Reviewerly/exHarmony huggingface dataset:
Development of Interactive E-Modules Assisted by The Kvisoft Flipbook Maker Application on Algebraic Forms Grade VII

&lt;em&gt;The research objectives are to develop a mathematics learning media product that can be accessed via android with the help of the Kvisoft Flipbook Maker application. The type of research is Research and Development (R&amp;amp;D) with a 4-D Thiagarajan research model which consists of 4 stages (Define, Design, Development, and Disseminate). The subjects of this study were 10 students of class VII SMP N 3 Bantul. &lt;em&gt;Data collection techniques were performed through questionnaires and validation sheets&lt;/em&gt;. Based on the results of expert validation, the e-module-based learning media is feasible to be used in the mathematics learning process and is included in the very good category with an ideal percentage of 90.85%. The results of the response of educators with the ideal percentage of 87.5% in the excellent category while the results of the responses of students with the ideal percentage of 92% in the excellent category. This shows that the learning media developed is suitable for use in the mathematics learning process and e-module-based media products can be further developed in the learning process to encourage educators to be more creative and innovative in creating interesting and fun media for students.&lt;/em&gt;
XPGraph: XPline-Friendly Persistent Memory Graph Stores for Large-Scale Evolving Graphs

Traditional in-memory graph storage systems have limited scalability due to the limited capacity and volatility of DRAM. Emerging persistent memory (PMEM), with large capacity and non-volatility, provides us an opportunity to realize the scalable and high-performance graph stores. However, directly moving existing DRAM-based graph storage systems to PMEM would cause serious PMEM access inefficiency issues, including high read and write amplification in PMEM and costly remote PMEM accesses across NUMA nodes, thus leading to the performance bottleneck. In this paper, we propose XPGraph, a PMEM-based graph storage system for managing large-scale evolving graphs, by developing an XPLine-friendly graph access model with vertex-centric graph buffering, hierarchical vertex buffer managing, and NUMA-friendly graph accessing. Experimental results show that XPGraph achieves 3.01× to 3.95× higher update performance and up to 4.46× higher query performance, compared with the state-of-the-art in-memory graph storage system implemented on a PMEM-based system.
Classifying Social Media Using Deep Packet Inspection Data

This article aims at using deep packet inspection data to classify social media on the network. Social media are interactive applications and services that allow users to share and create content or communicate on the internet. We have used a social media classification approach to identify and categorize most of the applications and services used for social networking. The paper's contribution uses the deep packet inspection (DPI) data for social media classification. The DPI data represents different real-time features processed by the DPI system. These features represent the network usage of various users classified into service, service category, and time of the service used (timestamp). The DPI system captured the network traffic data and usage trend of a user based on four categories (e.g., streaming, web browsing, gaming, and others) but also in unique applications within these categories (e.g., Snapchat, Facebook, Amazon, WhatsApp, YouTube, Tik Tok, Netflix, and others). The social media classification performed real-time stratification of the DPI results. Maven Systems Worx granted its resources and tools to obtain the DPI data from various networks. We have analyzed the data from September 2021 to April 2022. Packets and bytes have expressed the network traffic to detect the service and category with the highest number of traffic. The web and streaming categories with Netflix, Facebook, and YouTube services were the most utilized. Facebook and YouTube influenced the throughput fluctuation, but Tik Tok's throughput was minimalistic (the less the traffic, the less throughput). Mostly, network traffic is also encrypted and there are benefits to using network traffic decryption. The throughput fluctuates due to subscriber activity having Bing, Apple Music, Netflix, and Google detected as the most utilized social media services. Lastly, the streaming category obtained the highest throughput.
Many-body coherence and entanglement probed by randomized correlation measurements

We show how coherences between identical constituents of a many-body quantum state can be interrogated by suitable correlation functions, and identify sufficient conditions under which low-order correlators fully characterize many-body coherences, as controlled by the constituents' mutual distinguishability. Comparison of correlators of different order detects many-body entanglement.
Device-independent verification of Einstein–Podolsky–Rosen steering

