Reviewerly/exHarmony huggingface dataset:
Recapture Detection to Fight Deep Identity Theft

The progress made in deep learning has allowed the deployment of more powerful biometric authentication systems instead of traditional ones based on passwords or PIN codes. Facial recognition is widely used on smartphones to grant user access. However, advances in deep learning also improve methods for doctoring images and videos. A fraudulent user can use these methods to steal the identity of another person. It is very easy for impostors to present to the smartphone an image or video of the victim's face displayed on another screen. In this paper, we describe the security risks when a facial recognition system is attacked by presenting an image, a video or an interactive deepfake displayed on a screen. We also present a deep learning-based method to detect this kind of attack.
Image steganography with using QR code

This article deals with digital representation of images as well as presents design and implementation of message security of data represented in images by using the Quick Response (QR) code encryption technique. In the proposed solution, the QR code is written in the least significant bit (LSB). In our presented case the simulations are performed in MATLAB development tool. The results are presented by multiple stego images using multiple cover images and secret messages that are embedded into them. The motivation is ever-increasing demands on the security of hidden data represented in images. The proposed solution is evaluated by parametric values as Peak signal-to-noise ratio (PSNR), mean squared error (MSE) and subsequently compared with well-known proposed solutions. The aim of this article is to design a solution that will come with high level of security and at the same time with lowest possible level of perceptibility. The system can be used to secure data in the medical field, respectively in the protection of copyrighted data.
Continual Learning Based Ship Detection in Multiple Weathers

Ship detection in complex weathers is an important application of object detection approaches in real life. In recent years, ship detection approaches for single weather have gradually matured. However, there are few ship detection approaches for multiple weathers, and traditional ship detection approaches are only applicable to a specific weather and have poor generalization to other weathers. In this paper, we propose a continual learning based approach for ship detection to solve the task of ship detection in various weather conditions, while enabling the model to be capable of expanding to new weather conditions. We also present a ship detection dataset under three types of weather, including rain, fog, and snow, using weather effect simulation methods.
C++ Source Code Rejuvenation for an Improved Exception Specification

Exception handling is a beneficial language construct in modern programming languages. However, C++'s type system does not really conform to these elements. As a con-sequence, developers have to pay attention to avoid mistakes because of the missing compiler support. Moreover, C++11provides an approach in which exceptions appear in the function's signature in an inverse manner compared to the earlier standards. Static analysis is an approach in which we reason about a program based on the source with no execution of the analyzed code. It can be used for many purposes, for instance, finding bugs, refactoring the code or measure code complexity. In this paper, we analyze how the older style exception speci-fications can be rejuvenated for the modern idioms. Explicitly marking the functions as having a guaranteed exception-free execution is the primary way since C++11. We develop a static analyzer tool for providing hints for these specifications. We evaluate our method with the analysis of open sources projects.
On the Friendliness of Physical vs. Virtual Manipulators in Music Software - from a Perspective of Software Developers

Modern recordings are based on computer recording and computer editing. Therefore, in fact, a real, complete tool for a modern musician is, in most cases, not only an instrument but an instrument supported by powerful software tools, in which control values may be entered using virtual controllers displayed on the screen or with physical manipulators like switches, sliders or knobs, similar to those used in classical devices. The aim of this research was to find answers to the questions: “Are physical manipulators, in your opinion, better than virtual ones? Why? Does having more manipulators always mean better? Is there any optimal number of them? If so, what?” and a second one, “What methods do you use to limit the number of manipulators and shorten the program handlers?” The study was carried out using the in-depth interview method in a hybrid way, as a part of a broader interview with music software developers about HCI factors and the perceived differences between creating emulators of existing music devices and creating completely new music software, including plugins. The selection of respondents included, on the one hand, one of the most recognizable Polish plug-in companies on the world market - PSP audioware, where interviews were conducted with all owners and members of the group, which, due to their specialization, allowed for a multidisciplinary approach; and on the other hand, internationally known creators working for the D16group, Newfangled Audio and Eventide.
Self-Supervised Superpixel Correspondences For Video Object Segmentation

