Reviewerly/exHarmony huggingface dataset:
A new microarchitecture hardware proposal for the use of the improved template matching method in face similarity detection

Template matching is a technique used in many different areas in the literature to search for a small template image in a larger source image. The execution time of the technique can be quite high depending on the size of the source image, since the search made in the large image is realized pixel by pixel in the form of frames selected in the template size. This situation creates inconveniences in terms of real-time use of the technique. In addition, the results cannot be interpreted correctly due to the fact that there are many fake matches in the region where there are real matches. In this study, the technique was strengthened with a new and simple proposal called neighborhood pool to prevent fake matches. In order to reduce the long scan time to real-time execution time, the reinforced template matching technique was redesigned as an FPGA-based hardware architecture. This new micro-hardware architecture, which includes the neighborhood pool idea, was synthesized and embedded in the FPGA device by time analysis and tested on the device. For the tests of the study, the face matching scenario was chosen and the face image of a particular person was searched in the larger source image. The improved template matching technique in software-based tests reduced the execution time by about 4.5 times compared to the classical template matching method, and about 300 times in hardware testing, and it was observed that the system execution time became compatible with real-time. When the proposed new micro hardware architecture is compared with the CNN and template matching designs in the literature, it is clearly seen that it has a very low hardware requirement compared to them.
Perspective of small and medium enterprise (SME's) and their relationship with government in overcoming cybersecurity challenges and barriers in Wales

Small and medium enterprises (SMEs) have adapted their working methods in response to cyber threats, including security checks on internet-connected devices and securing social media accounts. To safeguard their data, SMEs are increasingly adopting intelligent software packages, despite the additional costs and need for skilled personnel. This study examines the perspectives of SMEs in Wales regarding their collaboration with the government to overcome cybersecurity challenges. Through qualitative research involving 34 SMEs, the study explores current cybersecurity practices, challenges faced, and interactions with government agencies. Wales, with its thriving economy and significant number of SMEs, provides a suitable context for the research. The findings will shed light on barriers and challenges encountered by SMEs in implementing effective cybersecurity measures, while also examining their perceptions of government agency involvement. By contributing insights to SME cybersecurity and government policy, the study aims to enhance government initiatives in supporting SMEs to tackle cybersecurity challenges effectively.
BAGGING BASED ENSEMBLE ANALYSIS IN HANDLING UNBALANCED DATA ON CLASSIFICATION MODELING

The purpose of this study is to Identify the algorithm of each method of handling the unbalanced class based on bagging based on the literature review. This study uses a bagging based ensemble method such as UnderBagging, OverBagging, UnderOverBagging, SMOTEBagging, Roughly Balanced Bagging and the last one is the Bagging Ensemble Variation. The data used is coded from the UCI Repository with 16 data, eight of which have class categories with low imbalance problems, and the rest are categorized as high imbalance problems. The number of classes used in this study amounted to two classes. The class with a small number is made into the minority class and the rest is made up as the majority class. The result of this research is the bagging based method gives better results when compared to classical methods such as the classification tree.
Lightweight digital video stabilization for small-size robot

Though constrained by payload and processing, small robots have gained applications in collecting visual information from the scene. Typically these small-size robots do not carry data loggers and send the video information to a hand-held device at a remote location for visual observations. Due to sophisticated processing and control limitations from mechatronics resources, the video captured by the robot is subjected to the effects of unintended motion, which requires digital methods for video stabilization. For a lightweight solution for video stabilization, we avoid use of any external hardware and develop a Singular Value Decomposition (SVD) based digital algorithm that avoids explicit feature tracking and motion estimation during stabilization. The process involves identifying a subspace with minimal dimensions that contains information of intentional motion alone. This work identifies the minimal subspace for video stabilization using the sliding window geometry method for practical implementation. Further, a shape-preserving filter is utilized to remove perturbations induced by the unintended motions, thereby resulting in the reconstruction of the stabilized video sequence. Experimental results on two different small-size robots viz spherical robot and Unmanned Aerial Vehicle (UAV) in indoor and outdoor settings, respectively, show quality outcomes without any change in parameters of the proposed filter design. Performance comparison with existing methods on the quality of stabilized video shows that the proposed stabilization method overcomes the non-availability of features for tracking due to large amplitudes and limited onboard resources. With the proposed video stabilization method, there is a potential for wider applicability of small-size robots in remote visual observations.
A heterogeneous network structure publishing security framework based on cloud-edge collaboration

