Reviewerly/exHarmony huggingface dataset:
A semantic backdoor attack against Graph Convolutional Networks

Graph convolutional networks (GCNs) have been very effective in addressing the issue of various graph-structured related tasks. However, recent research has shown that GCNs are vulnerable to a new type of threat called a backdoor attack, where the adversary can inject a hidden backdoor into GCNs so that the attacked model performs well on benign samples, but its prediction will be maliciously changed to the attacker-specified target label if the hidden backdoor is activated by the attacker-defined trigger. A semantic backdoor attack is a new type of backdoor attack on deep neural networks (DNNs), where a naturally occurring semantic feature of samples can serve as a backdoor trigger such that the infected DNN models will misclassify testing samples containing the predefined semantic feature even without the requirement of modifying the testing samples. Since the backdoor trigger is a naturally occurring semantic feature of the samples, semantic backdoor attacks are more imperceptible and pose a new and serious threat. In this paper, we investigate whether such semantic backdoor attacks are possible for GCNs and propose a semantic backdoor attack against GCNs (SBAG) under the context of graph classification to reveal the existence of this security vulnerability in GCNs. SBAG uses a certain type of node in the samples as a backdoor trigger and injects a hidden backdoor into GCN models by poisoning training data. The backdoor will be activated, and the GCN models will give malicious classification results specified by the attacker even on unmodified samples as long as the samples contain enough trigger nodes. We evaluate SBAG on four graph datasets and the experimental results indicate that SBAG is effective.
Facilitating inclusion in an evolving digital world

This piece addresses the technological environment, highlighting the impact of digital inaccessibility on the educational experience for all students, created by inaccessible technologies including websites, format of learning materials, and inadequate digital competence of students and faculty. The onset of the COVID-19 pandemic while exposing educational inequalities and the ‘digital divide’, also provided many opportunities to advance digital accessibility. Students continue to appreciate the flexibility offered by online learning environments which can empower them as independent learners. The author provides national and international examples of how HEIs can tackle the complexities of offering an effective hybrid learning environment that is accessible to all by providing innovative solutions which embed the student voice within the process and implementation.
Abstract P6-04-07: Primary Diagnosis of Breast Biopsies supported by AI versus Microscope: Multi-Site Clinical Reader Study

Abstract Objective This study aimed to clinically validate the use of an AI-based solution by pathologists for the primary diagnosis of breast core needle biopsies as compared with the gold standard practice (review on the microscope). Methods A two-arm prospective reader study comparing the performance of pathologists using an AI-based solution with pathologists using a microscope was performed at two sites (different staining and digital scanners). Both arms were compared to ground truth (GT) established by the consensus of two breast pathologists. Rates of major discrepancies between each arm and GT, as determined by an adjudicating pathologist, were compared. Results Eight pathologists participated in the study and reported on 385 cases (442 HES and 330 H&amp;E slides), each case being reported twice, once in each study arm. Pathologists first reviewed only H&amp;E/HES slides, if requested and available, they were provided with IHCs, while the AI results were on H&amp;E/HES only. The major discrepancy rates of the microscope arm and of the AI arm against GT were 4.42% and 3.12%, respectively, demonstrating a 29.4% reduction in major discrepancies. Pathologists with AI demonstrated very high accuracy for the detection of invasive carcinoma with sensitivity and specificity of 100% for both, as well as for DCIS/ADH with sensitivity of 92.4% and specificity of 97.8%. Conclusions This multi-site reader study reports diagnostic accuracy improvements by pathologists performing diagnosis and reporting with the support of a first read AI solution for breast biopsies. The AI solution performed accurately and generalized well for different staining platforms and different scanners. Thus, AI solutions could be used as significant aiding tools for pathologists in clinical decision-making in routine pathology practice, enhancing the quality and reproducibility of diagnosis. Citation Format: Anne Salomon, Alona Nudelman, Joanna Cyrta, Marina Maklakovski, Anat Albrecht Shach, Geraldine Sebag, Giuseppe Mallel, Ira Krasnitsky, Tali Feinberg, Manuela Vecsler, Judith Sandbank. Primary Diagnosis of Breast Biopsies supported by AI versus Microscope: Multi-Site Clinical Reader Study [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-07.
A Real-time Privacy System for Electric Vehicles using Blockchain Technology