Prior self-supervised video object segmentation models directly find pixel correspondences between pairs of frames. Instead, we propose a novel approach of employing superpixel features for learning visual correspondences between frames of a video sequence. With an attention mechanism, we train the model to reconstruct the next frame using superpixel features between adjacent frames of videos trained in a self-supervised manner. As superpixels are able to group pixels based on color and proximity, they reduce repetitive and noisy pixels that may confuse the model during tracking. In addition, the structural information provided by the superpixel features can help improve the segmentation of objects. We benchmark our method (S3CNet) against existing self-supervised pixel correspondence frameworks such as [12, 22, 24] and show that our self-supervised superpixel correspondence method performs better and is a viable alternative for visual tracking on the commonly-used DAVIS 2017 video segmentation benchmark.
Novel Approaches of Abdominal Computed Tomography Image Super Resolution based on Deep Residual Learning

Few studies present how to implement super resolution (SR) on abdominal Computed Tomography (CT) images. However, in the field of medical imaging, the development of an advanced SR system will potentially improve the clinical diagnosis and prognosis of various diseases, enabling the medical image generation machine to communicate its information better. The efficient sub-pixel convolutional network (ESPCN) is considered as the baseline network. Successfully implementing a residual learning strategy and proposing an approach for incorporating categorical conditions as the prior in the baseline, we proposed three novel deep residual learning based networks to restore more realistic texture for pancreatic tumour regions in abdominal CT images. The quality of super-resolved images is evaluated quantitatively by using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Our proposed networks show better SR performance compared with other existing deep learning based methods on the abdominal CT dataset.
An Image Encryption Algorithm Suitable for Medical Images

Security of data becomes more and more important each day. The importance is even more pronounced if the data contain sensitive information, such as those shown in medical images. There are already several solutions that can be used in order to protect medical images during their storage or transmission. The most popular of those is probably encryption by conventional encryption algorithms such as Advanced Encryption Standard. This proposal tries to investigate possibility of using dedicated image encryption algorithms that have applications in fields of steganography and biometric systems for encryption of medical images. The dedicated image encryption algorithms can be easily adjusted for processing medical images, what can be verified by several experiments. Main advantage of this proposal lies in an addition of a substitution block, which should increase the security of whole algorithm. The performance of our solution will be evaluated by several numerical parameters and those will be compared with results reported in similar works.
Image-Based Fashion Recommendation with Attention to Users' Interests

Building an effective fashion recommendation system is challenging due to the high level of subjectivity and the semantic complexity of the features involved. Users’ decision depends largely on their interest and the appearance of the product. Such information is often hidden in implicit feedback from users' purchase histories and product images. Most interest-based recommendation systems like Deep Interest Network (DIN) and Deep Interest Evolution Network (DIEN) only take advantage of product attributes and context review, which are all text information. There are also some studies focusing on the use of image features for a fashion product recommendation. They try to extract features from images and recommend products based on their similarity. However, for DIEN. It works not well when there are few interactions between users and items, the model can not find users' interests effectively. On the other hand, these image-based recommendation systems tend to ignore an important factor: the user's interest. We propose a new system trying to find users' interests by introducing the visual information of the product and our image-based deep interest attention model based on DIEN. Product attributes and user preference can both be represented by introducing visual information. We can model similar attribute items in the same place and find user interests more effectively. We conducted a series of experiments on our own dataset to compare user click-through rate (CTR) AUC and Accuracy using our model with DIEN and other existing models.Our experimental results demonstrate that visual information effectively aids CTR prediction and achieved better recommendation results.
Video-based Person Re-identification Method based on Attention and Feature Fusion

Aiming at the problems of low accuracy caused by low quality frames such as occlusion and noise, and the inability to effectively utilize local features of cross-channel in video surveillance, a video-based person re-identification method based on attention and feature fusion was proposed. First, channel mutual attention is added to ResNet50 residual network to mine the local features of each channel and its adjacent channels to enhance the expression of local features. Then, temporal attention was used to assign different weights to different quality frames to reduce the weights of occluded frames and noisy frames, and fuse multiple frames according to weight distribution to improve the discrimination ability of the model. Finally, the cross-entropy loss function of label smoothing and the triple loss function of hard mining are combined for supervised training to improve the generalization performance of the model. Experiments on MARS and DukeMTMC-VideoReID datasets show that the method can improve the accuracy of video-based person re-identification.
Interpretation Study of Unknown Oracle Bones Based on Self-training Learning

