Reviewerly/exHarmony huggingface dataset:
Deep Learning Approaches for Image Classification

Deep learning models can achieve a higher accuracy result compared with traditional machine learning algorithm. It is widely useful in different areas, especially in images classification area. In recent years, because of the improvement of hardware and the discovery of new deep learning network structures, the accuracy and reliability of deep learning model used in image classification have been greatly improved. However, in the field of images classification with deep learning technology, the reviews of the recent researches are lack. This paper will make a review about the recent researches of images classification based on deep learning. It includes the latest studies to improve the performance about deep learning. Additionally, the potential problems and challenges on deep learning technology and the possible future improvement and research direction are analyzed and discussed in the review.
Adaptive Rotated Convolution for Rotated Object Detection

Rotated object detection aims to identify and locate objects in images with arbitrary orientation. In this scenario, the oriented directions of objects vary considerably across different images, while multiple orientations of objects exist within an image. This intrinsic characteristic makes it challenging for standard backbone networks to extract high-quality features of these arbitrarily orientated objects. In this paper, we present Adaptive Rotated Convolution (ARC) module to handle the aforementioned challenges. In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images, and an efficient conditional computation mechanism is introduced to accommodate the large orientation variations of objects within an image. The two designs work seamlessly in rotated object detection problem. Moreover, ARC can conveniently serve as a plug-and-play module in various vision backbones to boost their representation ability to detect oriented objects accurately. Experiments on commonly used benchmarks (DOTA and HRSC2016) demonstrate that equipped with our proposed ARC module in the backbone network, the performance of multiple popular oriented object detectors is significantly improved (\eg +3.03\% mAP on Rotated RetinaNet and +4.16\% on CFA). Combined with the highly competitive method Oriented R-CNN, the proposed approach achieves state-of-the-art performance on the DOTA dataset with 81.77\% mAP. Code is available at \url{https://github.com/LeapLabTHU/ARC}.
A 3-Layer PSN for Image Encryption Utilizing Fractional-Order Chen Hyperchaotic Map and Cryptographically-Secure PRNGs

Image encryption is increasingly becoming an important area of research in information security and network communications, as digital images are widely used in various applications and are vulnerable to various types of attacks. In this research work, a color image cryptosystem that is based on a 3-layer permutation-substitution network (PSN) is proposed. For every layer, an encryption key and an S-box are generated and utilized. Those are based on a four-dimensional (4D) dynamical Chen system of a fractional-order, the Mersenne Twister, OpenSLL, Rule 30 Cellular Automata and Intel’s MKL. The sequential application of Shannon’s ideas of diffusion and confusion for 3 times guarantees a total distortion of any input plain image, resulting in a totally encrypted one. Apart from the excellent and comparable performance to counterpart algorithms from the literature, showcasing resistance to visual, statistical, entropy, differential, known plaintext and brute-force attacks, the proposed image cryptosystem provides an exceptionally superior performance in 2 aspects: a vast key space of 2^1658 and an average encryption rate of 3.34 Mbps. Furthermore, the proposed image cryptosystem is shown to successfully pass all the tests of the NIST SP 800 suite.
Hyper-convolutions via implicit kernels for medical image analysis

The convolutional neural network (CNN) is one of the most commonly used architectures for computer vision tasks. The key building block of a CNN is the convolutional kernel that aggregates information from the pixel neighborhood and shares weights across all pixels. A standard CNN’s capacity, and thus its performance, is directly related to the number of learnable kernel weights, which is determined by the number of channels and the kernel size (support). In this paper, we present the hyper-convolution, a novel building block that implicitly encodes the convolutional kernel using spatial coordinates. Unlike a regular convolutional kernel, whose weights are independently learned, hyper-convolution kernel weights are correlated through an encoder that maps spatial coordinates to their corresponding values. Hyper-convolutions decouple kernel size from the total number of learnable parameters, enabling a more flexible architecture design. We demonstrate in our experiments that replacing regular convolutions with hyper-convolutions can improve performance with less parameters, and increase robustness against noise. We provide our code here: https://github.com/tym002/Hyper-Convolution.
Human Resource Management in Islamic Educational Institutions to Improve Competitiveness in Society 5.0 Era

