AI-enabled biometrics in recruiting: Insights from marketers for managers
بیومتریک با استفاده از هوش مصنوعی در جذب نیرو: بینش بازاریابان برای مدیران-2020
Both researchers and practitioners are only in the early stages of examining and understanding the ap- plication of artificial intelligence (AI) in terms of marketing themselves as employers or the open jobs they have. AI has the potential to significantly affect how firms reach, identify, attract, and select human capital. We examine factors that can influence a job candidate’s intent to complete AI-enabled recruiting processes, especially the influence of a firm’s use of biometrics in that process. The results show that (1) social media can increase technology use motivation and AI-enabled recruiting with (2) trendiness as a first stage boundary condition and (3) biometrics as a second stage boundary condition. We contribute to marketing knowledge by identifying that for managers wanting to influence job seekers’ technology use motivation in order to increase their participation in AI-enabled recruiting; they must focus on the indirect effects of trendiness, biometrics, and their social media usage.
Keywords: AI-enabled | Biometrics | Technology use motivation | Trendiness | Recruiting | Social media usage
BAMHealthCloud: A biometric authentication and data management system for healthcare data in cloud
BAMHealthCloud: یک سیستم احراز هویت بیومتریک و سیستم مدیریت داده برای داده های مراقبت های بهداشتی در ابر-2020
Advancements in the healthcare industry have given rise to the security threat to the ever growing emedical data. The healthcare data management system records patient’s data in different formats such as text, numeric, pictures and videos leading to data which is big and unstructured. Also, hospitals may have several branches in different geographical locations. Sometimes, for research purposes, there is a need to integrate patients’ health data stored at different locations. In view of this, a cloud-based healthcare management system can be an effective solution for efficient health care data management. But the major concern of cloud-based healthcare system is the security aspect. It includes theft of identity, tax fraudulence, bank fraud, insurance frauds, medical frauds and defamation of high profile patients. Hence, a secure data access and retrieval is needed in order to provide security of critical medical records in healthcare management system. Biometric based authentication mechanism is suitable in this scenario since it overcomes the limitations of token theft and forgetting passwords in the conventional token idpassword mechanism used for providing security. It also has high accuracy rate for secure data access and retrieval. In the present paper, a cloud-based system for management of healthcare data BAMHealthCloud is proposed, which ensures the security of e-medical data access through a behavioral biometric signature-based authentication. Training of the signature samples for authentication purpose has been performed in parallel on Hadoop MapReduce framework using Resilient Backpropagation neural network. From rigorous experiments, it can be concluded that it achieves a speedup of 9 times, Equal error rate (EER) of 0.12, the sensitivity of 0.98 and specificity of 0.95. Performance comparison of the system with other state-of-art-algorithms shows that the proposed system preforms better than the existing systems in literature
Keywords: Biometric | Authentication | Healthcare | Cloud | Healthcare cloud | Hadoop
Bio-natural gas industry in China: Current status and development
صنعت گاز زیست طبیعی در چین: وضعیت فعلی و توسعه-2020
China has promoted its biogas industry for a long time and begun to support bio-natural gas (BNG; also known as biogas upgrading or biogas-to-biomethane) projects for the first time in 2015 at the central government level. This study presented a comprehensive overview of the BNG industry in China, including its status quo, national strategic planning, upgrading technologies, investment cost, potential, and opportunities and challenges. A total of 65 BNG demonstration projects were approved by the central government between 2015 and 2017, and 197 projects are expected to have been built by 2020 as part of the 13th Five-Year Plan (2016–2020). BNG is an emerging industry, and its development provides several opportunities, including a huge natural gas demand, national policy incentives, integrated agriculture, and reduced carbon emissions. The challenges and barriers to such developments include the high upgrading cost, fluctuating natural gas prices, unsound market access mechanism for biomethane and biofertilizer, scarce competition-oriented feedstock resources, incomplete standard system and cross-sectorial management, imperfect policy and subsidy mechanism, and lagging technology and equipment. China can learn from other developed countries in four ways. First, the country must enhance the cost effectiveness of its subsidies. Then, China must highlight the operations of its BNG industry and evaluate its performance; prioritize digestate management in the planning stage; improve its project service level and reinforce research and development. The findings of this work provide a valuable reference for other developing countries that intend to address energy shortage issues and integrate waste management into their regional planning.
