Reviewerly/exHarmony huggingface dataset:
Situation-Aware Cluster and Quantization Level Selection Algorithm for Fast Federated Learning

In federated learning (FL), which clients and quantization levels are selected for the deep model parameters has a significant impact on learning time as well as learning accuracy. This is not a trivial issue because it is also significantly affected by factors, such as computational power, communication capacity, and data distribution. Considering these factors, we formulate a joint optimization problem for clustering and selecting clusters with quantization levels. Due to the high complexity of the formulated problem, we propose a situation-aware cluster and quantization level selection (SITUA-CQ) algorithm. In this algorithm, the FL server first assembles clients into clusters to mitigate the impact of biased data distributions and determines the most suitable clusters and quantization levels based on their computing power and channel quality. Extensive simulation results show that SITUA-CQ can reduce the round time by up to 80.3% compared to conventional algorithms.
Image Enhancement Using Adaptive Region- Guided Multi-Step Exposure Fusion Based on Reinforcement Learning

In photography, accurate exposure is key to taking high-quality photos, particularly images with uneven exposure levels. Global exposure operations are usually difficult to effectively strengthen the various regions of the image. To achieve a balanced exposure level in different regions of the image, we propose a novel algorithm inspired by the luminance masking frequently employed by professional photographers. We use reinforcement learning to adaptively generate guiding regions and adjusting parameters as a basis for multi-step exposure fusion, to enhance the overall quality of the image. Firstly, reinforcement learning is employed to automatically segment the single image to be enhanced into multiple sub-images, with corresponding appropriate adjusting parameters for each sub-image generated. Then, the input image is enhanced using the local adjusting parameters, yielding a set of images with varying enhancing degrees. Finally, these images are fused in an exposure process to obtain the final result. Experimental results show that our method not only generates intuitive and interpretable guiding regions, but also its performance is comparable to that of other contemporaneous methods.
Deep learning-based vehicle speed estimation using the YOLO detector and 1D-CNN

This paper addresses vehicle speed estimation using visual data obtained from a single video camera. The proposed method accurately predicts the speed of a vehicle, using the YOLO algorithm for vehicle detection and tracking, and a one-dimensional convolutional neural network (1D-CNN) for speed estimation. The YOLO algorithm outputs bounding boxes around detected objects in an image, which is, in our case, the vehicle whose speed is to be predicted. As input to our 1D-CNN speed estimation model, we introduce a novel feature based on the change of area of the bounding box around the vehicle. The feature, referred to as the changing bounding box area (CBBA), is obtained by calculating the area of the bounding box, frame-to-frame, as the vehicle approaches the camera. The shape of the CBBA curve remains closely the same for all vehicles, with differences conditioned by the value of the observed vehicle’s speed. The proposed method is trained and tested on the VS13 dataset. Experiments show that it is able to accurately predict the vehicle’s speed with an average error of 2.76 km/h, with the best performing vehicle having the average error of just 1.31 km/h. The proposed method exhibits the robustness as a key advantage, eliminating the need for prior knowledge of real-world dimensions such as the vehicle size, road width, camera distance and angle in relation to the road etc.
QR Code Encryption for improving Bank information and Confidentiality

Nowadays, storing confidential documents using cloud services is getting popular due to advantages regarding security and fast processing operations. One of the major services of cloud computing is cloud storage, which is conversely raises some issues regarding information security. Storing information for cooperation such as a bank in the cloud needs a fast processing operation to avoid any bottleneck in the work procedure. Therefore, Quick Response (QR) code is highly required to be utilized in these operations. The advantage of the QR code is easier for reading information by just scanning using any reader available on smart devices. The methodology is to integrate the security with QR code to save the text bank information. This is done by converting text to the QR code as an image form, then applying a cryptography algorithm to the QR code image then upload it to the cloud storage. Later on, once the plaintext is required to be reconstructed, the same operations are applied exactly to the encryption phase. In which the same algorithm will be applied for decryption then a QR code reader is used to preview the target bank text. For security matters, a key is used to be embedded in the protection operation. The type of encryption is proposed to be a symmetric One-time-pad (OTP) cryptography algorithm.
Authentication System Based on Event-related Potentials Using AR Glasses

