Reviewerly/exHarmony huggingface dataset:
Stochastic Circuits for Computing Weighted Ratio With Applications to Multiclass Bayesian Inference Machine

Bayesian inference is one method of statistical inference in machine learning. It predicts the probability that a given test belongs to a certain class and is widely used in various applications such as medical diagnosis, spam classification and fraud detection. The conventional binary architecture of computing the posterior probability is inefficient in practical implementation, which is involved in multiplication, addition and division operations. Recently, it has been shown that simple Muller C-elements, the asynchronous logic units, can perform stochastic Bayesian inference motivated by its truth table when the data is encoded as the bit-stream. The Bayesian inference machine is therefore implemented with low hardware cost. However, such an architecture is employed to compute the posterior probability of two classes only. This brief presents two stochastic circuit designs for computing the weighted ratio with multiple weights for generalized multi-class Bayesian machines. The first design is mainly based on the JK flip flop and multiplexers. The second approach is to construct the finite state machine (FSM) by manipulating the correlation between the input bit-streams. The FSM-based design requires fewer random number sources (RNSs) as compared to the JK flip flop-based implementation. These facts lead to a reduction of hardware area and energy. Simulation results show that the accuracy of the proposed JK flip flop-based and FSM-based designs is almost the same in the tested data sets. As compared to the traditional binary design, the circuit area of the proposed stochastic design is improved by 96% at least in the cases of three and four classes. The consumed energy per operation is reduced by 58.1% at least in the cases of three and four classes.
Deep sequential pattern mining for readability enhancement of Indonesian summarization

In text summarization research, readability is a great issue that must be addressed. Our hypothesis is readability can be accomplished by using text representations that keep the meaning of text documents intact. Therefore, this study aims to combine sequential pattern mining (SPM) in producing a sequence of a word as text representation with unsupervised deep learning to produce an Indonesian text summary called DeepSPM. This research uses PrefixSpan as an SPM algorithm and deep belief network (DBN) as an unsupervised deep learning method. This research uses 18,774 Indonesian news text from IndoSum. The readability aspect is evaluated by recall-oriented understudy for gisting evaluation (ROUGE) as a co-selection-based analysis; Dwiyanto Djoko Pranowo metrics, Gunning fog index (GFI), and Flesch-Kincaid grade level (FKGL) as content-based analysis; and human readability evaluation with two experts. The experiment result shows that DeepSPM yields better than DBN, with the F-measure value of ROUGE-1 enhanced to 0.462, ROUGE-2 is 0.37, and ROUGE-L is 0.41. The significance of ROUGE results also be tested using T-Test. The content-based analysis and human readability evaluation findings are conformable with the findings of co-selection-based analysis that generated summaries are only partially readable or have a medium level of readability aspect.
Partitioning intensity inhomogeneity colour images via Saliency-based active contour

Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model.
The role of Louvain-coloring clustering in the detection of fraud transactions

&lt;span lang="EN-US"&gt;Clustering is a technique in data mining capable of grouping very large amounts of data to gain new knowledge based on unsupervised learning. Clustering is capable of grouping various types of data and fields. The process that requires this technique is in the business sector, especially banking. In the transaction business process in banking, fraud is often encountered in transactions. This raises interest in clustering data fraud in transactions. An algorithm is needed in the cluster, namely Louvain’s algorithm. Louvain’s algorithm is capable of clustering in large numbers, which represent them in a graph. So, the Louvain algorithm is optimized with colored graphs to facilitate research continuity in labeling. In this study, 33,491 non-fraud data were grouped, and 241 fraud transaction data were carried out. However, Louvain’s algorithm shows that clustering increases the amount of data fraud of 90% by accurate.&lt;/span&gt;
The preliminary study of carbon x-change rakyat using blockchain application