The ability of human resources to utilize technology in each of their activities is a competency needed in the era of society 5.0.Meanwhile, Islamic educational institutions in Indonesia in general are slow in responding to changes and developments, so the readiness of human resources in Islamic educational institutions in welcoming the era of society 5.0 is very worrying.Therefore, this study aims to reveal how human resource management is carried out in Islamic educational institutions in Indonesia.The study was conducted using qualitative methods.Data were collected from three types of Islamic educational institutions, namely traditional, modern and integrated educational institutions.These three types of institutions represent all types of Islamic educational institutions in Indonesia.Data were collected by observation, interviews, as well as documentation studies.Data were analyzed through interactive techniques.Based on the results, first, traditional Islamic educational institutions have an istiqamah attitude in carrying out existing and natural management, so as not to give special treatment to human resources in welcoming the era of society 5.0.Second, modern Islamic educational institutions apply TQM in preparing human resources to welcome the era of society 5.0.Third, integrated Islamic educational institutions accommodate the changes while still relying on instilling student morals.These three types of Islamic educational institutions have different responses in welcoming the era of society 5.0 showing the existence of Islamic educational institutions to welcome the era.
ANALISIS FAKTOR PENYEBAB KERUSAKAN NASKAH KUNO KOLEKSI ABDUL MULKU ZAHARI DI KELURAHAN BAADIA KECAMATAN MURHUM KOTA BAUBAU PROVINSI SULAWESI TENGGARA

This study aims to determine and explain the types of damage and the factors causing the damage and solutions to repair the ancient manuscripts collected by Abdul Mulku Zahari in Baadia Village, Murhum District, Baubau City.Research using qualitative research with inductive reasoning is drawing conclusions from things that are specific then states it into things that are general in nature, with data collection methods consisting of observation, literature studies, interviews and documentation. &#x0D; The results of research there are several types of damage and ancient manuscripts in the collection of Abdul Mulku Zahari in Baadia Village, Murhum District, Baubau City, namely: pen streaks, yellowing paper, black spots, ink seeping and widened, torn and faded writing. Factors causing damage to ancient manuscripts in the collection of Abdul Mulku Zahari In the Baadia Village, Murhaum District, Baubau City are divided into four factors causing damage, namely: damage caused by temperature and humidity including fungal factors caused by human damage that comes from the owner or colleagues. fellow of the owner of the manuscript itself, damage caused by insects including termites, rats, nerds and cockroaches, damage factor caused by nature, caused by water that causes ink to seep and widen so that the writing on the manuscript is not clearly read and makes the text corrupt . Efforts to prevent damage to the collection of Abdul Mulku Zahari manuscripts are to put anti-insect drugs such as mothballs and Slica Gel and regulate air temperatures and humidity that are ideal for ancient manuscripts, so that fungus does not multiply which can damage the ancient manuscripts. as well as digitizing scripts to maintain script files in order to stay awake in digital form.
Construction of innovation and entrepreneurship education ecosystem model of colleges based on vocational education

With the gradual implementation of the national innovation-driven development strategy and the rapid growth of new technologies, new formats and new product markets, it has provided new requirements for the innovative training of Chinese colleges.In order to further stimulate their awareness of innovation and entrepreneurship, cultivate their entrepreneurial spirit, and enhance their innovation and entrepreneurship skills, the relevant regulations promulgated by the State Council and the Ministry of Education have promoted the rapid development of innovation and entrepreneurship education in higher education in China.However, there are also many difficulties in the process of education, such as: on the one hand, the science and technology entrepreneurship competition is also in full swing.However, the talent construction of dual-innovation teaching is also relatively thin, in addition, the scientific and technological entrepreneurial talents within the campus are relatively scattered and the utilization rate is not high.Therefore, on the basis of educational ecology and other theories, it is very important to establish a dynamic college innovation and entrepreneurship education ecosystem, and study the relationship and operation mechanism of each role in the ecosystem, so as to promote the coordinated development of various elements.
AI-Assisted Drone Localization of Arbitrary Objects using Aruco Markers

This conference paper presents a novel approach for localizing arbitrary objects using drones and AI assisted image processing.The system utilizes aruco markers, which are small square markers that are easily identifiable by image processing algorithms, in combination with AI to accurately determine the location of the objects in real-time.The system was tested in a variety of environments and demonstrated high accuracy and robustness.The results of this research have potential applications in areas such as warehouse management, search and rescue, and precision agriculture.
Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem

Recent research on the robustness of deep learning has shown that Vision Transformers (ViTs) surpass the Convolutional Neural Networks (CNNs) under some perturbations, e.g., natural corruption, adversarial attacks, etc. Some papers argue that the superior robustness of ViT comes from the segmentation on its input images; others say that the Multi-head Self-Attention (MSA) is the key to preserving the robustness [30]. In this paper, we aim to introduce a principled and unified theoretical framework to investigate such argument on ViT’s robustness. We first theoretically prove that, unlike Transformers in Natural Language Processing, ViTs are Lipschitz continuous. Then we theoretically analyze the adversarial robustness of ViTs from the perspective of Cauchy Problem, via which we can quantify how the robustness propagates through layers. We demonstrate that the first and last layers are the critical factors to affect the robustness of ViTs. Furthermore, based on our theory, we empirically show that unlike the claims from existing research, MSA only contributes to the adversarial robustness of ViTs under weak adversarial attacks, e.g., FGSM, and surprisingly, MSA actually comprises the model’s adversarial robustness under stronger attacks, e.g., PGD attacks. We release our code via https://github.com/TrustAI/ODE4RobustViT .
R2-AD2: Detecting Anomalies by Analysing the Raw Gradient

