Reviewerly/exHarmony huggingface dataset:
High Throughput Implementation of AES Algorithm Using Radiation Tolerant FPGA for Secure LST-SW Algorithm

Advanced Encryption Algorithms have become a solution for securing data in different fields. In a hardware implementation, we can achieve more flexibility and performance compared to the software implementation which is more vulnerable to outside attacks. This paper describes a secure implementation of the LST-SW algorithm which is one of the important parameters computed by the earth observation satellite. The AES algorithm is implemented using a pipelined architecture in order to increase the throughput of the algorithm. The implementation has been successfully done by radiation-hardened Virtex-4QV XQR4VSX55 FPGAs using Xilinx ISE 14.7. The hardware results of the proposed design show that this implementation can achieve a throughput of 1989.97 Mbps and take 14 % of the slice in area utilization with 7 blocks of RAM. Compared to the previous works, this implementation improves the metric of the throughput and achieves the best trade-off between the resource consumption since we have used pipelined architecture which takes more resources.
Fractal analysis segmentation of medical images: Application to breast cancer mammography image

This study discusses a new approach to the classification of mammographic medical images of the breast based on the calculation of the fractal dimension by the method of planar triangles of images segmented by Markovian modeling. The relaxation algorithm used is Iterated Conditional Modes (ICM). To do this, and based on the criterion of the energy of the neighborhood system cliques and some of the cluster cliques while relying on an energy function adapted to the irregular neighborhood models of t image. This new approach pushed us to orient this study towards the detection of pathologies to extract the tumoral part in the patient such that the fractal dimension is a useful tool in this study for the characterization and segmentation of mammographic images in the medical field. This approach has been used for the characterization of both healthy and pathological states of breast cancer. In order to apply this new approach, we used a database of mammographic medical images from the Kenitra-Morocco reproductive health reference center (CRSRKM) which contains mammographic images of the breast of normal and pathological cases.
Fall recognition system using convolutional neural network

Fall imposes a great challenge not only to our families but also to the economy of our countries. Due to fall, there is a high mortality rate of elderly and disabled people. Modern technologies are utilized to provide secure and safe living environments. Although the use of computer vision and deep learning techniques provides an interesting solution to mitigate fall, they still experience overfitting problem due to scarcity of fall data. In this proposed system, we work towards improving the fall recognition accuracy of convolutional neural network (CNN) by performing hyperparameter tuning and applying data augmentation techniques. The experiment shows that the proposed model achieves interesting results with an accuracy of 99.8%. It is clear that CNN approach provides the optimal solution for identifying falls from activities of daily living.
Analysis of Threat Intelligence Information Exchange via the STIX Standard

Threat Information exchange is a highly relevant topic in today's environment of increasing data breaches, hacks and scams. Standardized formats for exchanging such information exist, but if and how they are used by an active community is determinant for gaining information from such provided information. We provide an in depth analysis of the current state of the Structured Threat Information Expression (STIX) standard, consisting of 5 different active threat information providers. Based on an analysis of 480,867 threat information objects, we find that the STIX standard is not used to its full capabilities, and lacks usefulness due to the quality and up-to-dateness of the information. We give suggestions for future improvements of standards based threat information exchange, such as more adherence to the core standard, and fostering an active community.
A complete human verified Turkish caption dataset for MS COCO and performance evaluation with well-known image caption models trained against it

The procedure of generating natural language captions for an image is known as image captioning. Automatic image captioning is a particularly challenging task that stands at the junction of Computer Vision and Natural Language Processing. It has a variety of applications, including text-based image retrieval, assisting visually impaired users, and human-robot interaction. The majority of publications on the subject focus on the English language, which is an analytical language with characteristics differing from the agglutinative Turkish language. This work introduces the Turkish MS COCO dataset that extends the original MS COCO collection with captions in the Turkish language; experimental results surpass the current state-of-the-art for the Turkish image captioning field. Furthermore, the newly introduced database is also applicable for the study of machine translation. On the Turkish MS COCO dataset, the best performance has been achieved with the Meshed Memory Transformers with a Bleu-1 score of 0.72. The database is publicly available at https://github.com/BilgiAILAB/TurkishImageCaptioning. It is desired that the Turkish MS COCO dataset with the proposed benchmark will be an excellent resource for future studies on Turkish image captioning.
Cross line structured light scanning system based on measuring arm

