Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN BAHAN AJAR FLIPBOOK MANFAAT ENERGI KELAS IV DI SEKOLAH DASAR

This study aims to produce flipbook teaching materials for theme 2, subtheme 2, the benefits of learning energy 1 in class IV at SDN Semplak 1 and to determine the feasibility of flipbook teaching materials. This development uses the ADDIE model. The research begins with a needs analysis, making a design, developing a flipbook teaching material design, then being validated by experts, revising the product, and conducting a limited trial (implementation). The total average value of the validation results of material, media, and language experts shows that flipbook teaching materials are very feasible to use with an average acquisition percentage of 91.90%. In the limited test stage, 29 students gave a very good response to the flipbook teaching materials used. Based on the results of the study, it was concluded that this flipbook teaching material was tested, interesting and very suitable for students to use for learning.
AI Explainability and Governance in Smart Energy Systems: A Review

Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major concern for stakeholders hindering a fast uptake of AI in the energy sector. This paper provides a review of AI explainability and governance in smart energy systems. We collect 3,568 relevant papers from the Scopus database, automatically discover 15 parameters or themes for AI governance in energy and elaborate the research landscape by reviewing over 150 papers and providing temporal progressions of the research. The methodology for discovering parameters or themes is based on "deep journalism", our data-driven deep learning-based big data analytics approach to automatically discover and analyse cross-sectional multi-perspective information to enable better decision-making and develop better instruments for governance. The findings show that research on AI explainability in energy systems is segmented and narrowly focussed on a few AI traits and energy system problems. This paper deepens our knowledge of AI governance in energy and is expected to help governments, industry, academics, energy prosumers, and other stakeholders to understand the landscape of AI in the energy sector, leading to better design, operations, utilisation, and risk management of energy systems.
Statistics of entanglement transformation with hierarchies among catalysts

The distribution of typical bipartite pure states is studied within the framework of state transformation via local operations and classical communication (LOCC). We report the statistics of comparable and incomparable states in different dimensions for single- and multicopy regimes, and we establish a connection between state transformation and the difference between the entanglement contents of the initial and the target states. From the analysis of catalyst resources, required to further otherwise impossible LOCC transformations between pairs, we demonstrate a universal pattern in the average and minimum entanglement of the randomly generated catalysts. Furthermore, we introduce a concept of hierarchy between different kinds of catalysts, and we show how they can not only aid in the conversion of incomparable states, but they can also act as a less costly resource toward this goal. We confirm the existence of catalysts, referred to as strong catalysts, which can activate LOCC transformation between pairs at the single-copy level, when it is initially impossible even with multiple copies.
Development of an Information Model of The Portal of Scientific Knowledge by Means of Semantic Web Technology

The approach to the development of the portal of scientific knowledge model is described. The main purpose of the portal is to provide meaningful access to scientific and educational information resources of this field of knowledge and comprehensive information processing services. The portal of scientific knowledge will provide systematization and integration of scientific knowledge, data and information resources into a single information space, meaningful and effective access to them, as well as support for their use in solving various scientific and educational tasks. One of the approaches to the creation of such portals is the use of Semantic Web technologies, relevant standards and tools in the collection, processing, storage, search and dissemination of information. It should be noted that our approach naturally integrates the most important components of Semantic Web technology, in particular, the use of ontology to represent the semantics of information resources and support their intellectual analysis.
Cross-Domain Authentication Scheme for IoT Devices Based on BlockChain

With the popularity of Internet of Things (IoT) technology, the scale of IoT is gradually becoming huge and demand-driven to form multi-domain IoT, and the demand for cross-domain access of devices in multi-domain scenarios has greatly increased. Secure cross-domain authentication is an important step in securing multi-domain IoT. The current cross-domain authentication architecture for IoT devices has limitations such as single point of failure, low authentication efficiency, poor scalability and other limitations. To address these problems, this paper designs a blockchain-based cross-domain authentication scheme for IoT terminals, which adopts an authentication architecture based on a combination of blockchain and edge computing technology to eliminate the problems of traditional cross-domain authentication for IoT devices and achieve distributed authentication. Considering that IoT devices have the characteristics of heterogeneity, massiveness, and limited resources, a lightweight and secure certificateless signature scheme is introduced in the cross-domain authentication process, eliminating complex certificate management and key escrow, and achieving secure and efficient cross-domain authentication of IoT terminals.
MS-GCN: Multi-Stream Graph Convolution Network for Driver Head Pose Estimation

