Reviewerly/exHarmony huggingface dataset:
High-order time lacunarity feature-aided multiple hypotheses tracking for underwater active small targets in high-clutter harbor environment

Active tracking of underwater small targets is a great challenge with kinematic information alone. This is because the active sonar often encounters multipath propagation and the induced clutter can even mask target echoes. Recently, high-order time lacunarity (HOT-Lac) has shown its ability in effectively highlighting "blob" targets from high clutter harbor environments. Hence, this paper proposes a HOT-Lac aided track scoring mechanism to solve the ambiguity of data association within the framework of Multiple Hypotheses Tracking (MHT). Specifically, the trajectory consistency of potential targets is captured by a momentum accumulation of the HOT Lac feature, which can inherit the historical information for the whole track. Meanwhile, due to the separability of the distribution of target and clutter in the HOT-Lac feature space, the probabilities of the target hypothesis and null hypothesis are modeled by the online computation of the HOT-Lac feature. Finally, the cumulative likelihood ratio based on HOT-Lac is integrated into MHT to score the potential tracks. Experiments in several real-world harbor scenarios demonstrate that the proposed HOT-Lac feature-aided tracker can suppress false tracks accurately and quickly.
КРИТЕРІАЛЬНІ ПОКАЗНИКИ ЯКОСТІ ОБРОБКИ МУЛЬТИСЕНСОРНОЇ ІНФОРМАЦІЇ В УМОВАХ ДЕСТАБІЛІЗУЮЧИХ ВПЛИВІВ

The approach to determining the criterion indicators of the quality of digital information processing in the multi-sensor information-controlled system of the car under the influence of destabilizing factors is considered. It was established that the process of processing information that enters the information-controlled system in digital form from various sensors involves the calculation of discrete convolutions of matrices of relevant operators and arrays of initial data. This process includes procedures for forming an array of responses to the observed information flow, an array of responses to the restored (restored) information flow, and forming conditions for restoration (restoration) of the observed information flow. The approach to assessing the quality of digital information processing in terms of determining subjective and objective assessments and methods of obtaining them is analyzed. The basis of these methods is the procedure of direct inversion of the estimated correlation matrix of random realizations of the observed multidimensional information process. Systems with an inverse operator for processing a multidimensional information array are quite sensitive to destabilizing influences: to random perturbations of the coefficients of the recovery (processing) operator and internal noise of the system along with internal system perturbations. The need to take into account destabilizing influences in the process of processing digital information in the car's information-controlled system necessitates the development of a general methodology for studying the influence of destabilizing factors on the quality of image processing. Based on the application of inverse synthesis methods, analytical expressions for criterion quality indicators of digital image processing are proposed, which are not limited to the upper limit of quality assessment and allow analytical evaluation and quantitative determination of the quality level of discrete images restored by direct inversion at the stage of system synthesis. Keywords: car, information-controlled system, image, restoration, restoration operator, criterion indicators, multisensory information, quality of information processing. internal noise, destabilizing factors.
Effect of Argumentation-Based Teaching on Students’ Understanding, Reasoning and Decision-Making Concerning Food Preservatives

Misuse of food preservatives in the society is alarming that it is necessary to raise students’ awareness about the danger to their health. Educating children about healthy food requires more than just promoting understanding but more importantly practicing reasoning and decision making. This study analyses the impact of lessons designed to improve students’ understanding, reasoning, and decision making on the use of food preservatives. Participants of the study were 30 students of 7th grade junior high school taught by a teacher who has been trained on argumentation-based teaching strategies. During the lessons students were provided with dilemmatic cases that stimulate their thinking followed by discussion before finally making a decision. Data were collected prior and after the lessons using a three-tier test that require students to provide answers that consist of explanation, reason, and evidence. The study shows improvement of students’ understanding of the content, reasoning skills, and decision making. Moreover, the study also reveals that the majority of the students tend to view food preservatives from the usefulness side but ignore the danger. The findings suggest that argumentation-based teaching strategy results in students’ deeper understanding of the content, reasoning and decision-making skills. This study recommends that efforts to promote students’ awareness of food preservatives and healthy diet can be done through a systematic and well-planned teaching strategy that encourage students to reason and to think deeply about the positive and the negative sides of food preservatives
Using sequential particle methods and non-parametric distributions in bayesian evaluations of abundance and catch at age time series

