Reviewerly/exHarmony huggingface dataset:
Evaluation of a correlation phase recovery method to be used in temporal speckle pattern interferometry with noise contaminated specklegrams

When a phase distribution is evaluated in temporal speckle pattern interferometry, the accuracy of the retrieved phase distribution depends on how the different phase recovery methods deal with the influence of low-frequency noise and intensity bias. In this paper, we show that the proposed correlation method can effectively recover the phase distribution in time series of speckle interferograms that are affected by sets of adjacent non-modulated pixels, combined with very small phase changes between successive frames. The performance of the proposed method is analyzed when the retrieved optical phase distributions are coded in noisy temporal speckle pattern interferometry signals presenting large regions of adjacent low-modulated pixels. It is also compared with the data measured with the well-known one-dimensional Fourier transform technique, and the three-dimensional directional wavelet transform method. The performance of the proposed phase retrieval technique is compared with these last two methods using computer-simulated speckle interferograms, and the advantages and limitations of the proposed method are discussed. Finally, the performance of the proposed correlation technique to process real data is illustrated.
Threshold Determination for False Track Probability in the Multipath ML-PMHT

We analyze the likelihood distribution for a multipath model in which no target is present, focusing on the peculiarities of the maximum-likelihood probabilistic multi-hypothesis tracker (ML-PMHT) when multiple peaks emerge. We study analytically the case in which peaks are separated and for the general case we propose a numerical algorithm based on sampling. The final distribution is used to compute a parameter-dependent false alarm probability threshold.
Resisting Multiple Advanced Persistent Threats via Hypergame-Theoretic Defensive Deception

Existing defensive deception (DD) approaches apply game theory, assuming that an attacker and defender play the same, full game with all possible strategies. However, in deceptive settings, players may have different beliefs about the game itself. Such structural uncertainty is not naturally handled in traditional game theory. In this work, we formulate an attackdefense hypergame where multiple advanced persistent threat (APT) attackers and a single defender play a repeated game with different perceptions. The hypergame model systematically evaluates how various DD strategies can defend proactively against APT attacks. We present an adaptive method to select an optimal defense strategy using hypergame theory for strategic defense as well as machine learning for adaptive defense. We conducted in-depth experiments to analyze the performance of the eight schemes including ours, baselines, and existing counterparts. We found the DD strategies showed their highest advantages when the hypergame and machine learning are considered in terms of reduced false positives and negatives of the NIDS, system lifetime, and players’ perceived uncertainties and utilities. We also analyze the Hyper Nash Equilibrium of given hypergames and discuss the key findings and insights behind them.
Digitizing the Epistolary: <i>Dracula Daily</i> and Embodied Contemporary Reading

Abstract This review discusses the online phenomenon of Dracula Daily, an internet substack that emails users sections of the epistolary novel Dracula in real-time, rearranged chronology. It examines the unique appeal of Dracula for virtual dissemination, examines how the epistolary format changes the context in virtual form, how readers read online, community reading, and whether or not the success of Dracula Daily can be replicated with other classic novels.
Advanced Information Securing by Combining Fortified Binary Image Steganography and Asymmetric Encryption Standard

In information security, image steganography is one of the information securing process. Image steganography is mostly used in medical fields for hiding medical prescription data behind the medical images. Designing steganographic algorithms for empirical cover sources, such as digital images, is very perplexing due to the lack of accurate models. The most successful approach today avoids estimating the cover source distribution because this task is infeasible for complex and non-stationary sources. Instead, the steganography problem is formulated as source coding with fidelity constraint the sender entrenches the message with minimizing the distortion. Practical algorithms that entrench near the theoretical payload–distortion bound are available for a very general class of distortion functions. With the current framework, the only task left to the sender is essential for the design of the distortion function. Here information is which wants to be confidential that information's are stored behind the image pixels. However, due to increasing crypt analysis attacks and binary image de stenographic process image steganography has very less security. So improving the security in image steganography process, the current system provide the asymmetric encryption standard for improving more security in steganography.
Development of students’ multiple intelligence-based junior high school learning devices: Preliminary research

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Yerizon, Atus Amadi Putra, Junita; Development of students’ multiple intelligence-based junior high school learning devices: Preliminary research. AIP Conference Proceedings 7 February 2023; 2698 (1): 060019. https://doi.org/10.1063/5.0122474 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Modified Euclidean-Canberra blend distance metric for kNN classifier