3D point cloud contains plentiful topological geometric features, which are crucially important for the task of driver head pose estimation. However, most recent works only take RGB or RGB-D data as input, without utilizing the topological features in 3D point cloud. In this work, we propose a Multi-Stream Graph Convolution Network (MS-GCN) to incorporate topological, local, and global facial information for driver head pose estimation. The introduced MS-GCN contains three Graph Convolution Network (GCN) streams, designed to finely extract topological features, local features and global features respectively. In particular, RepVggis introduced to extract the appearance feature, followed by one stream of GCN to capture the global facial information. Another stream of GCN is used to capture the local appearance feature related to local facial landmarks. Besides, the third stream is introduced to process the topological geometric feature from 3D facial landmarks. Note that the sparse 3D facial landmarks are located from dense point cloud as the input of the third topological GCN in this work. The three streams are finally fused based on the attention mechanism. To the best of our knowledge, this is the first attempt to construct 3D graph on the sparse 3D facial landmarks point cloud to efficiently extract topological information for head pose estimation. Extensive experiments are conducted on 300W-LP, AFLW2000 and BIWI datasets, and the results show that our method achieves state-of-the-art results among landmark-based methods, and is also competitive with landmark-free methods.
Application of Natural Language Processing Using Cosine-Similarity Algorithm in Making Chatbot Information on the New Capital City of the Republic of Indonesia

The new capital city (IKN) of the Republic of Indonesia has been ratified and inaugurated by President Joko Widodo since January 2022. Unfortunately, there are still many Indonesian citizens who do not understand all the information regarding the determination of the new capital city. Even though the Indonesian Government has created an official website regarding the new capital city (www.ikn.go.id) the information is still not optimal because web page visitors are still unable to interact actively with the required information. Therefore, the development of the Chatting Robot (Chatbot) application is deemed necessary to become an interactive component in obtaining information needed by users related to new capital city. In this study, a chatbot application was developed by applying Natural Language Processing (NLP) using the Term Frequency-Inverse Document Frequency (TF-IDF) method for term weighting and the Cosine-Similarity algorithm to calculate the similarity of the questions asked by the user. The research successfully designed and developed a chatbot application using the Cosine-Similarity algorithm. The testing phase of the chatbot model uses several scenarios related to the points of NLP implementation. The test results show that all scenarios of questions asked can be responded well by the chatbot.
Ristretto: An Atomized Processing Architecture for Sparsity-Condensed Stream Flow in CNN

Low-precision quantization and sparsity have been widely explored in CNN acceleration due to their effectiveness in reducing computational complexity and memory requirements. However, to support variable numerical precision and sparse computation, prior accelerators design flexible multipliers or sparse dataflow separately. A uniform solution that simultaneously exploits mixed-precision and dual-sided irregular sparsity for CNN acceleration is still lacking. Through an in-depth review of existing precision-scalable and sparse accelerators, we observe that a direct combination of low-level multipliers and high-level sparse dataflow from both sides is challenging due to their orthogonal design spaces. To this end, in this paper, we propose condensed streaming computation. By representing non-zero weights and activations as atomized streams, the low-level mixed-precision multiplication and high-level sparse convolution can be unified into a shared dataflow through hierarchical data reuse. Based on the condensed streaming computation, we propose Ristretto, an atomized architecture that exploits both mixed-precision and dual-sided irregular sparsity for CNN inference. We implement Ristretto in a 28nm technology node. Extensive evaluations show that Ristretto consistently outperforms three state-of-the-art CNN accelerators, including Bit Fusion, Laconic, and SparTen, in terms of performance and energy efficiency.
Improved TOA and Pulse Width Estimation for Wideband Signal in Electronic Warfare Systems

Estimating the parameters of the enemy's threat signal is an essential task in electronic warfare support systems. Among the parameters, time of arrival (TOA) and pulse width (PW) are commonly used to estimate carrier frequency and pulse amplitude (PA). It is indisputable that the estimation performance of the carrier frequency and PA depends on the TOA and PW accuracy. Therefore, it is crucial to estimate TOA and PW accurately. Recently, a method using wavelet denoising has been studied to increase the estimation accuracy of signal parameters. However, using wavelet denoising has a performance deterioration problem due to signal distortion when a wideband signal is intercepted. Thus, in this paper, we analyse the cause of distortion and propose a denoising technique with a sinc filter bank-based constant false alarm rate (CFAR) detection. In addition, we propose an estimator exploiting a differential moving average filter to improve the estimation performance even in a low signal-to-noise ratio (SNR). Simulation results show that the proposed denoising method performs better than the conventional method.
ATM Security System Modeling Using Face Recognition with FaceNet and Haar Cascade