We describe an approach to analyzing time series for two variables connected through the state model with abundance and catch data sets and cohort and catch equations as an example. First, we create a deterministic model with parameters that maximizes the closeness of given data and data generated by a model. Then, we obtain cohort stochastic models using the difference between initial and modeled data. They are represented as hidden Bayesian models with abundances as states and catches as observations. Using these models, one can evaluate posterior densities and calculate averages, deviations, etc. As a general matter, the recursive equations met by posterior densities have no analytic solutions. We describe several particle methods that may be used for density approximations and following calculations of their statistical quantities. All generated sample densities are smoothed with non-parametric kernel density estimation. The Fishmetica package was extended with functions for generating samples and weights for filtering, predicting and smoothing densities. Numerical simulations were conducted for a test data set. Several extensions of the approach are proposed including an additional option for comparing the basic models with the use of a likelihood function.
Security and Performance of Knowledge-Based User Authentication for Smart Devices

A secure authentication system ensures that the claimant is the genuine user attempting to access the system and that it is not susceptible to misidentification, forgetfulness, or reproduction. While technological advancements in the authentication process continue to advance, most authentication systems still have room for improvement, particularly in terms of accuracy, tolerance to various security attacks, noise, and scalability as the number of smart devices grows. In this chapter, the authors look at the security, effectiveness, and drawbacks of knowledge-based, ownership-based, location-based, and social-based authentication systems, as well as some unresolved issues and potential future research directions.
Machine Learning Applied to Media Libraries for Insights, Search, and Segmentation

Recent advances in machine learning (ML) have produced a new form of semantic indexing that lets users enhance searches and gain new insights into their media libraries. Unlike typical search systems that use extracted metadata, semantic indexing allows users to find relevant material without the need to tag the media with selections from a predefined taxonomy. With semantic search, users can simply enter unstructured text, and the system will find the best matching media clips. This article extends the use of this technology to gather analytics on the data, which can then be further correlated to generate various insights. This new form of media indexing can be performed with the contrastive language-image pretraining (CLIP) model from OpenAI for images and similar models for video and audio. These models encode media into embeddings that can be searched to find the closest semantic similarity, enhanced with learned cultural knowledge. These systems have the benefit of finding media based on keywords, synonyms, and summaries. The systems can also be used for analytics and insights, such as segmentation, shot detection, and creating a 2D map to display correlations. This article ends with a discussion of the next steps, including using knowledge graphs for semantic search.
The role of the Karelian-Finnish epic «Kalevala» in the ethno-cultural education of younger schoolchildren

The article deals with the issues of ethnocultural education of younger schoolchildren and the features of their solution in the modern information world; the results of an empirical study are presented, showing the insufficiently formed level of knowledge of the culture of the Karelian people by children on the example of the Kalevala epic, the development of which is mandatory in the educational space of the Republic of Karelia; examples of the use of the ideas of the synthesis of arts in the course of creating a choreographic work based on the Kalevala epic have been studied. The work is based on the author's observations of the process of studying the Karelian-Finnish epic “Kalevala” by students of primary school age in the conditions of the city of Petrozavodsk. The main research methods were theoretical analysis and experimental and practical work with an experimental group of younger schoolchildren. The author has studied the possibilities of applying the synthesis of arts in the formation of knowledge about the Kalevala epic. The epos “Kalevala” is included in the national-regional component of the educational program, the implementation of which makes it possible to achieve the tasks of ethno-cultural development and education of schoolchildren. However, in the conditions of multitasking pedagogical practice, forced to respond to all the challenges of the information world, the issues of preserving cultural traditions and transferring ethnocultural experience in the modern world become difficult to solve and depend on the stereotypes formed by modern mass media.
GepH: Entity Predictor for Hindi News

In this era, news is not only generated continuously with high speed but also growing in its amount by different web sources like talent hunt, news agencies, and so on. To predict the exact class of news depending on its topic, GepH (Grouped entity predictor for Hindi) is proposed using entity extraction and grouping. Entity extraction is popular for English corpus. Hindi is a national language due to its resource scarceness not being explored so much by researchers. More than 1,270 news are processed to apply entity extraction, clustering, and classification using the vector space model for Hindi (VSMH), Synset vector space model for Hindi (SVSMH), and grouped entity document matrix for Hindi (GEDMH). Synset-based dimension reduction techniques are used to get improved accuracy. Evaluation of HAC using three matrices shows the best performance of GEDMH for varied datasets. Thus labelled corpus obtained after applying HAC (Hierarchical agglomerative clustering) to GEDMH is used as a training dataset and predictions are done using random forest and Naïve Bayes. The Naïve Bayes classifier implemented using the proposed GEDMH performs the best. GepH shows 0.8 purity, 0.4 entropy, and 0.3 as error rate for 1,273 Hindi news.
A Comprehensive Review of Privacy Preserving Data Publishing (PPDP) Algorithms for Multiple Sensitive Attributes (MSA)

