Reviewerly/exHarmony huggingface dataset:
Bounds for Haralick features in synthetic images with sinusoidal gradients

Introduction: The gray-level co-occurrence matrix (GLCM) reduces the dimension of an image to a square matrix determined by the number of gray-level intensities present in that image. Since GLCM only measures the co-occurrence frequency of pairs of gray levels at a given distance from each other, it also stores information regarding the gradients of gray-level intensities in the original image. Methods: The GLCM is a second-order statical method of encoding image information and dimensionality reduction. Image features are scalars that reduce GLCM dimensionality and allow fast texture classification. We used Haralick features to extract information regarding image gradients based on the GLCM. Results: We demonstrate that a gradient of k gray levels per pixel in an image generates GLCM entries on the k th parallel line to the main diagonal. We find that, for synthetic sinusoidal periodic gradients with different wavelengths, the number of gray levels due to intensity quantization follows a power law that also transpires in some Haralick features. We estimate bounds for four of the most often used Haralick features: energy, contrast, correlation, and entropy. We find good agreement between our analytically predicted values of Haralick features and the numerical results from synthetic images of sinusoidal periodic gradients. Discussion: This study opens the possibility of deriving bounds for Haralick features for targeted textures and provides a better selection mechanism for optimal features in texture analysis applications.
Low-noise single-photon counting superconducting nanowire detectors at infrared wavelengths up to 29 µm

We report on the extension of the spectral sensitivity of superconducting nanowire single-photon detectors to a wavelength of 29 µm. To our knowledge, this represents the first demonstration of a single-photon counting detector at these long infrared wavelengths. We achieve saturated internal detection efficiency from 10 to 29 µm, while maintaining dark count rates below 0.1 counts per second. Extension of superconducting nanowire single-photon detectors to this spectral range provides low-noise and high-timing-resolution photon counting detection, effectively providing a new class of single-photon sensitive detectors for these wavelengths. These detectors are important for applications such as exoplanet spectroscopy, infrared astrophysics, physical chemistry, remote sensing, and direct dark-matter detection.
Posiform planting: generating QUBO instances for benchmarking

We are interested in benchmarking both quantum annealing and classical algorithms for minimizing quadratic unconstrained binary optimization (QUBO) problems. Such problems are NP-hard in general, implying that the exact minima of randomly generated instances are hard to find and thus typically unknown. While brute forcing smaller instances is possible, such instances are typically not interesting due to being too easy for both quantum and classical algorithms. In this contribution, we propose a novel method, called posiform planting , for generating random QUBO instances of arbitrary size with known optimal solutions, and use those instances to benchmark the sampling quality of four D-Wave quantum annealers utilizing different interconnection structures (Chimera, Pegasus, and Zephyr hardware graphs) and the simulated annealing algorithm. Posiform planting differs from many existing methods in two key ways. It ensures the uniqueness of the planted optimal solution, thus avoiding groundstate degeneracy, and it enables the generation of QUBOs that are tailored to a given hardware connectivity structure, provided that the connectivity is not too sparse. Posiform planted QUBOs are a type of 2-SAT boolean satisfiability combinatorial optimization problems. Our experiments demonstrate the capability of the D-Wave quantum annealers to sample the optimal planted solution of combinatorial optimization problems with up to 5, 627 qubits.
Continuous Auditing and Continuous Certification in MEDINA – Security Auditor’s View

<ns3:p>This paper discusses views on continuous auditing and continuous certification in the context of the MEDINA EU project and the security auditing industry. Based on an introduction of MEDINA, the notions of continuous auditing and continuous certification are introduced from a security auditor’s perspective to discuss the opportunities and challenges related to these topics. The paper also discusses further actions beyond this project in order to provide feedback on how continuous auditing and certification can be developed and introduced to the market.</ns3:p>
Transformasi pustakawan dalam meningkatkan kemampuan mahasiswa terhadap penelusuran informasi di dunia maya