In a vehicular energy network (VEN), an efficient transfer of energy among vehicles is realized while increasing the mobility of vehicles in a large geographic location. However, the security and privacy of vehicle owners are not fully explored in the existing literature. Today, because of the exponential rise in the number of vehicle owners in VEN, the problems of traffic congestion, energy consumption, etc., are created. The issues can be alleviated if certain information about vehicles such as the speed, energy consumption price, and location, is efficiently collected. Besides, effective communication is required for ensuring proper and authentic dissemination of traffic information among vehicles while preserving their data privacy. As a consequence, our study suggests a blockchain-based system for privacy preservation. In the proposed system, trust among vehicles is achieved using the Nash bargaining optimization method. The method is employed to maximize the payoffs of vehicles. Additionally, an improved super-increasing weighted sequence is used to preserve the privacy of vehicles by considering two essential parameters: energy consumption and price. Furthermore, the Paillier encryption mechanism is employed to securely transmit vehicles' information across the network. The proposed system has undergone a security study, which reveals that it is resistant to privacy and security-related threats. The performance of the proposed system shows that the system is efficient and reliable.
Recommended methods for teaching resources in public English MOOC based on data chunking

In order to overcome the problems of time-consuming and high recommendation error in traditional public English MOOC teaching resource recommendation methods, this paper proposes a new public English MOOC teaching resource recommendation method based on data partition. The data of public English MOOC teaching resources are collected, and hierarchical clustering algorithm is used to preprocess public English MOOC teaching resources to support the recommendation demand of mobile MOOC teaching resources. According to the preprocessing results, the data block algorithm is used to divide the resource data iteratively. Finally, we calculate the similarity of users' resource use and preference, and construct the public English MOOC teaching resource recommendation model based on the index weight results. Comparative validation results show, in the conventional method, the proposed method recommended consuming less and less precision compared to the recommended.
Classification of cervical cancer using machine learning techniques: a review

One of the most prevalent and deadliest malignancies in women is cervical malignancy. It is the fourth major cause of death among women worldwide. Women's lives can be saved if this malignancy is identified at a precancerous stage because it is fully curable at an early stage. The most widely used screening procedure for cervical malignancy is the pap test. As a result, computerised identification and classification of cervical malignancy from pap images have become essential because they provide a precise, reliable, and fast investigation of the disease's progress. This paper provides a comprehensive overview of the automatic identification of cervical malignancy, its causes, risk factors, and symptoms. This paper also discusses the following: the pap test, which is widely used in screening, the computer-aided diagnosis requirement for the classification of cervical cells, the classes of cervical cells for segmentation as well as classification, and the existing techniques for classifying cervical cells.
SRU-based Multi-angle Enhanced Network for Semantic Text Similarity Calculation of Big Data Language Model

As a fundamental problem of natural language processing (NLP), the calculation of semantic text similarity plays a crucial role in a variety of big data application situations. In the process of text similarity modeling, however, owing to the complexity and ambiguity of Chinese semantics, effectively capturing the semantic interaction characteristics of Chinese text only from a single angle is impossible. This study proposes a deep learning-based computational model for semantic text similarity called SRU-based multi-angle enhanced network (SMAEN). Specifically, the authors firstly combine character-grained embeddings and word-granularity embeddings obtained from the pre-trained model to represent text. The text is encoded using a bidirectional simple recurrent unit (Bi-SRU) network, and the local text similarity is represented using a soft-aligned attention technique. In addition, the authors integrate Bi-SRU with an improved convolutional neural network (CNN) for global similarity modeling to capture semantic, time, and spatial characteristics of short text interaction. Finally, they employ a pooling layer to aggregate the calculation results into a fixed-length vector and a multi-layer perceptual (MLP) classifier to make a determination. Experimental results on Chinese public datasets LCQMC and PAWS-X show that the proposed method fully captures semantic interaction features from multiple angles and achieves advanced performance. This method can produce better matching results and enhance the accuracy of large data analysis. It is applicable to numerous scenarios involving large data, such as information retrieval and recommendation systems.
AI tools could imperil institutional cybersecurity