In today’s world different data sets are available on which regression or classification algorithms of machine learning are applied. One of the classification algorithms is k-nearest neighbor (kNN) which computes distance amongst various rows in a dataset. The performance of kNN is evaluated based on K-value and distance metric used where K is the total count of neighboring elements. Many different distance metrics have been used by researchers in literature, one of them is Canberra distance metric. In this paper the performance of kNN based on Canberra distance metric is measured on different datasets, further the proposed Canberra distance metric, namely, Modified Euclidean-Canberra Blend Distance (MECBD) metric has been applied to the kNN algorithm which led to improvement of class prediction efficiency on the same datasets measured in terms of accuracy, precision, recall, F1-score for different values of k. Further, this study depicts that MECBD metric use led to improvement in accuracy value 80.4% to 90.3%, 80.6% to 85.4% and 70.0% to 77.0% for various data sets used. Also, implementation of ROC curves and auc for k= 5 is done to show the improvement is kNN model prediction which showed increase in auc values for different data sets, for instance increase in auc values form 0.873 to 0.958 for Spine (2 Classes) dataset, 0.857 to 0.940, 0.983 to 0.983 (no change), 0.910 to 0.957 for DH, SL and NO class for Spine (3 Classes) data set and 0.651 to 0.742 for Haberman’s data set.
Application of action and posture feature information matching algorithm in college aerobics

With the increasingly rich recreational activities of college students, diversified learning needs and complex physical education resources bring challenges to college physical education. In order to optimize the teaching effect of calisthenics in colleges and universities, this paper proposes a matching method of posture features based on dynamic time warping. Firstly, the dynamic time warping algorithm is introduced, and then the matching model of posture features of calisthenics is constructed on this basis. Finally, the application effect of the model is tested and analyzed. The results show that the model can capture the video frame accurately, and its matching accuracy reaches 94.8%, which greatly improves the accuracy of aerobics action recognition. Good posture matching effect is conducive to teachers to obtain a clear learning situation of students, and provide a reference for adjusting the teaching progress and teaching methods of calisthenics. Under the teaching mode of this model, the average professional score of the students in calisthenics reaches 85 points, which is 25 points higher than that under the convolutional neural network model. It also proves the validity and feasibility of this method in the course of calisthenics in colleges and universities, which is beneficial to enhance the physical quality of college students and enrich the content of calisthenics teaching.
Reward estimation with scheduled knowledge distillation for dialogue policy learning

Formulating dialogue policy as a reinforcement learning (RL) task enables a dialogue system to act optimally by interacting with humans. However, typical RL-based methods normally suffer from challenges such as sparse and delayed reward problems. Besides, with user goal unavailable in real scenarios, the reward estimator is unable to generate reward reflecting action validity and task completion. Those issues may slow down and degrade the policy learning significantly. In this paper, we present a novel scheduled knowledge distillation framework for dialogue policy learning, which trains a compact student reward estimator by distilling the prior knowledge of user goals from a large teacher model. To further improve the stability of dialogue policy learning, we propose to leverage self-paced learning to arrange meaningful training order for the student reward estimator. Comprehensive experiments on Microsoft Dialogue Challenge and MultiWOZ datasets indicate that our approach significantly accelerates the learning speed, and the task-completion success rate can be improved from 0.47%∼9.01% compared with several strong baselines.
Geological foundations for determination of anisotropic directions in geostatistical modeling on the example of Verkhnee orebody of the Mnogovershinnoe gold deposit

Background. When calculating reserves using geostatistical methods, variographic analysis can occasionally encounter difficulties in determining the direction of the search ellipsoid, particularly in deposits with a lognormal distribution of the useful component. The search ellipsoid defines the main directions of mineralization anisotropy, thus comprising a key element in the process of interpolating grades into a block model. The correct interpolation of grades provides a reliable estimate of reserves. Aim. To determine the influence of the orebody morphology on anisotropic directions. Materials and methods. Data from a report on the technical and economic substantiation of the resource and estimation parameters of the Mnogovershinnoe gold deposit for 1975 were used, along with a database comprising catalogues of borehole coordinates, mining outputs, inclinometry and exploratory test data. Three-dimensional computer modeling was performed in the Micromine min­ing and geological information system. Using the example of the Verkhnee orebody of the Mno­govershinnoe gold deposit, statistical and geostatistical studies were carried out to calculate the reserves. The as-calculated reserves were compared with those estimated by a polygonal method and approved by the State Commission for Mineral Reserves (GKZ) USSR in 1975. Results. The conducted comparison of the geological interpretation of the orebody under study, obtained by both wireframe modeling based on exploratory test data and the search ellipsoid constructed by variographic analysis, сonfirmed the influence of the orebody morphology on the axis directions of the minimal variability of mineralization. The orientation of the search ellipsoid was determined to be consistent with the geological interpretation of the orebody. Verification of the interpolated block model showed the estimate to be close to a reliable value. The comparison of the calculated reserves with those of 1975 showed their good convergence.
How students and teachers voyaged from physical classroom to Emergency Remote Teaching in COVID-19 crisis: A case of Nepal

