Reviewerly/exHarmony huggingface dataset:
Breast Cancer Classification Using Various CNN Models

Breast cancer is one of the leading causes of death among women globally. Early detection of tumors is possible through breast imaging, which is the first step in identifying abnormalities. Histopathology is a type of imaging techniques used to create a histopathological image of diseases in body tissues. This paper examines the use of Convolutional Neural Networks (CNNs) to classify benign and malignant tumors in the 400× and 100× BreaKHis datasets. We compare the results obtained using different types of convolutional neural networks models based on fastai to determine the highest accuracy level of classification among them, as well as the effect of image augmentation on the results. The first dataset showed that DenseNet 121 had the highest accuracy of 98%, followed by ResNet 34 (97.7%), and AlexNet 97%, respectively. The second dataset showed that ResNet 34 had the highest accuracy of 99.6%. It was also observed that enlarging the data before classifying had a negative impact on the results. Thus, the best solution for this kind of medical image classification is ResNet 34 without large magnification.
Double Layer Message Security For 3D Object Using Mathematical Sequences and Secret Keys

It’s It’s been so crucial lately to emphasize data security applying as the world depends on data exchange extensively almost in all domains. Steganography is one of the main techniques used to ensure information security in storage and during communication. We accomplish this novel technique by using two main layers. In the first layer, the secret message is compressed using Gzip or encrypted using Advanced Encryption Standard (AES). In the next layer, the binary representation of the secret message is embedded into vertices of x, y, and z-coordinates of the 3D object using the Gray code sequence, Recamán’s sequence, and Lucas sequence. This binary representation of secret message is embedded using the Least Significant Bit (LSB) technique according to the value of the used sequence. Different performance measurements are computed and evaluated to ensure that the 3D object quality remains preserved such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), embedding rate (ER), Normalized Hausdorff Distance (NHD) and maximum embedding capacity. The proposed scheme is compared with its counterparts in the literature. Outstanding numerical results are achieved, showing the superiority of the proposed schemes in the terms of performance, capacity, and security.
Neural Network-Based Classification of Hand Gestures Using Electromyography Sensor Data

This article focuses on the creation of a machine learning-based method for recognising gestures from electromyography (EMG) signals. In order to categories hand movements such as “thumbs up,” “wave out,” and “hand at rest,” two neural network model were trained and, evaluated using EMG signals obtained from Myo armbands. Traditional back propagation training was used in the first neural network while Bayesian regularisation were use in the second network. The second model performed better with fewer epochs despite having the same number of hidden layers as the first. The findings, indicated that the developed models could correctly categories the various arm movements, with the second model achieving higher level of accuracy. The models’ efficacy in generalising to new, not only untested subjects but also demonstrated by the models’ validation and testing. This study of this paper show how the machine learning and neural network could be used to recognise gestures from EMG signal. These techniques; can be applied to a variety of fields including rehabilitation and human-computer interaction.
A 32-bit Quantum Encryption Algorithm Using Dynamic Pauli Gates

By utilizing quantum cryptography, a real-time secure algorithm is created that makes use of the quantum properties of photons. An algorithm utilizing symmetric key is presented, taking into account the fundamental principles of both classical and quantum computing. Messages in this algorithm consist of blocks, each of which consists of 32 bits of space. To begin with, the process of classical encryption is divided into two processes, rotation process and permutation process, all controlled by the key that determines the order in which the bits of the block message are rotated. Secondly, encrypting block messages in bits is converted to qubits. In the third step, the quantum bits are applied to a certain quantum gate, such as Not(x), Control Not (CNot) and Toffoli (T), to change the value of that quantum bits. Fourth, in order to create non-orthogonal ciphertext states, Pauli gates are utilized and controlled by a key in the second stage. Eavesdroppers cannot detect the encryption of messages that have changed from orthogonal states to non-orthogonal states because quantum computing is based on quantum mechanical principles. It has been demonstrated that the proposed algorithm increases message space. In addition, various algorithm analyses have proven the improved strength of our proposed algorithm against various attacks, including brute force attacks.
Famous Digital Signatures Used In Smart Contracts

This paper introduces the process of digital signatures and their importance in smart contracts. There are a lot of digital signature algorithms. Some of these algorithms, such as Elgamal algorithm, Schnorr algorithm and Elliptic Curve algorithm, are suitable and efficient to be used in some applications, such as smart contracts and achieve high security andgood results. Schnorr was described by Claus Schnorr. It is based on the intransigence of specific discrete logarithm problems and is renowned for its simplicity. Elgamal algorithm was developed in 1984. It has quick performance, low power consumption, and is resistant to adaptively chosen-cipher text attacks. The Curve signature was developed in 1985. It has a performance that is quicker than Elgamal algorithm. Also, it consumes less power than Elgamal algorithm, and it is more resistant to different types of attacks. On the other hand, others are slow and have low security.
Fast Simple Image Encryption Technique Based on Chaos Based System