Privacy preserving data publishing (PPDP) provides a suite of anonymization algorithms and tools that aim to balance the privacy of sensitive attributes and utility of the published data. In this domain, extensive work has been carried out to preserve the privacy of single sensitive attributes. Since most of the data obtained from any domain includes multiple sensitive attributes (MSAs), there is a greater need to preserve it. The data sets with multiple sensitive attributes allow one to perform effective data analysis, research, and predictions. Hence, it is important to investigate privacy preserving algorithms for multiple sensitive attributes, which leads to higher utilization of the data. This chapter presents the effectiveness and comparative analysis of PPDP algorithms for MSAs. Specifically, the chapter focuses on privacy and utility goals and illustrates implications of the overall study, which promotes the development of effective privacy preservation techniques for MSAs.
Effectiveness of the chemical ludo game on shifting equilibrium material and the factors that influence on learning outcomes

This research is a continuation of development research with a 4-D model developed by Rini Fauza and Iswendi with the title Development of Ludo Chemistry Games as Learning Media on Material Shifting Equilibrium and Factors Affecting It Class XI SMA/MA which is valid but has not been tested for practicality and its effectiveness. This study aims to determine the level of practicality and effectiveness of chemical ludo games. The research design was the Pretest-Posttest Control Group Design. The research sample was XI MIPA 1 as the experimental class and XI MIPA 2 as the control class. Practicality data were analyzed with the practicality percentage and effectiveness data with the N-Gain test. The data analysis results on the practicality of teachers and students are in the very practical category. The results of the N-Gain analysis in the experimental class were 80% in the effective category and in the control class 74% in the quite effective category, which was supported by the learning outcomes of the experimental class, which were higher than the control class. The average posttest scores for the experimental and control classes were 84.83 and 81.17, respectively. The learning outcomes of the experimental class increased from the control class where tcount (2.465) &gt; ttable (2.002) at a significant level α = 0.05. Ludo chemistry games as learning media are very practical and effective in improving student learning outcomes in the cognitive domain of class XI SMAN 1 Lubuk Basung.
Grand Theft Auto-Based Cycling Simulator for Cognitive Enhancement Technologies in Dangerous Traffic Situations

While developing traffic-based cognitive enhancement technology (CET), such as bike accident prevention systems, it can be challenging to test and evaluate them properly. After all, the real-world scenario could endanger the subjects' health and safety. Therefore, a simulator is needed, preferably one that is realistic yet low cost. This paper introduces a way to use the video game Grand Theft Auto V (GTA V) and its sophisticated traffic system as a base to create such a simulator, allowing for the safe and realistic testing of dangerous traffic situations involving cyclists, cars, and trucks. The open world of GTA V, which can be explored on foot and via various vehicles, serves as an immersive stand-in for the real world. Custom modification scripts of the game give the researchers control over the experiment scenario and the output data to be evaluated. An off-the-shelf bicycle equipped with three sensors serves as a realistic input device for the subject's movement direction and speed. The simulator was used to test two early-stage CET concepts enabling cyclists to sense dangerous traffic situations, such as trucks approaching from behind the cyclist. Thus, this paper also presents the user evaluation of the cycling simulator and the CET used by the subjects to sense dangerous traffic situations. With the knowledge of the first iteration of the user-centered design (UCD) process, this paper concludes by naming improvements for the cycling simulator and discussing further research directions for CET that enable users to sense dangerous situations better.
クロスラインレーザとカメラを併用したロックボルト孔の位置および方向計測

In this paper, we propose a new position and direction measurement method of rock bolt hole with a cross line laser. A combination of the light-sectioning method and a hole detection in an image is used to estimate a hole position and direction. Experimental results in a tunnel under construction show that the proposed method can measure positions and directions with high accuracy.
Few-shot classification with task-adaptive semantic feature learning

Few-shot classification aims to learn a classifier that categorizes objects of unseen classes with limited samples. One general approach is to mine as much information as possible from limited samples. This can be achieved by incorporating data from multiple modalities. However, existing multi-modality methods only use additional modality in support samples while adhering to a single modal in query samples. Such approach could lead to information imbalance between support and query samples, which confounds model generalization from support to query samples. Towards this problem, we propose a task-adaptive semantic feature learning mechanism to incorporate semantic features for both support and query samples. The semantic feature learner is trained episodic-wisely by regressing from the feature vectors of the support samples. It is utilized to predict semantic features for the query samples. Such method maintains a consistent training scheme between support and query samples and enables direct model transfer from support to query data, which significantly improves model generalization. We conduct extensive experiments on four benchmarks in both inductive and transductive settings. Results show that the proposed TasNet outperforms state-of-the-art methods with an improvement of 1% to 5% in classification accuracy, demonstrating the superiority of our method. The exhaustive ablation studies further validate the effectiveness of our framework. The code is available at: https://github.com/pmhDL/TasNet
Research and design of program complexity measurement technology based on OINK framework

