Reviewerly/exHarmony huggingface dataset:
MarginalizedAadhaar

opinion Share on MarginalizedAadhaar: India's Aadhaar biometric ID and mass surveillance Author: Subhashish Panigrahi O Foundation O FoundationView Profile Authors Info & Claims InteractionsVolume 29Issue 2March - April 2022 pp 16–19https://doi.org/10.1145/3517173Published:25 February 2022Publication History 0citation382DownloadsMetricsTotal Citations0Total Downloads382Last 12 Months382Last 6 weeks44 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Discrete and Continuous Wavelet Expansions

This paper proposes a new approach to the construction of wavelet decomposition, which is suitable for processing a wide range of information flows. The proposed approach is based on abstract functions with values in linear topological spaces. It is defined by embedded spaces and their projections. The proposed approach allows for adaptive ways of decomposition for the initial flow depending on the speed changes of the last one. The initial information flows can be real number flows, flows of complex and p-adic numbers, as well as flows of (finite or infinite) vectors, matrices, etc. The result is illustrated with examples of spline-wavelet decompositions of discrete flows, and also with the example of the decomposition of a continuous flow.
Hybrid password meters for more secure passwords – a comprehensive study of password meters including nudges and password information

Supporting users with secure password creation is a well-explored yet unresolved research topic. A promising intervention is the password meter, i.e. providing feedback on the user's password strength as and when it is created. However, findings related to the password meter's effectiveness are varied. An extensive literature review revealed that, besides password feedback, effective password meters often include: (a) feedback nudges to encourage stronger passwords choices and (b) additional guidance. A between-subjects study was carried out with 645 participants to test nine variations of password meters with different types of feedback nudges exploiting various heuristics and norms. This study explored differences in resulting passwords: (1) actual strength, (2) memorability, and (3) user perceptions. The study revealed that password feedback, in combination with a feedback nudge and additional guidance, labelled a hybrid password meter, was generally more efficacious than either intervention on its own, on all three metrics. Yet, the type of feedback nudge targeting either the person, the password creation task, or the social context, did not seem to matter much. The meters were nearly equally efficacious. Future work should explore the long-term effects of hybrid password meters in real-life settings to confirm the external validity of these findings.
Morozkinskoye gold deposit (southern Yakutia): age and ore sources

The paper presents the results of the comprehensive isotope geochemical (Re-Os, Pb and δ34S) study of sulfide mineralization of the Morozkinskoye deposit. The ore zones of the deposit are localized in the syenite massif of Mount Rudnaya, which is located within the Central Aldan ore region (southern Yakutia). Gold mineralization is represented by vein-disseminated or vein type mineralization and is manifested in acidic low-temperature metasomatites – beresites (Qz-Ser-Ank-Py). For the first time we obtained an age estimate of the gold mineralization ~ 129 ± 3 Ma, which the synchronism of the hydrothermal ore process in the beresites, which formed the Morozkinskoye deposit, and magmatic crystallization of the syenites of Mount Rudnaya (~130 Ma). The osmium initial isotopic composition of the studied sulfides indicates a mixed mantle-crustal source of sulfide mineralization. New lead isotopic data of syenites indicate the predominance of mantle lead and an insignificant role of the lower – crust lead, while the isotopic composition of pyrite denotes the presence of the upper crustal material in the ore genesis. The sulfide δ34S values vary from –2.3 to +0.6 ‰ and indicate a predominantly magmatic source of sulfur in the ores.
Bias, journalistic endeavours, and the risks of artificial intelligence

Artificial intelligence is increasingly used throughout all processes of the news cycle. AI also has untapped corrective potential. By learning to point readers to diverse, quality, and/or legitimate news after exposure to 'fake news', 'false narratives', and disinformation, AI plays a powerful role in cleaning up the information ecosystem. Yet AI systems often 'learn' from training data that contains historical inaccuracies and biases, with results proven to embed discriminatory attitudes and behaviours. Because this training data often does not contain personal information, regulation of AI in the news production cycle is largely overlooked by legal commentators. Accordingly, this chapter lays out the risks and challenges that AI poses in both journalistic content creation and moderation, especially through machine-learning in the post-truth world. It also assesses the media's rights and responsibilities for using AI in journalistic endeavours in light of the EU's AI draft regulation legislative process.
Real-Time Image Enhancement Using DCT Techniques for Video Surveillance