In this paper, a novel image encryption technique based on chaotic maps is described. Encrypting an image with chaotic maps ensures that the safe cipher characteristics of confusion and diffusion are met. Moreover, chaos-based image cryptosystems have many features compared to the classical cryptographic techniques such as fast, effective security, minimum computing overheads, extremely sensitive to the control parameters and initial settings. These all features make effective encryption techniques with the best performance. Four chaotic maps are used in the proposed scheme to generate the encryption key, initial vector, and ciphering encryption S-Boxes. The maps used are the Chebyshev map, 1D improved quadratic map, 1D Improved Logistic Map and Altered Sine-Logistic based Tent map ASLT.
Towards The First Sustainable Building in The British University in Egypt Campus: Applied Study on The Wind Tunnel Building

Recently the energy market in Egypt has been experiencing a dramatic increase in fuel prices; that fact has raised a substantial social concern as it is directly proportional to the electricity tariff. Additionally, climate change related issues have glow up because of CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions. Herein, we demonstrate a real applied study on a targeted building, wind tunnel building, in The British University in Egypt campus, Cairo, Egypt. Initially, a techno-economical study to compare three alternatives of electric power supply systems was carried out. The first alternative is the direct connection to the grid; the second is a photovoltaic grid-connected system; the last is a hybrid on-grid/off-grid system. Consequently, the select model is implemented, where real measurements are taken against forecasted one. The paper is a seed toward applied studies with direct impact on society.
Correcting Kernel-based Network for SingleImage Haze

Existing dehazing methods based on classical theory often focus on two problems: the estimation of the transmission and ambient light, which is prone to suffering from blurry image along haze boundaries. To address the issue, we propose a novel network based on correcting kernel-based for single-image haze removal(CKNet), which gradually improve the performance of removing haze by a kernel correct (KC)block designed. We first construct a U-like framework for fog removal network, then combine it with a kernel correction network constructed using small convolutional neural networks to predict the kernel parameters more suitable for the fog scenario, and finally form the optimal network parameters after continuous iterations. Extensive experimental results show the effectiveness of our method compared with other state-of-the-art methods based on deep leaning and monochromatic atmospheric scattering model.
A Deep Learning Technique for early diagnosis of Skin Cancer disease using Biomedical data

Skin cancer is arguably known as one of the most prevalent cancers. However, it is also one of the most preventable and treatable types of cancer if caught early. An automated skin cancer detection system can help catch potential skin cancers early and prevent the disease from progressing to a more advanced and dangerous stage compared with manual detection methods, which are often prone to human error and may not always be accessible to people living in remote areas. Recently, convolutional neural networks (CNNs) have taken a front seat in today’s technology as they automatically classify images based on their visual features. In this paper, deep learning was utilized as a tool to further enhance and increase accuracy in reference to skin cancer detection in its initial stages through CNN and transfer learning. Achieved results in classifying skin cancer into 7 classes comprise of 78% in regards to the basic CNN, however the transfer learning with VGG16 resulted in an accuracy rate of 84%. Another considered approach is to segregate the two main skin cancer types, melanoma and benign, and this resulted in an accuracy rate of 92% when tested on the basic CNN model. However, it did not improve from 92% when tested after adding the VGG16 pretrained model, so another approach has been considered, a combination of two pre-trained models has implemented subsequently in attempt to improve results. This approach acquired 95% accuracy rate when tested on unseen data classifying the skin lesions into two classes.
Towards Content-Based Image Retrieval for Encrypted Images over Cloud Computing: Review of Recent Trends