COVID-19 pandemic forced schools to shift from traditional to remote teaching globally. Most schools in Nepal, however, could not adopt remote teaching. Only some schools, particularly those located in urban areas initiated remote teaching after the outbreak of the pandemic in 2020. This article, thus, reports an analysis of secondary students and teachers’ experiences of remote learning. It as qualitative research employed semi-structured interview and observation of online classes to investigate students’ ways of managing remote learning and teachers’ strategies of teaching through online mode. This paper reports that both students and teachers were, at first, intimidated by the new mode of learning. Remote learning, albeit it was observed as an alternative mode of education where there was minimum information and communication technology (ICT) infrastructure, seems to increase the digital divide in rural areas. Students, particularly from government schools, struggled to manage their remote learning due to limited access to digital technology, the sudden shift from the physical classroom to remote learning and a lack of government support. Thus, parents play a vital role in influencing their children’s attitudes toward remote learning. It is essential to equip students and teachers with ICT and minimal ICT skills and provide with subsidy on digital devices and the internet for students to completely implement remote learning.
Differential Analysis of Non-Markov Ciphers

In this article, for Markov ciphers, we prove that they are resistant to differential cryptanalysis and some statements made for MS are obtained. The upper estimates of the probabilities of integer differentials are significantly improved when compared to previously known results. Our differential cryptanalytic algorithm finds weak subkeys that have more than 80 bits and 128 bits for 128-bit keys.
Self-contained Entity Discovery from Captioned Videos

This article introduces the task of visual named entity discovery in videos without the need for task-specific supervision or task-specific external knowledge sources. Assigning specific names to entities (e.g., faces, scenes, or objects) in video frames is a long-standing challenge. Commonly, this problem is addressed as a supervised learning objective by manually annotating entities with labels. To bypass the annotation burden of this setup, several works have investigated the problem by utilizing external knowledge sources such as movie databases. While effective, such approaches do not work when task-specific knowledge sources are not provided and can only be applied to movies and TV series. In this work, we take the problem a step further and propose to discover entities in videos from videos and corresponding captions or subtitles. We introduce a three-stage method where we (i) create bipartite entity-name graphs from frame–caption pairs, (ii) find visual entity agreements, and (iii) refine the entity assignment through entity-level prototype construction. To tackle this new problem, we outline two new benchmarks, SC-Friends and SC-BBT , based on the Friends and Big Bang Theory TV series. Experiments on the benchmarks demonstrate the ability of our approach to discover which named entity belongs to which face or scene, with an accuracy close to a supervised oracle, just from the multimodal information present in videos. Additionally, our qualitative examples show the potential challenges of self-contained discovery of any visual entity for future work. The code and the data are available on GitHub. 1
DeltaShield: Information Theory for Human- Trafficking Detection

Given a million escort advertisements, how can we spot near-duplicates? Such micro-clusters of ads are usually signals of human trafficking (HT). How can we summarize them to convince law enforcement to act? Spotting micro-clusters of near-duplicate documents is useful in multiple, additional settings, including spam-bot detection in Twitter ads, plagiarism, and more. We present InfoShield , which makes the following contributions: practical , being scalable and effective on real data; parameter-free and principled , requiring no user-defined parameters; interpretable , finding a document to be the cluster representative, highlighting all the common phrases, and automatically detecting “slots” (i.e., phrases that differ in every document); and generalizable , beating or matching domain-specific methods in Twitter bot detection and HT detection, respectively, as well as being language independent. Interpretability is particularly important for the anti-HT domain, where law enforcement must visually inspect ads. Our experiments on real data show that InfoShield correctly identifies Twitter bots with an F1 score over 90% and detects HT ads with 84% precision. Moreover, it is scalable, requiring about 8 hours for 4 million documents on a stock laptop. Our incremental version, DeltaShield , allows for fast, incremental updates, with minor loss of accuracy.
Design Fictioning of a Second-Order Kind: Runaway Cybernetics, Futures of Work, Possibilities of Engagement