ATM or Automated Teller Machines are widely used by people nowadays. The existing conventional ATM is vulnerable to crimes because of the rapid technology development. A total of 270,000 reports have been reported regarding credit card fraud and this was the most reported form of identity theft in 2019. A secure and efficient ATM is needed to increase the overall experience, usability, and convenience of the transaction at the ATM. To provide better security for the conventional ATM, this paper proposed a face recognition system using FaceNet combined with Haar Cascade Classifier. Haar Cascade is used to detect the haar features based on the face features and stored to the FaceNet model as the face recognition model. The scale factor from the Haar Cascade is also modified a few times to get a better accuracy and processing time. With the proposed method, the Personal Identification Number (PIN) will be replaced by face recognition. Every time the customers insert their card, the system will detect and start to identify the face. If it does not match, the card will be blocked. This research uses the Face Recognition Dataset from Kaggle. By using this dataset, the proposed system achieves the highest accuracy with 90.93%.
Spectrum of Joint Action of Factors in the Binary Theory of Sufficient Causes

The paper presents basic constructions of the Boolean formalization of the binary theory of sufficient causes, which is a two-level version of a commonly accepted causality model in epidemiology, toxicology and evidence-based medicine. This model aims to clarify relationships between the possible causes of a response and the response itself. It is turned out that basic relations between causes and response can be effectively represented through the Boolean framework. Of interest in that theory as well as in epidemiology is a problem of interaction (joint action) of the causes (factors) under consideration. The common approach to that is a semantic analysis of a representation of response as a logical function of given binary factors. In the Boolean model of the binary sufficient causes framework this can be implemented algebraically through the action of an automorphism group on a Boolean algebra. Every orbit of that action can be treated as a type of joint action of given factors. The main content of the paper is devoted to the introduction of integer invariants of joint action (named degrees of joint action) which allow us to order all the types of joint action according to the strength of this action.
Comprehensive Analysis of the Object Detection Pipeline on UAVs

An object detection pipeline comprises a camera that captures the scene and an object detector that processes these images. The quality of the images directly affects the performance of the object detector. Current works focus on independently improving the image quality or object detection models but neglect the importance of joint optimization of the two subsystems. This paper aims to tune the detection throughput and accuracy of existing object detectors in the remote sensing scenario by optimizing the input images tailored to the object detector. We empirically analyze the influence of two selected camera calibration parameters (camera distortion correction and gamma correction) and five image parameters (quantization, compression, resolution, color model, and additional channels) for these applications. For our experiments, we utilize three Unmanned Aerial Vehicle (UAV) data sets from different domains and a mixture of large and small state-of-the-art object detector models to provide an extensive evaluation of the influence of the pipeline parameters. Finally, we realize an object detection pipeline prototype on an embedded platform for a UAV and give a best practice recommendation for building object detection pipelines based on our findings. We show that not all parameters have an equal impact on detection accuracy and data throughput. Using a suitable compromise between parameters, we can achieve higher detection accuracy for lightweight object detection models while keeping the same data throughput.
Stability of clinical prediction models developed using statistical or machine learning methods

Clinical prediction models estimate an individual's risk of a particular health outcome, conditional on their values of multiple predictors. A developed model is a consequence of the development dataset and the chosen model building strategy, including the sample size, number of predictors and analysis method (e.g., regression or machine learning). Here, we raise the concern that many models are developed using small datasets that lead to instability in the model and its predictions (estimated risks). We define four levels of model stability in estimated risks moving from the overall mean to the individual level. Then, through simulation and case studies of statistical and machine learning approaches, we show instability in a model's estimated risks is often considerable, and ultimately manifests itself as miscalibration of predictions in new data. Therefore, we recommend researchers should always examine instability at the model development stage and propose instability plots and measures to do so. This entails repeating the model building steps (those used in the development of the original prediction model) in each of multiple (e.g., 1000) bootstrap samples, to produce multiple bootstrap models, and then deriving (i) a prediction instability plot of bootstrap model predictions (y-axis) versus original model predictions (x-axis), (ii) a calibration instability plot showing calibration curves for the bootstrap models in the original sample; and (iii) the instability index, which is the mean absolute difference between individuals' original and bootstrap model predictions. A case study is used to illustrate how these instability assessments help reassure (or not) whether model predictions are likely to be reliable (or not), whilst also informing a model's critical appraisal (risk of bias rating), fairness assessment and further validation requirements.
Detection of Counter-Forensic Incidents Using Security Information and Incident Management (SIEM) Systems