With the integration of different fields, a large amount of heterogeneous data converges into a heterogeneous network with different types of nodes and edges. Then it is published by the cloud platform for applications such as recommendation services and public opinion analysis. However, in traditional cloud platform data publishing, one is privacy leakage due to the long transmission delay to the cloud platform, and the other is privacy leakage due to privacy attacks during cloud platform data publishing. In addition, there is an imbalance between privacy and availability in the privacy protection of the network structure between heterogeneous nodes. To address these issues, we propose a heterogeneous network structure publishing security framework based on cloud-edge collaboration. In this framework, we design a non-interactive edge privacy protection center and provide a heterogeneous network data publishing privacy protection model (HNPP). First, the network between heterogeneous nodes is transformed into an equivalent homogeneous network based on trivial closure. Then, we use differential privacy and random perturbations to achieve the homogeneous network structure reconstruction. Finally, we only rely on generation rules and reconstructed equivalent homogeneous networks to generate the heterogeneous network structure. We conducted extensive experimental analysis on real datasets, and the results show that the original edge retention rate of the network after privacy protection is above 0.88, and the clustering precision is above 0.92. It shows that the HNPP model is able to balance the privacy and availability of heterogeneous network structure publishing.
A Content Word Augmentation Method for Low-Resource Neural Machine Translation

Transformer-based neural machine translation (NMT) models have achieved state-of-the-art performance in the machine translation community. These models learn the translation knowledge from the parallel corpus through the attention mechanism automatically. However, the model fails to consider the semantic importance of words, where content words play a more important role than functional words in a sentence. This issue is particularly prominent for low-resource translation tasks, where insufficient parallel data results in poor translation quality. To alleviate this issue, a content word augmentation (CWA) method is proposed to improve the encoder for low-resource translation tasks. The main steps are as follows: Firstly, words in a sentence are classified into content and function words based on the content word selection algorithm; Next, two fusion strategies are employed by incorporating the word embedding of content words into the NMT model to augment the encoder. The results of experiments on several translation tasks show that the CWA method outperforms the strong baseline, significantly improving the BLEU score range from 0.24 to 0.57.
Synthetic Network Traffic Data Generation and Classification of Advanced Persistent Threat Samples: A Case Study with GANs and XGBoost

The need to develop more efficient network traffic data generation techniques that can reproduce the intricate features of traffic flows forms a central element in secured monitoring systems for networks and cybersecurity. This study investigates selected Generative Adversarial Network (GAN) architectures to generate realistic network traffic samples. It incorporates Extreme Gradient Boosting (XGBoost), an Ensemble Machine Learning algorithm effectively used for the classification and detection of observed and unobserved Advanced Persistent Threat (APT) attack samples in the synthetic and new data distributions. Results show that the Wasserstein GAN architectures achieve optimal generation with a sustained Earth Mover distance estimation of $$10^{-3}$$ between the Critic loss and the Generator loss compared to the vanilla GAN architecture. Performance statistics using XGBoost and other evaluation metrics indicate successful generation and detection with an accuracy of 99.97% a recall rate of 99.94%, and 100% precision. Further results show a 99.97% $$f_1$$ score for detecting APT samples in the synthetic data, and a Receiver Operator Characteristic Area Under the Curve (ROC_AUC) value of 1.0, indicating optimum behavior, surpassing previous state-of-the-art methods. When evaluated on unseen data, the proposed approach maintains optimal detection performance with 100% recall, 100% Area Under the Curve (AUC) and precision above 90%.
RECO-ASCON: Reconfigurable ASCON hash functions for IoT applications

The need for reconfigurable lightweight cryptographic processors with low power consumption for securing the Internet of Things (IoT) with adaptive functionalities is getting attracted due to security properties, such as data fidelity, provenance, and privacy concerns. This paper presents RECO-ASCON, a reconfigurable security processor that supports both cryptographic Hash and Hasha ASCON algorithms. The proposed design is implemented in Chisel hardware construction language, an open-source hardware library. It has been evaluated in various hardware platforms such as FPGAs, embedded systems and ASICs with various technology nodes and design flows. In the PYNQ-Z1 board, RECO-ASCON can be mapped to five processors in total with a Micro-Blaze soft-core processor for streaming data to all of them while running at 200 MHz without producing any timing violations. Moreover, mapping the proposed RECO-ASCON processor on the Nexsys4-DDR Xilinx Kit (ARTIX-7 chip) would cost ∼ two times fewer resources and run 1.25 × faster than the existing implementation of just one of the ASCON hash functions on the same family of the chip. We implement the proposed processor in 28 nm industry technology with commercial design flow, it achieves a maximum frequency of 1.12 GHz while achieving highest energy efficiency compared to the state-of-the-art implementations. The total area is only 9170.82um2 in 28 nm. To further evaluate the design, we also implement it with all open-source design tools and process technology libraries in 45 nm, it achieves 1 GHz maximum frequency and consumes only 18225um2 in 45 nm. Overall, the proposed reconfigurable processor is compact and more energy efficient, making it a perfect candidate to be deployed in wide range of IoT systems.
Improvement of DBSCAN Algorithm Based on K-Dist Graph for Adaptive Determining Parameters

