Reviewerly/exHarmony huggingface dataset:
Security-Level Improvement of IoT-Based Systems Using Biometric Features

The Internet of Things (IoT) is reported as a main research topic in the current decade. It will be possible to connect smart devices to each other using IoT, a platform such as the Internet. However, the expansion and intrusion of such a large network raises some new security issues and risks related to the disclosure of user confidential information where these devices are subject to hacker threats and intrusions. Traditional security systems were password based. In this paper, after reviewing the actions taken in this regard, the improvement level of biometric security compared with traditional password-based methods will be proven in section three using the Markov model. By considering the results of the evaluation, the probability of occurrence of security problems is decreased by 90.71% by applying biometric features. Then, multi-layer security architecture with biometric features and coding systems is suggested to increase security. In the first layer, the fingerprint recognition algorithm is dependent on the module, and the U.are.U 5100 module provides more security than others. In the second layer, the Hash mechanism of the MD5 algorithm is, on average, 63.21% more efficient. By determining the properties of the first two architectural layers and ultimately for the IoT application layer, empirical methods and hardware platforms for the Internet of things are used. Concerning the simulation results, the suggested mechanism enhances the system security by 120.38% on average, which is 106.23, 110.45, and 144.46% of relative improvement compared with IoT sensors, controller layer mechanisms, and application layer mechanisms, respectively.
Multi-Teacher Distillation With Single Model for Neural Machine Translation

Knowledge distillation (KD) is an effective strategy for neural machine translation (NMT) to improve the performance of a student model. Usually, the teacher can guide the student to be better by distilling the soft label or data knowledge from the teacher itself. However, the data diversity and teacher knowledge are limited with only one teacher model. Though a natural solution is to adopt multiple randomized teacher models, one big shortcoming is that the model parameters and training costs are largely increased with the number of teacher models. In this work, we explore to mimic multiple teacher distillation from the sub-network space and permuted variants of one single teacher model. Specifically, we train a teacher by multiple sub-network extraction paradigms: sub-layer reordering, layer-drop, and dropout variants. In doing so, one teacher model can provide multiple outputs variants and causes neither additional parameters nor much extra training cost. Experiments on <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$8$</tex></formula> IWSLT datasets: (IWSLT14 En <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\leftrightarrow$</tex></formula> De, En <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\leftrightarrow$</tex></formula> Es, and IWSLT17 En <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\leftrightarrow$</tex></formula> Fr, En <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\leftrightarrow$</tex></formula> Zh) and the large WMT14 EN <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\to$</tex></formula> DE translation tasks show that our method even achieves nearly comparable performance with multiple teacher models with different randomized parameters, both word-level, and sequence-level knowledge distillation. Our code is available at GitHub\footnote{https://github.com/dropreg/RLD}
Observability Criteron Guidance for Passive Towed Array Sonar Tracking

Nonlinear sonar contact tracking always presents a significant observability challenge. Observability as it relates to sonar tracking is the metric used to determine if a state estimate is uniquely identifiable given a history of measurements. Poor observability is troublesome to all nonlinear tracking filters to varying degrees. Observability criteria are used to assess the degree of observability and many observability criteria require, in some way, an observer maneuver. But when observability criteria are evaluated with real data they are evaluated in the presence of “micromaneuvers” due to unmodeled forces. The issue associated with these micromaneuvers is that they satisfy, mathematically, observability criteria but hold little practical value for obtaining trustworthy state estimates. This article chooses a recent observability criterion and examines its utility for passive sonar tracking with a towed array. The analysis performed provides guidance to all similar observability criteria when applied to the passive sonar tracking with a towed array and implies such criteria will be most valuable in applications with minimal navigation perturbations.
A Unified Framework for Non-universal SNARKs

We propose a general framework for non-universal SNARKs. It contains (1) knowledge-sound and non-black-box any-simulation-extractable (ASE), (2) zero-knowledge and subversion-zero knowledge SNARKs for the well-known QAP, SAP, QSP, and QSP constraint languages that all by design have relatively simple security proofs. The knowledge-sound zero-knowledge SNARK is similar to Groth's SNARK from EUROCRYPT 2016, except having fewer trapdoors, while the ASE subversion-zero knowledge SNARK relies on few additional conditions. We prove security in a weaker, more realistic version of the algebraic group model. We characterize SAP, SSP, and QSP in terms of QAP; this allows one to use a SNARK for QAP directly for other languages. Our results allow us to construct a family of SNARKs for different languages and with different security properties following the same proof template. Some of the new SNARKs are more efficient than prior ones. In other cases, the new SNARKs cover gaps in the landscape, e.g., there was no previous ASE or Sub-ZK SNARK for SSP or QSP.
Discursive Toolkits of Anti-Muslim Disinformation on Twitter