Although conventional biometric authentication methods have been developed for identification, they still involve some problems with hacking, copying, and theft. In this study, we propose a biometric authentication system based on electroencephalography (EEG) using the rapid serial visual presentation (RSVP) paradigm with stimuli of photographs of people displayed on augmented reality (AR) glasses. The event-related potentials (ERPs) of subjects were detected while they were exposed to two types of photographs, one that they were familiar with and the other that they were not. The amplitude of P3a, P3b and late positive potential (LPP) was smaller when subjects were exposed to familiar photographs compared to photographs of unfamiliar people. However, there was no change in the latency of P3a, P3b and LPP. Finally, the performance of the system was evaluated in terms of false rejection rate (FRR) and false acceptance rate (FAR). Thus, we identified the unique potential of EEG as a biometric authentication method by analyzing the response of ERPs during the RSVP paradigm.
Exploring Multimodal Data Approach in Natural Language Processing Based on Speech Recognition Algorithms

Speech-to-text systems work as an interpreter today. Since each domain has its own terminology and constraints, its effectiveness depends on the language's capacity to comprehend grammar, spot patterns, and relate to the intended domain frequently. The primary aspect units in audio-to-text conversion are complete sentences or phraseological units that explain the overall idea of the process rather than a single word.
SDN-based Internet of Video Things Platform Enabling Real-Time Edge/Cloud Video Analytics

The trend of recent years is the continuous development of the Internet of Things (IoT). Among such things, a significant share is occupied by visual sensors and video cameras that generate large amounts of data. In turn, the need to attract significant storage resources, transmission throughput, and processing power is an inevitable solution for real-time video analytics. Thus, the combination of smart cameras with the computing paradigm of Cloud/Edge and IoT architectures form the next generation of video surveillance systems, called the “Internet of Video Things” (IoVT In this paper, a new IoVT platform is developed that, in addition to harmoniously combining Edge/Cloud computing, uses SDN to overcome challenges such as flexible management, control, and maintenance of IoVT devices. In particular, within the proposed IoVT platform, an algorithm for the dynamic selection of Edge or Cloud computing is implemented using an SDN controller to provide effective video analytics in real-time. This algorithm considers such parameters as the priority of computational tasks, the number of video streams, and the image quality with the ability to adapt to a specific application by software configuration of the IoVT platform. We also demonstrate the effectiveness of the proposed solutions on real equipment and discuss several promising areas of application of the developed platform.
A Quantum-Classical Hybrid Classifier Using Multi-Encoding Method for Images

Image data grows in volume and size more quickly than classical processing power. Thanks to its entanglement and super-position qualities, quantum computing, which is based on quantum physics, has a lot of potential for speed and processing capacity. For high power challenges, it is therefore highly common to try to utilize quantum computing units rather than classical computing units. In this study, a hybrid quantum-classical approach was proposed for utilizing quantum computers’ advantages in image classification. This hybrid technique uses a variational quantum circuit (VQC) on the quantum computer side. To overcome the qubit restriction in the VQC, multiple amplitude encoding was used as the data encoding method. In the classical computer part of the proposed approach, the preprocessing of the image, the convolution operation, and the optimization of the parameters of the single-qubit rotation gates in the VQC were performed. The proposed approach was trained and tested on two different data sets. The accuracy rates acquired in 2-layers and 4-layers VQCs within the data sets were evaluated in the test results. The proposed approach was evaluated against studies that were similar in the literature. Compared to similar studies, it was observed that it is more successful in terms of the number of parameters used and quantum cost. As a result, the effectiveness of the proposed approach was verified.
Recursive quantum circuits for generating sequency ordering Walsh–Hadamard transform

In the field of information processing, Walsh–Hadamard transforms (WHTs) are extensively employed. However, there exists a dearth of WHT algorithms based on sequence ordering that can be employed for quantum information processing. This paper presents a pioneering attempt to address this issue by proposing a recursive circuit for quantum version of WHT (QWHT) based on sequence ordering, which can effectively process discrete signals, images, and videos. To provide a more intuitive representation of the sequency ordering QWHT, we design quantum circuits to implement both the QWHT and its inverse transform. Furthermore, simulation results on the IBM platform demonstrate the effectiveness of the algorithm. Meanwhile, comparative analysis reveals that the proposed sequency ordering QWHT approach is superior to the equivalent quantum version of the conventional WHT algorithm. Based on the complexity analysis, our methods are exponentially faster than traditional WHTs.
Coexisting point attractors, multi-transient behaviors, area-preserving chaotic systems, non-degenerate hyperchaotic systems derived from a simple 3D discrete system

Abstract In this paper, we propose a simple 3D discrete system with a variety of interesting dynamic behaviors. When the control parameters of the discrete system are set to different appropriate values, the system is transformed into four distinct systems, namely a discrete system with coexisting point attractors, a discrete system with novel multi-transient behaviors, an area-preserving map, and a non-degenerate hyperchaotic system. This transient transition behavior is manifested as a switch between multiple quasi-periodic flows. This multi-transient behavior is rarely reported in discrete systems. In addition, to meet the requirements of chaotic secure communication, relevant experiments prove that the pixel scrambling effect of the proposed area-preserving map is better than that of the 3D digital Arnold map. Moreover, a PRNG is constructed by quantizing the proposed non-degenerate hyperchaotic system, and the PRNG can pass the NIST SP-800-22 test and show good randomness.
Transformative teacher leadership experiences in schools in creating an innovative educational culture: The case of Lithuania

