Reviewerly/exHarmony huggingface dataset:
IMPROVING THE IMAGE-TO-SPEECH SYSTEM ACCURACY THROUGH INTEGRATION OF OPTICAL CHARACTER RECOGNITION AND LANGUAGE PROCESSING TECHNIQUES

Image-to-speech systems are a type of technology allowing for the conversion of visual information, such as images or videos, into auditory output. These systems use complex algorithms and machine learning techniques to recognize and describe visual content, allowing individuals who are visually impaired or blind to access in-formation that would otherwise be inaccessible to them. Image-to-speech systems are becoming increasingly sophisticated and can be integrated into a variety of devices, from smartphones to smart glasses. This article presents an approach to improving the accuracy of the image-to-speech system by incorporating multiple techniques. The proposed system begins by using Tesseract, an optical character recognition (OCR) engine, to extract text infor-mation from images. However, OCR is often imperfect and produces errors, which can impact the accuracy of image-to-speech models. To address this issue, the Text-Davinci-002 engine was applied for post-processing OCR output, which can help to correct errors and improve the accuracy of the extracted text. Finally, the Microsoft Speech API was employed in order to generate speech from the extracted text. By integrating these three techniques, image-to-speech system accuracy was significantly improved. An example of the generated synthetic dataset showed that the proposed techniques improve image-to-speech system accuracy both on word and character levels, and also perform punctuation error correction. This approach can be useful in various applications, including reading text from images, translating written text to speech, and assisting people with visual im-pairments.
Aspect-based automated evaluation of dialogues

Evaluating human dialogue is a complex task, as our conversation are never structured. There are, however, cases where there is some structure in our conversation, e.g., in a typical call center, dialogue between a call center agent and customer revolves around certain topics of conversation. These dialogues can be evaluated based on some pre-specified criteria as well as sub-criteria. This evaluation is typically done manually, which can be be time-consuming, motivating the need for an automated system that employs Artificial Intelligence (AI) algorithms to evaluate dialogues efficiently. In this paper, we have proposed a novel dialogue-evaluation framework that leverages recent advancements in deep learning research. The contributions of this work are two fold. Firstly, we introduce a straightforward end-to-end framework – CallAI, for evaluating dialogues in any domain, based on some predefined hierarchical criteria. Secondly, we present a novel algorithm – TAABLM, utilizing a novel combination of aspect-based learning along with traditional TF-IDF features for text. We show in this paper, that TAABLM outperforms conventional baselines such as BERT, LSTM, etc, delivering improved performance in automated dialogue evaluation, whereas CallAI offers a simple yet elegant framework for an AI-based solution to hierarchical dialogue evaluation. We demonstrate the efficacy of our proposed framework and proposed algorithm on three datasets, where we quantify performance in terms of an aggregated dialogue-score as well as in terms of either accuracy or AuROC metrics.
Managing cloud security in the presence of strategic hacker and joint responsibility

AbstractThe widespread use of cloud computing has brought cloud security to the forefront. The cloud provider and the firm assume varying degrees of joint responsibility for cloud security with cloud service models including IaaS, PaaS, and SaaS, to defend the strategic hacker. This paper builds a game-theoretical model to study cloud security management, in which we find that ignoring the strategic hacker leads to the dislocation security investment decisions (overinvestment or underinvestment) for the provider and the firm in bilateral refund contracts (BRCs). The strategic hacker’s attack effort is inverse U-shaped with cloud service models, leading to a free-riding problem between the provider and the firm. Furthermore, from the perspective of social welfare maximization, both the provider and the firm would underinvest or overinvest in cloud security. To solve the problem, we propose two new contract mechanisms: one is an internal effort-based contract, in which the provider oversees the firm internally and the compensation rate depends on the firm’s effort once the breach occurs. The other is an external effort-based contract, in which the monitoring agency supervises the efforts of the provider and the firm. We compare the two new contracts with BRCs and obtain the optimal choice for principals.Keywords: Strategic hackerjoint responsibilitycloud securitydislocation investmentmechanism design Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 The subscripts C, F, H, M represent the cloud provider, the firm, the hacker and the monitor agency, respectively. The superscripts I, II, III, IV, V represent the cases that the BRC, the existence of the non-strategic hacker, the benchmark, the internal effort-based contract, the external effort-based contract, respectively.2 Source: https://www.alibabacloud.com/help/en/legal/latest/alibaba-cloud-international-website-product-terms-of-service.3 In practice, the relationship between the monitoring cost and security effort may be ambiguous. The variable costs increase with the agent’s effort since more effort requires more monitoring cost. For simplicity, we omit the monitoring cost as 12MeF2.Additional informationFundingThe authors are extremely grateful to the anonymous referees for their valuable and helpful comments. This work was supported by the Shanghai Social Science Foundation (No. 2022ZGL009), the National Natural Science Foundation of China (No. 71801035, 71872037, 71901058, 71832001), and the Fundamental Research Funds for the Central Universities.
Instrumentation of India’s First Regional Earthquake Early Warning System and Site Characterization of Its Stations