Entanglement lies at the heart of quantum mechanics, and has been identified an essential resource for diverse applications in quantum information. If entanglement could be verified without any trust in the devices of observers, i.e., in a device-independent (DI) way, then unconditional security can be guaranteed for various quantum information tasks. In this work, we propose an experimental-friendly DI protocol to certify the presence of entanglement, based on Einstein-Podolsky-Rosen (EPR) steering. We first establish the DI verification framework, relying on the measurement-device-independent technique and self-testing, and show it is able to verify all EPR-steerable states. In the context of three-measurement settings as per party, it is found to be noise robustness towards inefficient measurements and imperfect self-testing. Finally, a four-photon experiment is implemented to device-independently verify EPR-steering even for Bell local states. Our work paves the way for realistic implementations of secure quantum information tasks.
Accelerated Digitalization of the Epidemiological Measures: Overcoming the Technological and Process Complexities of Establishing the EU Digital COVID Certificate in Slovenia

In order to facilitate free movement of EU citizens during the COVID-19 pandemic, in early 2021 the European Commission proposed the establishment of an EU Digital COVID Certificate. By 1 July 2021, all EU Member States have successfully implemented the EU Digital COVID Certificate. The technological and procedural complexities encountered while establishing the EU Digital COVID Certificate in Slovenia are reviewed in this article.This research employs a case study methodology. Controlled focus group sessions comprising eighteen eminent experts (not including medical and other expert groups involved in the epidemiological measures) in charge of the EU Digital COVID Certificate and other national eHealth services in Slovenia were used as the primary data collection technique. Focus group discussions were preceded by an all-encompassing review of the literature and the examination of numerous materials covering the EU Digital COVID Certificate-related content.The study findings reveal that the technological and process complexities are associated with the fragmented data sources and complicated and abundant business rules used for the generation and verification of the EU Digital COVID Certificate. However, despite the technological, process and other impediments that arose during the establishment of the EU Digital COVID Certificate in Slovenia, it can be argued that the approach used and stakeholder commitment, especially in critical pandemic conditions, offered the much-desired harmonisation and application of this digital service at the EU level.The study highlights the importance of a sound and coherent model for the impending establishment of cross-border eHealth services and suggests that the ad hoc implementation of such challenging and delicate digital solutions in the future will only be feasible with the prior construction of robust and interoperable digital health infrastructures across the EU Member States.
Artificial optoelectronic spiking neuron based on a resonant tunnelling diode coupled to a vertical cavity surface emitting laser

Abstract Excitable optoelectronic devices represent one of the key building blocks for implementation of artificial spiking neurons in neuromorphic (brain-inspired) photonic systems. This work introduces and experimentally investigates an opto-electro-optical (O/E/O) artificial neuron built with a resonant tunnelling diode (RTD) coupled to a photodetector as a receiver and a vertical cavity surface emitting laser as a transmitter. We demonstrate a well-defined excitability threshold, above which the neuron produces optical spiking responses with characteristic neural-like refractory period. We utilise its fan-in capability to perform in-device coincidence detection (logical AND) and exclusive logical OR (XOR) tasks. These results provide first experimental validation of deterministic triggering and tasks in an RTD-based spiking optoelectronic neuron with both input and output optical (I/O) terminals. Furthermore, we also investigate in simulation the prospects of the proposed system for nanophotonic implementation in a monolithic design combining a nanoscale RTD element and a nanolaser; therefore demonstrating the potential of integrated RTD-based excitable nodes for low footprint, high-speed optoelectronic spiking neurons in future neuromorphic photonic hardware.
Spoofing keystroke dynamics authentication through synthetic typing pattern extracted from screen-recorded video

Abstract As the inter-connectivity in cyberspace continues to increase exponentially, privacy and security have become two of the most concerning issues that need to be tackled in today’s state of technology. Therefore, password-based authentication should no longer be used without an additional layer of authentication, namely two-factor authentication (2FA). One of the most promising 2FA approaches is Keystroke Dynamics, which relies on the unique typing behaviour of the users. Since its discovery, Keystroke Dynamics adoption has been continuously growing to many use cases: generally to obtain access to a platform through typing behaviour similarity, into a continuous keystroke monitoring on e-learning or e-exams platforms to detect illegitimate participants or cheaters. As the adoption of Keystroke Dynamics continues to grow, so does the threats that are lurking in. This paper proposes a novel exploitation method that utilizes computer vision to extract and learn a user’s typing pattern just from a screen-recorded video that captures their typing process. By using a screen-recorded video, an attacker could eliminate the needs to inject a keylogger into the victim’s computer, thus rendering the attack easier to perform and more difficult to detect. Furthermore, the extracted typing pattern can be used to spoof a Keystroke Dynamics authentication mechanism with an evasion rate as high as 64%, a considerably alarming rate given the impact it yields if the attacks are successful, which allows an attacker to pretend, mimic, and falsely authenticate as the victim (i.e., total account takeover). This paper also shows that from a screen-recorded video, one can produce a staggering statistical similarity in keystroke timing patterns as if they used an actual keylogger, and the extracted patterns can be potentially used to spoof the Keystroke Dynamics authentication service. To the author’s best knowledge, there is no precedence of previous research that suggests this kind of attack (i.e. using video to spoof keystroke dynamics). This research can be used as the baseline for future research in this area.
Multi-Stage Classification-Based Deep Learning for Gleason System Grading Using Histopathological Images