The importance of teacher leaders has been increasingly emphasized in innovative educational practices. In this study, we present the experiences of innovative teacher leaders (N = 10) in Lithuania. The sample of respondents is qualitative-purposive. Teachers who have been awarded for their innovative practice took part in the study. In addition, teachers nominated by their school communities as innovative educators who are making a difference in their schools were also included. The data analyzed by the method of critical analysis of qualitative research discourse will help the reader understand what helps teacher leaders develop innovative educational culture practices. Additionally, the data reveal how teacher leaders themselves understand leadership, which motivates them to work innovatively. We found that innovative teachers in Lithuania perceive the phenomenon of transformational leadership in four categories, namely: attention to others, initiating change, community building, development, and improvement. The participants in the study also described the meaning of innovative teacher behavior, which is revealed in the analysis of two categories: quality of education and educational change. The third component, innovative organizational culture, was discussed by presenting teachers’ insights on three categories: planning, implementation, and motivation. Teacher leaders support, create and nurture a culture of innovation by motivating and encouraging their colleagues to improve. The findings of this study are useful for school communities wanting to make changes, as they provide answers to what motivates teachers to change their practices and to mobilise other educators for common goals.
Implementing quantum Fourier transform using three qubits

Abstract Using the circulant symmetry of a Hamiltonian describing three qubits, we realize the quantum Fourier transform. This symmetry allows us to construct a set of eigenvectors independently on the magnitude of physical parameters involved in the Hamiltonian and as a result the entanglement will be maintained. The realization will be leaned on trapped ions and the gate implementation requires an adiabatic transition from each spin product state to Fourier modes. The fidelity was numerically calculated and the results show important values. Finally, we discuss the acceleration of the gate by using the counter-driving field.
A Methodology and Framework to Determine the Isolation Capabilities of Virtualisation Technologies

The capability to isolate system resources is an essential characteristic of virtualisation technologies and is therefore important for research and industry alike. It allows the co-location of experiments and workloads, the partitioning of system resources and enables multi-tenant business models such as cloud computing. Poor isolation among tenants bears the risk of noisy-neighbour and contention effects which negatively impacts all of those use-cases. These effects describe the negative impact of one tenant onto another by utilising shared resources. Both industry and research provide many different concepts and technologies to realise isolation. Yet, the isolation capabilities of all these different approaches are not well understood; nor is there an established way to measure the quality of their isolation capabilities. Such an understanding, however, is of uttermost importance in practice to elaborately decide on a suited implementation. Hence, in this work, we present a novel methodology to measure the isolation capabilities of virtualisation technologies for system resources, that fulfils all requirements to benchmarking including reliability. It relies on an immutable approach, based on Experiment-as-Code. The complete process holistically includes everything from bare metal resource provisioning to the actual experiment enactment.
Predicting Inference Latency of Neural Architectures on Mobile Devices

Due to the proliferation of inference tasks on mobile devices, state-of-the-art neural architectures are typically designed using Neural Architecture Search (NAS) to achieve good tradeoffs between machine learning accuracy and inference latency. While measuring inference latency of a huge set of candidate architectures during NAS is not feasible, latency prediction for mobile devices is challenging, because of hardware heterogeneity, optimizations applied by machine learning frameworks, and diversity of neural architectures. Motivated by these challenges, we first quantitatively assess the characteristics of neural architectures and mobile devices that have significant effects on inference latency. Based on this assessment, we propose an operation-wise framework which addresses these challenges by developing operation-wise latency predictors and achieves high accuracy in end-to-end latency predictions, as shown by our comprehensive evaluations on multiple mobile devices using multicore CPUs and GPUs. To illustrate that our approach does not require expensive data collection, we also show that accurate predictions can be achieved on real-world neural architectures using only small amounts of profiling data.
HHVM Performance Optimization for Large Scale Web Services

HHVM is commonly developed for large online web services, yet there remains much room for optimizing HHVM performance. This paper discusses challenges and techniques in optimizing HHVM performance for Meta's web service. We begin by evaluating the effectiveness of semantic request routing, a request routing method aimed at enhancing code cache performance in HHVM, and examine its implications for optimizing HHVM performance. Second, we characterize HHVM performance for a large-scale datacenter and identify the challenges brought by uncontrollable confounding factors. Finally, we present the performance management framework for autotuning HHVM performance at scale.
Hierarchical DP-K Anonymous Data Publishing Model Based on Binary Tree