The Earthquake Early Warning System Laboratory, Centre of Excellence in Disaster Mitigation & Management, Indian Institute of Technology Roorkee has developed India's first Earthquake Early Warning System (EEWS). For this, Uttarakhand, a Himalayan state of the Republic of India, was selected for setting up the regional EEWS. Seismic sensors have been installed in the central seismic gap region, an area where strong and higher magnitude earthquakes are predicted and likely to occur in the future. A total of 169 sensors have been installed in this seismically active region. The control room has been set up in the Earthquake Early Warning System Laboratory. The data is streamed to the laboratory through dedicated VPNoBB network of BSNL and SWAN. The server does the processing of the data in real time. Warning of the earthquakes that occurred in the instrumented region is issued through two modes. Under first mode, sirens have been installed in the Government-owned buildings of Dehradun, Haldwani, and District Emergency Operation Center in all 13 districts of Uttarakhand. In the second mode, earthquake warning are issued through the mobile app ‘Uttarakhand Bhookamp Alert’, which has now been renamed now as ‘Bhukamp Disaster Early Vigilante (BhuDEV)’. This app was launched to the public on 4th August, 2021 by the honorable Chief Minister of Uttarakhand. In this article, site classification for the instrumented field sites is performed. It is vital for analyzing the seismic hazard of an area and understanding the damage patterns caused by earthquakes. In the present study, 169 sites of the seismic network array of EEWS for Uttarakhand are classified on the basis of geological settlement at the locations of the stations.
Seismological Data Quality Controls—A Synthesis

Data quality is of utmost importance in assessing seismic hazards and risk mitigation related to geohazards. Erroneous data incorporation may lead to inaccurate observations and judgments. Lack of data or clipped data can be dealt with easily, but errors in the data (e.g., timing, amplitudes) or in the metadata (e.g. location, gain) can induce some strong bias in the estimation of geohazards. As a result, strict and standard quality controls are required to guarantee the accuracy of the assessments. This chapter will discuss some of the common issues with seismological data and offer tips on identifying inaccuracies. Despite recent efforts by the seismological community, there is no uniform framework to identify the errors in seismological data nor to perform some established quality controls. Therefore, we will examine the current state of the art and best practices in seismological quality controls. Some recommendations for new implementations, including some cutting-edge technologies like “Dynamic Time Warping” will be suggested. Missing data (i.e., gaps) are permanent, but all erroneous data do not need to be discarded. In some cases, identifying the problem in the data or metadata allows for reprocessing. The problems with data availability will be examined first, then the challenges with data quality. Finally, a discussion on the metadata errors will be provided.
Joint polarization-color coding strategy based on Stokes parameter for robust strip-edge detection in 3D measurement

Stripe-edge detection of color fringe is crucial in structured light measurement. However, the robustness of color stripe-edge detection is affected by the color crosstalk, scattering, and the color filter in the color camera. This can lead to a decrease in imaging quality. In order to avoid the influence of color crosstalk, this paper analyzes the polarization properties of RGB channels and proposes a joint polarization-color coding strategy based on the Stokes parameter and a corresponding stripe-edge detection method. Compared with the traditional color-coded fringe measurement system, the monochrome camera can distinguish the color-coded fringe so that the influence induced by color crosstalk is eliminated completely. Furthermore, the influence induced by the scattering is reduced. The experiment shows that the robustness of strip-edge detection is remarkably improved, the nonparallelism of detected edges reduced by 32.4%, and the nonuniformity of detected edges reduced by 44.1%.
Factors Affecting the Adoption of Cryptocurrency

This paper examines factors affecting the adoption of cryptocurrency across 158 countries worldwide. To this end, we collected cryptocurrency adoption data from Chainalysis’s reports and macroeconomic data from the World Development Indicators platform. We find that greater import volumes, larger population size, more sufficient levels of the labor force, higher unemployment rate, and a higher level of electricity access are associated with a greater level of cryptocurrency adoption. On the other hand, a higher level of government spending and a greater level of domestic savings are associated with a lower level of cryptocurrency adoption. In addition, we also find that the population size and level of the labor force have a negative impact on the three subcomponents of the cryptocurrency adoption index including (i) centralized service value received (CeFi); (ii) the volume of exchange trading (P2P); and (iii) the received DeFi value (DeFi). We find that while the import volumes and level of electricity access have an opposite relationship with the centralized service value received and the DeFi value received, GDP has a negative effect on the DeFi value received. Meanwhile, greater government spending and higher domestic savings are associated with a greater level of exchange trade volume P2P. In terms of urbanization, whereas it shows a positive impact on the exchange trade volume P2P, it has the opposite effect on the DeFi value received.
Improving Computation and Memory Efficiency for Real-world Transformer Inference on GPUs