In this work, we introduced an automated diagnostic system for Gleason system grading and grade groups (GG) classification using whole slide images (WSIs) of digitized prostate biopsy specimens (PBSs). Our system first classifies the Gleason pattern (GP) from PBSs and then identifies the Gleason score (GS) and GG. We developed a comprehensive DL-based approach to develop a grading pipeline system for the digitized PBSs and consider GP as a classification problem (not segmentation) compared to current research studies (deals with as a segmentation problem). A multilevel binary classification was implemented to enhance the segmentation accuracy for GP. Also, we created three levels of analysis (pyramidal levels) to extract different types of features. Each level has four shallow binary CNN to classify five GP labels. A majority fusion is applied for each pixel that has a total of 39 labeled images to create the final output for GP. The proposed framework is trained, validated, and tested on 3080 WSIs of PBS. The overall diagnostic accuracy for each CNN is evaluated using several metrics: precision (PR), recall (RE), and accuracy, which are documented by the confusion matrices.The results proved our system's potential for classifying all five GP and, thus, GG. The overall accuracy for the GG is evaluated using two metrics, PR and RE. The grade GG results are between 50% to 92% for RE and 50% to 92% for PR. Also, a comparison between our CNN architecture and the standard CNN (ResNet50) highlights our system's advantage. Finally, our deep-learning system achieved an agreement with the consensus grade groups.
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning

Anomaly detection has wide applications in machine intelligence but is still a difficult unsolved problem. Major challenges include the rarity of labeled anomalies and it is a class highly imbalanced problem. Traditional unsupervised anomaly detectors are suboptimal while supervised models can easily make biased predictions towards normal data. In this paper, we present a new supervised anomaly detector through introducing the novel Ensemble Active Learning Generative Adversarial Network (EAL-GAN). EAL-GAN is a conditional GAN having a unique one generator <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">versus</i> multiple discriminators architecture where anomaly detection is implemented by an auxiliary classifier of the discriminator. In addition to using the conditional GAN to generate class balanced supplementary training data, an innovative ensemble learning loss function ensuring each discriminator makes up for the deficiencies of the others is designed to overcome the class imbalanced problem, and an active learning algorithm is introduced to significantly reduce the cost of labeling real-world data. We present extensive experimental results to demonstrate that the new anomaly detector consistently outperforms a variety of SOTA methods by significant margins.
Classification of hyperchaotic, chaotic, and regular signals using single nonlinear node delay-based reservoir computers

The Lyapunov exponent method is generally used for classifying hyperchaotic, chaotic, and regular dynamics based on the equations modeling the system. However, several systems do not benefit from appropriate modeling underlying their dynamic behaviors. Therefore, having methods for classifying hyperchaotic, chaotic, and regular dynamics using only the observational data generated either by the theoretical or the experimental systems is crucial. In this paper, we use single nonlinear node delay-based reservoir computers to separate hyperchaotic, chaotic, and regular dynamics. We show that their classification capabilities are robust with an accuracy of up to 99.61% and 99.03% using the Mackey–Glass and the optoelectronic oscillator delay-based reservoir computers, respectively. Moreover, we demonstrate that the reservoir computers trained with the two-dimensional Hénon-logistic map can classify the dynamical state of another system (for instance, the two-dimensional sine-logistic modulation map). Our solution extends the state-of-the-art machine learning and deep learning approaches for chaos detection by introducing the detection of hyperchaotic signals.
The psychological impact of the Metaverse