Video surveillance at night time is very difficult task to identify the objects under different environmental conditions. The surveillance task plays a vital role for easily identifying the abnormal scenarios. This tracking data is quite helpful for armed services and civilians. The proposed algorithm enhances the intensity of real-time dark images. This can be achieved by gathering the daytime image and night time image of same place, and the noise can be removed by background subtraction after that image is fused with same place daytime image by discrete cosine transformation (DCT) technique. This algorithm provides best results in terms of reducing noise, and it justifies through quality metrics like peak signal-to-noise ratio (PSNR) and mean squared error (MSE).
Raising Health Awareness in Rural Communities: A Randomized Experiment in Bangladesh and India

Abstract Delivering validated information to rural areas is a major challenge in low-income countries. In this paper, we study information provision to rural communities in the context of a global outbreak of an infectious disease—COVID-19. Two weeks after the initial lockdown in March 2020, we conducted a randomized experiment in rural Bangladesh and India to disseminate health information over the phone. We find that relative to information provided via SMS, phone calls can significantly improve people's awareness and compliance with health guidelines. We also find compliance to be substantially higher among women, which also persists after three months of the campaign.
<i>Call for Special Issue Papers:</i> Cloud Computing and Big Data for Cognitive IoT

Big DataVol. 10, No. 1 Calls for PapersCall for Special Issue Papers: Cloud Computing and Big Data for Cognitive IoTDeadline for Manuscript Submission: August 15, 2022Guest Editors:Dr. Elena Verdu, Dr. Yuri Vanessa Nieto, and Dr. Nasir SaleemGuest Editors:Dr. Elena VerduUniversidad Internacional de la Rioja, Logroño, SpainSearch for more papers by this author, Dr. Yuri Vanessa NietoUniversidad Nacional Abierta y a Distancia UNAD, Bogotá, ColombiaSearch for more papers by this author, and Dr. Nasir SaleemFaculty of Engineering and Technology, Gomal University, Dera Ismail Khan, PakistanSearch for more papers by this authorPublished Online:10 Feb 2022https://doi.org/10.1089/big.2021.29048.cfp2AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"Call for Special Issue Papers: Cloud Computing and Big Data for Cognitive IoT." Big Data, 10(1), pp. 83–84FiguresReferencesRelatedDetails Volume 10Issue 1Feb 2022 InformationCopyright 2022, Mary Ann Liebert, Inc., publishersTo cite this article:Guest Editors:Dr. Elena Verdu, Dr. Yuri Vanessa Nieto, and Dr. Nasir Saleem.Call for Special Issue Papers: Cloud Computing and Big Data for Cognitive IoT.Big Data.Feb 2022.83-84.http://doi.org/10.1089/big.2021.29048.cfp2Published in Volume: 10 Issue 1: February 10, 2022Online Ahead of Print:January 27, 2022PDF download
Multi-step-ahead Host Load Prediction with VMD-BiGRU-ED in Cloud Computing

Cloud computing resource load has the characteristics of nonlinearity, non-smoothness, high noise and high fluctuation, and it is difficult to achieve better prediction results by using a separate prediction model. Therefore, in this paper, we propose a combined VMD-BiGRU-ED prediction model based on VMD algorithm, GRU and encoder-decoder structure, which combines the advantages of strong VMD signal decomposition ability and high GRU prediction accuracy to accurately predict cloud computing resource load. To evaluate our model, we conduct experiments using the cluster public dataset provided by Alibaba Cluster-trace-v2018. The experimental results show that our proposed combined VMD-BiGRU-ED prediction model outperforms the single prediction model and other combined prediction models in terms of prediction accuracy.
Tremor Waveform Extraction and Automatic Location With Neural Network Interpretation

Active faults release tectonic stress imposed by plate motion through a spectrum of slip modes, from slow, aseismic slip, to dynamic, seismic events. Slow earthquakes are often associated with tectonic tremor, nonimpulsive signals that can easily be buried in seismic noise and go undetected. We present a new methodology aimed at improving the detection and location of tremors hidden within seismic noise. After identifying tremors with a classic convolutional neural network (CNN), we rely on neural network attribution to extract core tremor signatures. We observe that the signals resulting from the neural network attribution analysis correspond to a waveform traveling in the Earth&#x2019;s crust and mantle at wavespeeds consistent with seismological estimates. We then use these waveforms signatures to locate the source of tremors with standard array-based techniques. We apply this method to the Cascadia subduction zone, where we identify tremor patches consistent with existing catalogs. This approach allows us to extract small signals hidden within the noise, and to locate more tremors than in existing catalogs.
Utilization of Both Machine Vision and Robotics Technologies in Assisting Quality Inspection and Testing