In this article, we investigate the socio-technical ecology of Twitter, including the technological affordances of the platform and the user-generated discursive strategies used to create and circulate anti-Muslim disinformation online. During the first wave of Covid-19, right-wing followers claimed that Muslims were spreading the virus to perform Jihad. We analyzed a sample of 7000 tweets using Critical Discourse Analysis to examine how the online disinformation accusing Muslims in India was initiated and sustained. We identify three critical discourse strategies used on Twitter to spread and sustain the anti-Muslim (dis)information: (1) creating mediatized hate solidarities, (2) appropriating instruments of legitimacy, and (3) practicing Internet Hindu vigilantism. Each strategy consists of a subset of discursive toolkits, highlighting the central routes of discursive engagement to produce disinformation online. We argue that understanding how the technical affordances of Social Networking Sites are leveraged in quotidian online practices to produce and sustain the phenomenon of online disinformation will prove to be a novel contribution to the field of disinformation studies and Internet research.
Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction

The nonstationary time series is generated in various natural and man-made systems, of which the prediction is vital for advanced control and management. The neural networks have been explored in the time series prediction, but the problem remains in modeling the data's nonstationary and nonlinear features. Referring to the time series feature and network property, a novel network is designed with dynamic optimization of the model structure. Firstly, the echo state network (ESN) is introduced into the broad learning system (BLS). The broad echo state network (BESN) can increase the training efficiency with the incremental learning algorithm by removing the error backpropagation. Secondly, an optimization algorithm is proposed to reduce the redundant information in the training process of BESN units. The number of neurons in BESN with a fixed step size is pruned according to the contribution degree. Finally, the improved network is applied in the different datasets. The tests in the time series of natural and man-made systems prove that the proposed network performs better on the nonstationary time series prediction than the typical methods, including the ESN, BLS, and recurrent neural network.
Practical Blockchain-Based Financial Assets Tokenization

Financial institutions own balanced portfolios with different types of assets, resulting in a low-risk investment. At the same time, assets cannot be broken down into smaller parts with comparable risk for resale owing to regulators' restrictions caused by a lack of auditability. Blockchain overcomes this issue with smart contracts. By now, the way to tokenize financial assets into a commodity is known. In this paper, we provide technical details for tokenization. Namely, an artificial dataset from Forex data is constructed for demonstration purposes; two tokenization algorithms are implemented and applied to the dataset; ERC1155-based smart contract is implemented and applied to the algorithms' output to perform sold-out and secondary market steps. The source code is openly available.
Making Private Function Evaluation Safer, Faster, and Simpler

In the problem of two-party private function evaluation (PFE), one party $$\mathsf {P}_{\mathsf {A}}$$ holds a private function f and (optionally) a private input $$x_A$$ , while the other party $$\mathsf {P}_{\mathsf {B}}$$ possesses a private input $$x_B$$ . Their goal is to evaluate f on $$x_A$$ and $$x_B$$ , and one or both parties may obtain the evaluation result $$f(x_A, x_B)$$ while no other information beyond $$f(x_A, x_B)$$ is revealed. In this paper, we revisit the two-party PFE problem and provide several enhancements. We propose the first constant-round actively secure PFE protocol with linear complexity. Based on this result, we further provide the first constant-round publicly verifiable covertly (PVC) secure PFE protocol with linear complexity to gain better efficiency. For instance, when the deterrence factor is $$\epsilon = 1/2$$ , compared to the passively secure protocol, its communication cost is very close and its computation cost is around $$2.6\times $$ . In our constructions, as a by-product, we design a specific protocol for proving that a list of ElGamal ciphertexts is derived from an extended permutation performed on a given list of elements. It should be noted that this protocol greatly improves the previous result and may be of independent interest. In addition, a reusability property is added to our two PFE protocols. Namely, if the same function f is involved in multiple executions of the protocol between $$\mathsf {P}_{\mathsf {A}}$$ and $$\mathsf {P}_{\mathsf {B}}$$ , then the protocol could be executed more efficiently from the second execution. Moreover, we further extend this property to be global, such that it supports multiple executions for the same f in a reusable fashion between $$\mathsf {P}_{\mathsf {A}}$$ and arbitrary parties playing the role of $$\mathsf {P}_{\mathsf {B}}$$ .
API-m-FAMM: A focus area maturity model for API Management