Abstract The Metaverse is no revolution, but another step in the evolution of the internet. Today we can observe various 3D virtual worlds with different focuses and purposes, like meeting spaces, education, or classic gaming. Most of these applications run on computer or smart phone, some of these 3D platforms support virtual reality glasses, or even further wearables like haptic gloves. Nevertheless, the general interconnection of these platforms is still lacking. Due to this, user information, including avatars, cannot be reused for other applications. Even if the research company Gartner predicts that already “by 2026, 25% of the people will spend at least 1 h a day in the Metaverse for work, shopping, education, social and/or entertainment,” the vision still did not manifest yet. This gives society and academia time to start a productive discussion. The article wants to support this ambition by first discussing a potential definition of what the Metaverse will be, including different types for different purposes. After that this, the text predicts the arrival, including required circumstances, then later the expected psychologic impact of the expected scenarios on the regular users. As human psychology depends on experience, environment and, of course, the contact with other humans, such a scenario will have a relevant impact, up to a different understanding of reality, as people frictionless move from physical to virtual world, and even interact on complete different virtual worlds. Existing articles regarding the connections between online (like video games and social media) and offline behavior focused on particular cohorts. As the Metaverse will change work and private life, wide parts of society are voluntary and non-voluntary affected, which limits the possibility to transfer the insights from earlier scenarios to this topic. This article focuses on shifted Socialization, Roles, Perception, and the relating Humanization of Artificial Intelligence, as it enables the algorithm, consciously or subconsciously, to influence the human mind. Graphical Abstract
Improving quality control in the routine practice for histopathological interpretation of gastrointestinal endoscopic biopsies using artificial intelligence

Background Colorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. Lack of a sufficient pathologist workforce can cause an increase in human errors, threatening patient safety. Therefore, we developed a digital pathology total solution combining artificial intelligence (AI) classifier models and pathology laboratory information system for GI endoscopic biopsy specimens to establish a post-analytic daily fast quality control (QC) system, which was applied in clinical practice for a 3-month trial run by four pathologists. Methods and findings Our whole slide image (WSI) classification framework comprised patch-generator, patch-level classifier, and WSI-level classifier. The classifiers were both based on DenseNet (Dense Convolutional Network). In laboratory tests, the WSI classifier achieved accuracy rates of 95.8% and 96.0% in classifying histopathological WSIs of colorectal and gastric endoscopic biopsy specimens, respectively, into three classes (Negative for dysplasia, Dysplasia, and Malignant). Classification by pathologic diagnosis and AI prediction were compared and daily reviews were conducted, focusing on discordant cases for early detection of potential human errors by the pathologists, allowing immediate correction, before the pathology report error is conveyed to the patients. During the 3-month AI-assisted daily QC trial run period, approximately 7–10 times the number of slides compared to that in the conventional monthly QC (33 months) were reviewed by pathologists; nearly 100% of GI endoscopy biopsy slides were double-checked by the AI models. Further, approximately 17–30 times the number of potential human errors were detected within an average of 1.2 days. Conclusions The AI-assisted daily QC system that we developed and established demonstrated notable improvements in QC, in quantitative, qualitative, and time utility aspects. Ultimately, we developed an independent AI-assisted post-analytic daily fast QC system that was clinically applicable and influential, which could enhance patient safety.
Turning Federated Learning Systems Into Covert Channels

Federated learning (FL) goes beyond traditional, centralized machine learning by distributing model training among a large collection of edge clients.These clients cooperatively train a global, e.g., cloud-hosted, model without disclosing their local, private training data.The global model is then shared among all the participants which use it for local predictions.In this paper, we put forward a novel attacker model aiming at turning FL systems into covert channels to implement a stealth communication infrastructure.The main intuition is that, during federated training, a malicious sender can poison the global model by submitting purposely crafted examples.Although the effect of the model poisoning is negligible to other participants, and does not alter the overall model performance, it can be observed by a malicious receiver and used to transmit a single bit.
Unsupervised Multimodal Image-to-Image Translation: Generate What You Want

Unsupervised image-to-image translation is one of the most important research topics in the field of computer vision. Traditional unsupervised image-to-image translation methods can only learn one-to-one mapping relation between two domains. However, the target domain is multimodal in general. Therefore, some existing methods generate diverse and multimodal outputs for a given source image by introducing random style codes or noise vectors. But they can only generate images with random modal of target domain. In some scenarios, we expect the generated images have desired modal. To solve this problem, we propose a novel framework for unsupervised multimodal image-to-image translation, called MuGAN, which can learn one-to-many mapping relations from source domain to target domain. And by controlling the modal label of target domain, our approach can generate images with desired modal. We verify the effectiveness and superiority of our framework by qualitative and quantitative evaluations on several image datasets.