Transformer models have emerged as a leading approach in the field of natural language processing (NLP) and are increasingly being deployed in production environments. Graphic processing units (GPUs) have become a popular choice for the transformer deployment and often rely on the batch processing technique to ensure high hardware performance. Nonetheless, the current practice for transformer inference encounters computational and memory redundancy due to the heavy-tailed distribution of sequence lengths in NLP scenarios, resulting in low practical performance. In this article, we propose a unified solution for improving both computation and memory efficiency of the real-world transformer inference on GPUs. The solution eliminates the redundant computation and memory footprint across a transformer model. At first, a GPU-oriented computation approach is proposed to process the self-attention module in a fine-grained manner, eliminating its redundant computation. Next, the multi-layer perceptron module continues to use the word-accumulation approach to eliminate its redundant computation. Then, to better unify the fine-grained approach and the word-accumulation approach, it organizes the data layout of the self-attention module in block granularity. Since aforementioned approaches make the required memory size largely reduce and constantly fluctuate, we propose the chunk-based approach to enable a better balance between memory footprint and allocation/free efficiency. Our experimental results show that our unified solution achieves a decrease of average latency by 28% on the entire transformer model, 63.8% on the self-attention module, and reduces memory footprint of intermediate results by 7.8×, compared with prevailing frameworks.
Multisecret‐sharing scheme with two‐level security and its applications in blockchain

Abstract A ‐threshold secret sharing and multisecret‐sharing scheme based on Shamir's secret sharing scheme are introduced with two‐level security using a one‐way function. Besides, its application in a smart contract‐enabled consortium blockchain network is discussed. The proposed scheme is thoroughly examined in terms of security and efficiency. Privacy, security, integrity, and scalability are also analyzed while applying it to the blockchain network.
Improving Access to Medical Information for Multilingual Patients using Pipelined Ensemble Average based Machine Translation

Machine translation has shown potential in improving access to medical information and healthcare services for multilingual patients. This research aims to enhance machine translation accuracy in the medical field, specifically for translating from Hindi to English. The study introduces a new approach that dynamically allocates decoding parameters using regression models, overcoming the limitations of fixed parameters in the decoder. A comprehensive dataset is created to address limited data availability, enabling regression models to predict optimal pruning parameters. The main motivation for the study is the introduction of a regression method for optimizing pruning parameters, which is a novel approach in this context. The proposed approach outperforms existing methods, achieving improved translation accuracy. Standard metrics such as the BLEU score are used to evaluate translations. Ensemble average and pipeline approaches further enhance performance. The improved performance of the proposed models can be attributed to the ensemble of diverse models (Extra Trees, LightGBM, XGBoost, and Random Forest) that employ various techniques to reduce overfitting, enhance prediction accuracy, and improve translation by correcting prediction errors. The study contributes to facilitating the translation and sharing of medical literature, promoting collaboration and knowledge exchange across languages. The research demonstrates the effectiveness of the regression method for optimizing pruning parameters in machine translation, leading to improved translation accuracy in the medical field. The proposed models offer promising results, paving the way for enhanced machine translation systems and promoting collaboration and knowledge exchange in the medical domain. The source code is available at https://huggingface.co/debajyoty/statistical-regression-Based-MT/tree/main/Statistical-Regression-SMT.
Adaptive weighted federated Kalman filtering based on Mahalanobis distance and its application in navigation

Abstract Due to the federal Kalman filter is used to directly fuse the measurement information into the main filter without processing, resulting in the problem of reduced filtering accuracy. An adaptive weighted federated Kalman filtering based on Mahalanobis distance was proposed in this paper. By calculating the Mahalanobis distance between the predicted value and the measurements of the system, the random fluctuation of the measurements is detected. The statistical characteristics of the system measurement noise are adjusted at any time according to random fluctuations in the measurements. And then by using a adaptive amplification factor to dynamically adjust the measurement noise in the subsystems, and reduce the impact of measurement information contamination in subfilters on the main filter. The adaptive federated information distribution coefficient is used to realize the global information fusion of the federal Kalman filter method, to reduce the influence of inaccurate estimation of subfilters on the main filter.Simulation results and comparison analysis prove that the filtering performance of the proposed is better than the traditional federated Kalman filter (FKF) and adaptive FKF, which can improve the accuracy of the integrated navigation system.
Exploring the Role of Public Libraries in Bangladesh in the Fourth Industrial Revolution Era: Readiness, Challenges, and Opportunities

