Reviewerly/exHarmony huggingface dataset:
Secure and transparent password storing mechanism using blockchain

Blockchain Technology has emerged in last decade and despite being a new Technology it is already taken over the Internet.Blockchain Technology serves a major role for the Project that works on trust, because of its transparency and not being centralized.We would like to expand the Blockchain Technology towards Password Managers.Password Managers are applications that can help you store all online credentials with at most security.In this Paper we will be discussing how the Adaptation of Blockchain Technology can be used in the field of Password Managers to make them secure and how the Blockchain Technology can help with the existing issues with Password Managers, and how we can create a system of nodes using the Blockchain Technology to efficiently and securely save the credentials.
Kalimat Teks Eksposisi Karya Siswa Kelas VIII SMP

This study aims to describe the structure of single sentence patterns of seventh-grade students of SMPN 1 Guguak, Lima Puluh Kota Regency. This type of research uses descriptive qualitative. The object of this research is a student worksheet in the form of an exposition text, with the research subject being the students of SMP N 1 Guguak, Lima Puluh Kota Regency. The results showed 89 data elements in a single sentence pattern in the exposition text of class VIII students of SMP N 1 Guguak, Lima Puluh Kota Regency. The sentence structure with the S + P pattern consists of 2 data (2%), the use of the S + P + O pattern element is 17 sentences from 89 data (19%), the use of the S + P + Pel pattern element is 20 sentences from 89 data (24%), the use of the S+P+Ket pattern element contained 15 sentences from 89 data (7%), the use of the S+P+O+Pel pattern element contained 18 sentences from 89 data (22.5%), and the S sentence element pattern +P+O+Ket there are 17 sentences from 89 data (18%). In conclusion, the most widely used single sentence pattern element in the exposition text of VIII grade students of SMP N 1 Guguak, Lima Puluh Kota Regency is the S+P+Pel pattern.&#x0D; Keywords: Sentence Pattern Structure, Exposition Text
Breast Tumor Classification using Short-ResNet with Pixel-based Tumor Probability Map in Ultrasound Images

Breast cancer is the most common form of cancer and is still the second leading cause of death for women in the world. Early detection and treatment of breast cancer can reduce mortality rates. Breast ultrasound is always used to detect and diagnose breast cancer. The accurate breast segmentation and diagnosis as benign or malignant is still a challenging task in the ultrasound image. In this paper, we proposed a classification model as short-ResNet with DC-UNet to solve the segmentation and diagnosis challenge to find the tumor and classify benign or malignant with breast ultrasonic images. The proposed model has a dice coefficient of 83% for segmentation and achieves an accuracy of 90% for classification with breast tumors. In the experiment, we have compared with segmentation task and classification result in different datasets to prove that the proposed model is more general and demonstrates better results. The deep learning model using short-ResNet to classify tumor whether benign or malignant, that combine DC-UNet of segmentation task to assist in improving the classification results.
Identifying regions of interest in mammogram images

Screening mammography is the primary preventive strategy for early detection of breast cancer and an essential input to breast cancer risk prediction and application of prevention/risk management guidelines. Identifying regions of interest within mammogram images that are associated with 5- or 10-year breast cancer risk is therefore clinically meaningful. The problem is complicated by the irregular boundary issue posed by the semi-circular domain of the breast area within mammograms. Accommodating the irregular domain is especially crucial when identifying regions of interest, as the true signal comes only from the semi-circular domain of the breast region, and noise elsewhere. We address these challenges by introducing a proportional hazards model with imaging predictors characterized by bivariate splines over triangulation. The model sparsity is enforced with the group lasso penalty function. We apply the proposed method to the motivating Joanne Knight Breast Health Cohort to illustrate important risk patterns and show that the proposed method is able to achieve higher discriminatory performance.
ConNet: Designing a Fast, Efficient, and Robust Crowd Counting Model Through Composite Compression

Counting the number of people in a specific area is crucial in maintaining proper crowd safety and management especially in highly-congested indoor scenarios. Recent convolutional neural network (CNN) approaches integrate auxiliary sub-networks to increase the accuracy of the model in estimating crowd size. However, these models require large computational costs due to additional calculations, resulting in an impractically slow inference speed for real-world applications. In this paper, we propose a fast, efficient, and robust crowd counting model called Condensed Network or ConNet. We utilize a composite technique composed of multiple compression methods to reduce the number of parameters of our proposed model. ConNet attains counting accuracy on par with state-of-the-art crowd counting methods on benchmark datasets featuring indoor scenes, while significantly reducing parameter count and increasing inference speed. Moreover, ConNet still performs accurately even with extreme changes in lighting conditions, image resolutions, and camera orientations. Our smallest model ConNet-04 has 61.0× less parameters and is at most 9.0× faster than the baseline approach. Our code and trained models are publicly available at https://github.com/mikatej/ConNet .
Home-Based Real-Time Abnormal Movement Detection System Deployed on On-Device Artificial Intelligence