Keywords: Biogas | Bio-natural gas | Biomethane | Potential | China
Automated face recognition in forensic science: Review and perspectives
تشخیص خودکار جهره در علم پزشکی قانونی: بررسی و چشم انداز-2020
With recent technological innovations, the multiplication of captured images of criminal events has brought the comparison of faces to the forefront of the judicial scene. Forensic face recognition has become a ubiquitous tool to guide investigations, gather intelligence and provide evidence in court. However, its reliability in court still suffers from the lack of methodological standardization and empirical validation, notably whenusingautomatic systems,whichcompare imagesandgenerate a matchingscore.Although the use of such systems increases drastically, it still requires more empirical studies based on adequate forensic data (surveillance footage and identity documents) to become a reliable method to present evidence in court. In this paper, we propose a review of the literature leading to the establishment of a methodological workflow to develop a score-based likelihood-ratio computation model using a Bayesian framework. Different approaches are proposed in the literature regarding the within-source and between-sources variability distributions modelling. Depending on the data available, the modelling approach can be specific to the case or generic. Generic approaches allow interpreting the score without any available images of the suspect. Such model is henceforth harder to defend in court because the results are not anchored to the suspect. To make sure the computed score-based LR is robust, we must assess the performance of the model with two main characteristics: the discriminating power and the calibration state of the model. We hence describe the main metrics (Equal Error Rate and Cost of log likelihood-ratio), and graphical representations (Tippett plots, Detection Error Trade-off plot and Empirical Cross-Entropy plot) used to quantify and visualize the performance characteristics.
Keywords: Facial comparison | Biometric system | Likelihood ratio | Score | Calibration
TAPSTROKE: A novel intelligent authentication system using tap frequencies
TAPSTROKE: رویکرد سیستم احراز هویت هوشمند با استفاده از فرکانسهای آهسته-2019
Emerging security requirements lead to new validation protocols to be implemented to recent authen- tication systems by employing biometric traits instead of regular passwords. If an additional security is required in authentication phase, keystroke recognition and classification systems and related interfaces are very promising for collecting and classifying biometric traits. These systems generally operate in time- domain; however, the conventional time-domain solutions could be inadequate if a touchscreen is so small to enter any kind of alphanumeric passwords or a password consists of one single character like a tap to the screen. Therefore, we propose a novel frequency-based authentication system, TAPSTROKE, as a prospective protocol for small touchscreens and an alternative authentication methodology for existing devices. We firstly analyzed the binary train signals formed by tap passwords consisting of taps instead of alphanumeric digits by the regular (STFT) and modified short time Fourier transformations (mSTFT). The unique biometric feature extracted from a tap signal is the frequency-time localization achieved by the spectrograms which are generated by these transformations. The touch signals, generated from the same tap-password, create significantly different spectrograms for predetermined window sizes. Finally, we conducted several experiments to distinguish future attempts by one-class support vector machines (SVM) with a simple linear kernel for Hamming and Blackman window functions. The experiments are greatly encouraging that we achieved 1.40%–2.12% and 2.01%–3.21% equal error rates (EER) with mSTFT; while with regular STFT the classifiers produced quite higher EER, 7.49%–11.95% and 6.93%–10.12%, with Hamming and Blackman window functions, separately. The whole methodology, as an expert system for protecting the users from fraud attacks sheds light on new era of authentication systems for future smart gears and watches.
Keywords: Tapstroke | Keystroke | Authentication | Biometrics | Frequency | Short time Fourier transformation | Support vector machines
Preserving privacy in speaker and speech characterisation
حفظ حریم خصوصی در شخصیت پردازی و گفتار-2019
Speech recordings are a rich source of personal, sensitive data that can be used to support a plethora of diverse applications, from health profiling to biometric recognition. It is therefore essential that speech recordings are adequately protected so that they cannot be misused. Such protection, in the form of privacy-preserving technologies, is required to ensure that: (i) the biometric profiles of a given individual (e.g., across different biometric service operators) are unlinkable; (ii) leaked, encrypted biometric information is irreversible, and that (iii) biometric references are renewable. Whereas many privacy-preserving technologies have been developed for other biometric characteristics, very few solutions have been proposed to protect privacy in the case of speech signals. Despite privacy preservation this is now being mandated by recent European and international data protection regulations. With the aim of fostering progress and collaboration between researchers in the speech, biometrics and applied cryptography communities, this survey article provides an introduction to the field, starting with a legal perspective on privacy preservation in the case of speech data. It then establishes the requirements for effective privacy preservation, reviews generic cryptography-based solutions, followed by specific techniques that are applicable to speaker characterisation (biometric applications) and speech characterisation (non-biometric applications). Glancing at non-biometrics, methods are presented to avoid function creep, preventing the exploitation of biometric information, e.g., to single out an identity in speech-assisted health care vspeaker characterisation. In promoting harmonised research, the article also outlines common, empirical evaluation metrics for the assessment of privacy-preserving technologies for speech data.