Recent advances in imaging and computing technology generate tremendous image data daily. Searching image collections has been made easier with the introduction of some content-based image retrieval (CBIR) approaches. The research area of CBIR is currently an active topic. It is becoming increasingly important to process and store big data in a parallel and distributed manner using CBIR, especially cloud computing platforms. Cloud computing provides high amounts of computation power that can be accessed at low costs. However, cloud servers are not entirely secure, and data owners raise privacy concerns; thus, the encrypted image and encrypted feature are uploaded to the cloud server to ensure privacy. This paper presents the essential techniques of secure content-based image retrieval over cloud computing. We will compare and contrast several approaches for CBIR, especially with cloud computing.
Hybrid- Blowfish Security Strengths Using Side Channel Countermeasures

in today’s digital age, the need for secure communication has become increasingly crucial. Cryptography has become an essential aspect of modern-day communication, where transmitting and storing sensitive information securely is paramount. By using encryption techniques, cryptography protects sensitive information stored in computer systems, transmitted through networks and exchanged among individuals. It is vital for organizations to implement robust cryptography practices in order to safeguard their critical data and protect themselves from cyber-attacks. The misuse or breach of sensitive information can result in serious consequences such as financial loss, reputational damage and data theft. Overall, We introduce new algorithm Hybrid-Blowfish by combination between blowfish and counter-measure of SCA using Masking string, we compared DES, 3DES, AES, blowfish, RSA and Hybrid-Blowfish across the tests like frequency test, serial test, encryption time, decryption time, memory used, avalanche effect, entropy and no of bits required for encoding optimally and the result demonstrate the effectiveness and superiority of Hybrid-Blowfish compared to traditional Blowfish and increase the cyber-security strength by approximately 25%
Metamorphoses of A. T. Gretchaninov’s musical language in the 1910s. The cycle of piano pieces “Pastels” Op. 61

The research aims to shed light on the metamorphoses of the musical language of the famous Russian composer A. T. Gretchaninov, active in the late XIX century – the first half of the XX century, which occurred in the 1910s. The paper pays special attention to the identification of the stylistic singularity of “Pastels” Op. 61 (1912-1913), a cycle of piano pieces. The scientific originality of the research is connected with the fact that A. T. Gretchaninov’s creative work has not been previously considered in the context of the changes that took place in the composer’s musical style in the 1910s. As a result, it has been revealed that the novelty of Gretchaninov’s musical language in the period under consideration was due to both changes in Russian art in general (in particular, the influence of impressionism on the composer’s work turned out to be significant) and crises in Alexander Tikhonovich’s personal and creative life. In particular, this trend can be traced in the history of creation of the cycle “Pastels”: during a difficult, critical period, Gretchaninov turned to composing music for children, in which he would find solace, but the piano pieces in the cycle turned out to be generally sad in an unchildlike way and psychologically saturated with deeply “adult” feelings and experiences.
IMPLEMENTASI MEDIA PEMBELAJARAN INTERAKTIF SENI BUDAYA KELAS VII DI SMP N 8 BATAM NONGSA BERBASIS ANDROID MENGGUNAKAN ADOBE ANIMATE

This study aims to produce learning media in the form of android-based applications for junior high school students. Based on research on student learning outcomes at SMPN 8 BATAM in the subject of Cultural Arts, it is a subject that has a low score compared to other subjects, namely 71. This is shown by data from 39 students, there are 24 students (61.53%) who get a score below the Minimum Completeness Criteria (KKM), which is 68, while the remaining 15 students (38.67%) score above the KKM. In this way, the researcher is interested in designing an Android-based Interactive Learning Media for Cultural Arts Class VII application using Adobe Animate. This prompted the author to build a multimedia-based application using the waterfall method. The conclusion obtained in this study is that interactive art and culture learning media applications can be used as additional learning media that are interesting and not boring.
IMPLEMENTASI FUZZY LOGIC DALAM MENENTUKAN HARGA PERBAIKAN ALTERNATOR MENGGUNAKAN METODE MAMDANI

The alternator is the component of the generator that produces electricity. A short circuit in the alternator causes a surge in electric current so that it can cause an explosion of sparks, it is necessary to prevent an electric short circuit so that maintenance is needed to repair the alternator. Problems in making decisions also occur in companies, in transactions that are used by companies that are still done manually, in determining the price of alternator repairs it is still based on estimates and during the price determination process you have to wait for confirmation from the company's leadership so that the process takes longer and the error rate is relatively high. Fuzzy logic mamdani one method that can be used to overcome this problem. This research will get the best price quickly and minimize the error rate in pricing and adapt it to the implementation design in MATLAB. In the process of determining the repair price, several weighted criteria are required as input variables, namely alternator capacity, level of difficulty and warranty period, which will then produce output variables, namely alternator repair prices. This study aims to assist companies in determining the price of alternator repairs.
RANCANG BANGUN APLIKAIS PEMESANAN AIR GALON PADA DEPOT WINDY BERBASIS ANDROID

