Reviewerly/exHarmony huggingface dataset:
Efficient block contrastive learning via parameter-free meta-node approximation

Contrastive learning has recently achieved remarkable success in many domains including graphs. However contrastive loss, especially for graphs, requires a large number of negative samples which is unscalable and computationally prohibitive with a quadratic time complexity. Sub-sampling is not optimal. Incorrect negative sampling leads to sampling bias. In this work, we propose a meta-node based approximation technique that is (a) simple, (b) canproxy all negative combinations (c) in quadratic cluster size time complexity, (d) at graph level, not node level, and (e) exploit graph sparsity. By replacing node-pairs with additive cluster-pairs, we compute the negatives in cluster-time at graph level. The resulting Proxy approximated meta-node Contrastive (PamC) loss, based on simple optimized GPU operations, captures the full set of negatives, yet is efficient with a linear time complexity. By avoiding sampling, we effectively eliminate sample bias. We meet the criterion for larger number of samples, thus achieving block-contrastiveness, which is proven to outperform pair-wise losses. We use learnt soft cluster assignments for the meta-node construction, and avoid possible heterophily and noise added during edge creation. Theoretically, we show that real world graphs easily satisfy conditions necessary for our approximation. Empirically, we show promising accuracy gains over state-of-the-art graph clustering on 6 benchmarks. Importantly, we gain substantially in efficiency; over 2x reduction in training time and over 5x in GPU memory reduction. Additionally, our embeddings, combined with a single learnt linear transformation, is sufficient for node classification; we achieve state-of-the-art on Citeseer classification benchmark. code:https://github.com/gayanku/PAMC
A novel fast hybrid face recognition approach using convolutional Kernel extreme learning machine with HOG feature extractor

The ability to identify a person's face from a digitized photo or video frame against a database of faces is known as facial recognition. In the past few years, algorithms that use deep learning to recognize faces have become more popular. The majority of them are predicated on extremely accurate but complicated Convolutional Neural Networks (CNNs), which require a lot of computational power, storage space, and a number of training epochs before they provide satisfying results, and are notably difficult to implement. In an effort to reduce the training time by reducing the number of epochs and increase accuracy, this paper introduces a novel fast hybrid face recognition approach HOG-CKELM, based on CNN that makes use of Kernel based Extreme Learning Machines (KELM) and Histogram of Oriented Gradients (HOG) as facial feature extractor. The effectiveness of the proposed hybrid face recognition technique is evaluated using AT & T, Yale, and JAFFE datasets. When compared to traditional HOG-CNN based techniques, the experimental evaluation indicates that the proposed method for face recognition is capable of achieving excellent performance in terms of accuracy and training time.
FGDA-GS: Fast guided decision attack based on gradient signs for skeletal action recognition

The black-box adversarial attacks for skeletal action recognition currently construct high-quality adversarial examples by sacrificing queries and perturbation imperceptibility. To overcome the above drawbacks, we propose a novel fast guided decision attack based on gradient signs for skeletal action recognition (FGDA-GS) scheme. Firstly, to reduce the queries, we guide the adversary to attack along the gradient direction obtained by sampling in a custom Gaussian distribution and design a gradient direction estimation method based on successful history information queries, which can quickly determine the updated direction of the effective perturbation without a large query. Secondly, to ensure the imperceptibility of the adversarial examples, we design a manifold projection function matching the first-order derivatives based on joint position constraints, joint angle constraints, and bone-length constraints to generate complete and natural skeletal adversarial examples that are invisible to the human eye. Finally, comprehensive experimental results demonstrate that FGDA-GS can generate strong-natured, highly effective adversarial examples with significantly reduced query. Specifically, FGDA-GS achieves a 100% attack success rate on different datasets and models with reduced queries by hundreds to thousands and reduced time by more than half. Particularly in the untargeted attack setting, compared to BASAR, FGDA-GS outperforms in terms of average joint position deviation, average joint acceleration deviation, average joint angle acceleration deviation, and average bone-length deviation percentage for the STGCN model on the NTU dataset, with percentage reductions of 91.13%, 91.81%, 80.60%, and 81.08%, respectively. We also conducted experiments on defense methods to validate our proposed scheme's effectiveness further.
MGGAN: A multi-generator generative adversarial network for breast cancer immunohistochemical image generation