For the shortcomings of an unstable clustering effect and low accuracy caused by the manual setting of the two parameters Eps and MinPts of the DBSCAN (density-based spatial clustering of applications with noise) algorithm, this paper proposes an adaptive determination method for DBSCAN algorithm parameters based on the K-dist graph, noted as X-DBSCAN. The algorithm uses the least squares polynomial curve fitting method to fit the curve in the K-dist graph to generate a list of candidate Eps parameters and uses the mathematical expectation method and noise reduction threshold to generate the corresponding MinPts parameter list. According to the clustering results of each group of parameters in the Eps and MinPts parameter lists, a stable range of cluster number changes is found, and the MinPts and Eps corresponding to the maximum K value in the stable range are selected as the optimal algorithm parameters. The optimality of this parameter was verified using silhouette coefficients. A variety of experiments were designed from multiple angles on the artificial dataset and the UCI real dataset. The experimental results show that the clustering accuracy of X-DBSCAN was 21.83% and 15.52% higher than that of DBSCAN on the artificial and real datasets, respectively. The X-DBSCAN algorithm was also superior to other algorithms through comprehensive evaluation and analysis of various clustering indicators. In addition, experiments on four synthetic Gaussian datasets of different dimensions showed that the average clustering indices of the proposed algorithm were above 0.999. The X-DBSCAN algorithm can select parameters adaptively in combination with the characteristics of the dataset; the clustering effect is better, and clustering process automation is realized.
Access Control Management System for Edge Computing Environment Using <scp>Tag‐Based</scp> Matching and Cache Injection

With increasing attention to the secondary use of data, privacy protection laws such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) have come into force to protect residents from unauthorized exploitation of personal data. Flow management is an effective method to track the distribution of user data in a multi‐domain network environment. Transparency in the relationship between data and service providers is essential to track the status of data provision. Vendor and Consumer Relationship Management (VCRM) manages this relationship by regulating relevant flow information. VCRM is expected to collaborate with the edge‐computing environment in the management of regional unit flow, wherein all access to personal user data should be restricted in accordance with the stakeholders' requests. Accordingly, this study proposes a packet processing function that collaborates with VCRM to manage the access control of flows passing through a regional network. Moreover, this paper proposes methods to reduce packet processing time on the switch, and requests to the switch controller. Using tag‐based matching and cache injection methods, the number of requests to the controller decreased to 33.4% of the original figure. Our proposed method also reduces the total processing time on flow management switches in the network environment in the analysis of encrypted TCP streams such as HTTP over TLS and MQTT over TLS. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
An underwater image enhancement strategy based on pyramid attention mechanism

Due to the low contrast and color distortion of underwater images caused by the absorption and scattering of light by water, this paper proposes an underwater image enhancement strategy based on the attention mechanism of pyramids. Based on FUnIE-GAN, the feature extraction module uses MobileNet to replace the VGG16 model in the original U-Net structure, which reduces the number of network model parameters and accelerates the inference speed of the network model. Furthermore, the pyramid attention module is introduced into the generative network. The multi-scale pyramid feature and attention mechanism collection is beneficial to enhance the network feature extraction ability and improve the model performance. Experiments are carried out on EUVP dataset. The results show that the underwater images enhanced by our method are better in terms of subjective and objective aspects and have improved sharpness, color correction and contrast. The average values of peak signal-to-noise ratio and structural similarity were 21.398 and 0.742, respectively that is better than the other comparison methods.
Coherence, superposition, and Löwdin symmetric orthogonalization