Inspecting the smoothness and durability of the exterior and interior surfaces of manufactured products such as large passenger aircraft requires utilizing manufacturing precision instruments. Manual techniques are mainly adopted in inspections, which are, however, much costly and have low efficiency. Moreover, manual operation is more prone to missing and false inspections. To cope with these issues, effective precision instruments have been needed to devise intelligent inspections for impalpable damages as necessary tools. When many inspection tasks are investigated, very few public datasets are available to recognize the intelligent inspection of precision instruments. On the other hand, precision instruments can be applied to a wide variety of damages. YOLO V3 was proposed based on acquiring and processing the image appearances, which deal with the surface inspection of the precision instrument in this study. The image dataset of the impalpable damage was initially established. More specifically, the YOLO V3 detection network is leveraged to roughly calculate the location of the damage appearances and identify the damage type. Afterward, the designed level set algorithm was employed to obtain more accurate damage locations in the image block utilizing the characteristics of different types of damages. Finally, a quantitative analysis was performed employing the refined detection results. A deep architecture that can intelligently conduct damage detection was proposed. Besides, the proposed method exhibited a strong tolerance for unknown types of damages and excellent flexibility and adaptability. Extensive experimental results demonstrated that the proposed method can alleviate the shortcomings of the traditional inspection methods. Thus, it provided technical guidance for the applications of the intelligent orbital inspection of the robots. The proposed method with a more precise and noninvasive inspection technique remarkably accelerates the inspection time of impalpable damages on surfaces.
SSL/TLS Encrypted Traffic Application Layer Protocol and Service Classification

Network traffic protocols and service classification are the foundations of network quality of service (QoS) and security technologies, which have attracted increasing attention in recent years. At present, encryption technologies, such as SSL/TLS, are widely used in network transmission, so traditional traffic classification technologies cannot analyze encrypted packet payload. This paper first proposes a two-level application layer protocol classification model that combines packets and sessions information to address this problem. The first level extracts packet features, such as entropy and randomness of ciphertext, and then classifies the protocol. The second level regards the session as a unit and determines the final classification results by voting on the results of the first level. Many application layer protocols only correspond to one specific service, but HTTPS is used for many services. For the HTTPS service classification problem, we combine session features and packet features and establish a service identification model based on CNN-LSTM. We construct a dataset in a laboratory environment. The experimental results show that the proposed method achieves 99.679% and 96.27% accuracy in SSL/TLS application layer protocol classification and HTTPS service classification, respectively. Thus, the service classification model performs better than other existing methods.
MKGN: A Multi-Dimensional Knowledge Enhanced Graph Network for Multi-Hop Question and Answering

Machine reading comprehension with multi-hop reasoning always suffers from reasoning path breaking due to the lack of world knowledge, which always results in wrong answer detection. In this paper, we analyze what knowledge the previous work lacks, e.g., dependency relations and commonsense. Based on our analysis, we propose a Multi-dimensional Knowledge enhanced Graph Network, named MKGN, which exploits specific knowledge to repair the knowledge gap in reasoning process. Specifically, our approach incorporates not only entities and dependency relations through various graph neural networks, but also commonsense knowledge by a bidirectional attention mechanism, which aims to enhance representations of both question and contexts. Besides, to make the most of multi-dimensional knowledge, we investigate two kinds of fusion architectures, i.e., in the sequential and parallel manner. Experimental results on HotpotQA dataset demonstrate the effectiveness of our approach and verify that using multi-dimensional knowledge, especially dependency relations and commonsense, can indeed improve the reasoning process and contribute to correct answer detection.
Opportunities and threats of digital transformation management education

The changes taking place in the world presuppose the inevitable entry of business, science, education into the digital space. For such a serious step, Russian organisations in any ﬁeld of activity need a comprehensive readiness of the entire system, supported not only by the enthusiasm of employees and the ability of managers to experiment. Without thoughtful high-quality preparation of all system elements and connections, digital transformation can turn out to be an untimely costly measure that hinders the development of organisations. The article analyses the barriers to the economic digitalisation and management education that affect the digital transition success. Education is viewed as a leading factor in socio-economic development, therefore, the demand for digitalisation is addressed primarily to education. The article raises the problem of the lack of a convincing scientiﬁc psychological and pedagogical concept of digital learning, which could be used by subjects of education as a basic one. The results of a students’ survey of management specialties of the university showed: the professional competencies of modern managers necessary for activities in the era of the digital revolution have to be expanded, the question arises of the reasonable disciplines inclusion that correspond to real needs in the educational programs of the management university.
Biological Image Segmentation Using Region-Scalable Fitting Energy with B-Spline Level Set Implementation and Watershed