AbstractThis column examines readiness for 4IR technology adoption in public libraries in Bangladesh. It explores the current state of adoption, the challenges faced during implementation, and the potential benefits. With the convergence of advanced technologies such as AI, IoT, blockchain, and autonomous systems, libraries can transform into vital community centers and preserve cultural heritage. The findings provide an overview of the current state of adoption, identify opportunities and challenges, and emphasize the need for further research to understand the impact of these technologies on libraries in Bangladesh.Keywords: Public libraryFourth Industrial Revolution4IRBangladeshchallengesopportunities Disclosure statementNo potential conflict of interest was reported by the authors.
T-Gen: Time series data generator for inverse analysis of machine learning models

A method was developed to generate diverse time series data automatically. Following the discrete Fourier transform (DFT) for time series data, the real and imaginary parts of the complex numbers in the frequency domain representation were modified using random numbers to generate new ones. Then, an inverse DFT was performed to generate virtual time series data. This innovative approach can propose diverse time series data in case studies involving actual processes and design time variation in each process variable to meet the target values of objective variables through the inverse analysis of machine learning models constructed with process data.
Conditioned Cooperative training for semi-supervised weapon detection

Violent assaults and homicides occur daily, and the number of victims of mass shootings increases every year. However, this number can be reduced with the help of Closed Circuit Television (CCTV) and weapon detection models, as generic object detectors have become increasingly accurate with more data for training. We present a new semi-supervised learning methodology based on conditioned cooperative student-teacher training with optimal pseudo-label generation using a novel confidence threshold search method and improving both models by conditional knowledge transfer. Furthermore, a novel firearms image dataset of 458,599 images was collected using Instagram hashtags to evaluate our approach and compare the improvements obtained using a specific unsupervised dataset instead of a general one such as ImageNet. We compared our methodology with supervised, semi-supervised and self-supervised learning techniques, outperforming approaches such as YOLOv5 m (up to ＋19.86), YOLOv5l (up to ＋6.52) Unbiased Teacher (up to ＋10.5 AP), DETReg (up to ＋2.8 AP) and UP-DETR (up to ＋1.22 AP).
Low-epsilon adversarial attack against a neural network online image stream classifier

An adversary intercepts a stream of images between a sender and a receiver neural network classifier. To minimize its footprint, the adversary only attacks a limited number of images within the stream. The adversary is interested in maximizing the number of successfully conducted attacks among all performed attacks. Upon the arrival of each image and before the arrival of the following image, the adversary must irrevocably decide whether it wants to attack the current image or not. The target model is a fixed deep neural network that may use any form of regularization. The adversary has query access to the target model, which can feed images and obtain the loss, which may contain regularization and classification loss terms. Since this paper’s proposed method needs classification loss term alone, it also suggests a novel method in which the adversary estimates the regularization loss term and eliminates it. All images are partitioned into three groups based on their after-attack classification loss and treated according to their group. Moreover, this paper provides some promising test results on various datasets.
Shortest path core-selection incentive for federated learning platform with medical applications

As the main technology to solve data islands and mine data value, federated learning has been widely researched and applied, and more and more federated learning platforms are emerging. The federated learning platform organizes users, devices and data to collaborate in a crowdsourcing manner and complete specific federated learning tasks. This paper designs the shortest path core-selection incentive mechanism by combining the VCG auction mechanism and the core concept of cooperative games. This mechanism solves the problems of overpayment, false-name attack, and deviation from the core of the VCG mechanism, and saves the expenditure of the federated learning task issuer. It adopts the VCG-nearest principle in the core selection, so that the federated learning task participants get rewards as close as possible to the outcome of VCG mechanism. This mechanism can guarantee four economic attributes: incentive compatibility, individual rationality, alliance rationality, and maximization of social efficiency. Medical experimental results illustrate the effectiveness of the mechanism.
Defensive cyber security: continuous controls enforcement and infrastructure hygiene

With the exponential rise in cyber attacks, organisations need to implement mature cyber security strategies for handling cyberthreats at scale and minimising the risk to the business. In this article, we look at the cyber security model of continuous assessment and controls enforcement (CACE), including the importance of maintaining infrastructure cyber hygiene.