The oracle bone script is the oldest writing system in China, and as the origin of Chinese characters, it is also the earliest cultural heritage in China. With the development of computer technology, deep learning has become an important technology of image classification and played a very significant role in oracle research. In this paper, oracle bone inscription recognition is proposed based on semi-supervised self-training learning, which learns the relationship between oracle bones, clementi, seal script and official script, and uses other existing font features to learn oracle bone characters. In the experiment, the detection accuracy of oracle script fonts has reached 87.9%, and the probability of character recognition is 28.2%, which proves that there is a certain relationship in the development of oracle bone inscriptions.
A chatbot for Academic advising

Academic advising is a crucial and challenging task at the beginning of each term. It remains a manual process in Saudi universities, that needs to be automated. Our solution consists of a chatbot as a digital academic advisor helping students make logical decisions based on analyzing data like what course must be essential or have more required courses, and answer the common questions. This chatbot is knowledge-based and is always available, students can use it to plan the semester courses, as well. It collects the data and develops them to build better decisions.
Missing Children Identification using Face Recognition

In India a limitless wide variety of kids are pronounced lacking each year. Among the lacking baby instances a huge percent of kids continue to be untraced. This approach gives a unique technique for figuring out the pronounced lacking baby from the images of kids available, with the assist of face popularity. The public is able to upload the suspicious baby image into a web portal with landmarks and location. From the repository the registered image can be compared with the lacking baby. Classification of the enter baby picture graph is finished and image with pleasant in shape may be decided on from the database of lacking kids. For this, a deep getting to know version is skilled to effectively discover the lacking baby from the lacking baby picture graph database provided, the usage of the facial picture graph uploaded with the aid of using the public. The Convolutional Neural Network (CNN) is an enormously powerful deep getting to know tech for picture graph primarily based totally packages is followed for recognition of face. The descriptors of face are extracted from the snapshots the usage of a pre-skilled CNN version. Compared with regular deep getting to know packages, our set of rules makes use of convolution community simplest as an excessive stage function extractor and the kid popularity is achieved with the aid of using the KNN classifier.
Protecting the Cloud-Based Healthcare Data Repository: Overview of Hashing Algorithm

The benefits of digital transformation has largely been felt in almost every part of professions with inclusion of healthcare industry. Healthcare industry is known to be reached with pool of data which could be historical in nature, thus requires to be kept in a secured and reliable locations. Cloud platform has been used to make data and information available for the users in distributed locations, while many methods and approaches have been provided to preserve the sanctity of a platform. However, less or no study has been conducted on the use of hashing algorithm, which has been proven reliable in protecting the data in an online domain. The objective of this study is to explore the capacity of hashing algorithm towards securing the healthcare data repository in the cloud. The study designs a cloud-based procedural model in form of flowchart to protect the healthcare data repository by using the concept of hash algorithm as basis. Therefore, the model was validated and represented by Pseudocode which shows the reliability of the designed procedural model. Hence, the use of hashing algorithm in protecting the healthcare data repository would assist the healthcare industries in strengthening the curation of data in the cloud system.
Gender and Age Prediction Using Deep Learning

The human species has advanced to the point where the twenty-first century marks the start of previously unimaginable achievements. By observing at a person using a camera, picture, or video, the aforementioned technologies can be utilized to establish their age and gender. Our project will walk you through the entire process, including the many approaches and algorithms that can be used, which one is the most accurate, and how everything works together. It will also highlight its importance and how it may be implemented to better our daily life. The major purpose of this project is to develop a detector gender and age that can determine a person's gender and age based on their performance Keeping track of the projected numbers andcarrying out the calculations Analyzing stored data aids in determining model accuracy.
Breast Cancer Detection by Using Radient Based Algorithm on Mammogram Images

One of the most common cancers, particularly among women, is breast cancer. Cancer that originates in the breast tissue is called breast cancer. Indications of bosom disease could remember a protuberance for the bosom. Fluid emerges from the nipple by changing shape and dimpling the skin. When cells in the breast begin to grow out of control, breast cancer develops. Through screening and precise identification of masses, microcalcifications, and structural bends, mammography is the most effective and reliable method for the early detection of breasttumors. Breast disease is the leading cause of death for women worldwide. It is evident that recognizing danger early can aid in the investigation of a woman's infection and significantly increase the likelihood of survival. To find an abnormality in mammogram images, this novel segmentation technique, which is based on Iterative algorithms like the Markov random field (MRF) model, is proposed here. This algorithm processes the label with the lowest energy for all iterations. A label and boundary MRF can have a highly compressed relation thanks to this approach.
DigiBlock: Digital Self-sovereign Identity on Distributed Ledger based on Blockchain