Keywords: Data privacy | Voice biometrics | Standardisation | Cryptography | Legislation
Based blockchain-PSO-AES techniques in finger vein biometrics: A novel verification secure framework for patient authentication
روش های مبتنی بر بلاکچین-PSO-AES در بیومتریک رگ های انگشت: یک چارچوب تأیید صحت جدید برای احراز هویت بیمار-2019
The main objective of this study is to propose a novel verification secure framework for patient authentication between an access point (patient enrolment device) and a node database. For this purpose, two stages are used. Firstly, we propose a new hybrid biometric pattern model based on a merge algorithm to combine radio frequency identification and finger vein (FV) biometric features to increase the randomisation and security levels in pattern structure. Secondly, we developed a combination of encryption, blockchain and steganography techniques for the hybrid pattern model. When sending the pattern from an enrolment device (access point) to the node database, this process ensures that the FV biometric verification system remains secure during authentication by meeting the information security standard requirements of confidentiality, integrity and availability. Blockchain is used to achieve data integrity and availability. Particle swarm optimisation steganography and advanced encryption standard techniques are used for confidentiality in a transmission channel. Then, we discussed how the proposed framework can be implemented on a decentralised network architecture, including access point and various databases node without a central point. The proposed framework was evaluated by 106 samples chosen from a dataset that comprises 6000 samples of FV images. Results showed that (1) high-resistance verification framework is protected against spoofing and brute-force attacks; most biometric verification systems are vulnerable to such attacks. (2) The proposed framework had an advantage over the benchmark with a percentage of 55.56% in securing biometric templates during data transmission between the enrolment device and the node database.
Keywords: Finger vein | Blockchain | Cryptography | Steganography | RFID | CIA
Is biometrics the answer to crypto-currency crime?
آیا بیومتریک جواب جرم رمزنگاری ارزی است؟-2019
Crypto-currency – commonly defined as digital assets that use cryptography to secure transactions without the need for a central banking authority – is rising in popularity and being widely adopted across the globe. According to research by the University of Cambridge1, some 3 million people are estimated to be actively trading in crypto-currencies today, and many are already using crypto to pay for items such as hotels, games and even their rent.
Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network
همجوشی امتیاز موازی ECG و اثر انگشت را برای احراز هویت انسان بر اساس شبکه های عصبی کانولوشن-2019
Biometrics have been extensively used in the past decades in various security systems and have been deployed around the world. However, all unimodal biometrics have their own limitations and disadvantages (e.g., fingerprint suffers from spoof attacks). Most of these limitations can be addressed by designing a multimodal biometric system, which deploys over one biometric modality to improve the performance and make the system robust to spoof attacks. In this paper, we proposed a secure multimodal biometric system by fusing electrocardiogram (ECG) and fingerprint based on convolution neural network (CNN). To the best of our knowledge, this is the first study to fuse ECG and fingerprint using CNN for human authentication. The feature extraction for individual modalities are performed using CNN and then biometric templates are generated from these features. After that, we have applied one of the cancelable biometric techniques to protect these templates. In the authentication stage, we proposed a Q-Gaussian multi support vector machine (QG-MSVM) as a classifier to improve the authentication performance. Dataset augmentation is successfully used to increase the authentication performance of the proposed system. Our system is tested on two databases, the PTB database from PhysioNet bank for ECG and LivDet2015 database for the fingerprint. Experimental results show that the proposed multimodal system is efficient, robust and reliable than existing multimodal authentication algorithms. According to the advantages of the proposed system, it can be deployed in real applications
Keywords: Authentication | CNN | ECG | Fingerprint | Multimodal biometrics | MSVM
An elliptic curve cryptography based mutual authentication scheme for smart grid communications using biometric approach
رمزنگاری منحنی بیضوی مبتنی بر طرح احراز هویت متقابل برای ارتباطات شبکه هوشمند با استفاده از روش بیومتریک-2019
Smart grid (SG) provides a suitable adjustment in the amount of power generation by providing the ability to supervise consumer behavior. SG uses in the smart system to encourage cultural heritage because it is accountable for providing power without any interruption. SG is one of the vital components to authorize smart systems with a lot of smart features to attract visitors to come and visit heritage. In SG, environment security and privacy are the major concern for communications. An authentication protocol provides secure communication between users and service provider for security and privacy purpose. Several authentication protocols are available in the literature. However, they are enabled to known security attacks easily or they are not computationally efficient for SG communication. In the present paper, we design an ECC-based mutual authentication protocol for smart grid communication using biometric approach. The present framework satisfy various security features such as replay attack, user anonymity, man in the middle attack, key freshness, message authentication, session key agreement, impersonation attack, non-traceability and non-transferability. Further, the proposed protocol takes much less communication and computation costs compared with other existing protocols in SG environment. Therefore, our scheme is convenient for practical application in SG communication
Keywords: Biometric | Elliptic curve cryptography | Fuzzy extractor | Mutual authentication | Smart grid security and privacy