Reviewerly/exHarmony huggingface dataset:
Verifiable XOR-based visual secret sharing scheme for hyperspectral images

Hyperspectral images (HSIs) are the spectral images that provide spatial and spectral information. Unlike multispectral images, these images consist of 100 to 200 bands, which provide a large amount of data to identify minute details of the scene with the help of the spectral signatures. This information is valuable and should be secured while transmitting the HSI over the network. Visual cryptography is a well-known cryptographic method for securing images. It helps in securely transmitting images among n users. It converts visual data into unreadable shares that are transmitted and, stacking these shares together will reveal the image. Many visual secret sharing (VSS) schemes have been proposed in the past decade, which made it easier to hide the visual information from unauthorized users. We have proposed a verifiable XOR-based VSS method for the secure transmission of HSIs. We have introduced a preprocessing step for the image with a band selection technique, which reduces the size of the image and eliminates redundancy. It also includes the detection of the tampered shares. Embedding a verifiable bit in each pixel is performed to test the integrity of the shares. We have assessed the visual quality of the recovered image using quantitative measuring parameters. We also compared them with existing VSS methods. The proposed method recovers the HSI with a self-similarity index of 95% to 99%. The proposed method’s experimental results show that the HSI is restored with better visual quality.
Design and Implementation of English Online Translation Platform Based on Multi-strategy Analysis

It is difficult to ensure that the translation of existing machine translation system is completely accurate, and it usually needs manual intervention and modification to form an accurate translation. If the translation of machine translation system is modified by a general text editing system, we propose an overall design scheme of an interactive intelligent online assistant translation platform based on multiple strategies. This platform realizes integrated processing of multiple translation modes based on rule analysis, analogical reasoning and statistical knowledge. Based on the needs of users, the overall layout of the system is realized by C/S mode, and the system is divided into server and client. By setting up Android test environment, the functions of the system are tested, in which the query module and translation module can run normally, which verifies the correctness of the above design scheme and provides a reference for online translation in English campus.
RETRACTED: Investigation and reflection on multimedia-assisted English classroom teaching

With the rapid development of information technology, multimedia-assisted instruction has been widely used in classroom teaching, especially in English classroom teaching, the importance of multimedia teaching has been generally recognized. However, there are still some problems in the practical application of multimedia teaching, which deserve further study. Taking 400 students and 50 teachers as the subjects of investigation, through my own English classroom teaching practice, observation and investigation, this paper explores and analyses the real situation of multimedia English teaching from many aspects, and reflects on how to improve multimedia English teaching more effectively in the multimedia environment. In English teaching, we should correctly view the position of multimedia in teaching, correctly understand the traditional English teaching relative to multimedia, organically combine multimedia English teaching with traditional English teaching, and pay attention to people-oriented, teacher-student interaction and emotional exchange in multimedia classroom teaching. Finally, according to the survey results, we can know that what is important is not the improvement of teaching methods and equipment but whether the teaching concept is updated.
Cryptocurrency and crime

Cryptocurrencies have been increasingly involved in financial crimes. This chapter aims to classify the roles of cryptocurrencies in financial crimes, and the applicable laws, accordingly. The chapter also tries to identify possible flaws in the existing legal system. The chapter begins by elaborating the definition and development of cryptocurrency around the globe and pinpointing the features of cryptocurrency that are used by financial criminals. The chapter discusses further the direct and indirect involvements of cryptocurrency in financial crimes and the core and applicable laws and acts. Through carefully analysing relevant primary and secondary legislation of the UK, this chapter identifies some potential issues in the legal system: micropayment, individual activities and jurisdictions, where the unharmonised jurisdiction scheme has caused delays in some prosecution proceedings. The chapter concludes that cryptocurrency has been a convenient tool for those committing financial crimes. Despite the effort of Financial Action Taskforce, the legal system hasn’t yet caught up with technology development. The chapter provides the first review and classification of the laws and acts applied to the cryptocurrency crimes in the UK. It also calls for an internationally harmonised legal system. The discovered flaws may urge authorities to improve the existing regulatory frameworks and legal provisions.
Towards Repairing Scenario-Based Models with Rich Events

Repairing legacy systems is a difficult and error-prone task: often, limited knowledge of the intricacies of these systems could make an attempted repair result in new errors. Consequently, it is desirable to repair such systems in an automated and sound way. Here, we discuss our ongoing work on the automated repair of scenario-based models: fully executable models that describe a system using scenario objects that model its individual behaviors. We show how rich, scenario-based models can be model-checked, and then repaired to prevent various safety violations. The actual repair is performed by adding new scenario objects to the model, and without altering existing ones - in a way that is well aligned with the principles of scenario-based modeling. In order to automate our repair approach, we leverage off-the-shelf SMT solvers. We describe the main principles of our approach, and discuss our plans for future work.
Towards cascaded V-Net for automatic accurate kidney segmentation from abdominal CT images