With the rise of the digital era, the massive shift towards everything being digital is evident. People have a plethora of digital data points to denote them in digital world. Digital personal data points, also known as Digital Identity, contains Personally Identifiable Information (PII) which is private to every individual. Managing the cycle of private, high security data from its creation till verification is a massive undertaking. As there are instances where personal data are breached, centralized servers fail to respond, attacks compromise the entire system, it becomes difficult to rely on such systems. Blockchain is a propitious technology that is immutable, decentralized, tamper-proof, highly secure and easy to use. This paper proposes to use Blockchain and leverage its advantages to establish a self-sovereign digital identity management system, collision resistant encrypted document, which ensures the integrity of document issued. It presents DigiBlock as a powerful solution to the current scenario with a robust role based permission system leveraging Distributed Ledger Technology (DLT), which provides advantage over centralized system with its exceptional sovereignty, storage-control, cost-free, security, privacy, transparency and portability features.
Applications of Deep Learning for Improved Recognition from Some High-Level Human Activities Using Sensors Data

More than half of the population of the world owns a smartphone, and many individuals are beginning to utilize smartwatches. Many real-world smartphones or smartwatch-based sensing applications are becoming available. To gain a better understanding of human behaviour, these applications recognize human activities using accelerometers and gyroscope sensors built into smartphones. In this research, we looked at the accelerometer and gyroscopes on both the smartphone and the smartwatch, as well as their combinations, to see which combination performs best for the underlying algorithms. This work demonstrates how to automatically extract discriminative features for activity recognition using Long Short Term Memory (LSTM) method, a deep learning approach. The results reported in this article show that using a smartwatch accelerometer and/or a combination of any two or four sensors can produce good results. However, we will endeavour to improve the accuracy of activity detection using raw sensor data.
Heterogeneous Cloud Systems and Criteria for Enhanced Performance

Cloud systems are managed on the basis of au-tonomous systems. We present criteria that are suitable for optimization or improvement of modern cloud systems. In every operating system, task scheduling is very important. In clouds systems, where large amount of tasks runs on numerous machines, optimized task scheduling leads to significant reduction of computing time. Cloud providers have to comply with service level agreement from technical and from the quality of service point of view. For this reason, we specify quality of service criteria and limits for service level agreement violation. Clouds are virtualized by virtual machines or containers. We show approaches for power consumption minimization. Fog computing helps to improve middleware technology between cloud computing and the IoT devices. We specify criteria for correct decomposition of parallel and distributed application. We conclude that understanding of effective work of the system, can improve design and implementation of parallel and distributed application.
Machine Intelligence Enabled Parametric Speaker: An Invention Towards Experience Sound

This work proposes to channelize the conventional longitudinal behavior of sound which diverges in every direction after its generation to a beam like behavior seen in transverse waves, which is already conceived in a device named as parametric speaker. On top of this, using technologies such as AI, self-learning, and decision-making capabilities can be endorsed in the hardware-based device, exhausting its full capabilities. The goal of the proposed work is to provide an invisible screening and privacy to the individuals in public and semi-public areas associated with verbal mode of communication. The underlying work focuses on using the unconventional behavior of sound, possessing beam like properties produced through Artificial means by interference and Superposition principle, applicable to sound which is constructed by Parametric Speaker also known as directional Speaker. The following work in this paper attempts to showcase the possibilities of using and harnessing this specific sound properties with sophisticated technologies such as ML and Deep Learning to create semi private areas in public spaces which can not only help people within different age groups (mostly elderly population) but can also help people who are handicapped (such as visually impared people who can only receive information through auditory channels), the scope of the proposed work is not only limited to impared people but, also to general population, with the objective to increase the conventional information transfer system into personalized data delivery service through voice and speech.
Modified Convolutional Neural Network for Fashion Classification

Fashion classification is a domain which finds its applications in various fields like e-commerce platforms, social media and criminal identification with clothing similarity or dissimilarity. In this paper, we have used a modified version of convolutional neural network for classification and encompassing the identification of clothing items. Within the fashion classification category, we mainly concentrate on the multi-class classification of different types of apparels. The modified convolution neural network is applied on fashion classification data which reduces over-fitting. Here we have compared the accuracy of the CNN models and have achieved train accuracy and test accuracy of around 93% and 90% respectively which are better than previous works done by other researchers.