The measurement system based on measuring arm and line structured light has a wide range of applications. In order to improve the efficiency of scanning, this paper uses two single-line structured light to form the crosshair structured light, and combines the measuring arm to form the scanning measurement system. Use Zhengyou Zhang calibration method and calibration board to complete all the parameter calibration of sensors and cameras and measuring arm hand-eye calibration, for complex curved surface objects, extract the Cross line structure light optical center location has the ambiguity problem, based on the two single-line structured light periodic control to solve the problem of light extraction polysemy. Experimental results show that this method can satisfy the function of crosshair structured light scanning large complex surface.
Kalman Supervised Network for Improved Model Predictions

In this paper, we propose a Neural Network to im-prove long-term state predictions without measurements based on Kalman filter observations. It is well known that the Kalman Fil-ter is an iterative algorithm composed of two phases: predict and update. The update corrects predictions based on measurements. Predictions rely exclusively on the embedded physical model. This research aims to learn the underlying dynamics of the system under observation from the estimates of a standard Kalman Filter that supervises a Neural Network. Then, the Kalman Supervised Net (KSN) can be used to improve predictions learning from Kalman filter corrections. Numerical results show the advantages of the proposed solution when predicting the state of a spring-mass-damper system without using acceleration measurements.
Exam Reporting System (Case Study: Faculty Of Engineering, Universitas Islam Riau)

The Exam Reporting System (Case Study: Faculty of Engineering, Universitas Islam Riau) is a reporting system that assists the faculty's leadership (Dean) in recapitulating test files conducted by the test supervisor during exam implementation at the Faculty of Engineering, Universitas Islam Riau. The construction of this system is based on a policy or SOP (Standard Operating Procedure) for the exam implementation defined by the leadership of the Faculty of Engineering, Universitas Islam Riau, with each case study having its SOP. The manual recapitulation method is slow and takes a long time (1–4 weeks). Therefore, an alternative in the form of a system that can speed up and shorten the recapitulation process is required. This system is meant to deliver exam reporting solutions through services. The outcomes of this service procedure (transitioning from a manual to a digital system) will be faster and easier to obtain. Not only that but the system can also be used at any time and from any location as long as you have internet connectivity, making it more efficient and versatile. The system was tested and evaluated using a questionnaire at the Faculty of Engineering, Universitas Islam Riau, and the results were analyzed using the Likert approach. The processing results have an index of 80.5 per cent, indicating that this technology is very suitable for use in the Faculty of Engineering Universitas Islam Riau.
Optimal cooperative control of mobile robots based on Pontryagin's Minimum Principle

This work presents the cooperative control with optimization for positioning of mobile robots based on Pontryagin's Minimum Principle. The problem to be solved is the minimum energy related to the control actions of the robots. The objective function is subject to the equations of states of the triangular formation of the 3 mobile robots and is restricted to boundary conditions that include the desired form parameters. The mathematical model of the system is based on the kinematic transformation of the position of the robots towards the parameters of the formation. To solve the differential equations obtained, the shooting method is programmed for two initial value problems. The results show the evolution of the robots' positions and the positioning errors that tend to zero, validating this proposal through simulation. In addition, the control actions allow determining the energy index to compare it with a previous work, achieving a lower value.
Depth Maps Comparisons from Monocular Images by MiDaS Convolutional Neural Networks and Dense Prediction Transformers

This paper introduces a comparative case study on inferring depth maps from calibrated single-view monocular images with two deep learning architectures, convolutional neural networks and dense prediction transformers. Three monocular depth estimation models, MiDaS Small-CNN, MiDaS Hybrid-DPT and MiDaS Large-DPT, were used to produce depth maps of input images. By applying a 4x4 disparity-to-depth mapping matrix, Q, from the calibrated intrinsic camera matrix K, 2D input images were transformed to 3D point clouds. This study assesses computational efficiency of using each MiDaS model to generate depth map and corresponding polygon file at different image resolutions. In addition, it briefly monitors the difference in outlier points detected as well as it provides an idea on the original perspectives from objects' point-of-view in the opposite direction of the camera from the 2D input image. The production-rate analysis of the three models to produce depth map and polygon file results in MiDaS-Small CNN performing 9% faster than MiDaS Hybrid-DPT with 5% less memory overhead, and 44% faster than MiDaS Large-DPT with 38% less memory overhead. Taking both computational efficiency and accuracy into consideration, MiDaS Hybrid-DPT stands out overall for use as a close-range monocular image 2D-to-3D model.
Design and Development of Cloud based QR Coded Watermarking DLP system