Cyber criminals can use many different methods to completely lose their traces after their actions or to complicate the work of forensic experts. Detection of such suspected anti-forensic activities can guide the detection of cyber crimes that have been experienced but not yet detected. One of the tools that can be used for this purpose is SIEM (Security Information and Event Management) systems. With proper configuration and management, these systems facilitate the work of security analysts in attack detection and incident response stages. In addition, it facilitates the discovery of evidence after a cybercrime and reduces evidence collection times. In this study, logs and log collection methods that can be produced by systems that are likely to be found in an enterprise infrastructure were examined. It has been researched how to detect sample anti-forensics attack types with the help of a selected SIEM application and the results are shown on a created use case template.
Efficient One-Shot Sports Field Image Registration with Arbitrary Keypoint Segmentation

Automatic sports field registration aims at projecting a given image taken with unknown camera parameters to a known 3D coordinate system in order to obtain higher-level information like the position and speed of players. Existing methods generally detect specific visual landmarks on the field and then use an iterative refinement to get closer to the desired calibration. They are usually only compared in terms of precision on a standard benchmark without considering other metrics. However, execution speed is also important, mainly in the context of live broadcast TV and sports analysis. This work introduces a new automatic field registration method achieving excellent performance on the WorldCup Soccer benchmark, while neither depending on specific visible landmarks nor any refinement, resulting in a very high execution speed one-shot model. Finally, to complement the usual Soccer benchmark, we introduce a new Swimming Pool registration benchmark which is more challenging for the task at hand. Code and dataset available at https://github.com/njacquelin/sportsfieldregistration.
Topologically-Consistent Magnitude Pruning for Very Lightweight Graph Convolutional Networks

Graph convolution networks (GCNs) are currently main-stream in learning with irregular data. These models rely on message passing and attention mechanisms that capture context and node-to-node relationships. With multi-head attention, GCNs become highly accurate but oversized, and their deployment on cheap devices requires their pruning. However, pruning at high regimes usually leads to topologically inconsistent networks with weak generalization.In this paper, we devise a novel method for lightweight GCN design. Our proposed approach parses and selects subnet-works with the highest magnitudes while guaranteeing their topological consistency. The latter is obtained by selecting only accessible and co-accessible connections which actually contribute in the evaluation of the selected subnetworks. Experiments conducted on the challenging FPHA dataset show the substantial gain of our topologically consistent pruning method especially at very high pruning regimes.
Relational Visual Information explains Human Social Inference: A Graph Neural Network model for Social Interaction Recognition

Humans effortlessly recognize social interactions from visual input. The best models of this ability are generative inverse planning models, which make predictions by simulating agents' inferred goals, suggesting humans use a similar process of mental simulation. However, growing behavioral and neuroscience evidence suggests that recognizing social interactions is a visual process, separate from complex mental simulation. Yet despite their success in other domains, visual neural network models have been unable to reproduce human-like interaction recognition. We hypothesize that humans rely on relational visual information in particular, which is lacking from standard neural networks, and develop a new relational, graph neural network model, SocialGNN. Unlike prior models, SocialGNN accurately predicts human interaction judgments across both animated and natural videos. These results suggest that humans can make complex social interaction judgments without explicit simulation or inference about agents’ mental states, and that structured, relational visual representations are key to this behavior.
Quantum capacities of transducers

High-performance quantum transducers, which faithfully convert quantum information between disparate physical carriers, are essential in quantum science and technology. Different figures of merit, including efficiency, bandwidth, and added noise, are typically used to characterize the transducers' ability to transfer quantum information. Here we utilize quantum capacity, the highest achievable qubit communication rate through a channel, to define a single metric that unifies various criteria of a desirable transducer. Using the continuous-time quantum capacities of bosonic pure-loss channels as benchmarks, we investigate the optimal designs of generic quantum transduction schemes implemented by transmitting external signals through a coupled bosonic chain. With physical constraints on the maximal coupling rate [Formula: see text], the highest continuous-time quantum capacity [Formula: see text] is achieved by transducers with a maximally flat conversion frequency response, analogous to Butterworth electric filters. We further investigate the effect of thermal noise on the performance of transducers.