Reviewerly/exHarmony huggingface dataset:
Reversible Audio Steganography using Least Prime Factor and Audio Interpolation

Security of data is critical given the rapid development of information technology due to the illegal access to personal or confidential data. This has made security methods to hide secret messages urgent to implement, one of which is hiding a secret message (payload) into a medium (cover), like an audio file. However, this general technique has some drawbacks, mainly are the small space to hold the data and the large difference between the stego-audio and the original audio. In addition, there is a risk of irreversible payload upon extraction. To solve this problem, we propose using the Least Prime Factor of the difference between the interpolated data and the original audio to find the sample space, partition the payload using the specified sample space, and embed the decimal payload into the interpolated sample. The result shows that the method can work well on those payload capacity and stego-audio quality issues.
The Research and Design of Migrant Workers' Real-name Back Salary Governance Platform Based on Big Data Technology

With the rapid development of China&#x0027;s reform and opening up, industrialization and urbanization, the number of migrant workers is increasing, which is widely distributed in all walks of life in cities and has made great contributions to the modernization of cities. However, the problem of back salary faced by migrant workers is also increasing. If it is not properly solved, it will inevitably lead to many social contradictions and disputes. In addition, migrant workers have poor awareness of rights protection, which makes it difficult for regulatory authorities to conduct real-time monitoring. With the development of new technology and the advent of the networking era in real-name registration system, it provides a new method for comprehensively managing the problem of back salary of migrant workers. This platform relies on the real-name system to record the identity information and labor attendance of migrant workers at construction sites, which can guide migrant workers to improve their risk awareness, improve their awareness of legal rights protection, and further protect their legitimate rights and interests.At present, the big data management system of the real-name system for migrant workers in China has yet to be established, and the government&#x0027;s ability to apply the real-name system data to migrant workers still needs to be improved. Based on this, the government supervision department of migrant workers urgently needs to design and develop a back salary governance platform from the perspective of real-name management to realize the whole-process data supervision of the construction projects, personnel, attendance and salary distribution of migrant workers.
Use of Electronic Seizure Diaries and Decision Trees to Predict Seizure Outcome for Patients with Epilepsy

Epilepsy is a neurological disorder that causes unpredictable recurrent seizures.Most people with epilepsy dwell in fear of having unpredictable seizures.In attempts to predict future seizure occurrences, investigators have used data from electronic seizure diaries and machine-learning methods, like decision trees.Using individual patient e-diary data, the purpose of this study is to build patient specific decision trees to 1) determine decision trees overall accuracy in predicting seizures and depicting seizure predictors that influence seizure outcome, and 2) identify seizure predictors that have the most influence on seizure outcome.Patients (n=64) were examined, and their e-diary data was used to build patient specific decision trees.Using a 5-point Likert scale, patients e-diaries entailed information on how they rated the probability of experiencing subsequent seizures and rated their mood, predictive symptoms, stress, and seizure counts.Since e-diaries were recorded in the morning and in the evening, seizures for each patient were assessed by half days.R Programming software was used to generate the decision trees and depict seizure predictors that had the most influence on patient's seizure outcome.A confusion matrix was performed to obtain the decision trees performance accuracy.Patients were categorized into groups based on certain seizure predictors that they shared.The results showed that for decision trees overall accuracy in predicting seizures and depicting seizure predictors that influenced seizure outcome, 49% of decision trees had an accuracy of 100%; 37% of decision trees had an accuracy ranging between 90-99%; and 13% of decision trees had an accuracy of <90%.Additionally, the results showed that there were more seizure predictors that had influence on patient's seizure outcome in the morning than in the evening.This work introduces non-invasive precision medicine, with intentions to develop more personalized and reliable health care treatments for people with epilepsy.
Preparation for Success in 2022 [President’s Message]

Looking back to my last letter from 2021, I found that I spent most of it recapping 2021 events but not much time describing preparations for 2022. Therefore, this letter may be a continuation from December. First of all, I would like to welcome our new Administrative Committee (AdCom) members beginning three-year terms starting in 2022: Ronny Haensch, chair of the 2021 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest and current GRSS e-newsletter editor; Keely Roth, current GRSS publicity and public relations chair; and Mengdao Xing, who has been very active with the GRSS in China. Also returning for another three-year term are Paul Rosen, chair of GRSS Global Activities, and Alejandro Frery, chair of the GRSS Future Publications Committee. I would also like to thank the AdCom for allowing me to serve a second one-year term as president for 2022.
Linear Structure Extraction and Analysis of Remote Sensing Information in Postmasburg Region, South Africa

