Reviewerly/exHarmony huggingface dataset:
Quanvolutional Neural Network Applied to MNIST

At present, quantum computing and its applications are still in research. Nonetheless, the need to accelerate significantly computational processing that requires a considerable amount of time through classical computing for solving complex problems; are just a few reasons why quantum machine learning algorithms are being implemented in this field. Image classification is a frequent computer vision problems to solve using deep learning algorithms, evaluating their performance via well-known datasets. In this work, we compare the performance of the LeNet5 neural network with a quantum version of itself, in which a fixed non-trainable quantum circuit is used as a quanvolution kernel. The contribution of this work focuses on analyzing the disadvantages and advantages of a quanvolution kernel in image classification problems. The results show that using a quanvolutional layer achieves a favorable performance tradeoff over a classical CNN LeNet5 model. We used the MNIST hand-written digits dataset to perform the evaluation using well-known metrics such as accuracy, precision, F1 score, latency, throughput, and others.
The Lateral Strategy of University Development in the BANI World

The research discusses the features of the BANI world compared with the SPOD and VUCA world, which consist in increasing the chaotization of processes and increasing the likelihood of unexpected adverse events with devastating consequences (“black swans”). Under these conditions, the method of extrapolating past events to the future, which is characteristic of strategic planning in the SPOD world, does not work. An important direction of the strategic management of the organization in Industry 4.0 is to increase the organization’s antifragility. The research formulates the main requirements for universities in Industry 4.0, such as anticipating the needs of tomorrow’s labor market, the integration of science and education, and an interdisciplinary approach to research. These can be met with a barbell strategy or a lateral strategy. Lateral strategy is about overcoming patterns of strategic thinking. The research provides examples of how Volgograd universities respond to global and local agendas when developing lateral development strategies. However, to increase their antifragility and ability to respond to the challenges of the BANI world, universities need to continue to increase their efforts to implement lateral strategies in such areas as strengthening horizontal connections in social space, personalized education, and an edutainment approach.
Multi-object tracking via deep feature fusion and association analysis

We describe a tracking-by-detection framework for multi-object tracking (MOT). It first detects the objects of interest in each frame of the video, followed by identifying association with the object detected in the previous frame. A deep association network is described to perform object feature matching in the arbitrary two frames to infer association degree of objects, and then similarity matrix loss is used to calculate association between each object in different frames to achieve an accurate tracking. The novelty of the work lies in the design of a multi-scale fusion strategy by gradually concatenating sub-networks of low-resolution feature maps in parallel to the main network of high-resolution feature maps, in the construction of a deeper backbone network which can enhance the semantic information of object features, and in the use of a siamese network for training a pair of discontinuous frames. The main advantage of our framework is that it avoids missing detection and partial detection. It is particularly suitable for solving the problem of object ID switch caused by occlusion, entering and leaving of objects. Our method is evaluated and demonstrated on the publicly available MOT15, MOT16, MOT17 and MOT20 benchmark datasets. Compared with the state-of-the-art methods, our method achieves better tracking performance, and is therefore, more suited for MOT tasks.
Truth-value unconstrained face clustering for identity resolution in a distributed environment of criminal police information systems

The work of criminal police in contemporary society is characterized by the proliferation of data and information to be processed, a more significant limitation of access to personal data, increased public monitoring, and higher expectations in the efficiency of identifying perpetrators. Also, all information collected during investigations is distributed through police systems separated by different levels and organization units, and often there are insufficient human and material resources. Resolving the perpetrator’s identity in those circumstances is a complex task, and police decision support systems must group all available evidence related to specific persons. For that purpose, this paper proposes a new approach to unconstrained pairwise clustering of face feature vectors extracted from the histogram of oriented gradients descriptor, named Truth-value clustering (TVC), based on non-axiomatic logic and graphs. The clustering approach was experimentally tested with six different face image databases. They were created to simulate unconstrained conditions like IARPA Janus Benchmark-B Face Dataset (IJB-B), IMDb-Wiki, Labeled Faces in the Wild, and YouTube Faces. The results of the proposed solution are compared with other state-of-the-art methods, showing that the approach gives, in summary, significantly better results. Application of the IJB-B protocol created for testing face clustering showed that the new approach gives better results by an average of 8.25% (σ=4.2). The main advantage over other methods is the possibility of utilizing mechanisms from non-axiomatic logic such as revision, which can then acquire new knowledge based on information from different nodes of the distributed environment consisting of various police information systems.
A Human-Centric Cybersecurity Framework for Ensuring Cybersecurity Readiness in Universities