With the expansion of software system scale, the study of software complexity has become a hot topic in software engineering. However, the domestic research on software complexity analysis technology is not mature, especially the measurement and evaluation methods of software complexity are not perfect. In order to solve the problem of prediction and evaluation of program structure complexity in software engineering more effectively, this paper proposed a program complexity measurement technique based on OINK framework. The technology uses the data sharing interface design to analysis target program by extracting the complex relationship between OINK components. On this basis, the technology adopts the layered software architecture to realize the automatic design of the function of the measurement data acquisition module, the complexity measurement module and the data management module of measurement results, thus, the structure complexity of the target program can be analyzed more clearly and accurately. At the same time, this technique applies multiple measurement methods to quantify the complexity of program structure, such as McCabe, HalStead, and Line Count. Experimental results show that this method can effectively measure the complexity of program structure. The solution on software complexity based on the open source ONIK framework will be open up worldwide, and will be continuously supported and improved by global communities and teams under the constraints of common driving forces.
Recursive spatial-temporal clustering-based target detection with millimeter-wave radar point cloud

Abstract Millimeter-wave (mmWave) radar plays a vital role in a wide range of applications such as security surveillance and environmental monitoring. This work investigates target detection with radar point cloud measurements in the slow-motion scenario. In contrast to the existing spatial domain clustering-based target detection methods, we adopt a recursive spatial-temporal clustering (STC)-based method to detect targets in the spatial and temporal domain jointly. Specifically, the points belonging to targets are obtained by clustering with a distance metric defined in the spatial-temporal domains. In addition, to ensure the feasibility of the proposed method for practical real-time implementation, a speed-up scheme that intends to reduce the computational complexity induced by clustering in both spatial and temporal dimensions is developed. We demonstrate the efficacy of the proposed recursive STC-based method through experimental mmWave radar point cloud data where multiple people walk simultaneously in an open space. The proposed method achieves decent target detection performance improvement compared to a widely-used clustering method for target detection while its computation time is negligible compared to radar data reception time.
A formal, diagrammatic, and operational study of normative relations

Abstract In this work, we provide an extensive analysis of Hohfeld’s theory of normative relations, focusing in particular on diagrammatic structures. Our contribution is threefold. First, we specify an extensional formal language to represent the main notions in the two families of normative relations identified by Hohfeld (i.e. the deontic and the potestative family). Our primary focus is on the part of the theory concerning potestative relations. In this regard, we assign a key role to the concept of ability, which is treated as a primitive notion and used to formulate three fine-grained definitions of power (outcome-centered, change-centered and force-centered). Second, on the basis of these definitions we build Aristotelian diagrams of opposition for deontic and potestative relations, improving, extending and systematizing previous proposals formulated in the literature. Third, we present a model-theoretic interpretation and a logic programming (ASP) implementation of the proposed framework, elaborating on the procedural dimension of normative reasoning.
The significance and role of digitalization in the development of a universal culture and its impact on modern society

The article discusses the importance and role of digital culture in the development of a universal culture and its impact on modern society. The development and contradictory nature of the impact of digitalization are analyzed. The study is based on the analysis of scientific and popular science sources. The article shows that the formation of digital culture as an independent industry occurs in the second half of the 20th century. First of all, this is due to the creation of a personal computer and other digital devices, the emergence of the global Internet as a mass form of communication, as well as the introduction of various practices of using computer technology in the daily life of each person. The author comes to the conclusion that the formation of such a new type of culture as digital culture should be based on a systematic and integrated approach.
New US cybersecurity strategy will boost resilience

Significance The strategy -- the first major revision since September 2018 -- seeks to usher in a new era of more aggressive regulation around cybersecurity. The administration intends to place greater responsibility on larger organisations to secure cyberspace and less on individuals and small businesses by incentivising the private sector to increase cybersecurity investment. Impacts The immunity software companies have long enjoyed around vulnerabilities in their code may soon end. Washington is likely to allocate more resources to cybersecurity education and diversity-focused training programmes. Preventing ransomware attacks and arresting cybercriminals will be treated as a national security priority, not as cybercrime prevention.