In this paper, the Manganese ore field in Postmasburg region of South Africa is selected as the research area. Landsat8 remote sensing data were used to enhance the linear structure by principal component analysis, convolution filtering, morphological analysis and false color synthesis, and the linear structure information in the study area is extracted by visual interpretation. The results show that, Linear structure is mainly distributed in SN direction. In the area with high density of linear structure, tectonic activity is strong and fault structure is developed, which is a favorable area for deposit development. The watershed of the mountain and the wide and gentle syncline of the hillside are the main sections of thick and large ore bodies. The experimental analysis shows that Landset8 remote sensing data is very suitable for linear structure information extraction. Through the analysis of linear structure in this area, it can better guide the determination of prospecting target area.<sup>1</sup>
Diagnostic Accuracy of Thin Film in Detection of Scanty Parasitaemia Malaria Taking Thick Film as Gold Standard

Background: Analyzing a blood film is the most accurate approach to determine the number of parasites present. People question which blood film and counting method is the most accurate and can be used again and over again. Objective: To see if thin peripheral blood smear films may identify malaria with little parasitaemia, thick peripheral blood smear films were used as the gold standard. Material and Methods: The study was conducted by the National Institute of Child Health in Karachi's Department of Paediatric Medicine and was a cross-sectional one. 177 people were admitted to the hospital. A vein yielded about 5 milliliters of blood for the experiment. As long as there was a single control band and two testing bands, the test result was positive for both P. falciparum and P. viridans. When the control band and the test band were found to be present, the test verified the presence of P. falciparum. Results were deemed negative if neither the test nor control bands appeared. Comparison of thin peripheral blood smear accuracy to the gold standard of thick peripheral blood smear accuracy was used to assess the thin blood smear diagnostic accuracy. Results: Sixty-one percent of the patients were men, and only 39 percent were women. The average age was 30 years and 15 months. The P.falciparum was 66.0%, the P.vivax was 22.7%, and the P.ovale was 11.3%. With a thick peripheral blood smear film, 52.5 percent were found to have scanty parasitaemia malaria, and 48 percent were found to have it with a thin peripheral blood smear film. The accuracy, specificity, and sensitivity were 95.3%, 87.5%, and 90.9%, respectively. Conclusion: The results of the study showed that thin blood smear films are accurate diagnostic tools 90.9% of the time. It could be used as a second way to confirm a diagnosis of malaria. Keywords: Diagnostic Accuracy, Thin Peripheral Blood Smear Film, Parasitaemia Malaria, Thick Peripheral Blood Smear Film
Approaching optical metrology with multiple light sources and compressive sensing

Complex optical surfaces such as aspheres and freeforms are used in optical systems to reduce aberrations or to achieve high optical performance with a compact design and less optical surfaces. Due to limited acceptance angles of conventional interferometric techniques, there is still no satisfactory solution for their form measurement that is at the same time precise, flexible, and fast. Often these surfaces are surveyed by pointwise measurement, or the aperture problem is overcome by elaborately compensating wave front deviations either through compensator lenses or computer-generated holograms. Alternatively, several subapertures are used to capture the whole surface. These, however, have to be recorded in sequence since the superposition of multiple independent wave fields cannot be assigned a time-independent wave front. Instead, we present a compressive sensing approach for Multiple Aperture Shear-Interferometry (MArS) which captures multiple overlapping subapertures simultaneously and allows a flexible measurement of aspheres with multi-spot illumination. MArS uses the mutual intensity as the primary measurand which is still well defined for superposed mutually incoherent wave fields. The mutual intensity is sparse in phase space for there are only a finite number of distinct wave fields at every surface point. Utilizing this sparsity, the presented compressive sensing approach avoids superflously large space-bandwidth products and significantly reduces the number of necessary measurements.
FUTURESHOCK AND CONTINUOUS DEVELOPMENT: EXPANDING THE SPACE OF EDUCATION AND MOBILITY OF THE PERSON

Aim. The purpose of the work is to consider the concept of "personality mobility" as a concept of education that is significant in the context of a permanent futureshock. Methodology. The following methods were used: a) analysis of theoretical ideas about lifelong education, personal mobility, transdisciplinarity and continuity of education of a professional of the future; b) analysis of pedagogical and research experience in the field of modern education; c) local expert survey; d) content analysis of respondents' answers to open-ended questions. Results. Based on the study, the article presents a look at the purpose of education and knowledge in the life of a modern person acting in the information society. It is proved that continuous education is no longer an arbitrary choice of a person, but an imperative, not only social and economic, but physical, emotional, cultural. Research implications. Conclusions are drawn about the need in the context of "educational impact" to diagnose the temporal properties of the subject of learning, for example, in order to assess the appropriateness of a particular learning step, assess a person's readiness to present his level of learning, assess a particular stage of individual development in terms of sensitivity to the next portion knowledge, assessment of motivation, desire for the next "reset" and resumption of learning.
Continuous Model Evaluation and Adaptation to Distribution Shifts: A Probabilistic Self-Supervised Approach