Headline INT: AI tools could weaken institutional cybersecurity
Legal Issues of Intellectual Property Rights in NFT

NFT는 소유권, 저작권 등의 진위 여부를 확인하는 수단으로서 일종의 디지털 권리증명서라 할 수 있는데, 저작권법적 쟁점과 관련하여, NFT 거래를 통해 디지털 콘텐츠의 저작권자와 NFT의 보유자가 분리되는 현상이 발생하므로 소유권과 저작권을 준별하여야 하고, NFT 대상 저작물의 진위 여부에 따라 나누어 문제되는 개별 저작권의 침해 여부를 살펴보아야 한다. NFT 거래에 따라 권리소진원칙을 재검토할 필요성이 있고, 아직 입법화되지 않은 추급권을 사실상 실현할 수 있다. NFT 거래 플랫폼의 책임과 관련하여 온라인서비스제공자로서의 구성 가능성에 대하여 검토할 필요가 있다. 표지법적 쟁점과 관련하여, 상품의 동일, 유사를 중심으로 한 상표권 침해에 관하여 논의하여야 하고, 상품주체 혼동행위, 저명상표 희석행위, 성과모용행위를 중심으로 부정경쟁행위 해당 가능성에 대하여 살펴볼 필요가 있다. 발명의 특허적격성 문제와 함께 침해추적이 어렵다는 점, NFT 거래를 통한 특허권소진의 가능성 및 NFT 기술의 연구노트에서의 활용방안에 대한 논의 등이 NFT 관련 특허법적 쟁점이라고 할 수 있다. NFT 거래 및 관련 시장이 급성장하면서 지식재산권법 전반에 걸쳐 새로운 법적 쟁점이 계속하여 출현하고 있으나, 섣불리 법제화 하거나 규제하기보다는 시장에서 당사자간의 규약 및 실무관행의 정착을 지켜보면서 관련 산업계 및 학계의 충분한 검토 및 논의를 거쳐 제도가 형성될 수 있기를 기대해본다. NFT is a kind of digital rights certificate as a means of verifying the authenticity of ownership, copyright, etc. In relation to copyright law issues, a phenomenon in which the copyright holder of digital content and the holder of the NFT are separated through NFT transactions occurs, so ownership It is necessary to distinguish between copyright and copyright, and to check whether individual copyrights are infringed according to the authenticity of the NFT target work. According to NFT transactions, there is a need to review the principle of exhaustion of rights, and the right to follow-up, which has not yet been legislated, can be practically realized. Regarding the responsibility of the NFT trading platform, it is necessary to review the possibility of being configured as an online service provider. Regarding the issue of the labeling law, it is necessary to discuss trademark infringement centering on the same or similar products, and to examine the possibility of unfair competition focusing on product subject confusion, well-known trademark dilution, and imitation of performance. Issues related to NFT-related patent law include the difficulty of tracking infringement along with the issue of patent eligibility of the invention, the possibility of exhausting patent rights through NFT transactions, and the discussion of how to utilize NFT technology in research notes. As NFT transactions and related markets grow rapidly, new legal issues continue to emerge throughout the intellectual property law. It is hoped that a system can be formed through discussion and discussion.
Variance-based sensitivity analysis of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">Λ</mml:mi></mml:math> -type quantum memory