The notions of coherence and superposition are conceptually the same; however, an important distinction exists between their resource-theoretic formulations. Namely, while basis states are orthogonal in the resource theory of coherence, they are not necessarily orthogonal in the resource theory of superposition. Owing to the nonorthogonality, the manipulation and characterization of superposition states require significant efforts. Here, we demonstrate that the L\"{o}wdin symmetric orthogonalization (LSO) method offers a useful means for characterizing pure superposition states. The principal property of LSO is that the structure and symmetry of the original nonorthogonal basis states are preserved to the greatest extent possible, which prompts us to study the role of LSO in identifying the hierarchical relations of resource states. Notably, we reveal that the maximally coherent states turn into the states with maximal superposition with the help of LSO: in other words, they are equivalent under the action of symmetric orthogonalization. Our results facilitate further connections between coherence and superposition, where LSO is the main tool.
Distilling Distribution Knowledge in Normalizing Flow

In this letter, we propose a feature-based knowledge distillation scheme which transfers knowledge between intermediate blocks of teacher and student with flow-based architecture, specifically Normalizing flow in our implementation. In addition to the knowledge transfer scheme, we examine how configuration of the distillation positions impacts on the knowledge transfer performance. To evaluate the proposed ideas, we choose two knowledge distillation baseline models which are based on Normalizing flow on different domains: CS-Flow for anomaly detection and SRFlow-DA for super-resolution. A set of performance comparison to the baseline models with popular benchmark datasets shows promising results along with improved inference speed. The comparison includes performance analysis based on various configurations of the distillation positions in the proposed scheme.
Entanglement entropy of Compton scattering with a witness

Unitarity and the optical theorem are used to derive the reduced density matrices of Compton scattering in the presence of a witness particle. Two photons are initially entangled wherein one photon participates in Compton scattering, while the other is a witness, i.e., does not interact with the electron. Unitarity is shown to require that the entanglement entropy of the witness photon does not change after its entangled partner undergoes scattering. The final mutual information of the electron's and witness particle’s polarizations is shown to be nonzero for low-energy Compton scattering. This indicates that the two particles became correlated in spite of no direct interaction. Assuming an initial maximally entangled state, the change in entanglement entropy of the scattered photon’s polarization is calculated in terms of Stokes parameters. A common ratio of areas occurs in the final reduced density matrix elements, von Neumann entropies, Stokes parameter, and mutual information. This common ratio consists of the Thomson scattering cross-section and an accessible regularized scattering area.
The Chilling Effects of Surveillance and Human Rights: Insights from Qualitative Research in Uganda and Zimbabwe

Abstract States are increasingly developing and deploying large scale surveillance and AI-enabled analytical capabilities. What is uncertain, however, is the impact this surveillance will have. Will it result in a chilling effect whereby individuals modify their behaviour due to the fear of the consequences that may follow? Understanding any such effect is essential: if surveillance activities interfere with the processes by which individuals develop their identity, or undermine democratic processes, the consequences may be almost imperceptible in the short term but profound over the long term. Currently, surveillance-related chilling effects are not well understood, meaning that insufficient weight is given to their potentially society-wide impacts. This article seeks to help redress this balance. Drawing on empirical research in Zimbabwe and Uganda it highlights how State surveillance has chilled behaviour, with significant implications for rights essential to individual development and democratic functioning, specifically the rights to freedom of expression and to freedom of assembly. Importantly, this qualitative research identifies a pattern of common themes or consequences associated with surveillance in general, allowing us to move beyond hypothetical or individual experiences, and providing a greater understanding of the nuances of surveillance-related effects that can help inform decision-making surrounding large scale digital surveillance.
Single-Electron-Transistor Compact Model for Spin-Qubit Readout

Quantum computers process information stored in quantum bits (qubits), which must be controlled and read out by a traditional electronic interface. Co-designing and cooptimizing such a quantum-classical complex system requires efficient simulators to emulate the qubits and their interaction with classical electronics. For spin-qubit readout, a single electron transistor (SET) is often employed. To build a toolset that can co-simulate the spin qubit system with the classical control and readout interface, a compact and efficient SET model is needed. This paper presents a new compact empirical SET model based on state-of-the-art SET measurement and extracted by a custom function-fitting python program. Within the target source-drain voltage range of ±1000μV , the model is accurate for circuit (SPICE) simulation. Furthermore, the empirical model is represented by a set of equations that enables instantaneous output response requiring a negligible simulation time. With this new SET model, a quantum-electronics co-simulator such as SPINE can now be enhanced to simulate the readout in addition to the control circuits of spin qubits, thus enabling the design of the complete integrated circuit (IC) required for large-scale quantum computers.