Abdominal kidney segmentation plays an essential role in diagnosis and treatment of kidney diseases, particularly in surgical planning and clinical outcome analysis before and after kidney surgery. It still remains challenging to precisely segment the kidneys from CT images. Current segmentation approaches still suffer from CT image noises and variations caused by different CT scans, kidney location discrepancy, pathological morphological diversity among patients, and partial volume artifacts. This paper proposes a fully automatic kidney segmentation method that employs a volumetric convolution driven cascaded V-Net architecture and false positive reduction to precisely extract the kidney regions. We evaluate our method on publicly available kidney CT data. The experimental results demonstrate that our proposed method is a promising method for accurate kidney segmentation, providing a dice coefficient of 0.95 better than other approaches as well less computational time.
EFFECT OF DEPTH MULTI-SKILLING STRATEGIES ON EMPLOYEE PERFORMANCE IN COMMERCIAL BANKS IN KENYA: A CASE OF KISUMU COUNTY

The purpose of the study was to establish the effect of depth multi-skilling strategy on employee performance in commercial banks in Kenya, with a case of Kisumu County. The study adopted a survey research design. The target population was two hundred and thirty eight employees of commercial banks in Kisumu County from which sample of one hundred and forty nine employees were drawn through stratified random sampling technique. One hundred and thirteen (113) questionnaires were dully filled, returned and analyzed using Statistical Package for Social Sciences to facilitate descriptive and inferential analysis of the study variables. Linear regression results indicated that depth multi-skilling strategy significantly explained variance in employee performance. Pearson correlation results showed strong positive relationship between depth multi-skilling strategy and employee performance. Commercial banks provided employees with complex, specialized tasks and in-depth experience or mastery of work. The study concluded that depth multi-skilling play a significant role in employee performance in commercial banks. The study recommended that challenge and achievement opportunities should be provided through complex job assignments to improve employee and organizational performance. Key Words: Depth Multi-skilling, Employee Performance, Human Resource Flexibility, Commercial Banks CITATION: Okeyo, B. A., &amp; Juma, D. (2021). Effect of depth multi-skilling strategies on employee performance in commercial banks in Kenya: A case of Kisumu County. T he Strategic Journal of Business &amp; Change Management, 8 (1), 435 – 446.
Developing a Media to Teach Chemical Technology to Students with Hearing Impairments

This study aims to develop chemical technology learning media through the production of dishwashing soap using sign language for students with hearing impairments. This study used a six-step research method by Borg and Gall, namely needs analysis, planning, video validation, media testing, media revision, and testing the revised media. The results of this study indicate that to create audiovisual media, instrument validation is necessary. Validation of the instrument serves to verify the videos. After the videos are validated, the videos must be edited based on the experts suggestions until the videos are declared valid and ready to use. The videos are tested on eight students with hearing impairments to teach chemical technology and dishwashing soap making. The results of the main field experiments indicates that the experiments are ineffective because the tests conducted during the COVID-19 pandemic outbreak. Besides, learning for students with hearing impairments is generally conducted face-to-face and the media is accommodated by some learning aids for material delivery.
Linearly convergent gradient-free methods for minimization of parabolic approximation

Finding the global minimum of non-convex functions is one of the main and most difficult problems in modern optimization. In the first part of the paper, we consider a certain class of "good" non-convex functions that can be bounded above and below by a parabolic function. We show that using only the zeroth-order oracle, one can obtain the linear speed $\log \left(\frac{1}{\varepsilon}\right)$ of finding the global minimum on a cube. The second part of the paper looks at the nonconvex problem in a slightly different way. We assume that minimizing the quadratic function, but at the same time we have access to a zeroth-order oracle with noise and this noise is proportional to the distance to the solution. Dealing with such noise assumptions for gradient-free methods is new in the literature. We show that here it is also possible to achieve the linear rate of convergence.
The Design Definition and Research of In-Car Digital AI Assistant