Today’s air pollution is detrimental to the environment, particularly in Indonesia. Carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) and nitrogen oxide (NO&lt;sub&gt;x&lt;/sub&gt;) are present in the atmosphere due to air pollution. Many individuals employ reforestation to lessen the influence of CO&lt;sub&gt;2&lt;/sub&gt; and NO&lt;sub&gt;x&lt;/sub&gt; gases on the atmosphere. However, in the digitalized era, lowering carbon emissions may also be accomplished through a carbon credit exchange. Thus, in this study we investigate the performance of the carbon x-change rakyat (CXR) based on blockchain platform utilizing the stress test approach. We provided four scenarios with 10,000 to 100,000 transactions evaluated on the CXR blockchain system i.e., transfer, insert, remove, and update. The outcome demonstrates CXR’s effectiveness with 100% success and 0% failure rate based on testing and statistical computations calculation. The mean absolute error (MAE), variance accounted for (VAF), and percent error (PE) are obtained with values ranging from 0.38% to 4.67%. In this study, the transaction per-second (TPS) is used to calculate include error request (IER) and exclude error request (EER) values around 312 to 746 milliseconds (ms). In addition, the TPS of CXR based on blockchain platform is a capability to create and trace database carbon certificate ownership (nonfinancial activity). It means CXR based on the blockchain platform has a fast response to process carbon certificate ownership for transactions across local and international countries in the world.
Enhancing cryptographic protection, authentication, and authorization in cellular networks: a comprehensive research study

This research article provides an extensive analysis of novel methods of cryptographic protection as well as advancements in authentication and authorization techniques within cellular networks. The aim is to explore recent literature and identify effective authentication and authorization methods, including high-speed data encryption. The significance of this study lies in the growing need for enhanced data security in scientific research. Therefore, the focus is on identifying suitable authentication and authorization schemes, including blockchain-based approaches for distributed mobile cloud computing. The research methodology includes observation, comparison, and abstraction, allowing for a comprehensive examination of advanced encryption schemes and algorithms. Topics covered in this article include multi-factor authentication, continuous authentication, identity-based cryptography for vehicle-to-vehicle (V2V) communication, secure blockchain-based authentication for fog computing, internet of things (IoT) device mutual authentication, authentication for wireless sensor networks based on blockchain, new secure authentication schemes for standard wireless telecommunications networks, and the security aspects of 4G and 5G cellular networks. Additionally, in the paper a differentiated authentication mechanism for heterogeneous 6G networks blockchain-based is discussed. The findings presented in this article hold practical value for organizations involved in scientific research and information security, particularly in encryption and protection of sensitive data.
An introduction to double stain normalization technique for brain tumour histopathological images

Stain normalization is an image pre-processing method extensively used to standardize multiple variances of staining intensity in histopathology image analysis. Staining variations may occur for several reasons, such as unstandardized protocols while preparing the specimens, using dyes from different manufacturers, and varying parameters set while capturing the digital images. In this study, a double stain normalization technique based on immunohistochemical staining is developed to improve the performance of the conventional Reinhard’s algorithm. The proposed approach began with preparing a target image by applying the contrast-limited adaptive histogram equalization (CLAHE) technique to the targeted cells. Later, the colour distribution of the input image will be matched to the colour distribution of the target image through the linear transformation process. In this study, the power-law transformation was applied to address the over-enhancement and contrast degradation issues in the conventional method. Five quality metrics comprised of entropy, tenengrad criterion (TEN), mean square error (MSE), structural similarity index (SSIM) and correlation coefficient were used to measure the performance of the proposed system. The experimental results demonstrate that the proposed method outperformed all conventional techniques. The proposed method achieved the highest average values of 5.59, 3854.11 and 94.65 for entropy, TEN, and MSE analyses.
The $U(1)_{L_\mu-L_\tau}$ breaking phase transition, muon $g-2$, dark matter, collider and gravitational wave

Abstract Combining the dark matter and muon <?CDATA $ g-2 $?> anomaly, we study the <?CDATA $ U(1)_{L_\mu-L_\tau} $?> breaking phase transition, gravitational wave spectra, and direct detection at the LHC in an extra <?CDATA $ U(1)_{L_\mu-L_\tau} $?> gauge symmetry extension of the standard model. The new fields include vector-like leptons ( <?CDATA $ E_1,\; E_2,\; N $?> ), the <?CDATA $ U(1)_{L_\mu-L_\tau} $?> breaking scalar S , and the gauge boson <?CDATA $ Z' $?> , as well as the dark matter candidate <?CDATA $ X_I $?> and its heavy partner <?CDATA $ X_R $?> . A joint explanation of the dark matter relic density and muon <?CDATA $ g-2 $?> anomaly excludes the region where both <?CDATA $\min(m_{E_1},m_{E_2},m_N,m_{X_R})$?> and <?CDATA $\min(m_{Z'},m_S)$?> are much larger than <?CDATA $ m_{X_I} $?> . In the parameter space accommodating the DM relic density and muon <?CDATA $ g-2 $?> anomaly, the model can achieve a first-order <?CDATA $ U(1)_{L_\mu-L_\tau} $?> breaking phase transition, whose strength is sensitive to the parameters of the Higgs potential. The corresponding gravitational wave spectra can reach the sensitivity of U-DECIGO. In addition, the direct searches at the LHC impose stringent bounds on the mass spectra of the vector-like leptons and dark matter.
A versatile dataset for intrinsic plagiarism detection, text reuse analysis, and author clustering in Urdu