The escalating number of cyberattacks on universities worldwide resulted in universities losing valuable information assets leading to disruption of operations and loss of reputation. The research sought to explore a framework for human-factor vulnerabilities related to cybersecurity knowledge and skills, which enabled cybercriminals to manipulate human elements into inadvertently conveying access to critical information assets through social engineering attacks. Descriptive and inferential statistics were used to test the data, and Pearson's correlation statistics were used to measure the statistical relationships and association of variables. The results revealed that students and staff are vulnerable to social engineering attacks and their ability to protect themselves and other information assets is limited mainly due to poor cybersecurity knowledge and skills resulting from poor cybersecurity awareness and education.
Data-Driven Intelligent Platforms—Design of Self-Sovereign Data Trust Systems

Urban areas provide the seedbed conditions for a variety of agglomeration advantages, including incubator conditions for the ICT sector. This study aims to present the foundations for a data-driven digital architecture based on the notion of open access platform organisations (e.g., platform cities). The principles of coordinated multi-actor data handling and exchange mechanisms centre in particular on privacy and confidentiality regulations. These are highlighted and tested on the basis of the data exchange architecture in a particular Industry 4.0 sector, viz., the medical–pharmaceutical sector. To cope with these issues, self-sovereign data trust systems are designed and tested using an OpenDSU data environment. Several building blocks of this architecture are presented and assessed. The conclusion of this study is that OpenDSU technology offers promising departures for handling privacy-sensitive and confidential data exchange in open platform organisations, such as smart cities.
Exploring lottery ticket hypothesis in few-shot learning

Lottery Ticket Hypothesis (LTH) [14] has gathered great focus since being proposed. Researchers then succeed in figuring out alternative ways to find the ”winning ticket” and extending the vanilla version to various kinds of situations ranging from image segmentation to language pretraining. However, these works all emphasize fully supervised learning with plenty of training instances, whilst they ignore the important scenario of learning from few examples, i.e., Few-Shot Learning (FSL). Different from classical many-shot learning tasks, the common FSL setting assumes the disjoint of source and target categories. To the best of our knowledge, the lottery ticket hypothesis has for the first time, systematically studied in few-shot learning scenarios. To validate the hypothesis, we conduct extensive experiments on several few-shot learning methods with three widely-used datasets, miniImageNet, CUB, CIFARFS. Results reveal that we can even find “winning tickets” for some high-performance methods. In addition, our experiments on Cross-Domain FSL further validates the transferability of the found “winning tickets”. Furthermore, the process of finding LTH can be costly. So we study the early-stage LTH for FSL via exploring the Inverse Scale Space(ISS). Empirical results validate the efficacy of early-stage LTH.
BMAR

A requirement of cross-modal signal processing is accurate signal alignment. Though simple on a single device, accurate signal synchronization becomes challenging as soon as multiple devices are involved, such as during activity monitoring, health tracking, or motion capture---particularly outside controlled scenarios where data collection must be standalone, low-power, and support long runtimes. In this paper, we present BMAR, a novel synchronization method that operates purely based on recorded signals and is thus suitable for offline processing. BMAR needs no wireless communication between devices during runtime and does not require any specific user input, action, or behavior. BMAR operates on the data from devices worn by the same person that record barometric pressure and acceleration---inexpensive, low-power, and thus commonly included sensors in today's wearable devices. In its first stage, BMAR verifies that two recordings were acquired simultaneously and pre-aligns all data traces. In a second stage, BMAR refines the alignment using acceleration measurements while accounting for clock skew between devices. In our evaluation, three to five body-worn devices recorded signals from the wearer for up to ten hours during a series of activities. BMAR synchronized all signal recordings with a median error of 33.4 ms and reliably rejected non-overlapping signal traces. The worst-case activity was sleeping, where BMAR's second stage could not exploit motion for refinement and, thus, aligned traces with a median error of 3.06 s.
C-DONN: compact diffractive optical neural network with deep learning regression