The storage and retrieval of photonic quantum states, quantum memory, is a key resource for a wide range of quantum applications. Here we investigate the sensitivity of $\mathrm{\ensuremath{\Lambda}}$-type quantum memory to experimental fluctuations and drift. We use a variance-based approach, focusing on the effects of fluctuations and drift on memory efficiency. We consider shot-to-shot fluctuations of the memory parameters, and separately we consider longer timescale drift of the control field parameters. We find the parameters that a quantum memory is most sensitive to depend on the quantum memory protocol being employed, where the observed sensitivity agrees with physical interpretation of the protocols. We also present a general framework that is applicable to other figures of merit beyond memory efficiency. These results have practical ramifications for quantum memory experiments.
Pengembangan E-Modul Berbasis Problem Based Learning pada Materi Sistem Pernapasan untuk Meningkatkan Hasil Belajar Kognitif Siswa SMP

The effectiveness of use of printed teaching materials such as student textbooks, such as textbooks, opens up opportunities for integrating a teaching material supplement with the latest information technology to support the achievement of 21st century skills. One of them is the development of an e-module based on Problem Based Learning. This research aims to develop valid, practical, and effective Problem Based Learning e-modules. This research uses research and development methods with the 4D development model (Define, Design, Develop, and Disseminate). The test subjects in this study were class VIII students of SMP Muhammadiyah 17 Pon Village, Sei Bamban District, Serdang Bedagai Regency, North Sumatra Province. The results showed that: 1) the validation results of media experts obtained an average percentage of 77.63% in the valid category. The results of the material expert validation obtained an average percentage of 97.61% with a very valid category. The results of the linguist validation obtained an average percentage of 100% with a very valid category; 2) the results of the teacher's response assessment obtained an average percentage of 81.06% in the very practical category. The results of assessing the responses of students in the small group tryout obtained an average percentage of 87.22% in the very practical category, and in the large group tryout an average percentage of 87.84% was obtained in the very practical category; and 3) product effectiveness test using the N-gain test obtained an average score of 0.59 indicating an increase in cognitive learning outcomes with moderate criteria. Based on the results of testing the validity, practicality and effectiveness of the product, the e-module based on Problem Based Learning on respiratory system material developed is valid, practical and effective for teachers and students to use in the process of learning natural sciences.
Decentralized Blockchain Based Land Deed Verification and Reservation System in Bangladesh

The present land document reservation process that is done manually provides a lot of insecurity, unsafely, and many scopes for land deed fraud in terms of storing land documents and maintaining the details of ownership of specific land property. So, the current land deed reservation and verification method don’t seem reliable and efficient. To make it a reliable and safe transaction, we will use blockchain technology. We have created a blockchain system for land deed authentication utilizing the data encryption algorithm SHA-256. Using this system, land deed transactions will be safer and can store, verify, and preserve all of the relevant information of a land deed document. This proposed architecture store retrieves and detects attempts to modify copies using a variety of approaches, including several efficient technologies like Zero Knowledge proof, Public-key cryptography, and IPFS; It generates far more efficient solutions than the other systems. This method has been put out as a potential means of thwarting fraudulent land deeds and bringing delight to the public.
Special Vehicle Target Detection Based on Improved Yolov5 + Jetson NX

In military operations, UAVs can provide various intelligence to ground commanders. Among them, special vehicle targets are the most common. The special vehicle target images obtained by UAVs have the characteristics of large differences in target scales and complex environments. This paper proposes an improved yolov5 target detection algorithm based on Jetson NX deployment. First, we introduce the CBAM self-attention module to the yolov5 algorithm to improve the feature extraction ability of the network. Second, we used the MetaAconC activation function instead of SiLU to tune up the model performance. And we deploy the model on the Jetson NX platform through TensorRT. Its inference speed is increased from 17FPS to 33FPS, and the inference speed is increased by 94.1%, achieving the ability to implement target detection, and after TensorRT acceleration, the model performance did not decline, but slightly improved. Under various background conditions, the recognition rate of our method reaches 96.9% with a confidence threshold of 0.5, which has excellent target detection ability.
Improved LSB image steganography with high imperceptibility based on cover‐stego matching