The immunohistochemical technique (IHC) is widely used for evaluating diagnostic markers, but it can be expensive to obtain IHC-stained section. Translating the cheap and easily available hematoxylin and eosin (HE) images into IHC images provides a solution to this challenge. In this paper, we propose a multi-generator generative adversarial network (MGGAN) that can generate high-quality IHC images based on the HE of breast cancer. Our MGGAN approach combines the low-frequency and high-frequency components of the HE image to improve the translation of breast cancer image details. We use the multi-generator to extract semantic information and a U-shaped architecture and patch-based discriminator to collect and optimize the low-frequency and high-frequency components of an image. We also include a cross-entropy loss as a regularization term in the loss function to ensure consistency between the synthesized image and the real image. Our experimental and visualization results demonstrate that our method outperforms other state-of-the-art image synthesis methods in terms of both quantitative and qualitative analysis. Our approach provides a cost-effective and efficient solution for obtaining high-quality IHC images.
Secure Cloud Storage and Retrieval of Personal Health Data From Smart Wearable Devices With Privacy-Preserving Techniques

With the increasing awareness of personal health, personal health data management has become an important part of people's lives. Smart wearable devices (SWDs) collect people's personal health data, and then store the data on cloud. Authorized entities access the data to provide personalized health services. However, these personal health data contain a large amount of sensitive information, which may pose a significant threat to people's lives and property. To address this, this paper proposes a privacy-preserving solution. SWD data is encrypted, and secure indexes are created using Bloom filter and 0-1 encoding. Encrypted data and indexes are stored in a semi-trusted cloud. Only authorized entities can access the ciphertexts, ensuring secure personalized health management. Extensive experiments validate the scheme's efficiency in index construction, query token generation, and ciphertext search. Security analysis confirms no external entity, including the cloud, gains additional information during retrieval.
A Novel Graph-based Diversity-aware Rank Fusion Method Applied to Image Metasearch

While search result diversification is used to handle ambiguous or underspecified queries, rank aggregation is a widely used approach in metasearch. However, current aggregation methods assume that the input rankings are built only according to the relevance of the items, disregarding the inter-relationship between images in each ranking. Hence, these methods tend to be inadequate for diversity-oriented retrieval. In this work, we introduce a diversity-aware rank fusion method that is validated in the context of diverse image metasearch. The experimental findings indicate that the proposed method significantly improves the overall diversity of metasearch results, in comparison to the state-of-the-art positional and score-based fusion methods.
Common knowledge processing patterns in networks of different systems

Knowledge processing has patterns which can be found in biological neuron activity and artificial neural networks. The work explores whether an underlying structure exists for knowledge which crosses domains. The results show common data processing patterns in biological systems and human-made knowledge-based systems, present examples of human-generated knowledge processing systems, such as artificial neural networks and research topic knowledge networks, and explore change of system patterns over time. The work analyzes nature-based systems, which are animal connectomes, and observes neuron circuitry of knowledge processing based on complexity of the knowledge processing system. The variety of domains and similarity in processing mechanisms raise the question: if it is common in natural and artificial systems to see this pattern-based knowledge processing, how unique is knowledge processing in humans.
Two‐Mode Correlated Multiphoton Bundle Emission

Abstract The preparation of correlated multiphoton sources is an important research topic in quantum optics and quantum information science. Here, two‐mode correlated multiphoton bundle emission in a nondegenerate multiphoton Jaynes–Cummings model, which is comprised of a two‐level system coupled with two cavity modes is studied. The two‐level system is driven by a near‐resonant strong laser such that the Mollow regime dominates the physical processes in this system. Under certain resonance conditions, a perfect super‐Rabi oscillation between the zero‐photon state and the ()‐photon state of the two cavity modes can take place. Induced by the photon decay, the two‐mode correlated multiphoton bundle emission occurs in this system. More importantly, the results show that there is an antibunching effect between the strongly‐correlated photon bundles, so that the system behaves as an antibunched ()‐photon source. The work opens up a route toward achieving two‐mode correlated multiphoton source device, which has potential applications in modern quantum technology.
Entropy‐guided contrastive learning for semi‐supervised medical image segmentation

Abstract Accurately segmenting medical images is a critical step in clinical diagnosis and developing patient‐specific treatment plans. While supervised learning algorithms have achieved excellent performance in this area, they require a large amount of annotated data, which is often time‐consuming and difficult to obtain. As a result, semi‐supervised learning (SSL) has gained attention as it has the potential to alleviate this challenge by using not only limited labelled data but also a large amount of unlabelled data. A common approach in SSL is to filter out high‐entropy features and use the low‐entropy part to compute unsupervised loss. However, it is believed that the high‐entropy part is also beneficial for model training, and discarding it can lead to information loss. To address this issue, a simple yet efficient contrastive learning approach is proposed in this work for semi‐supervised medical image segmentation, called Entropy‐Guided Contrastive Learning Segmentation Network (EGCL‐Net). The proposed method separates the low‐entropy and high‐entropy features via the average of predictions, using contrastive loss to pull the intra‐class entropy representation distance close and push the inter‐class entropy representation distance away. Extensive experiments on the automated cardiac diagnosis challenge dataset, COVID‐19, and BraTS2019 datasets showed that: (1) EGCL‐Net can significantly improve performance by utilizing high‐entropy representation, and (2) the authors’ EGCL‐Net outperforms recent state‐of‐the‐art semi‐supervised methods in both qualitative and quantitative evaluations.
A hybrid part-of-speech tagger with annotated Kurdish corpus: advancements in POS tagging