A new method to improve the integration level of an on-chip diffractive optical neural network (DONN) is proposed based on a standard silicon-on-insulator (SOI) platform. The metaline, which represents a hidden layer in the integrated on-chip DONN, is composed of subwavelength silica slots, providing a large computation capacity. However, the physical propagation process of light in the subwavelength metalinses generally requires an approximate characterization using slot groups and extra length between adjacent layers, which limits further improvements of the integration of on-chip DONN. In this work, a deep mapping regression model (DMRM) is proposed to characterize the process of light propagation in the metalines. This method improves the integration level of on-chip DONN to over 60,000 and elimnates the need for approximate conditions. Based on this theory, a compact-DONN (C-DONN) is exploited and benchmarked on the Iris plants dataset to verify the performance, yielding a testing accuracy of 93.3%. This method provides a potential solution for future large-scale on-chip integration.
QR-GAN: Generative Adversarial Networks meet Quantile Regression

This study proposes a novel Quantile Regression Generative Adversarial Networks (QR-GAN), which uses quantile regression to improve GAN performance by mitigating issues of GAN such as non-convergence, mode collapse, and gradient problems. Instead of directly minimizing the 1-Wasserstein distance between real and generated data distributions, as in WGANs, the proposed method leverages the quantile loss to encourage the generator to produce a diverse range of outputs. Additionally, we conduct an in-depth analysis of the output space of the discriminator and the gradients of fake samples to identify the root causes of mode collapse. Our study reveals that constraining the discriminator to specific values can lead to mode collapse. The proposed QR-GAN method exhibits a high level of robustness against mode collapse and significantly improves the generation performance, as measured by the Frechet Inception Distance (FID) metric, compared to other GAN variants. These findings suggest that the proposed QR-GAN method has the potential to advance the state-of-the-art in GAN research and improve their applicability in a variety of domains.
A Video Question Answering Model Based on Knowledge Distillation

Video question answering (QA) is a cross-modal task that requires understanding the video content to answer questions. Current techniques address this challenge by employing stacked modules, such as attention mechanisms and graph convolutional networks. These methods reason about the semantics of video features and their interaction with text-based questions, yielding excellent results. However, these approaches often learn and fuse features representing different aspects of the video separately, neglecting the intra-interaction and overlooking the latent complex correlations between the extracted features. Additionally, the stacking of modules introduces a large number of parameters, making model training more challenging. To address these issues, we propose a novel multimodal knowledge distillation method that leverages the strengths of knowledge distillation for model compression and feature enhancement. Specifically, the fused features in the larger teacher model are distilled into knowledge, which guides the learning of appearance and motion features in the smaller student model. By incorporating cross-modal information in the early stages, the appearance and motion features can discover their related and complementary potential relationships, thus improving the overall model performance. Despite its simplicity, our extensive experiments on the widely used video QA datasets, MSVD-QA and MSRVTT-QA, demonstrate clear performance improvements over prior methods. These results validate the effectiveness of the proposed knowledge distillation approach.
A Group Signature Scheme With Selective Linkability and Traceability for Blockchain-Based Data Sharing Systems in E-Health Services

Recently, with the rapid improvement of e-health technology, a large amount of precious medical data has been accumulated in different entities, such as hospitals, clinics, and medical institutions, promoting the development of data sharing in e-health services. However, most of them lacks fine-grained functionalities: selective linkability and traceability, which are critical in an e-health environment. Furthermore, we observe that existing schemes mostly rely on centralized storage centers, which will lead to a single point of failure and privacy disclosure. In this article, we first construct a group signature scheme <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SLTGS</i> suitable for a data sharing environment. It supports selectively linking two different message-signature pairs to the same signer. Further, it provides an algorithm to trace the signer. Then, based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SLTGS</i> scheme, we leverage distributed technology (i.e., interplanetary file system (IPFS) and blockchain) and attribute-based encryption to propose a distributed data sharing scheme. We claim that our scheme meets anonymity, accountability, linkability, traceability, fine-grained and efficient access control, and distributed storage. Moreover, the proposed data sharing scheme yields a practical performance making it suitable for e-health applications.
Classification of measurement-based quantum wire in stabilizer PEPS

We consider a class of translation-invariant 2D tensor network states with a stabilizer symmetry, which we call stabilizer PEPS. The cluster state, GHZ state, and states in the toric code belong to this class. We investigate the transmission capacity of stabilizer PEPS for measurement-based quantum wire, and arrive at a complete classification of transmission behaviors. The transmission behaviors fall into 13 classes, one of which corresponds to Clifford quantum cellular automata. In addition, we identify 12 other classes.
Reporting Mobile Device-Mediated Text to Manage Action and Agency in Co-Present Conversation