Plagiarism detection (PD) is a process of identifying instances where someone has presented another person's work or ideas as their own. Plagiarism detection is categorized into two types (i) Intrinsic plagiarism detection primarily concerns the assessment of authorship consistency within a single document, aiming to identify instances where portions of the text may have been copied or paraphrased from elsewhere within the same document. Author clustering, closely related to intrinsic plagiarism detection, involves grouping documents based on their stylistic and linguistic characteristics to identify common authors or sources within a given dataset. On the other hand, (ii) extrinsic plagiarism detection delves into the comparative analysis of a suspicious document against a set of external source documents, seeking instances of shared phrases, sentences, or paragraphs between them, which is often referred to as text reuse or verbatim copying. Detection of plagiarism from documents is a long-established task in the area of NLP with remarkable contributions in multiple applications. A lot of research has already been conducted in the English and other foreign languages but Urdu language needs a lot of attention especially in intrinsic plagiarism detection domain. The major reason is that Urdu is a low resource language and unfortunately there is no high-quality benchmark corpus available for intrinsic plagiarism detection in Urdu language. This study presents a high-quality benchmark Corpus comprising 10,872 documents. The corpus is structured into two granularity levels: sentence level and paragraph level. This dataset serves multifaceted purposes, facilitating intrinsic plagiarism detection, verbatim text reuse identification, and author clustering in the Urdu language. Also, it holds significance for natural language processing researchers and practitioners as it facilitates the development of specialized plagiarism detection models tailored to the Urdu language. These models can play a vital role in education and publishing by improving the accuracy of plagiarism detection, effectively addressing a gap and enhancing the overall ability to identify copied content in Urdu writing.
Boosting semi-supervised learning with Contrastive Complementary Labeling

Semi-supervised learning (SSL) approaches have achieved great success in leveraging a large amount of unlabeled data to learn deep models. Among them, one popular approach is pseudo-labeling which generates pseudo labels only for those unlabeled data with high-confidence predictions. As for the low-confidence ones, existing methods often simply discard them because these unreliable pseudo labels may mislead the model. Unlike existing methods, we highlight that these low-confidence data can be still beneficial to the training process. Specifically, although we cannot determine which class a low-confidence sample belongs to, we can assume that this sample should be very unlikely to belong to those classes with the lowest probabilities (often called complementary classes/labels). Inspired by this, we propose a novel Contrastive Complementary Labeling (CCL) method that constructs a large number of reliable negative pairs based on the complementary labels and adopts contrastive learning to make use of all the unlabeled data. Extensive experiments demonstrate that CCL significantly improves the performance on top of existing advanced methods and is particularly effective under the label-scarce settings. For example, CCL yields an improvement of 2.43% over FixMatch on CIFAR-10 only with 40 labeled data.
Inline classification of polymer films using Machine learning methods

Improving the sortability of plastic packaging film waste (PPFW) is crucial for increasing the recycling rate in Austria as they account for 150,000 t of the annually produced 300,000 t of plastic packaging waste. Currently PPFW is thermally recovered, as it is impossible to separate the mechanically recyclable monomaterial films from the non mechanically-recyclable multimaterial films. In this study, machine learning models capable of classifying inline into monolayer and multilayer films of PPFW according to their spectral fingerprint taken in transflection were created. Feature selection methods, like PCA and MRMR F-Tests, identified the most relevant spectral ranges for classification, that show the least redundancy and highest relevance. This effective subset of features decreases the required complexity of the model while reducing prediction time without compromising accuracy. The resulting models achieved a prediction accuracy of 85 % on unseen specimens with minimal prediction latency, effectively showing the inline applicability of these models in sorting aggregates.
Bayesian probabilistic modeling in robosoccer environment for robot path planning

