutions of pre-school, primary, out-of-school, and alternative education. The authors propose an algorithm for a phased construction of a practice-oriented process of training education masters, which is developed based on their previous theoretical works. Its structure includes a set of individual research tasks aimed to create promising corporate social responsibility projects in institutions of pre-school, primary, special, out-of-school, and alternative education of various forms of ownership
Research on the Model of Music Sight-Singing Guidance System Based on Artificial Intelligence

Intelligent Guided Learning System (ITS) is a computer system that uses computers to imitate the experience and methods of teaching experts to assist in teaching; ITS provides learners with personalized learning resources and adaptive teaching methods, thus reducing students’ dependence on teachers and realizing independent learning. After years of research and development, and with the help of artificial intelligence technology, ITS has basically formed a stable structure and unified implementation specifications and has given birth to many excellent products in disciplines such as basic computer, language, medicine, and mathematics. Based on the above background, this paper takes the system structure of universal ITS as the basis, combines the characteristics of music sight-singing subject in teaching contents and teaching methods, researches the intelligent guidance system model of music sight-singing, and completes the design of the overall system architecture. The specific strategies and implementation methods of the teaching methods, resource recommendation, and ability assessment components of the teaching model are studied. The research includes the definition of the difficulty characteristics of the score, the design of the score recommendation algorithm, the design of the sight-singing scoring algorithm, and the experimental analysis of each algorithm. It is proposed that the difficulty feature-based score recommendation algorithm is the core component of resource recommendation in the teacher model, and the sight-singing scoring algorithm is the basis for ability assessment and learner model update.
Single-Shot High Dynamic Range Imaging via Multiscale Convolutional Neural Network

We propose a single-shot high dynamic range (HDR) imaging algorithm with row-wise varying exposures in a single raw image based on a deep convolutional neural network (CNN). We first convert a raw Bayer input image into a radiance map by calibrating rows with different exposures, and then we design a new CNN model to restore missing information at the under- and over-exposed pixels and reconstruct color information from the raw radiance map. The proposed CNN model consists of three branch networks to obtain multiscale feature maps for an image. To effectively estimate the high-quality HDR images, we develop a robust loss function that considers the human visual system (HVS) model, color perception model, and multiscale contrast. Experimental results on both synthetic and captured real images demonstrate that the proposed algorithm can achieve synthesis results of significantly higher quality than conventional algorithms in terms of structure, color, and visual artifacts.
Deep Learning Based Real Age and Gender Estimation from Unconstrained Face Image towards Smart Store Customer Relationship Management

The COVID-19 pandemic markedly changed the human shopping nature, necessitating a contactless shopping system to curb the spread of the contagious disease efficiently. Consequently, a customer opts for a store where it is possible to avoid physical contacts and shorten the shopping process with extended services such as personalized product recommendations. Automatic age and gender estimation of a customer in a smart store strongly benefit the consumer by providing personalized advertisement and product recommendation; similarly, it aids the smart store proprietor to promote sales and develop an inventory perpetually for the future retail. In our paper, we propose a deep learning-founded enterprise solution for smart store customer relationship management (CRM), which allows us to predict the age and gender from a customer’s face image taken in an unconstrained environment to facilitate the smart store’s extended services, as it is expected for a modern venture. For the age estimation problem, we mitigate the data sparsity problem of the large public IMDB-WIKI dataset by image enhancement from another dataset and perform data augmentation as required. We handle our classification tasks utilizing an empirically leading pre-trained convolutional neural network (CNN), the VGG-16 network, and incorporate batch normalization. Especially, the age estimation task is posed as a deep classification problem followed by a multinomial logistic regression first-moment refinement. We validate our system for two standard benchmarks, one for each task, and demonstrate state-of-the-art performance for both real age and gender estimation.
A Two-Path Network for Cell Counting