Abstract With the rapid growth of online content written in the Kurdish language, there is an increasing need to make it machine-readable and processable. Part of speech (POS) tagging is a critical aspect of natural language processing (NLP), playing a significant role in applications such as speech recognition, natural language parsing, information retrieval, and multiword term extraction. This study details the creation of the DASTAN corpus, the first POS-annotated corpus for the Sorani Kurdish dialect. The corpus, containing 74,258 words and thirty-eight tags, employs a hybrid approach utilizing the bigram hidden Markov model in combination with the Kurdish rule-based approach to POS tagging. This approach addresses two key problems that arise with rule-based approaches, namely misclassified words and ambiguity-related unanalyzed words. The proposed approach’s accuracy was assessed by training and testing it on the DASTAN corpus, yielding a 96% accuracy rate. Overall, this study’s findings demonstrate the effectiveness of the proposed hybrid approach and its potential to enhance NLP applications for Sorani Kurdish.
Internet of Things and User Privacy Protection

The object of this article is to discuss the features of the digital world based on the global network space and contemporary information technologies, in particular the Intern of Things (IoT). A brief survey of the main types of information specifying the place of the personal data is made in the introduction. Social aspects of the Internet space are discussed whit determining the negative consequences for privacy. The features of IoT including short presentation of the important versions (wireless sensor networks, machine-to-machine communication, and cyber-physical systems) are presented. And finally, the challenges of IoT for user privacy and personal data protection are defined.
DIAGNOSE OF MENTAL ILLNESS USING FORWARD CHAINING AND CERTAINTY FACTOR

The prevalence of mental disorders in Indonesia is increasingly significant, as seen from the 2018 Riskesdas data. Riskesdas records mental, emotional health problems (depression and anxiety) as much as 9.8%. This shows an increase when compared to the 2013 Riskesdas data of 6%. Based on these data, it can be said that many people still suffer from mental disorders. Meanwhile, the number of medical personnel, medicines and public treatment facilities for people with mental disorders is still limited. In addition, the lack of public awareness, concern and knowledge about mental health causes a lack of public interest in consulting a psychologist, so people tend to self-diagnose. One solution for self-diagnosis is to use an expert system. This study developed an expert system using the forward chaining method and certainty factor. Based on the research conducted, the results are as follows. First, the expert-based system that has been developed can help provide the results of a diagnosis that is carried out before there are complaints and will be detected early by efforts to increase awareness of the prevention of mental illness and reduce the tendency to self-diagnose. Second, applying the forward chaining method and certainty factor to this expert system can produce an accuracy rate of 95.918%. An expert has also validated these results; in this study, the expert was a psychologist at a hospital in Yogyakarta.
Knowledge-Based Semi-Automatic Selection of Personalized Learning Paths

The main problem of adaptive learning and tutoring is how to select appropriate learning objects (LOs) and sequences of objects, called “Learning path”. In this work we discuss results of our analysis of the research about Adaptive learning and personalized pathways. We propose a knowledge model for semi-automatic learning path generation or selection, based on learners' knowledge, performance, learning styles, competences, preferences, or other learning context properties.
Semantics-Based Knowledge Representation and Personalized Learning Content Development

Personalized E-learning resources are of great importance for every adaptive or intelligent tutoring system. In this paper we will discuss the role of semantic models for development of personalized e-learning content. We systematize the main concepts, needed for overall description of all components of the learning process and their relationships and propose a ontology, modeling general knowledge in all the sub-domains of e-learning domain and its important relationships. This ontology will be most useful for annotation of the learning objects to support personalized tutoring.
Targeting by Sensory-representative Systems in Advertising and PR Texts (Experience in Developing a Methodology for MGIMO Students)