The main goal of a route planning approach is to find a trajectory that safely transports the robot from one site to the next. Furthermore, it should provide an energy-efficient path so the computer can calculate it rapidly. This study develops a path-planning system for robots to approach the ball without collision. The Bayesian optimization algorithm (BOA) is used to identify the shortest path between the robot and the ball. BOA employs a probabilistic model to seek the optimum of an uncertain objective function efficiently. The performance of the BOA-based path planning system is compared to other optimization algorithms such as genetic algorithm, ant colony optimization, and firefly algorithm. BOA’s acquisition functions such as expected improvement, probability of improvement (PI), and upper confidence bound, are investigated. The exact locations of the robots and the ball are fed into optimization problems to discover the optimum path. The results reveal that the BOA system outperforms other systems in terms of computational time for planning the optimum path in dynamic situations and BOA-PI is the fastest algorithm.
Efficient VVC Encoding Using Hierarchical Parallelization: A Comprehensive Analysis

This paper presents and analyzes different parallelization strategies in VVenC, an open and optimized software encoder implementation of the Versatile Video Coding (VVC) standard. VVC has been developed to address the increasing demand for higher compression of digital video data, and it reduces the bitrate by around 50% for the same perceived quality compared to its predecessor, the High-Efficiency Video Coding (HEVC) standard. However, this increase in compression efficiency comes with an increase in computational complexity, particularly on the encoder side. VVenC integrates algorithmic optimizations for each coding tool in VVC and defines a set of five presets from faster to slower that provide Pareto-optimal tradeoffs between runtime and efficiency. Multithreading is employed to further reduce runtime while preserving most of the compression efficiency of each preset. With a hierarchical combination of pre-processing, picture-level and in-picture parallelization, VVenC achieves a 4× speedup for four threads. Further speedup using a higher number of threads depends on the video resolution and used encoder preset. For 16 threads, it ranges from 6 to 9 for high definition to 10-12 for ultra-high-definition video. Compared to previous work, the usage of temporal prediction for the adaptive loop filter reduces the associated coding efficiency loss from 0.4% to almost zero. A better scaling for higher numbers of threads can be achieved at the cost of a higher coding efficiency loss. In the presented framework, increased speedup by smaller block Coding Tree Unit (CTU) sizes, a combination of wavefront parallel processing and various tiles picture partitioning configurations is examined. Furthermore, results on a 20-core ARM-based Apple M1 computer indicate a better scaling for multithreading compared to ×86-based architectures. The analysis is complemented by profiling, which exhibits the overhead by idle threads and identifies the mode estimation as the main bottleneck of the presented framework.
Evaluation on model-driven knowledge graph and platform for grid operation and maintenance

With the continuous expansion of the power grid, the forms of faults are becoming increasingly complex, with a wide range of impacts and long maintenance cycles, posing increasingly severe challenges for power grid operators. In the current power system (PS), due to system limitations, there is a large amount of data, and the current computing system is limited by hardware and computing power, making it difficult to satisfy the requirements of real-time computing, comprehensive analysis, and expansion. This paper analyzed the technical basis of the knowledge graph (KG) of power grid operation and maintenance (O&amp;M), and clarified the key role of model driving in the current large-scale smart grid (SG), which can effectively improve the efficiency and reliability of data processing in the SG. This paper also analyzed the model-driven PG O&amp;M KG platform, and analyzed the role of particle swarm optimization (PSO) algorithm in PG operation and fault maintenance. After applying the PSO algorithm in this article, in the experimental results section, the fault rate of Transformer 5 was 2.1% lower than that of Transformer 4. The knowledge mapping model of grid O&amp;M and the particle swarm algorithm in this paper can significantly reduce the failure rate of the grid, which has wide extension value.
Target-Free Stereo Camera-GNSS/IMU Self-Calibration Based on Iterative Refinement

Camera and GNSS/IMU are widely used in autonomous vehicles and robots due to their light weight and low cost, in these cases, an accurate extrinsic calibration is essential for sensor fusion. Traditionally, specific vehicle movements or scenarios with known fiducial markers are required for the sophisticated calibration process, however, the former is difficult to eliminate the impact of drift made by the techniques used to estimate the motion of each sensor, and the latter is poorly automated and needs specialist human expertise. To tackle above problems, this paper proposes a target-free stereo camera-GNSS/IMU self-calibration method based on iterative refinement. The initial calibration parameters are calculated with relative pose constraints derived from visual odometry. To eliminate the impact of visual odometry drift, parameters are further refined by iteratively using paired relative pose constraints and absolute pose constraints, which are derived from scan-global map matching. Quantitative calibration results on simulated data show the robustness and accuracy of this approach with RMSEs of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> deg and 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> m for rotation and translation, respectively. Further experiments in real world confirm the superior of our method in natural environments.