The density map is an effective manner to infer how many cells a cell image contains, and it carries valuable information. However, a fine-grained density map requires rich spatial information to recover the distribution details. In this paper, we propose a cell counting network with two paths, i.e. detail path and context path, which respectively extract spatial details and semantics. The detail path encodes the spatial information with small convolutional kernels. The context path rapidly enlarges the receptive field and extracts multi-scale features with an atrous spatial pyramid pooling. At the end of the two paths, we design a feature fusion module to merge the high-level feature maps from the two paths. To decrease the parameters and computation, we directly upsample the fused feature maps to the input size and decode them to obtain the density map. The proposed model is evaluated on three cell datasets and a popular crowd dataset Shanghaitech Part-B. The experiments illustrated that the proposed model not only achieves superior performance on cell datasets but also generalizes well on the crowd dataset.
A Systematic Literature Review of Software Defect Prediction Using Deep Learning

The approaches associated with software defect prediction are used to reduce the time and cost of discovering software defects in source code and to improve the software quality in the organizations.There are two approaches to reveal the software defects in the source code.The first approach is concentrated on the traditional features such as lines of code, code complexity, etc.However, these features fail to extract the semantics of the source code.The second one is concentrated on revealing these semantics.This paper presents a Systematic Literature Review (SLR) of software defect prediction using deep learning models.This SLR is focused on identifying the studies that use the semantics of the source code for improving defect prediction.This SLR aims to analyze the used datasets, models and frameworks.Also, identifying the evaluation metrics to ensure their applicability in software defect prediction.IEEE Xplore, Scopus and Web of Science digital libraries were used to select the suitable primary studies.Forty (40) primary studies were selected that published by 15 December 2020 for analysis based on the quality criteria.The project levels that applied in the studies were: Within-project 52.5%, cross-project 17.5% and both within-project and cross-project 30%.The datasets used were: Promise dataset 68.18% and other datasets 31.82%.The most used deep learning model in the primary studies was: Convolutional Neural Network (CNN) by 35%.The most used evaluation metrics were: F-measure and Area Under the Curve (AUC).Software defect prediction using deep learning models is still a valuable topic and requires much research studies to enhance the performance of the defect prediction.
Managing the Product-Counterfeiting Problem with a Blockchain-Supported E-Commerce Platform

As a disruptive tool, blockchain technology can eradicate the product-counterfeiting problem in supply chains. However, a blockchain-supported platform charges an operating fee to legitimate manufacturers and retailers for product traceability and authentication. In this study, we employ enterprise profit-driven analytical models using Stackelberg equilibrium theory and highlight the values of blockchain-supported e-commerce platforms in addressing the product-counterfeiting problem. To measure the actual benefits of blockchain technology, we compare the profits of all agents in two different supply chains, traditional and blockchain-supported. Results show that the application of blockchain technology is not always beneficial to manufacturers, retailers, and customers. However, when the manufacturing cost of a legitimate manufacturer is sufficiently high, the manufacturer generates more profits using blockchain technology. Further, for a price-sensitive market, a retailer tends to trade in a blockchain-supported e-commerce platform if the retailer’s qualification in the platform is lower than that in a traditional supply chain, and the manufacturing cost of the counterfeit manufacturer in the platform is higher than that in a traditional supply chain.
Evaluation of the effect of music education on improving students’ mental health based on intelligent fuzzy system

Traditional control methods and modern accurate mathematical model control methods do not perform well in the evaluation of students’ mental health. In order to improve the evaluation effect of students’ mental health, this paper takes the intelligent fuzzy system as the control center and proposes an evaluation system to evaluate the effect of music education in promoting students’ mental health based on fuzzy neural network. Moreover, according to the working characteristics of the music education system, this paper interprets the design requirements of its control system in detail, and has an in-depth understanding of the fuzzy principle, neural network principle and fuzzy god network principle. Secondly, this paper completes the design of the actual orthosis control algorithm applied to the fuzzy neural network control system and the optimization of the fuzzy neural network algorithm. Finally, this paper realizes the intelligent processing of the non-linear pressure signal output by the corresponding strain, and uses music education to evaluate the students’ mental health and manage the rehabilitation effect. From the experimental research results, it can be seen that the system constructed in this paper has a certain effect.