The proposed methodology complements the arsenal of the usual methods of segmentation, which is traditionally based on socio-demographic and financial characteristics. It is based on the study of human perception and segments the audience by sensory-representative systems. Sensory-cognitive systems are the most important element in the process of any communication. The success of transmitting, consuming and assimilating information with minimal aberration largely depends on the ability and mood of the audience to perceive the text through one or more channels. In the article, we will first consider the theoretical basis of this method, then give examples and provide an opportunity to monitor the reception of new techniques with the help of control questions. The task of PR practice is to facilitate this process for the audience by applying this technique. By targeting the audience through the leading perception channel and using the studied algorithms, professionals achieve more effective communication by fixing the consumer's interest not only on the information provided, but also on the emotional response. The algorithmic structuring of the communication process, which is an integral part of the methodology, helps to subsequently increase efficacy of PR activities, and reduce the time spent on the production of texts. In addition to obtaining the professional skill of interacting with the target audience, the technique allows one to determine the leading channel of perception by the student himself/herself and to improve the self-organization of their studies and their cognitive process. The use of workshops in teaching opens up new opportunities for personal and professional growth.
Hemudu – Chinese early Neolithic culture

The subject of this research is the Chinese Neolithic Hemudu culture (&amp;#27827;&amp;#22982;&amp;#28193;&amp;#25991;&amp;#21270;), it will be 50 years since its discovery in 2023. Its age is estimated at 6500-7000 years. Based on the analysis of archaeological reports, materials of the Hemudu Museum and research of Chinese and Russian scientists, the authors put the light on the geographical location of the ancient settlement of Hemudu, the history of its discovery, its main typological features, which led to identifying it as a independent culture of the Neolithic period of Eastern China. Hemudu had a great influence on the cultural genesis of Chinese civilization. More than 40 prehistoric settlements are attributed to the Hemudu culture. In this article, the authors gave a brief description of one of them – the ruins of Xiangjiashan (&amp;#40094;&amp;#26550;&amp;#23665;&amp;#36951;&amp;#22336;). The main conclusions of the study are as follows: the discovery and identification of the Hemudu culture was a major breakthrough in Chinese Neolithic archeology. It proved that the Yangtze basin was also the birthplace of ancient Chinese culture and rewrote the history of the development of Chinese civilization. In terms of its typological features, the Neolithic settlement of Xiangjiashan is similar to Hemudu, which indicates their genetic connection. The remains of the early Xiangjiashan period filled the gap in the history of the Hemudu settlement, making it more logical and understandable for researchers. All finds made not only in the settlement of Hemudu, but also in Xiangjiashan are extremely important for understanding the development of Chinese culture.
Identify Fake Author in Indonesia Crime Cases: A Forensic Authorsip Analysis Using N-gram and Stylometric Features

cases of violations of the Law on Electronic Information and Transaction (UU ITE) which are dominated by cases related to hacking and fake documents. An interesting fact is that a number of cases were found with evidence that points to the falsification of texts and someone's authorship. Proofing authorship dispute cases in Indonesia has not reached an analysis of authorship, because of the difficulty of identifying personal identity in electronic texts, especially in short texts with limited characters and words. This study examines Indonesian text set to investigate and describe linguistic profiles based on N-gram analysis and style characteristics. The data source in this study is Corpora of electronic text sets from 50 unique authors, including 8 authors and evidence from criminal cases, which are limited to 2000 characters or 500 words. All texts are personal texts that are collected from volunteers and case documents that are permitted to be accessed. Data analysis was carried out by determining and calculating the n-grams, both on the character-level and word-level, and performing stylometric features that the Natural Language Toolkit (NLTK) library extracts. The results of the data analysis show that lexically, the character-level n-gram analysis, as the smallest n-unit, shows an important element of authorship attribution, such as the use of alphabetic and non-alphabetic characters, capitalization, and punctuation. Diction is a significant factor to identify the author's profile and distinguish between one author and another. The results of using the small text set are able to demonstrate authorship attribution to identify authors, with the various stylistic features, resulting in a classification accuracy of between 92% and 98.5%.
Adaptive E-Assessment Framework

The important part of the learning process is practice and evaluation. Evaluation is intended to determine students' abilities so that the learning process can be improved according to student needs. Unfortunately, exercises and evaluations that are appropriate to the student's personality are difficult to do traditionally by teachers. In order to make the evaluation process easier and more personal, a system that is able to assist the process of training and evaluating students is needed. The adaptive training system allows the system to provide questions that can increase students' motivation and abilities. In addition, the form of the questions can be in accordance with the circumstances of the students. A methodology based on qualitative research using the Grounded Theory method was applied in this study. The proposed framework consists of several layers, including the User layer, Input Output layer, Model and AI/Machine Learning layers. Input Output can consist of a variety hardware. The model layer can consist of student model and domain model. The AI/Machine Learning layer can consist of item classification modules, student condition monitoring, questions recommendations, student performance evaluations, learning path recommendations, gamification engine, prevention action, questions generator. It is intended that the framework created would satisfy the proper evaluation requirements of each student in order to encourage learning and enhance student abilities. This framework is intended for use in secondary schools.