With the acceleration of data opening and sharing process in the power industry, the risk of sensitive data leakage is also gradually increasing. Privacy protection is one of the hot issues of privacy leakage control technology research in data release, and k-anonymity is the hot topic of privacy protection research in recent years. In this paper, we propose a hierarchical DP-K anonymous data release model based on binary tree clustering for the existing k-anonymity scheme and for minimizing the amount of information loss. A binary tree-based clustering algorithm (BTCA) is proposed to classify similar data records into the same equivalent class, which can improve the effect of clustering, reduce the information loss caused by anonymous data set release, and improve the availability of data. The clustered anonymous data sets redistribute different privacy budgets according to the privacy rights of the quasi-identifier attributes, and realize the hierarchical protection of the data with different degrees of sensitivity through the differential privacy noise increase mechanism, which enhances the privacy of the data.
A Survey of Data Center Network Topology Structure

In the new application mode, data center network (DCN) is faced with many challenges. Firstly, this paper introduces the requirements of modern data center network, such as network scalability, network fault tolerance, network bandwidth, equipment overhead, etc. Then it introduces the characteristics of the traditional data center network architecture and divides the data center network into two types: switch-centered and server-centered. Then, the representative structures of the two types of structures are reviewed in detail, and the above requirements are compared. Finally, a summary of the paper is made and the future development direction is pointed out.
Development of Security Target for Router Based on ENISA Common Criteria Framework

With the development of new technologies, information and communication technologies (ICT) are everywhere in our daily life. For attackers, information and communication technology products are the entry point for attacks, so they can quickly obtain the necessary information or retrieve data to threaten users. The diversity of network devices also increases the probability of attacks, whether software, hardware, or firmware, which could be an opportunity for attackers. The EUCC is based on the ISO/IEC15408 Common Criteria for Information Technology Security Certification, an international framework agreement that provides guidelines for evaluating and certifying ICT products. When a developer submits a product for evaluation, they need to design a security target (ST) for the product, which includes an overview of the product, security features, and an assessment of potential security threats. During the development of the security target, a preliminary analysis of the possible threats on the network device is performed, the security objectives are proposed for the threats, and finally, the security functional requirements are designed. In this study, we take a router as an example and perform a gap analysis between the possible threats faced by an unregulated router and the development of a security standard document related to routers based on the Common Criteria framework. Finally, we present a list of features reported as compliant with the Common Criteria framework to improve their router products.
Motivations for the initiation of knowledge management activities in times of routine and emergency

Purpose This research seeks to understand, for the first time, what motivates knowledge-intensive organizations (KIOs) to initiate knowledge management (KM) activities in times of routine and emergency. The COVID-19 pandemic was placed at the center as a case study of an extreme crisis. Design/methodology/approach Based on the adoption of the qualitative-constructivist paradigm, the study was conducted among 52 KM professionals through in-depth interviews and focus groups. The data were analyzed using a thematic analysis method, according to the principles of the grounded theory approach. Findings The findings reveal that opportunities and risks are two types of catalysts which accelerate KM efforts in times of routine and emergency respectively. Due to KM’s support of the transition to flexible employment during COVID-19, the authors show that this field experienced real growth and prosperity in the “new normal.” KM initiatives were promoted during the COVID-19 crisis in light of gaps in retention, sharing, accessibility and development of knowledge. Originality/value Given that knowledge risks are a field with fragmented understanding, the results contribute to understanding the importance of risk management related to knowledge in times of crises and turmoil. The authors call for incorporating this niche into the overall risk management of the organization, while adopting a holistic and long-term perspective of KM. Furthermore, the authors uncover KM’s position in KIOs during the global pandemic. The paper proposes food for thought regarding informal knowledge sharing in virtual environments typical of the “Corona routine”.
Blockchain System for Trustless Healthcare Data Sharing with Hyperledger Fabric in Action

Healthcare data security is a much-discussed topic nowadays. Medical data storage and sharing require security, privacy, and trust between organizations. Different research organizations, medical institutes, or individual researchers publish their research results but sometimes they need to share the data as well. In this scenario, a secure and transparent medical data-sharing system is much needed. In this work, we are proposing a blockchain-based system for medical data sharing between organizations. The system ensures security, privacy, transparency, and trust, hence trust between the organizations is not necessary. Since the system requirement is permissioned, a private or permissioned blockchain is used. We have set up the blockchain infrastructure using hyperledger fabric, known for its flexibility and modular architecture.