Abstract Information security is an important factor when critical information is transferred. This paper concentrates on finding a method that can hide a message in an image without making any changes to it. The proposed method is novel and based on the Least Significant Bit (LSB) method with a high imperceptibility and capacity. Initially, the secret message bits are compressed using the LZW technique, then the compressed message bits are embedded into the LSB of the edge pixels in the cover image which have the same bit values. As a result, the cover image pixels remain unchanged and the stego image matches the cover image. The addresses of pixels that contain the compressed secret bits are maintained in a file called “location addresses” which the receiver uses to retrieve the compressed secret message. It is possible that the stego image will not be transmitted if it was previously specified by the sender and the receiver. Image quality metrics are applied to several standard images. The obtained PSNR for the stego image is infinity, SSIM and NCC are one, MSE and AD are zero for different embedding capacities in all images, which ensures the stego image's excellent imperceptibility.
Analysis of Jeju Olle posts Before and During the COVID-19 Pandemic Using Text Mining Techniques

The purpose of this study is to collect posts about Jeju Olle Trail uploaded to blogs and cafes (Naver, Daum) and to understand the perception and the behavior of Jeju Olle Trail users. After collecting posts with the keyword
Penerapan Media Kahoot pada Pembelajaran Bahasa Indonesia Siswa Kelas VII SMPN 2 Wanggarasi Kabupaten Pohuwato

This study aims to (1) describe the application of Kahoot in learning Indonesian, (2) describing student responses based on the reflection rubric on the application of Kahoot in learning Indonesian. The method used is a combination method with a descriptive research type through a qualitative approach. The results of the research show that the application of Kahoot In learning Indonesian, it is carried out through three stages of activity, namely the initial, core and final activities of learning. Student responses based on the reflection rubric on the application of Kahoot in learning Indonesian in the Agree and Strongly Agree categories, said that the appearance was attractive, easy to use, learning became easy to understand, and there was an increase in students’ motivation and interest in learning.
Recognizing human activities using light-weight and effective machine learning methodologies

<ns5:p><ns5:bold>Background</ns5:bold></ns5:p><ns5:p> </ns5:p><ns5:p> Human activity recognition is a dynamic and challenging task. It is a large field of research and development. It involves predicting the movement of a person from the raw sensor data using a machine learning model. To accurately detect human activities for e-health systems, several research attempts have been carried out using data mining and machine learning techniques, but there still is room to improve the performance. To this aim, human activities such as walking, standing, laying, sitting, walking upstairs, walking downstairs are predicted using prominent machine learning models. </ns5:p><ns5:p> The aim of human activity recognition is examining actions from photos or video clips. This serves as the driving force behind human activity identification systems' aim to accurately classify input data into the relevant activity category.</ns5:p><ns5:p> <ns5:bold>Methods </ns5:bold></ns5:p><ns5:p> Six machine learning techniques, including decision tree, random forest, linear regression, Naïve bayes, k-nearest neighbour, and neural networks algorithms, were used for human activity recognition.</ns5:p><ns5:p> <ns5:bold>Results </ns5:bold></ns5:p><ns5:p> The performance of decision tree, random forest, linear regression, Naïve bayes, k-nearest neighbor, and neural network algorithms was assessed with a human activity recognition dataset. From the results, the random forest classifier and neural network gave good results, whereas the Naïve bayes result was not satisfying.</ns5:p><ns5:p> <ns5:bold>Conclusions</ns5:bold></ns5:p><ns5:p> We classified the SITTING, STANDING, LAYING, WALKING, WALKING_DOWNSTAIRS, WALKING_UPSTAIRS activities with machine learning techniques with 98% of accuracy</ns5:p>