This paper introduces a Bayesian approach to estimating distribution shifts over the modelled variables and continuous model adaptations to mitigate the impact of such shifts. The method exploits probabilistic inference over sets of correlated variables in causal models describing data generating processes. By extending the models with latent auxiliary variables, probabilistic inference over sets of correlated variables enables estimation of the distribution shifts impacting different parts of the models. Moreover, the introduction of latent auxiliary variables makes inference more robust against distribution shifts and supports automated, self-supervised adaptation of the modelling parameters during the operation, often significantly reducing the adverse impact of the distribution shifts. The effectiveness of the method has been validated in systematic experiments using synthetic data.
Unsupervised dynamic modeling of medical image transformations

Spatiotemporal imaging has applications in e.g. cardiac diagnostics, surgical guidance, and radiotherapy monitoring, In this paper, we explain the temporal motion by identifying the underlying dynamics, only based on the sequential images. Our dynamical model maps the inputs of observed high-dimensional sequential images to a low-dimensional latent space wherein a linear relationship between a hidden state process and the lower-dimensional representation of the inputs holds. For this, we use a conditional variational auto-encoder (CVAE) to nonlinearly map the higher dimensional image to a lower-dimensional space, wherein we model the dynamics with a linear Gaussian state-space model (LG-SSM). The model, a modified version of the Kalman variational auto-encoder, is end-to-end trainable, and the weights, both in the CVAE and LG-SSM, are simultaneously updated by maximizing the evidence lower bound of the marginal likelihood. In contrast to the original model, we explain the motion with a spatial transformation from one image to another. This results in sharper reconstructions and the possibility of transferring auxiliary information, such as segmentation, through the image sequence. Our experiments, on cardiac ultrasound time series, show that the dynamic model outperforms traditional image registration in execution time, to a similar performance. Further, our model offers the possibility to impute and extrapolate for missing samples.
Passive Sensor Fusion and Tracking in Underwater Surveillance with the GLMB model

Passive sensors pose challenges in the localisation and tracking of targets due to the inherent ambiguity in the source range-specifically, accurate 2-D localisation using only a single sensor is not guaranteed. On the other hand, the underwater domain exhibits certain characteristics that induce some structure to the problem that help reduce the range ambiguity and lead to a favourable 2-D localisation and tracking performance. This work incorporates target motion analysis (TMA) into tracking algorithms by specifying target birth process parameters and demonstrates that the output exhibits contraction in the range uncertainty. In addition, we propose a likelihood model that incorporates cepstrum processing detections that exploit multi-path reflections from the sea surface and the sea bottom to reduce the range ambiguity. We use these models in the generalised labelled multi-Bernoulli (GLMB) multi-object model and the associated tracking filter with sequential Monte Carlo techniques. This model explicitly incorporates track labels in Bayesian recursions yielding coherent trajectory estimates consisting of the 2-D location estimates when used with passive detections.
A Feistel Network-based Prefix-Preserving Anonymization Approach, Applied To Network Traces

Network traces represent a critical piece of data for network security. Due to lack of expertise, companies are forced to outsource their network traces to third parties to perform analytics on the traces and provide security feedback and recommendations. However, these companies are reluctant to share their network traces, as they comprise sensitive information (e.g., IP addresses). Therefore, the network traces are anonymized to ensure the privacy of the data and preserve its utility. The latter guarantees that the essence of the data remains valid after anonymization, otherwise the analytics are useless. Existing solutions, such as CryptoPAN, preserves the data utility (by preserving the IP prefixes), but are vulnerable to semantic attacks.In this paper, we propose an anonymization solution, which is based on the Feistel, which is widely used in encryption systems, such as DES and Twofish. Our solution preserves both data privacy and its utility at the same time. We validate our solution using the Kddcup99 dataset and measure the data leakage (dual of privacy) provided by our solution. We evaluate the security of our solution using the avalanche property, which is widely used to measure the security of encryption systems. Moreover, the efficacy of our solution is evaluated against Injection attacks. Overall, the obtained results, avalanche property and resistance to Injection attacks, are appealing.
Event-Domain Knowledge in Inertial Sensor Based State Estimation of Human Motion