Organizations are increasingly connecting software applications using Application Programming Interfaces (APIs) to share data, services, functionality, and even complete business processes. However, the creation and management of APIs is non-trivial. Aspects such as traffic management, community engagement, documentation, and version management are often rushed afterthoughts. In this research, we present and evaluate a focus area maturity model for API Management (API-m-FAMM). A focus area maturity model can be used to establish the maturity level of an organization in a specific functional domain described through a number of areas. The API-m-FAMM addresses the areas Lifecycle Management, Security, Performance, Observability, Community, and Commercial. The model is constructed using established methods for the design of a focus area maturity model. It is grounded in literature and practice, and was developed and evaluated through a systematic literature Review, eleven expert interviews, and five case studies at software producing organizations. The model is described in detail, and its application is illustrated by six case studies. The evaluations are reported on, and show that the API-m-FAMM is an efficient tool for aiding organizations in gaining a better understanding of their current implementation of API management practices, and provides them with guidance towards higher levels of maturity. The detailed description of the construction of the API-m-FAMM gives researchers an example to further support the available methodologies, specifically how to combine design science research with these methodologies. Additionally, this study’s unique case study design shows that maturity models can be successfully deployed in practice with minimal involvement of researchers. The focus area maturity model for API Management is maintained on www.maturitymodels.org, allowing practitioners to benefit from its useful insights.
Studying Natural User Interfaces for Smart Video Annotation towards Ubiquitous Environments

Creativity and inspiration for problem-solving are critical skills in a group-based learning environment. Communication procedures have seen continuous adjustments over the years, with increased multimedia elements usage like videos to provide superior audience impact. Annotations are a valuable approach for remembering, reflecting, reasoning, and sharing thoughts on the learning process. However, it is hard to control playback flow and add potential notes during video presentations, such as in a classroom context. Teachers often need to move around the classroom to interact with the students, which leads to situations where they are physically far from the computer. Therefore, we developed a multimodal web video annotation tool that combines a voice interaction module with manual annotation capabilities for more intelligent natural interactions towards ubiquitous environments. We observed current video annotation practices and created a new set of principles to guide our research work. Natural language enables users to express their intended actions while interacting with the web video player for annotation purposes. We have developed a customized set of natural language expressions that map the user speech to specific software operations through studying and integrating new artificial intelligence techniques. Finally, the paper presents positive results gathered from a user study conducted to evaluate our solution.
A Novel Fragile Zero-Watermarking Algorithm for Digital Medical Images

The wireless transmission of patients’ particulars and medical data to a specialised centre after an initial screening at a remote health facility may cause potential threats to patients’ data privacy and integrity. Although watermarking can be used to rectify such risks, it should not degrade the medical data, because any change in the data characteristics may lead to a false diagnosis. Hence, zero watermarking can be helpful in these circumstances. At the same time, the transmitted data must create a warning in case of tampering or a malicious attack. Thus, watermarking should be fragile in nature. Consequently, a novel hybrid approach using fragile zero watermarking is proposed in this study. Visual cryptography and chaotic randomness are major components of the proposed algorithm to avoid any breach of information through an illegitimate attempt. The proposed algorithm is evaluated using two datasets: the Digital Database for Screening Mammography and the Mini Mammographic Image Analysis Society database. In addition, a breast cancer detection system using a convolutional neural network is implemented to analyse the diagnosis in case of a malicious attack and after watermark insertion. The experimental results indicate that the proposed algorithm is reliable for privacy protection and data authentication.
A Tacit-Knowledge-Based Requirements Elicitation Model Supporting COVID-19 Context

Effective software requirements elicitation plays a vital role in the success or failure of a project. However, ambiguity in the requirement&#x2019;s statements indicate the presence of a tacit knowledge, which ultimately act as a root cause of critical complications in later stages of software development as user&#x2019;s needs might remain hidden. Additionally, the existence of numerous stakeholders escalates the problem as their perceptions may contrast mainly due to their experiences and roles in a specific application domain. Hence, witlessness of relevant stakeholder(s) and ambiguous requirements cause the compromise for a product quality. Eventually, it paves the way towards the failure of a project. Furthermore, COVID-19 has affected all walks of life, more specifically requirements elicitation process as it heavily depends on human-to-human interaction. Motivated by this, current study aims at identifying the requirements elicitation techniques and challenges through a systematic literature review protocol. Furthermore, we have performed an exploratory study to identify the traditional elicitation techniques that can be used specifically for eliciting the tacit requirements. Additionally, we validate the top 15 critical challenges in a normal and pandemic scenario. To validate the result&#x2019;s authenticity and legitimacy, appropriate statistical tests have been applied on the obtained results. Based on the attained results, it is observed that transfer of tacit knowledge remains a most crucial challenge. To effectively handle the tacit knowledge challenge, we propose a novel conceptual model supporting COVID-19 context. Similarly, we employ expert-validation mechanism for empirically evaluation of the proposed conceptual model. Moreover, the current study provides the guidelines for the practitioners to mitigate the highlighted effects on the requirements elicitation process during current pandemic time. Finally, we believe that proposed conceptual model supports the practitioners in effectively gathering the tacit-knowledge based requirements in the COVID-19 context.
Automatic Counting Red Blood Cells in the Microscopic Images by EndPoints Method and Circular Hough Transform