The paper considers the role of agency in human interaction with mobile devices. We use multimodal conversation analysis to trace how mobile screen content is reproduced as locally relevant for updating information for co-present interlocutors. While informing-centered actions supported by mobile devices may sometimes have the character of an agentic intrusion into the local interaction, we show that the organization of device-accessed information and its meaningfulness is nonetheless positioned in relation to how device-supported updates are animated into social action by human participants. This research contributes to understanding how device-related content is sequentially incorporated into face-to-face interaction.
Accelerating Quantum Readiness for Sectors: Risk Management and Strategies for Sectors

The potential impact of quantum computing on various industries such as finance, healthcare, cryptography, and transportation is significant; therefore, sectors face challenges in understanding where to start because of the complex nature of this technology.Starting early to explore what is supposed to be done is crucial for providing sectors with the necessary knowledge, tools, and processes to keep pace with rapid advancements in quantum computing.This article emphasizes the importance of consultancy and governance solutions that aid sectors in preparing for the quantum computing revolution.The article begins by discussing the reasons why sectors need to be prepared for quantum computing and emphasizes the importance of proactive preparation.It illustrates this point by providing a real-world example of a partnership.Subsequently, the article mentioned the benefits of quantum computing readiness, including increased competitiveness, improved security, and structured data.In addition, this article discusses the steps that various sectors can take to achieve quantum readiness, considering the potential risks and opportunities in industries.The proposed solutions for achieving quantum computing readiness include establishing a quantum computing office, contracting with major quantum computing companies, and learning from quantum computing organizations.This article provides the detailed advantages and disadvantages of each of these steps and emphasizes the need to carefully evaluate their potential drawbacks to ensure that they align with the sector's unique needs, goals, and available resources.Finally, this article proposes various solutions and recommendations for sectors to achieve quantum-computing readiness.
Real-Time Surveillance System for Women’s Safety and Crime Detection in Public Area

When it comes to social problems womenharassment and crimes against women are the biggest problem amongst all. Despite, strict laws and punishments such incidents happen every now and then, that’s because there is a drawback in the surveillance system used by the law enforcement officers for the sake of curtailing women safety and crimes. According to the report of National Crime Records Bureau (NCRB), 4, 05,861 lakhs transgression cases were reported against women in India in the year 2019. The further proposed work will help in lessening down the crime rates by ameliorating the surveillance system with the implementation of the Convolution Neural Networks (CNN).
Defending Against Identity Threats Using Risk-Based Authentication

Abstract Defending against identity-based threats, which have predominantly increased in the era of remote access and working, requires non-conventional, dynamic, intelligent, and strategic means of authenticating and authorizing. This paper aims at devising detailed risk-scoring algorithms for five real-time use cases to make identity security adaptive and risk-based. Zero-trust principles are incorporated by collecting sign-in logs and analyzing them continually to check for any anomalies, making it a dynamic approach. Users are categorized as risky and non-risky based on the calculated risk scores. While many adaptive security mechanisms have been proposed, they confine identities only to users. This paper also considers devices as having an identity and categorizes them as safe or unsafe devices. Further, results are displayed on a dashboard, making it easy for security administrators to analyze and make wise decisions like multifactor authentication, mitigation, or any other access control decisions as such.
PENCIPTAAN MOTIF BATIK SUMBER IDE DARI ORNAMEN MASJID DAN MAKAM MANTINGAN

CREATION OF BATIK MOTIF FROM THE MANTINGAN MOSQUE AND TOMB ORNAMENTS &#x0D; Ufik Nur Ismia *)a, Muh Fakhrihun Naâ€™am *)b&#x0D; Department of Family Welfare Education, Faculty of Engineering State University of Semarang, E7 Campus Sekaran Gunungpati Semarang Building 50229&#x0D; Email: ufikjepara123@gmail.com&#x0D; &#x0D; Abstract: Batik motifs are one of the most important factors in realizing a work of batik, by taking the source of ideas such as: abstract motifs, flora and fauna motifs, ornaments, decorative carvings, and so forth. With various sources of ideas avaible, researchers are interested in the ornaments in , mosques and Mantingan tombs. Apart from being a historical legacy, batik business people in Jepara Regency have not used batik motifs as a source of ideas from ornaments, most of them still use Jepara carvings. The method used in this research is descriptive qualitative research method. The procedure of conducting research is to create a batik motifs that takes the source of the ideas of some of the ornaments in the mosque and the mantingan tomb, and then translates them into a written batik work. Research results by describing the work, the work produced is feasible and needs to be developed with a variety of attractive colors.&#x0D; Keyword: ornaments, Batik Motifs.