Abstract Nowadays, intelligent assistants, such as Apple’s Siri, are becoming integral to our daily lives, and they significantly change the way users interact with systems. Meanwhile, with the development of technologies, people continue to bring the experience of using intelligent products to the car, and the in-car intelligent assistant is also gradually being used. While there have been many studies focused on technologies, there are fewer studies dealing with design issues at the initial stage of the design, the stage prior to system implementation, such as how the personality of intelligent assistant should be, how to design the appearance, and the way of interacting, the designers had to rely on imagination. Therefore, we present a design method to define intelligent assistants, using the MBTI model combined with brand tonality to define the personality of intelligent assistants, then we determined the design direction of the appearance design based on user research and benchmark. Furthermore, the initial design scheme was screened from brand fitting and flexibility, and experts and users were invited to determine the most suitable scheme from the two dimensions of personality and symbolization. This design method allows designers and engineers to explore more precise design solutions and to improve the emotional design and user satisfaction of intelligent assistants before all the complex systems are ready to run.
MLLP-VRAIN Spanish ASR Systems for the Albayzin-RTVE 2020 Speech-To-Text Challenge

This paper describes the automatic speech recognition (ASR) systems built by the MLLP-VRAIN research group of Universitat Politècnica de València for the Albayzin-RTVE 2020 Speech-to-Text Challenge.The primary system (p-streaming 1500ms nlt) was a hybrid BLSTM-HMM ASR system using streaming one-pass decoding with a context window of 1.5 seconds and a linear combination of an n-gram, a LSTM, and a Transformer language model (LM).The acoustic model was trained on nearly 4,000 hours of speech data from different sources, using the MLLP's transLectures-UPV toolkit (TLK) and TensorFlow; whilst LMs were trained using SRILM (n-gram), CUED-RNNLM (LSTM), and Fairseq (Transformer), with up to 102G tokens.This system achieved 11.6% and 16.0%WER on the test-2018 and test-2020 sets, respectively.As it is streaming-enabled, it could be put into production environments for automatic captioning of live media streams, with a theoretical delay of 1.5 seconds.Along with the primary system, we also submitted three contrastive systems.From these, we highlight the system c2streaming 600ms t that, following the same configuration of the primary one, but using a smaller context window of 0.6 seconds and a Transformer LM, scored 12.3% and 16.9% WER points respectively on the same test sets, with a measured empirical latency of 0.81±0.09seconds (mean±stdev).This is, we obtained state-of-the-art latencies for high-quality automatic live captioning with a small WER degradation of 6% relative.
A Pilot Study in Surveying Clinical Judgments to Evaluate Radiology Report Generation

The recent release of many Chest X-Ray datasets has prompted a lot of interest in radiology report generation. To date, this has been framed as an image captioning task, where the machine takes an RGB image as input and generates a 2-3 sentence summary of findings as output. The quality of these reports has been canonically measured using metrics from the NLP community for language generation such as Machine Translation and Summarization. However, the evaluation metrics (e.g. BLEU, CIDEr) are inappropriate for the medical domain, where clinical correctness is critical. To address this, our team brought together machine learning experts with radiologists for a pilot study in co-designing a better metric for evaluating the quality of an algorithmically-generated radiology report. The interdisciplinary collaborative process involved multiple interviews, outreach, and preliminary annotation to design a larger scale study - which is now underway - to build a more meaningful evaluation tool.
Development of main group element digital encyclopedias as learning resources to support learning activities at home in COVID-19 pandemic condition

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 Munzil, Sutrisno Sutrisno, I. Wayan Dasna, Sutandyo Dwija Laksmana; Development of main group element digital encyclopedias as learning resources to support learning activities at home in COVID-19 pandemic condition. AIP Conf. Proc. 2 March 2021; 2330 (1): 020030. https://doi.org/10.1063/5.0043251 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
Effectiveness of Investments in Prevention of Geological Disasters

Research on geological disasters has made several achievements in monitoring, early warning, and risk assessment. Substantial resources are being invested in prevention projects, but, due to geographical and demographical complexity, incompleteness of data, and small number of samples, a quantitative analysis on the number of geological disasters and the entity of investments in their prevention is a difficult problem. In this work, the relation is studied between the amount of resources invested in prevention and the number of geological disasters in subsequent years. The analysis is performed on historical data, using statistical methods and a LSTM recurrent neural network.
The Design of High Precision Arbitrary Waveform Generator Based on DDS Technology and FPGA

Abstract Combining with the IP core advantage of FPGA, a signal generator which can generate arbitrary waveform is designed on the basis of direct digital synthesis technology in this paper. The INTEL company’s Cyclone IV series EP4CE6F17C8N is used as the control core and the front-end interface design is carried out by XMAL. The software control operation of the logical processing interface is realized through C#. The signal generator has the characteristics of single cycle waveform with 256 sampling points, clear spectrum analysis, higher purity and high precision. The arbitrary waveform displayed on oscilloscope is wonderful after filtering out high frequency signal by seven order butter worth filter.
Automated Data-Driven Approach for Gap Filling in the Time Series Using Evolutionary Learning