In response to the aging trend in society and to Human Augmentation beings for home-based activities, this paper proposes an Abnormal Movement Detection system, using the common at-home movements of standing up and hand tremors while picking up items for abnormal movement verification. This can be easily applied in ordinary homes or long-term care institutions; for those living alone with limited resources, there is no longer any need to purchase expensive monitoring equipment to achieve improved quality of life. Therefore, this research collected and built the own dataset as the first important step of the study. The proposed Abnormal Movement Detection system is implemented by designing a deep learning network. Several issues, including the network architecture, the novel method of data augmentation and the scoring method of expanding the intervals between abnormality levels, are studied. For achieving the home-based real-time detection, there are four main contributions of this paper. The first is that a training dataset was collected and established: From this, the pathognomonic movement categories are easy to observe in home activities and geometric data augmentation can be used to improve the related home activity video collection. The second is the abnormal behavior detection architecture: This architecture has several important function blocks including detecting object, detecting action, inspecting abnormal movement and reminding event, using Convolutional Neural Network combined with Long Short-Term Memory ([Formula: see text]) as the core network for abnormal motion detection. With movement abnormality evaluation based on different levels, it can judge abnormal behaviors and conduct model training, performance evaluation and architecture optimization with both public domain datasets and the movement dataset collected in this research project. The third is the proliferation of new attributes in the videos: New attributes are added to the original videos through a Generative Adversarial Network (GAN), producing new training videos; the effectiveness of two different generation methods is evaluated. Finally, the algorithms developed in this paper are deployed on resource-constrained On-device Artificial Intelligence (AI). Activity videos from a total of 20 people were collected; in all, 53 videos of StandUp and 60 videos of PickUpItems were obtained to establish the training dataset. When CNN and LSTM network were added to Batch Normalization (BN), and Global Average Pooling (GAP) replaced Fully Connected (FC) layers, the accuracy rate reached 98.4%. In terms of data augmentation, geometric transformations and GAN were used to estimate the performance. The experimental results showed that the geometric transformation using brightness adjustment had the highest accuracy rate of 98.6%. Finally, the Softmax layer using Phi-Softmax–tan(⋅) function was shown to be the best method to expand the intervals between abnormality levels.
Sequential parametrized motion planning and its complexity, II

This is a continuation of our recent paper [6] in which we developed the theory of sequential parametrized motion planning. A sequential parametrized motion planning algorithm produced a motion of the system which is required to visit a prescribed sequence of states, in a certain order, at specified moments of time. In [6] we analysed the sequential parametrized topological complexity of the Fadell - Neuwirth fibration which is relevant to the problem of moving multiple robots avoiding collisions with other robots and with obstacles in the Euclidean space. In [6] we found the sequential parametrised topological complexity of the Fadell - Neuwirth bundle for the case of the Euclidean space Rd of odd dimension as well as the case d=2. In the present paper we give the complete answer for an arbitrary d≥2 even. Moreover, we present an explicit motion planning algorithm for controlling multiple robots in Rd having the minimal possible topological complexity; this algorithm is applicable to any number n of robots and any number m≥2 of obstacles.
The reusability prior: comparing deep learning models without training

Abstract Various choices can affect the performance of deep learning models. We conjecture that differences in the number of contexts for model components during training are critical. We generalize this notion by defining the reusability prior as follows: model components are forced to function in diverse contexts not only due to the training data, augmentation, and regularization choices, but also due to the model design itself. We focus on the design aspect and introduce a graph-based methodology to estimate the number of contexts for each learnable parameter. This allows a comparison of models without requiring any training. We provide supporting evidence with experiments using cross-layer parameter sharing on CIFAR-10, CIFAR-100, and Imagenet-1K benchmarks. We give examples of models that share parameters outperforming baselines that have at least 60% more parameters. The graph-analysis-based quantities we introduced for the reusability prior align well with the results, including at least two important edge cases. We conclude that the reusability prior provides a viable research direction for model analysis based on a very simple idea: counting the number of contexts for model parameters.
Where Is the Research on Cryptographic Transition and Agility?