Neural networks follow a gradient-based learning scheme, adapting their mapping parameters by back-propagating the output loss. Samples unlike the ones seen during training cause a different gradient distribution. Based on this intuition, we design a novel semi-supervised anomaly detection method called R2-AD2. By analysing the temporal distribution of the gradient over multiple training steps, we reliably detect point anomalies in strict semi-supervised settings. Instead of domain dependent features, we input the raw gradient caused by the sample under test to an end-to-end recurrent neural network architecture. R2-AD2 works in a purely data-driven way, thus is readily applicable in a variety of important use cases of anomaly detection.
NOVICE SCHOOL PRINCIPALS IN EDUCATION AND THEIR EXPERIENCES OF PEDAGOGICAL LEADERSHIP IN PRACTICE

School leadership is found important for school development and student learning.Consequently, the interest in professional leadership education for principals has increased.In Sweden, professional leadership education for novice principals was made mandatory in 2010.Moreover, enhanced focus on leadership for teaching and learning in terms of 'pedagogical leadership' is highly topical.This study aims to deepen our knowledge of novice principals' experiences of pedagogical leadership practices and relate these to their paths toward principalship.The study follows a qualitative and situated design and adopts a practice-based approach.Observations were conducted in the educational and workplace practices of novice principals in Sweden and interviews were conducted with principals and teachers.Using a conceptual framework of Wenger (1998), the analyses show that principals experience challenges concerning pedagogical leadership if their competence and experience are not aligned with practice and context.This mismatch seems to impair their pedagogical leadership practice.In addition, a lack of leadership experience obstructs their learning in the professional leadership education for principals.
Indonesian Language Learning in Elementary Schools Through Life Skills Education Model

&lt;p&gt;&lt;em&gt;This research is a needs analysis research which will be used as the basis for personality factors in learning Indonesian. The purpose of this research is to describe Indonesian language learning in elementary schools through a life skills education model. The research method used is library research, whose data sources come from relevant previous research data in the form of textbooks, books, journal articles and other published manuscripts. The results of the study show that learning Indonesian in elementary schools through life skills education is more empowered in developing the communication potential of Indonesian both oral and written and can develop language skills (listening skills, reading skills, speaking skills and writing skills) which are very useful in perform communication interactions. In communicating activities, language skills are not carried out singly, but are used together to achieve communication goals. Another finding is that the learning process activities are used for the purpose of training students to use Indonesian more for communication purposes, and not being required to master more knowledge of Indonesian. As an effort to optimize learning Indonesian, it is necessary to create conditions for self-directed learning for students.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;strong&gt;Keywords&lt;/strong&gt;: &lt;em&gt;Learning, Indonesian Language, Life Skills Education.&lt;/em&gt;&lt;/em&gt;&lt;/p&gt;
Realizing health data interoperability in low connectivity settings: The case of VODAN-Africa

VODAN Africa has produced FAIR data in low resource settings. Federated machine actionable data is available in a triple store for visiting. Interoperability facets (I1–I3) were followed to achieve semantic interoperability. Vertical interoperability was also realized with DHIS2.
Design optimization for high-performance computing using FPGA

Abstract Reconfigurable architectures like Field Programmable Gate Arrays (FPGAs) have been used for accelerating computations in several domains because of their unique combination of flexibility, performance, and power efficiency. However, FPGAs have not been widely used for high-performance computing, primarily because of their programming complexity and difficulties in optimizing performance. We optimize Tensil AI's open-source inference accelerator for maximum performance using ResNet20 trained on CIFAR in this paper in order to gain insight into the use of FPGAs for high-performance computing. In this paper, we show how improving hardware design, using Xilinx Ultra RAM, and using advanced compiler strategies can lead to improved inference performance. We also demonstrate that running the CIFAR test data set shows very little accuracy drop when rounding down from the original 32-bit floating point. The heterogeneous computing model in our platform allows us to achieve a frame rate of 293.58 frames per second (FPS) and a \%90 accuracy on a ResNet20 trained using CIFAR. The experimental results show that the proposed accelerator achieves a throughput of 21.12 Giga-Operations Per Second (GOP/s) with a 5.21 W on-chip power consumption at 100 MHz. The comparison results with off-the-shelf devices and recent state-of-the-art implementations illustrate that the proposed accelerator has obvious advantages in terms of energy efficiency.
Formal Analysis of Trust and Reputation for Service Composition in IoT