Purpose. College libraries are no longer just waiting for users to come to visit and serve book return loans, but a new breakthrough is needed to seduce students. One of them is that librarians can act as educators. Librarians can provide education to users in accessing, searching, and using information sources effectively. This article discusses the role of librarians in digital information tracing training at UIN Walisongo Library.Methodology. The research method used is quantitative with a descriptive approach, which allows for a more detailed investigation of the stages taken by librarians in providing this training.Result and discussion. There are several stages carried out by librarians in providing digital information tracing training, namely the introduction of information sources, information tracing tools, search strategies, and citation management. Through this training, librarians guide students in understanding and mastering these steps, enabling them to access, search for, and use information more competently in an academic and research context. Conclusion. Reveal several stages undertaken by librarians in providing digital information tracing training, covering source introduction, tracking tool utilization, search strategy formulation, and citation management guidance. The pivotal role of librarians involves guiding students to understand and master stages of information retrieval. This transforms librarians into proactive educators, exceeding traditional roles. By transferring essential digital information tracing skills, librarians contribute to students' academic success, emphasizing their crucial role in shaping information-literate individuals.
Secure Digital Voting System on Blockchain

Every citizen's fundamental right, voting is a crucial part of democracy. Every nation benefits from blockchain-based elections since they allow for the conduct of elections online. It makes the election process safe, easy, and straightforward in contrast to the outdated (paper-based) and traditional (EVM) voting methods. The main goal of this project is to design and build a decentralised, blockchain- based voting and analysis system that might be used to set up election systems in countries where traditional, in-person voting with readily manipulable assurances is the norm. The goals of this system's design are to provide a safe voting process, save expenses, shorten wait times, eliminate inequalities brought on by different types of incorrect proxies, scale well, and operate independently of physical location. All in all, a reliable voting system will help the democratic system thrive. Voters may now cast their ballots from the convenience of their own homes thanks to our initiative, which will save them time and cut down on the number of invalid votes cast.
Optimalisasi kolaborasi mahasiswa dengan instansi praktik dalam menentukan minat praktik kerja perpustakaan

Purpose. The purpose of this study is to obtain a description of the collaboration carried out by students participating in Library Internship Programs with the institutions where they undergo their internships. Methodology. Using an online questionnaire as the instrument distributed to students participating in internships, responses were received from students from 127 provinces in Indonesia.Results and discussion. The information gathered indicates that library internship activities already reflect a diversity of interests, competencies, and objectives as part of the library internship experience. There is alignment between the activities carried out and various aspects of library management, collection development, and the complexity of seeking career opportunities in the library field. Conclusions. An analysis of the collaboration strategy between students and practical institutions in determining the type of library practice reveals several important aspects. Students must have a clear understanding of the learning objectives and library practice requirements through constructive dialogue with the practical institution. They also need to build a professional network and participate in activities that broaden their understanding of various types of practices. However, the lack of variety in the scope of practices suggests the need for guidance or alignment activities before the practical placement is carried out, allowing students to choose a practice type that aligns with their mentor's educational background.
Exploring Neighbor Correspondence Matching for Multiple-Hypotheses Video Frame Synthesis

Video frame synthesis, which consists of interpolation and extrapolation , is an essential video processing technique that can be applied to various scenarios. However, most existing methods cannot handle small objects or large motion well, especially in high-resolution videos such as 4K videos. To eliminate such limitations, we introduce a n eighbor c orrespondence m atching ( NCM ) algorithm for flow-based frame synthesis. Since the current frame is not available in video frame synthesis, NCM is performed in a current-frame-agnostic fashion to establish multi-scale correspondences in the spatial-temporal neighborhoods of each pixel. Based on the powerful motion representation capability of NCM, we propose a heterogeneous coarse-to-fine scheme for intermediate flow estimation. The coarse-scale and fine-scale modules are trained progressively, making NCM computationally efficient and robust to large motions. We further explore the mechanism of NCM, and find that neighbor correspondence is powerful since it provides multiple-hypotheses motion information for synthesis. Based on this analysis, we introduce a multiple-hypotheses estimation process for video frame extrapolation, resulting in a more robust framework, NCM-MH. Experimental results show that NCM and NCM-MH achieve 31.63 dB and 28.08 dB for interpolation and extrapolation on the most challenging X4K1000FPS benchmark, outperforming all the other state-of-the-art methods that use two reference frames as input.
Maria Martone, Gian Gaspare Napolitano e Novellieri inglesi e americani

The essay analyzes the section “Americani” in the anthology Novellieri inglesi e americani, published in 1944 by Salvatore De Carlo, edited by Maria Martone in collaboration with her husband Gian Gaspare Napolitano. The volume can be considered as the first answer to Elio Vittorini’s Americana, published by Bompiani in 1942. Though praising the importance of Vittorini’s work, Martone and Napolitano differentiate their text in terms of selective criteria. This essay wishes to demonstrate the influence exerted on Martone and Napolitano’s anthology by Henry Louis Mencken’s studies on the autonomy of the American language as opposed to British English (The American Language, 1919).
Optimization and performance investigation of 1-Toffoli gate quantum full adders for spin-torque-based n-qubit architecture