Gaps facing the industry as quantum safe algorithms move closer to standardization.
Protecting Autonomous Cars from Phantom Attacks

Enabling object detectors to better distinguish between real and fake objects in semi-autonomous and fully autonomous vehicles.
Contracting for Artificial Intelligence

Considering the promises and perils of contracting for the use of artificial intelligence tools and data.
Subpixel spot localization using multiscale anisotropic Gaussian tensor

Spot localization is a fundamental but crucial issue in many optical applications. However, the existing approaches either have limited localization accuracy for overlapping elliptic spots or are noise-sensitive. To overcome these limitations, a novel subpixel spot localization method based on the multiscale anisotropic Gaussian tensor is proposed. Firstly, an anisotropic Gaussian spot model is developed to better describe spots under perspective projection. Then, the anisotropic scale-space tensor representation is generated to separate overlapping spots and determine the initial centers. Afterwards, the scale-adapted anisotropic Gaussian kernel and its second-order partial derivates are constructed to obtain subpixel centers. The simulation results show that the highest score of 1.00 is achieved on overlapping elliptic spot images. When estimating homography matrix, the proposed method yields the reprojection error of 0.06 pixels and is more robust to noise. Thus, the proposed method achieves robust subpixel localization for overlapping elliptic spots.
Towards Dynamic Action Planning with user preferences in Automated Health Coaching

Health coaching is an evidence-based approach to help individuals adopt health behaviors such as physical activity (PA). However, human health coaching is limited by a lack of real-time data and scalability. In this paper, we present the architecture and functionality of a novel semi-automated health coach that we call BeSMART that automates the goal modification and dialogue functionalities of human health coaching using a commercial health tracker and mobile phone. Results from an acceptability study (N=10) indicate that users have difficulty fully completing all actions as initially planned. To build planning systems that can dynamically adapt action plans to such changes, we show that user preferences are vital, and that users find re-planning acceptable. Towards solving this problem, we extracted and categorized user preferences from health coaching sessions and propose extensions to Hierarchical Task Network (HTN) representations. When taking users personally re-planned activities into account, post-hoc analysis shows that users are much more likely to complete their overall PA goals.
Fast parallel computation of reduced row echelon form to find the minimum distance of linear codes

Finding the distance of linear codes is a key aspect to build error correcting codes, and also to design attacks in code-based post-quantum cryptography; however, it is a NP-hard problem difficult to be addressed. Metaheuristics, and more specifically genetic algorithms, have proven to be a promising tool to improve the search of an upper bound for the distance of a given linear code. In a previous work, it was demonstrated that the there exists a column permutation of a code matrix whose Reduced Row Echelon Form (RREF) contains a row of minimum weight, i.e. the code distance, although calculating RREF during fitness evaluation increases the time complexity of the algorithm substantially. In this work, we propose parallelization of multiple calculations of Reduced Row Echelon Forms simultaneously, and its integration into a fully parallelized design of a CHC evolutionary algorithm to overcome this limitation. Moreover, we demonstrate empirically a substantial improvement in time complexity for the approach in practical case studies to find the distance of linear codes over different finite fields.
A novel variational inference method for joint estimation of target and reflectors in household multipath environment

Compared to the situation in unobstructed scenarios, RF sensing information obtained in the household environment has significant multipath components. Multiple reflections of electromagnetic waves in the household caused multipath components which bring ambiguity to the linear programming association algorithm. In this paper, we propose a structured variational inference algorithm. By using multipath components wisely, the algorithm improved sensing accuracy and can jointly estimate reflector and target states. Considering the multi-frame association problem, a belief propagation algorithm is used to approximately estimate the discrete association variables. And then, mean-field variation is used to approximate the hierarchical measurement model. With simulation experiments, the proposed algorithm can achieve better estimation accuracy than traditional methods.
Implementation of orthogonal subspace projection on ASTER data for exploring phosphorite -a study in Chhatarpur, Madhya Pradesh, India

In this study, we have identified surface exposures of phosphorite using visible near infrared (VNIR) and shortwave infrared (SWIR) bands of Advanced space borne thermal emission and reflection radiometer (ASTER) data. We analysed the spectral contrast of dolomite and phosphorite samples collected from the ground and also from the pixels of the ASTER image composite. We observed the consistent spectral contrast between the samples of dolomite and phosphorite and used the same for delineating phosphorite within the dolomite and associated carbonate rocks. In this regard, we first delineated dolomite and associated carbonate rocks using feature oriented principal component of the iron rich carbonate rocks. Subsequently, we implemented partial spectral unmixing method, known as orthogonal subspace projection on ASTER VNIR and SWIR bands to delineate phosphorite within dolomite. We could trace a few new occurrences of phosphorite in the study area beyond the known occurrence at Hirapur open cast mining area. We have validated the identified new areas during field survey using a rapid colorimetric test for phosphate to ascertain the exploration importance of these targets. We further estimated P2O5 grade content of the phosphorite samples in these sites and found few of them have very high P2O5 content.
Project-scale prospectivity analysis for unconformity-related uranium deposits in the Badami Group of the western Kaladgi basin, India