Despite hectic security efforts in place today by large organizations, data breaches continue to occur, and identity theft is on the rise. Data leak prevention (DLP) is a suite of technologies aimed at restricting the loss of sensitive information that occurs in enterprises across the globe. By focusing on the location, classification, and monitoring of information at rest, in use and in motion, this solution can go far in helping an enterprise get a handle on what information it has, and in stopping the numerous leaks of information that occur each day. In this paper, first we suggest a policy for an enterprise to work on to prevent the data leakage and detection, depending upon access control and data location using QR code-based watermarking. QR code-based watermarking have been implemented using image and pdf files for detecting violations in accessed, downloaded, and uploaded files to central portal.
Lab ViewSimulation of a Flute Wind Musical Instrument Taking into Consideration the Fractional Behavior of Visco Thermal Losses in the Resonator

The process of numerically synthesizing and designing musical instruments has been growing in the last few years. Many techniques such as modulation, digital wave guides and others, succeeded in mimicking the sound of a musical instrument using the computer system. However, still most of the instruments, especially wind musical instruments, are fabricated from a hobbyist perspective and not from a scientific manner. For that, the aim of this work is to analyse all the physical and mathematical parameters of a flute musical instrument, and to create a simulator using Lab View graphical user interface that allows the design of a flute to be based on scientific inputs such as the length, the width, the number and the size of holes, the distance between holes and many other features. The results represent all parameters needed to design a flute and they were simulated and verified numerically.
Virtual Reality Game Based Interaction with Exergaming using Wearable Sensors

Now-a-days, children are more indulged in indoor activities instead of outdoor games. This plays a very negative role on their minds along with physical laziness and obesity. The proposed system that solves this issue uses virtual reality gaming interface and activity recognition system for the users to play outdoor games in their rooms. People can wear the headsets along with wearables and can enjoy 3D games while exercising. The system uses sensors (MPU6050) connected with nRF24L01 modules and Arduino responsible for sending the sensor data to the game. Moreover, the data by sensor is processed using a machine learning model, which predicts the game played by the user. The proposed system aims at recognizing six basic games in the IMSporting behaviors dataset, which utilizes triaxial accelerometers connected to the subject’s knee, wrist, and below neck areas to capture human activities i.e., badminton, basketball, table tennis, skipping, cycling and football. WISDM dataset is used to recognize activities such as jogging and walking. The system has achieved an accuracy of 92% by using median filter as a pre-processing technique, Fourier transform features extraction and artificial neural networks classifier. The system can be applied to many fields like education, offices, construction and sports. Extensive experimentation proved the validity of the proposed system.
Privacy Enhancing Approach for Healthcare System Based on Blockchain

Blockchains provide a decentralized, incorruptible, and accurate ledger for documenting digital asset transactions, revolutionizing the healthcare system. Revenue and data have grown the greatest in healthcare. Electronic health records have increased security concerns. Blockchain is used to secure sensitive data. However, Blockchains may also have security, scalability, and privacy difficulties (e.g., adhering to privacy legislation). Blockchain-based crypto-privacy remedies are evolving to address these fundamental shortcomings and equip individuals with frameworks to become incognito and manage their data throughout their electronic transfers in the ledger, allowing them to maintain their privacy. In this article, a blockchain-based healthcare system is proposed by employing differential privacy, which offers trade-offs between electronic health record (EHR) privacy and accessibility, speeds up (efficient) EHR storage and maintenance, and stakeholders can access medical data securely and efficiently while safeguarding patient privacy. Furthermore, this article’s primary goal is to describe the existing state of the art in blockchain-based healthcare systems with the proposed framework. A comparative study of previously published and the proposed research in the same field is also presented and discussed.
Intelligent Signs Language Understanding with Autonomous Landmarks for E-learning Context

Human sign language understanding has become very important task nowadays, which is attracting interest of researchers. In this research work, an efficient sign language recognition system has been presented. The system is based on extraction of landmarks. The Mediapipe and OpenCV (Open Computer Vision) are used for extraction of landmarks. The extracted landmarks are further utilized to generate new type of features. The machine learning (ML) based classifiers have been proposed to classify the hand signs from the extracted features. The random forest has been selected as a base classifier. It presented accuracy rates of 98.76% over ISL (Irish sign language) dataset and 98.68% over ASL (American Sign Language) dataset. Other ML-based classifiers are also selected to work with new efficient features such as support vector machines (SVM), k-nearest neighbors (KNN), decision trees and naive Bayes. The results of these classifiers have also been compared. The base classifier performance is very efficient when compared to others and it works in real time (15 to 30 processed frames per second).
Faceswap Deepfakes Detection using Novel Multi-directional Hexadecimal Feature Descriptor