ABSTRACT&#x0D; &#x0D; Water is one of the primary needs in human life, starting from daily activities such as drinking, bathing, cooking, to the field of industrial processing on a large scale, therefore good quality water is needed. Depot Windy is a refill drinking water business produced by PT. Lautan Bening which has been operating since 2016, with daily sales of around 200-300 gallons issued from 07.00 AM - 19.00 PM. However, there are problems related to the distribution of information between administrators, delivery staff and customers, so unwanted things often occur, such as gallons being stolen, gallons not being picked up, and payment transactions being delayed. In this journal, the author describes the development of an android-based gallon water ordering application using the SDLC (Software Development Life Cycles) method and the Waterfall development method which is connected via the user's cellphone. By creating good communication facilities, it can make it easier for sellers and customers to make transactions online and in real time, make it easier for customers to know the availability of gallons of water, and achieve the company's long-term goal of increasing sales.
Gesture-based Interaction for AR Systems: A Short Review

Gesture-based interaction constitutes the prominent interaction modality for Augmented Reality (AR) systems. In this state-of-the-art review, we classify the recent literature on hand gesture-based AR interaction techniques, concerning the various domains where AR is gaining a foothold. Based on the conducted analysis, a taxonomy is introduced for the classification of the various reported techniques on the grounds of their application domain, the context of use, gestures recognition method, and types of gestures used. Furthermore, the discussed analysis reveals that context of use is a factor to take into account when determining the gestures that should be employed when developing an AR application. At the same time, there is a trade-off between touch-based and mid-air gestures, regarding their simplicity and user engagement entailed, which needs to be carefully considered. In all cases, it is of utmost importance that AR gesture-based systems are designed following a human-centered approach. The analysis conducted and reported in this paper aims to motivate the informed development and integration of hand gesture interaction of AR applications in various contexts and provide information on the different approaches that can be employed.
Enhancing Action Recognition in Vehicle Environments With Human Pose Information

Monitoring driver behavior and recognizing driver actions is a crucial task in modern semi-autonomous driving conditions, where secondary activities, irrelevant to driving, should be minimized. The driver activity recognition problem represents a subclass of the widely studied action recognition task, but poses additional challenges stemming from the environment, the appearance of the participants, and the limited data availability for this specific task. Furthermore, the similarity of body movements and the nuanced changes when performing different actions further complicate the classification process. In this work, we explore the effectiveness of Temporal Segment Networks (TSNs) on the driver activity recognition task. Moreover, we propose a model to enhance the performance of such networks through the integration of information from pose landmarks, allowing for multi-modal fusion either in the early or late stages of the model, providing informed predictions for input videos. Thus, the simplicity of the TSN models is counterbalanced by the incorporation of prior knowledge, resulting in a fused model that outperforms more resource-demanding 3D architectures. The proposed method is evaluated on the Drive&Act dataset and demonstrates state-of-the-art performance, surpassing previous works by a margin of 8.01% using only RGB video as input.
Research through Design of Socially Cooperative Agents for Exoskeleton-based at Home Stroke Rehabilitation

This paper presents efforts to concretize the novel concept of a Socially Cooperative Feedback System through the use of Research through Design methodology that provides an iterative approach for macro-level management. The design stages follow the Double Diamond path of divergent exploration and convergent refinement. Within the design stages, we combined empathetic Design Thinking for research execution and Agile for stage-level management to prototype the interfaces of an exoskeleton for the rehabilitation of stroke patients at home as they age in place. The combination of these methods provided the flexibility needed for oversight and alignment of interfaces for the patient, therapists, and 3rd party stakeholders (i.e., family and friends).
Easy-to-Read Language Resources and Tools for three European Languages

Easy-to-Read (E2R) is a controlled language variant that makes any written text more accessible through the use of clear, direct and simple language. It makes content accessible to anyone with a minimum reading level, but it is mainly aimed at specific user groups. In recent years, important developments in the field of E2R have been carried out in many different languages in addition to English. The main objective of this paper is to present an updated overview of the existing tools and resources for E2R in German, Spanish and Basque. We aim to offer a benchmark for future studies that focus on these languages and their E2R variants.
Challenges with Voice Assistants for the Elderly in Semi-Public Spaces

Voice Assistants such as Amazon Alexa and Google Home have recently made inroads into all walks of life as is evident from their popularity, and the growing number of users. Traditionally, research on elderly people with voice assistants has focused on private spaces - bedrooms, kitchens, and living rooms. Due to privacy concerns, ethical issues, legal issues, and noisy environments, their use in public and semi-public spaces are discouraged. However, by carefully mitigating these concerns, voice assistants could still find applications in semi-public spaces for elderly people. This paper summarizes the preliminary insights from 8 interviews that we conducted with elderly people and throws light on the potential areas where voice assistants could be used in semi-public spaces.