Developing successful exploration strategies for finding new ore deposits is a major challenge for exploration geologists, particularly, for concealed deposits in terranes with thick sediment cover. Proterozoic unconformity-related uranium deposits occur at, below, or above, unconformable contacts between Archean and Paleoproterozoic crystalline basement and siliciclastic sediments of middle or later Proterozoic age, often at locations where fault systems cut across either one or both of the geologic units that lie above and below the unconformity. The present study implements project-scale prospectivity analyses to identify drilling targets in the Badami Group of the western Kaladgi basin, India. Topographic profiles; field observations of breccia and lineaments; surface and sub-surface sampling; petrography and geochemistry; drill-core lithologs; and interpreted aeromagnetic anomalies were used to build a generalized local geological model of unconformity-related uranium deposits in the study area. Based on the geological model and a generalised mineral systems model, hydrothermal alteration, proximity to lineaments, proximity to unconformity surface, and anomalous uranium content on the surface and in the groundwater were identified as key project-scale targeting criteria. Seven high-priority drilling targets were mapped based on expert driven prospectivity analysis constrained by a GIS-based fuzzy prospectivity modelling. Out of these, four target areas coincide with the targets identified by the Atomic Minerals Directorate (AMD) of India based on their independent investigation. The three new targets from this study are recommended as targets for detailed exploration and drilling. In addition to this, about eight additional low priority targets were identified for follow-up exploration activities. This paper demonstrates that the prospectivity mapping methods can be applied at the project scale. The strategy demonstrated in this study can be used for exploration targeting in similar basins like the western Kaladgi basin that are structurally less disturbed.
Development of AI Educational Datasets Library Using Synthetic Dataset Generation Method

Recently, as the impact of AI technology on society has increased, education to develop students' AI capabilities has been emphasized. Along with the importance of AI technology, the importance of datasets, which are an axis of technological development, is being emphasized, and many studies on datasets for AI are being conducted. In order to provide meaningful AI education to students from an educational perspective, this paper reconstructs libraries to utilize synthetic dataset generation libraries in different classroom instruction environments and confirms the applicability of educational purposes.
Trust and Rewards in a Two-Tier Consensus Architecture

Consensus algorithms based on the proof-of-stake (PoS) are becoming a viable alternative to proof-of-work-based distributed blockchain ledgers. In this work, we integrated PoS with Practical Byzantine Fault Tolerance (PBFT) to ensure truthful and honest behavior of all nodes, leaders and ordinary nodes alike, that participate in block validation and ordering processes in a permissioned network environment. The proposed scheme is modeled using Markov Chain and probabilistic analysis, and it is shown to ensure proper consensus-based decision making even in case of substantial probability of dishonest node behavior.
Interpretable Anomaly Detection for Lung Sounds Using Topology

In the medical field, research on computer-aided diagnosis using machine learning has been actively conducted. While machine learning can achieve high accuracy by collecting a large amount of data, low interpretability of machine learning is an important issue for achieving practical use in the medical field, where missing a disease may lead to fatal results. In this paper, we propose an anomaly detection method that takes the interpretability into account for diagnosing lung sounds. Furthermore, the proposed method incorporates the context information included in the sound data in the machine learning-based anomaly detection method to improve the detection performance while maintaining the interpretability of the detection results.
Faster Few-Shot Face Image Generation With Features of Specific Group Using Pivotal Tuning Inversion and PCA

Training Generative Adversarial Networks (GANs) requires large amounts of data, and creating sufficiently large datasets to generate images of faces with attributes of a specific group is difficult. In this study, we propose a few-shot method to rapidly generate high-quality images of faces with features associated with a specific group. Our proposed approach comprises two techniques, Pivotal Tuning Inversion (PTI) and Principal Component Analysis (PCA). PTI consists of two processes: Inversion and Pivotal Tuning. The images of the dataset are projected into the latent space as latent vectors by Inversion, and the pretrained weights of the generator are tuned to adapt to the dataset by Pivotal Tuning. The distribution with the features of the group is obtained from the latent vectors using PCA. Since training on the dataset requires only a short computational time by using these techniques, our method is faster than the previous few-shot methods in the image generation process. With three datasets each containing approximately 100 face images of members of specific groups, the experimental results showed that the proposed method achieved the generation of higher-quality images with the features of the group in a shorter computational time than all previous few-shot methods.