Abstract Quantum computing (QC) is suitable for reversible computing due to its inherent parallel processing ability and fast speed. It also helps to address the issue of high-power dissipation in classical computing. Moreover, QC gates are the sequence of elementary operations such as single-qubit rotation and two-qubit entanglement. Elementary quantum operations are required to be reduced for the realization of complex computing. In this paper, optimization of 1-Toffoli gate-based quantum full adders (QFAs) in terms of the number of elementary operations with the help of quantum library {R y , R z , $$\sqrt{\mathrm{SWAP}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mi>SWAP</mml:mi> </mml:msqrt> </mml:math> } is carried out. Moreover, the performance of two different 1-Toffoli QFAs is investigated in terms of execution time, fidelity, and number of electrons required to realize the QFAs. Improvement in fidelity is 0.7% and 0.57% for QFA1 and QFA2, respectively, compared to the fidelity of 2-Toffoli QFA. A 9.97% increase in execution time is mandatory for the QFA2 compared to QFA1. The QFA2 takes 5% more number of electrons in comparison to QFA1.
LGNN: a novel linear graph neural network algorithm

The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks. However, the existing graph neural network framework often uses methods based on spatial domain or spectral domain to capture network structure features. This process captures the local structural characteristics of graph data, and the convolution process has a large amount of calculation. It is necessary to use multi-channel or deep neural network structure to achieve the goal of modeling the high-order structural characteristics of the network. Therefore, this paper proposes a linear graph neural network framework [Linear Graph Neural Network (LGNN)] with superior performance. The model first preprocesses the input graph, and uses symmetric normalization and feature normalization to remove deviations in the structure and features. Then, by designing a high-order adjacency matrix propagation mechanism, LGNN enables nodes to iteratively aggregate and learn the feature information of high-order neighbors. After obtaining the node representation of the network structure, LGNN uses a simple linear mapping to maintain computational efficiency and obtain the final node representation. The experimental results show that the performance of the LGNN algorithm in some tasks is slightly worse than that of the existing mainstream graph neural network algorithms, but it shows or exceeds the machine learning performance of the existing algorithms in most graph neural network performance evaluation tasks, especially on sparse networks.
AI voices reduce cognitive activity? A psychophysiological study of the media effect of AI and human newscasts in Chinese journalism

Artificial Intelligence (AI) has been widely utilized in automated journalism writing and broadcasting in recent years. However, few systematic studies have been conducted on the differences in brain activation between human and artificial voices in newscasts. This paper aims to investigate the psychophysiological effects of the media in Chinese contexts when different agents (AI or human) broadcast different types (emotional/neutral) of news. Comparing the electrophysiological data of the participants’ EEG while listening to different newscast agents revealed that brain activity responses were greater when listening to a human newscast than to an AI voice newscast. And β bands in left posterior temporal lobe were significantly different, suggesting that participants’ brain were better at processing, comprehending auditory information, and storing working memory when listening to a human reporter than when listening to a voice generated by AI. Moreover, the ERP results and the interaction effect of content valence and agent voice demonstrated that the human voice generated greater cognitive effect, which may reflect participants’ trust in the credibility and fluency of the human voice news. This study demonstrates the importance of further research into cognitive effects of AI journalism.
Research on Trusted Management of Industrial Internet Identity Analysis Data Based on Blockchain

As an important part of the industrial internet, identity analysis data are growing with the expansion of the field involved in the industrial internet. The management of industrial internet identity analysis data faces many problems, such as complex types, a wide range of information, rapid growth, reduced security, etc. In view of the above problems, a trusted management model of industrial internet identity analysis data based on blockchain is first designed. Meanwhile, the identity analysis data information is analyzed and classified, and industrial data are divided into three levels according to the degree of privacy for hierarchical encryption. Secondly, the “on-chain + off-chain” storage model combining the blockchain main-slave chain and the off-chain database is designed to improve the efficiency of the whole model. Then, a collaborative consensus mechanism suitable for the main-slave multi-chain of the industrial internet is also designed, including slave-chain CIPBFT consensus, inter-chain cross-chain transmission protocol and main chain KZKP consensus. Finally, a prototype system is built to analyze the correctness, security, scalability and consensus efficiency of the model proposed in this study. The results show that the model proposed in this study can be applied to trusted management of data information for industrial internet identity analysis, and also provides an optimized solution for the same problem in fields of the industrial internet.
Photovoltaic Power Forecasting Approach Based on Ground-Based Cloud Images in Hazy Weather