State estimation can significantly benefit from prior knowledge about a system's dynamics and state. In this paper, we investigate a special class of prior knowledge: Events that correspond to a subset of the state space. This class of knowledge was introduced in pedestrian activity classification to improve the position estimation. We argue that the methodology can be generalized and applied to other applications in human motion tracking, in which the same class of knowledge is available. We apply this methodology to estimate the pose of climbers using inertial sensors and previously measured route maps. For our evaluation, we collected an open source dataset with 27 participants, including IMU data and ground truth positions of the hands. We detect gripping holds (as events), estimate the transition between holds in a least squares optimizer and use a particle filter to deploy the route map constraints (as state subset). In this scenario, our approach achieves a position error median of 0.132 m and thus demonstrates its possible effectiveness for this application class.
Location Differential Privacy Protection Method Based on Generative Adversarial Network

Differential privacy is often used in location privacy protection because of its strict reasoning and proof privacy guarantee. When users make continuous location query, it will cause noise superposition, which leads to the decline of query accuracy. At present, although differential privacy based on rule tree structure can reduce the query error, it will generate a lot of invalid zero nodes. The data structure is too large, and more improvement in the query accuracy can be further investigated. In this paper, we proposed a differential privacy location privacy protection method based on generative adversary network. Firstly, the definition of location data privacy protection under differential privacy mechanism is given, and then resume density aware network under differential privacy mechanism. Based on density aware network, the privacy protection problem of location data can be transformed into the distribution of fitting trajectory length. Finally, we use Markov chain to generate a new trajectory, and introduce the generative adversarial network to construct a set differential privacy protection method. Compared with other methods to improve the accuracy of differential privacy query, this method can effectively reduce the problem of query accuracy decline caused by noise superposition in continuous query, and can adapt to lbs location query service in different density environments.
Comprehensive Event Representations using Event Knowledge Graphs and Natural Language Processing

Recent work has utilised knowledge-aware approaches to natural language understanding, question answering, recommendation systems, and other tasks. These approaches rely on well-constructed and large-scale knowledge graphs that can be useful for many downstream applications and empower knowledge-aware models with commonsense reasoning. Such knowledge graphs are constructed through knowledge acquisition tasks such as relation extraction and knowledge graph completion. This work seeks to utilise and build on the growing body of work that uses findings from the field of natural language processing (NLP) to extract knowledge from text and build knowledge graphs. The focus of this research project is on how we can use transformer-based approaches to extract and contextualise event information, matching it to existing ontologies, to build comprehensive knowledge graph-based event representations. Specifically, sub-event extraction is used as a way of creating sub-event-aware event representations. These event representations are then further enriched through fine-grained location extraction and contextualised through the alignment of historically relevant quotes.
Authentication and authorization in cloud computing using blockchain

Cloud computing is a technology that creates a virtual environment by configuring various computing devices. It provides software, platform as well as infrastructure as a service. Most companies have shifting or have included online operations mode along with offline mode of operation to expand business. Organizations are using cloud services to save on infrastructure and other associative costs. Most cloud services are chargeable on the basic of usage. Authentication is an important aspect of cloud computing. Various authentication algorithms which are presently in use are having some limitations. This chapter reviews various existing authentication and authorization mechanisms used for the cloud environment and will discuss how the blockchain technology with its features like transparency, immutability, traceability can be used to provide authentication in cloud environment.
Predicate Logic Syntax and Semantics

Introduction to first-order Predicate Logic. Syntax and semantics, models and quantification. Implementation of “Term,” “Formula,” and “Model” objects for Predicate Logic in Python.
Sistem Mitigasi Terintegrasi Tanggap Darurat Kebakaran Berbasis Internet of Things

Fire is an event that most often effects both urban and rurals areas. This incident is very dangerous beacause it can cause loss and loss life. Ingeneral, these fire cannot be predicted when and where they will occur. The bigger the fire, the bigger the impact, to minimize the risk of fire, a tool is needed that can provide early fire information that can be monitored so that fires can be handled quickly. With the application of internet of things technology using an ESP32 board via internet network and assisted by fire sensors, gas sensors, and temperature sensors, they are able to detect potential fires early. The data displayed is in the form of fire status, gas leaks, and temperature monitoring So in this study, a tool is made that is able to make it easier to provide fire information with the average time needed for 15.81 seconds and the system is able to provide accurate location because the location set is the identity or address of each of the sensor devices. and gateways.&#x0D;
THE UNIQUE APPROACH TO CONDUCTING PHYSICS LABORATORY WORK AT SCHOOL

The article analyzes the main reasons that cause the lack of effectiveness of instructions to laboratory works on physics contained in textbooks and notebooks for laboratory robot. Front laboratory work is proposed to divide into five main groups. Examples of design work of each group. It is shown that the proposed comprehensive use instructions for frontal laboratory works, promotes the formation of students' experimental skills and research skills, enabling them to use the acquired theoretical knowledge and skills for experimentation of new products training activities