With the growing number of sophisticated deep learning algorithms and fake video generation applications, it is now possible to create highly realistic deepfake videos. Faceswap is the most commonly employed deepfakes approach, which is challenging to detect due to variations in the facial skin tone, illumination conditions, presence of accessories like glasses on the face, compression artifacts, etc. Existing local texture descriptors have achieved better performance on face recognition applications; however, they compute only the limited directional information while ignoring the magnitude details. This motivated us to develop a robust local texture descriptor to extract more directional and magnitude details from the adjacent pixels to effectively represent the video frames. For this purpose, we proposed a robust multi-directional hexadecimal feature descriptor (MDHFD) by combining the local hexadecimal pattern (LHeXDP) and Local Adjacent Neighborhood Magnitude Pattern (LANMP). LHeXDP calculates the orientation-based pattern by computing 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> - and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> -order derivatives at 0°, 45°, 90°, and 135° angles from each center pixel. LANMP computes the magnitude information from each central pixel to its adjacent pixels in horizontal, vertical, diagonal, and diagonal-back directions. Histograms of both the LHeXDP and LANMP are fused to compute a multi-directional feature vector, which is used to train a support vector machine to classify between the original or faceswap deepfakes video. We measured the performance of our system on the challenging faceswap subset of a diverse and large-scale Face Forensic++ and World Leaders datasets. Experimental results illustrate that the proposed method outperforms state-of-art methods for the detection of faceswap deepfakes.
A Framework for Secure CyberSavvy Internet Vote Casting System: Rebuilding Trust in Digital Electoral

Elections serve as a fundamental contributor to sound democratic governance, and voting allows for electing from amongst listed contestants a particular one. It is an anonymized expression of preference through the ballot. A contestant having desired threshold or above vote casting is declared to have won the election. Internet(i)-voting has gained significant importance in recent times because of the transparency requirement in the vote casting system besides allied advantages such as voter convenience and ease of access through the internet constrained only by the time bounds from a particular electoral. In 2005, Estonia successfully introduced the i-voting system, which requires an e-ID card or ID Mobile for vote casting, for its eligible voters to cast votes in the local, legislative, and European Parliamentary elections. Recent studies reveal that the issue of resentment for i-voting is not in accepting transformation or its espousal but the vulnerabilities linked with the technology (uncontrolled environment of the internet). That is, technological naiveness and lack of trust of the people in cyberspace are significant impediments to adopting the i-voting system, especially in the recent growth of hacking incidents and infiltration of the dark web. There are also concerns over forged vote casting on behalf of intimate members and sold-out votes. One of the accusations exerted by the foes is the adversative effect of the digital divide on elections. Procedural, political, and resource scarceness further accentuate the nonacceptance by the masses. Hence, this situation necessitates the need to propose and evolve, on top of existing technologies, cost-effective secure i-voting solutions that help gain voters’ trust, which is the prima facie of this research exertion.
TensorFormer: A Tensor-Based Multimodal Transformer for Multimodal Sentiment Analysis and Depression Detection

Sentiment analysis is an important research field aiming to extract and fuse sentimental information from human utterances. Due to the diversity of human sentiment, analyzing from multiple modalities is usually more accurate than from a single modality. To complement the information between related modalities, one effective approach is performing cross-modality interactions. Recently, Transformer-based frameworks have shown a strong ability to capture long-range dependencies, leading to the introduction of several Transformer-based approaches for multimodal processing. However, due to the built-in attention mechanism of the Transformers, only two modalities can be engaged at once. As a result, the complementary information flow in these Transformer-based techniques is partial and constrained. To mitigate this, we propose, TensorFormer, a tensor-based multimodal Transformer framework that takes into account all relevant modalities for interactions. More precisely, we first construct a tensor utilizing the features extracted from each modality, assuming one modality is the target while the remaining tensors serve as the sources. We can generate the corresponding interacted features by calculating source-target attention. This strategy interacts with all involved modalities and generates complementing global information. Experiments on multimodal sentiment analysis benchmark datasets demonstrated the effectiveness of TensorFormer. In addition, we also evaluate TensorFormer in another related area: depression detection and the results reveal significant improvements when compared to other state-of-the-art methods.