Haze constitutes a pivotal meteorological variable with notable implications for photovoltaic power forecasting. The presence of haze is anticipated to lead to a reduction in the output power of photovoltaic plants. Therefore, achieving precise forecasts of photovoltaic power in hazy conditions holds paramount significance. This study introduces a novel approach to forecasting photovoltaic power under haze conditions, leveraging ground-based cloud images. Firstly, the aerosol scattering coefficient is introduced as a pivotal parameter for characterizing photovoltaic power fluctuations influenced by haze. Additionally, other features, such as sky cloud cover, color attributes, light intensity, and texture characteristics, are considered. Subsequently, the Spearman correlation coefficient is applied to calculate the correlation between feature sequences and photovoltaic power. Effective features are then selected as inputs and three models—LSTM, SVM, and XGBoost—are employed for training and performance analysis. After comparing with existing technologies, the predicted results have achieved the best performance. Finally, using actual data, the effectiveness of the aerosol scattering coefficient is confirmed, by exhibiting the highest correlation index, as a pivotal parameter for forecasting photovoltaic output under the influence of haze. The results demonstrate that the aerosol scattering coefficient enhances the forecast accuracy of photovoltaic power in both heavy and light haze conditions by 1.083% and 0.599%, respectively, while exerting minimal influence on clear days. Upon comprehensive evaluation, it is evident that the proposed forecasting method in this study offers substantial advantages for accurately predicting photovoltaic power output in hazy weather scenarios.
Modeling and shadowing paraconsistent BDI agents

Abstract The Bdi model of rational agency has been studied for over three decades. Many robust multiagent systems have been developed, and a number of Bdi logics have been studied. Following this intensive development phase, the importance of integrating Bdi models with inconsistency handling and revision theory have been emphasized. There is also a demand for a tighter connection between Bdi -based implementations and Bdi logics. In this paper, we address these postulates by introducing a novel, paraconsistent logical Bdi model close to implementation, with building blocks that can be represented as Sql /rule-based databases. Importantly, tractability is achieved by reasoning as querying. This stands in a sharp contrast to the high complexity of known Bdi logics. We also extend belief shadowing, a shallow and lightweight alternative to deep and computationally demanding belief revision, to encompass agents’ motivational attitudes.
Active Learning in the Detection of Anomalies in Cryptocurrency Transactions

The cryptocurrency market has grown significantly, and this quick growth has given rise to scams. It is necessary to put fraud detection mechanisms in place. The challenge of inadequate labeling is addressed in this work, which is a barrier to the training of high-performance supervised classifiers. It aims to lessen the necessity for laborious and time-consuming manual labeling. Some unlabeled data points have labels that are more pertinent and informative for the supervised model to learn from. The viability of utilizing unsupervised anomaly detection algorithms and active learning strategies to build an iterative process of acquiring labeled transactions in a cold start scenario, where there are no initial-labeled transactions, is being investigated. Investigating anomaly detection capabilities for a subset of data that maximizes supervised models’ learning potential is the goal. The anomaly detection algorithms under performed, according to the results. The findings underscore the need that anomaly detection algorithms be reserved for situations involving cold starts. As a result, using active learning techniques would produce better outcomes and supervised machine learning model performance.
Application Specific Reconfigurable Processor for Eyeblink Detection from Dual-Channel EOG Signal

The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human–computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can be useful for paralyzed people in controlling wheelchairs. Eye blink features from EOG signals can be useful in drowsiness detection. In some applications of electroencephalograms (EEGs), eye blinks are considered noise. The accurate detection of eye blinks can help achieve denoised EEG signals. In this paper, we aimed to design an application-specific reconfigurable binary EOG signal processor to classify blinks and saccades. This work used dual-channel EOG signals containing horizontal and vertical EOG signals. At first, the EOG signals were preprocessed, and then, by extracting only two features, the root mean square (RMS) and standard deviation (STD), blink and saccades were classified. In the classification stage, 97.5% accuracy was obtained using a support vector machine (SVM) at the simulation level. Further, we implemented the system on Xilinx Zynq-7000 FPGAs by hardware/software co-design. The processing was entirely carried out using a hybrid serial–parallel technique for low-power hardware optimization. The overall hardware accuracy for detecting blinks was 95%. The on-chip power consumption for this design was 0.8 watts, whereas the dynamic power was 0.684 watts (86%), and the static power was 0.116 watts (14%).