Objectives Image segmentation plays an important role in the analysis and understanding of the cellular process.However, this task becomes difficult when there is intensity inhomogeneity between regions, and it is more challenging in the presence of the noise and clustered cells.The goal of the paper is propose an image segmentation framework that tackles the above cited problems.Material and methods A new method composed of two steps is proposed: First, segment the image using B-spline level set with Region-Scalable Fitting (RSF) active contour model, second apply the Watershed algorithm based on new object markers to refine the segmentation and separate clustered cells.The major contributions of the paper are: 1) Use of a continuous formulation of the level set in the B-spline basis, 2) Develop the energy function and its derivative by introducing the RSF model to deal with intensity inhomogeneity, 3) For the Watershed, propose a relevant choice of markers that considers the cell properties.Results Experimental results are performed on widely used synthetic images, in addition to simulated and real biological images, without and with additive noise.They attest the high quality of segmentation of the proposed method in terms of quantitative and qualitative evaluation.Conclusion The proposed method is able to tackle many difficulties at the same time: overlapped intensities, noise, different cell sizes and clustered cells.It provides an efficient tool for image segmentation especially biological ones.
Stacking Ensemble of Convolutional Neural Networks for Sign Language Recognition

Sign language involves the use of various hand gestures and motions for conveying information. It is predominantly used by the deaf community, while very rarely understood by others. Machine learning and deep learning models have been widely reported for automatic recognition of sign language from wearable and vision-based sensor data. In this paper, data from three multi-modality wireless sensors placed on the dominant hand of the signer is processed using a homogeneous ensemble of convolutional neural networks (CNN) to recognize the performed sign. 50 signs from the Indian sign language are performed by 5 subjects multiple times to develop the sensor signal database. The features extracted for the multi-modality data are given as input to randomly initialized CNN models to enable extraction of high-level features. The class probabilities at the output of the CNN models are stacked and interpreted by a meta-learner to determine the performed sign. Experimental results are presented to analyze the effect of including varying number of CNN models in the ensemble, as well to determine the best meta-learner for classification of the performed signs using the stacked CNN ensemble. The best average classification accuracy of 87.6&#x0025; is obtained for a stacked ensemble of 25 CNN models with multi-layer perceptron as the meta-learner, which is significantly better as compared to that of individual CNN models, were a maximum accuracy of just 79.5&#x0025; was obtained.
Application of Moire Profilometry in Three-Dimensional Profile Reconstruction of Key Parts in Railway

Moire profilometry (MP) is one of the three-dimensional (3D) topography measurement methods of structured light, which has the advantages of single frame reconstruction, high speed, no contact and high precision, and is suitable for dynamic measurement scenes. In this article, the digital MP is applied to the wheel tread measurement, the virtual grating is generated by computer to project to the object surface, the moire fringe pattern of the object is obtained by filtering, and finally the continuous phase pattern is obtained by phase unwrapping. The 3D shape reconstruction of the wheel tread is realized, and a new method of wheel tread detection is provided. At the same time, in this paper, the results of using different filters are compared, and the significance of the frequency domain filtering to MP is proved. It is necessary to choose a suitable filtering method according to different environmental conditions. At present, digital MP can be used in industrial static detection, and it can be extended to the dynamic detection of rolling wheels in the future, so as to improve the detection efficiency and realize the automatic detection of trains.
Design of engineering drawing recognition system based on Yolo V4

In order to realize the detection of devices in engineering drawings, this paper proposes an engineering drawing recognition method based on YOLOv4 algorithm, which is applied to the detection of component targets under circuit diagrams. Traditional drawing auditing is mostly done by manual means. With the development of information technology, applying image technology and pattern recognition to drawing auditing is a hot spot for the application of artificial intelligence in drawing auditing. YOLO is a simple and uncomplicated algorithm that can make predictions based on the whole picture information and learn the picture features more generally. The components (capacitors, resistors, inductors and other basic components) in the circuit diagram are identified, and their number and location are indicated.
Proposed model to decentralized storage of educational data for privacy preservation using Blockchain

Satoshi Nakamoto created the first decentralizedblock chain in 2008. Block chain is a relatively new and widely accepted kind of decentralized, distributed computing. The advantages of block chain technology are improved security and privacy, decreased expenses, enhanced speed, and consistency. Block chain has applications in a variety of areas, including IT, healthcare, finance, and educational institutions. For data storage, many organizations now adopt centralised cloud systems. Educational Institutes stored and shared their digitally stored data with other institutes or Vendors for various purposes like issuing certificate, so protecting the privacy while sharing the data is a problem of researchers. People create a large number of files every day and wish to save them somewhere. However, they usually rely on unsecure centralized systems. Block chain is a decentralized system that keeps files and data safe. The suggested model will eliminate all cloud storage limits. It uses a block chain and an interplanetary file system to operate. It divides educational data files into blocks and then encrypts each block with hash keysOur goal is to come up with a privacy-preserving method for sharing data via block chain. A model along with Blockchain principles and features to serve the requester and data owner authentication has been developed in a two-way system