In the paper, we propose an adaptive data-driven model-based approach for filling the gaps in time series. The approach is based on the automated evolutionary identification of the optimal structure for a composite data-driven model. It allows adapting the model for the effective gap-filling in a specific dataset without the involvement of the data scientist. As a case study, both synthetic and real datasets from different fields (environmental, economic, etc.) are used. The experiments confirm that the proposed approach allows achieving the higher quality of the gap restoration and improve the effectiveness of forecasting models.
Internet of things for power inspection system

According to the actual needs, this paper designs and implements a power inspection system based on the Internet of things GIS, including server and mobile terminal. It mainly includes basic information management, patrol task management, statistical query management, and other functions, and describes its design method in detail. Finally, this paper summarizes the key technologies of the power inspection system based on the Internet of things GIS and describes the realization of each functional module of the power inspection system, and through a more detailed description of the implementation process of each functional module of the system, and an example of operation to show the system.
EXPERIENCIAR, TRABALHAR E CONVIVER: O ARTESANATO COMO RECURSO PSICOSSOCIAL DE PREVENÇÃO SOCIAL E PRODUÇÃO DE SAÚDE MENTAL PARA OS USUÁRIOS DO CRAS ÁFRICA NA PERSPECTIVA PÓS PANDEMIA / EXPERIENCING, WORKING AND LIVING TOGETHER: HANDICRAFTS AS A PSYCHOSOCIAL RESOURCE FOR SOCIAL PREVENTION AND MENTAL HEALTH PRODUCTION FOR USERS OF CRAS ÁFRICA IN THE POST-PANDEMIC PERSPECTIVE

Experienciar, trabalhar e conviver: o artesanato como recurso psicossocial de prevenção social e produção de saúde mental para os usuários do cras áfrica na perspectiva pós pandemia Experiencing, working and living together: handicrafts as a psychosocial resource for social prevention and mental health production for users of cras áfrica in the post-pandemic perspective
Comparison of FairMOT-VGG16 and MCMOT Implementation for Multi-Object Tracking and Gender Detection on Mall CCTV

The crowd detection system on CCTV has proven to be useful for retail and shopping sector owners in mall areas. The data can be used as a guide by shopping center owners to find out the number of visitors who enter at a certain time. However, such information was still insufficient. The need for richer data has led to the development of more specific person detection which involves gender. Gender detection can provide specific information on the number of men and women visiting a particular location. However, gender detection alone does not provide an identity label for every detection that occurs, so it needs to be combined with a multi-person tracking system. This study compares two tracking methods with gender detection, namely FairMOT with gender classification and MCMOT. The first method produces MOTA, MOTP, IDS, and FPS of 78.56, 79.57, 19, and 24.4, while the second method produces 69.84, 81.94, 147, and 30.5. In addition, evaluation of gender was also carried out where the first method resulted in a gender accuracy of 65\% while the second method was 62.35\%.
COVID-19 and Rule 10b-5

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Selection of Characteristics by Hybrid Method: RFE, Ridge, Lasso, and Bayesian for the Power Forecast for a Photovoltaic System

Currently, the generation of alternative energy from solar radiation with photovoltaic systems is growing, its efficiency depends on internal variables such as powers, voltages, currents; as well as external variables such as temperatures, irradiance, and load. To maximize performance, this research focused on the application of regularization techniques in a multiparametric linear regression model to predict the active power levels of a photovoltaic system from 14 variables that model the system under study. These variables affect the prediction to some degree, but some of them do not have so much preponderance in the final forecast, so it is convenient to eliminate them so that the processing cost and time are reduced. For this, we propose a hybrid selection method: first we apply the elimination of Recursive Feature Elimination (RFE) within the selection of subsets and then to the obtained results we apply the following contraction regularization methods: Lasso, Ridge and Bayesian Ridge; then the results were validated demonstrating linearity, normality of the error terms, without autocorrelation and homoscedasticity. All four prediction models had an accuracy greater than 99.97%. Training time was reduced by 71% and 36% for RFE-Ridge and RFE-OLS respectively. The variables eliminated with RFE were Total Energy, Daily Energy, and Irradiance, while the variable eliminated by Lasso was: “Frequency”. In all cases we see that the root mean square errors were reduced for RFE. Lasso by 0.15% while for RFE-Bayesian Ridge by 0.06%.