The exponential growth in the number of smart devices connected to the Internet of Things (IoT) that are associated with various IoT-based smart applications and services, raises interoperability challenges. Service-oriented architecture for IoT (SOA-IoT) solutions has been introduced to deal with these interoperability challenges by integrating web services into sensor networks via IoT-optimized gateways to fill the gap between devices, networks, and access terminals. The main aim of service composition is to transform user requirements into a composite service execution. Different methods have been used to perform service composition, which has been classified as trust-based and non-trust-based. The existing studies in this field have reported that trust-based approaches outperform non-trust-based ones. Trust-based service composition approaches use the trust and reputation system as a brain to select appropriate service providers (SPs) for the service composition plan. The trust and reputation system computes each candidate SP’s trust value and selects the SP with the highest trust value for the service composition plan. The trust system computes the trust value from the self-observation of the service requestor (SR) and other service consumers’ (SCs) recommendations. Several experimental solutions have been proposed to deal with trust-based service composition in the IoT; however, a formal method for trust-based service composition in the IoT is lacking. In this study, we used the formal method for representing the components of trust-based service management in the IoT, by using higher-order logic (HOL) and verifying the different behaviors in the trust system and the trust value computation processes. Our findings showed that the presence of malicious nodes performing trust attacks leads to biased trust value computation, which results in inappropriate SP selection during the service composition. The formal analysis has given us a clear insight and complete understanding, which will assist in the development of a robust trust system.
Artificial Intelligence Produced Original Work: A New Approach to Copyright Protection and Ownership

The journey of copyright protection that started with the printing press in the 16th century entered a new era of challenges with the technological advancement of the 21st century. Copyright has rights and enforcement that are grounded in legislative enactments. This paper advocates that A. I.-produced work is original and deserves copyright protection. Artificial Intelligence (A. I.) has emerged as a powerful technology that has enabled the creation and assimilation of new and unique authorship. The amount of work that A. I. is producing in the fields of science, medicine, art, law, and literature is increasing dramatically. This paper addresses the question of why A. I. generated work deserves copyright protection and how it correlates with its ownership. A comparative analysis of the existing copyright laws in various jurisdictions is examined. A rundown of current challenges of digital copyright and future developments are discussed. The paper presents the idea of legal personhood and how it correlates with copyright work ownership. Five traditional ownership options are compared and considered. A hybrid ownership model that gives legal personality to the artificial intelligence (AI) system, its programmer, user, and the company under the umbrella of a legal entity like artificial personality (AiLE) is proposed. In most jurisdictions, legislative changes are required to address and provide a new foundation for copyright protection and ownership of AI. -produced original work. Hence, the need to address the current challenges of digital copyright and its rightful owner is essential in unleashing the true potential and further development of A. I.
Musical interpretation in the context of the art of recording

The purpose of the article is to identify and characterize the peculiarities of interpretation as a unique phenomenon of musical art in the context of the specifics of sound recording. Methodology. The method of cultural-historical analysis was applied, which contributed to the consideration of the process of development and transformation of interpretation in musical art, and also helped to trace the specifics of the evolution of the art of sound recording; the comparative method, thanks to which the features of the interpretation of a piece of music by the performer, sound engineer and listener are revealed; philosophical, cultural and art history methods of analysis, which contributed to the identification of cause-and-effect relationships and regularities in the functioning of the peculiarities of musical interpretation in the art of sound recording, its consideration, and understanding in the unity of all elements, the reproduction of the context of the socio-cultural space in which this phenomenon developed. Scientific novelty. The musical interpretation is investigated in the context of the specifics of the art of sound recording; the influence of postmodern tendencies on the formation of a new paradigm of interpretation of recorded musical work is revealed; the problem of interpretation in the art of sound recording is characterized at the level of performing an interpretation of a musical work, its fixation as an important semantic process that generates new cultural meanings and sound engineering activities. Conclusions. The study revealed that in the context of the art of sound recording, musical interpretation manifests itself on several levels, since in a recorded musical work, as a material object that is accessible to the senses "in infinite variability of reading" (according to J. Derrida), through the prism of the personality of the author of the musical work, the performer and sound engineer embody ideas about reality, and artistic information encoded and decoded by them takes on its final form only in the mind of the listener.