In this paper, I propose second-order design fictioning as a way of reframing the hegemonic fictions built around technology, work, and design. These forms of progress fictions are further amplified by uncritical developments in cybernetic technologies that have a complex and often problematic relationship to discourses around the politics of innovation and acceleration. Projects related to the speculation of post-work futures within a design studio and seminar are used as a heuristic to engage broader theoretical questions on the reductive ways in which the question of changes in technologies (human-machine relationships) are addressed in the context of the transition imagination towards alternative work futures. Within these projects, engaging frictions, i.e., questions concerning difference and sense-making in sites of disruption, are further problematized. To define the notion of a second-order in design fictioning, the paper draws on modes of engaging systemic complexity as theorised within recent discussions in design cybernetics, critical cybernetics, and Gregory Bateson&rsquo;s work on meta-communicational frames. Second-order design fictioning, as proposed here, delinks the better-known concept of design fiction&mdash;a methodological tool often used in the imagination of futures&mdash;from its preoccupation with objects, technology, and technocratically projected views of the future and innovation. It is hoped that second-order design fictioning would enable a better focus on the complex politics of the changing value frameworks (fictions) that drive these concepts.
Differential drive kinematics and odometry for a mobile robot using TwinCAT

&lt;abstract&gt;&lt;p&gt;In this paper, we propose a motion control system for a low-cost differential drive mobile robot. The robotic platform is equipped with two driven wheels powered by Beckhoff motors, instrumented with incremental encoders. The control system is designed and implemented using Beckhoff's TwinCAT 3 automation software, running on an industrial PC. The system is tested and experimentally tuned to achieve optimal performance. The method allows addressing both odometry motion accuracy and motion correction in order to obtain minimum trajectory errors. Test results on linear and angular robot trajectories show errors below 0.02 and 0.03%, respectively, after tuning of the motion parameters. The proposed approach can be expanded, tweaked and applied to other differential drive TwinCAT 3 based robotic solutions. This will contribute to expanding mobile robot applications to a variety of fields, such as industrial automation, logistics, warehouse management, health care, ocean and space exploration and a variety of other industrial and non-industrial activities.&lt;/p&gt;&lt;/abstract&gt;
Clipped Quantization Aware Training for Hardware Friendly Implementation of Image Classification Networks

Although deep neural networks (DNNs) show excellent performance in the image processing field, a massive amount of computation and memory access makes it difficult to deploy DNNs on mobile devices. To reduce the computation and the memory access, quantization shows great results. However, it is challenging to quantize the network into low bit-width without significant accuracy degradation. In this paper, we propose Clipped Quantization Aware Training (CQAT) to decrease the accuracy drop during the low bit-width quantization aware training. In the proposed CQAT, the original values are first quantized into 8-bit and then the quantized values are clipped so that only 4-bit for activations and 5-bit for weights are used. With the proposed technique, ResNet-18 for the CIFAR-100 dataset using 5-bit weight and 4-bit activation shows an accuracy drop of only 0.96% compared to the network using full precision weight and activation.
Computer Technology and RBF Algorithm English Online Test System Design

With the development of computer technology, it has been gradually applied to different fields and played an irreplaceable role. In the current school education, the information management system is also used for teaching and management. For example, the school uses an advanced online examination system, which improves the intelligence, standardization and process level of the examination. The purpose of this paper is to study the design of the English online test system based on the RBF algorithm. In order to achieve the goal of accuracy, efficiency and convenience in the online test system, the online test system can automatically grade and judge objective and subjective questions, and improve the intelligence and standardization of the test. The purpose is to study the design of the English online test system based on the RBF algorithm. In order to achieve the goal of accuracy, and to realize the automatic scoring and evaluation of objective and subjective questions in the online test system, a computer technology-based RBF algorithm is designed. The new online English test system provides a reliable tool for the improvement of teaching quality. This paper uses the experimental data analysis method, through the experimental data, and the data analysis obtained by tracking the algorithm operation process, to provide data support for the construction of the fitness function in the algorithm, the selection of reasonable parameters for the crossover probability and the mutation probability. The simulation results show that the convergence speed of the adaptive wavelet RBF neural network is 26 steps and 25 steps, respectively, and the relative errors are 2.96% and 2.60%, respectively.