Many diseases such as anemia and leukemia are detected by counting Red Blood Cells (RBCs or erythrocytes). Generally, there are manual and automatic method for RBCs counting. In the manual method, RBCs counting is performed by a hematologist with the help of special medical equipment. The manual method is tedious, time-consuming and dependent on equipment and hematologist&#x2019;s expert and accuracy. The automatic method is fast, high accuracy and independent of equipment and hematologist. It is performed with the assistance of a microscopic image of the person&#x2019;s blood and a computer system. In this paper, a new method is presented for counting incomplete or cropped RBCs by Circular Hough Transform (CHT) and another method called EndPoints method which will be described in the following. Two parallel tasks are performed on the input image. In the first work, the input image is segmented using thresholding and erosion on the green channel. In the second work, the input image is converted to a grayscale image and edged by the Canny method. Then, the segmented image is subtracted from the edged image and remove White Blood Cells (WBCs or leucocytes) from it. After margin is added, by defining EndPoints set estimated continuation of RBCs will be added to the image. Finally, CHT is applied to the image and it will calculate the number of RBCs. Also, new counting error and counting accuracy metrics are defined for counting problem. The method is evaluated on the fifteen images of the ALL-IDB1 database and achieved 97.14 overall accuracy.
Teaching Quantum Computing to High School Students

Quantum computing is a growing field at the intersection of physics and computer science. The goal of this article is to highlight a successfully trialled quantum computing course for high school students between the ages of 15 and 18 years old. This course was designed to bridge the gap between popular science articles and advanced undergraduate textbooks. Conceptual ideas in the text are reinforced with active learning techniques, such as interactive problem sets and simulation-based labs at various levels. The course is freely available for use and download under the Creative Commons “Attribution-NonCommercial-ShareAlike 4.0 International” license.
An LSTM Acceleration Method Based on Embedded Neural Network Accelerator

With the maturity of neural network technology, chips to accelerate neural network inference are emerging endlessly. Faced with the emerging complex neural network operators (such as LSTM) that are constantly evolving in neural network algorithms, it is unrealistic to modify the hardware design of the neural network inference chip to support the evolving new operators. Therefore, it has important research significance and practical value to make existing hardware support new operators through software. We propose an LSTM acceleration method based on an embedded neural network accelerator. Split the LSTM operator into multiple basic operators supported by the neural network accelerator by software, and optimize it. Finally, the embedded neural network accelerator supports LSTM operators quickly and efficiently. Experimental results show that the execution efficiency of LRCN model deployed on a low-power accelerator is x1.6 and X1.3 higher than that on CPU and GPU, respectively.
Stationary Behavior of Constant Stepsize SGD Type Algorithms

Stochastic approximation (SA) and stochastic gradient descent (SGD) algorithms are work-horses for modern machine learning algorithms. Their constant stepsize variants are preferred in practice due to fast convergence behavior. However, constant stepsize SA algorithms do not converge to the optimal solution, but instead have a stationary distribution, which in general cannot be analytically characterized. In this work, we study the asymptotic behavior of the appropriately scaled stationary distribution, in the limit when the constant stepsize goes to zero. Specifically, we consider the following three settings: (1) SGD algorithm with a smooth and strongly convex objective, (2) linear SA algorithm involving a Hurwitz matrix, and (3) nonlinear SA algorithm involving a contractive operator. When the iterate is scaled by 1/α, where α is the constant stepsize, we show that the limiting scaled stationary distribution is a solution of an implicit equation. Under a uniqueness assumption (which can be removed in certain settings) on this equation, we further characterize the limiting distribution as a Gaussian distribution whose covariance matrix is the unique solution of a suitable Lyapunov equation. For SA algorithms beyond these cases, our numerical experiments suggest that unlike central limit theorem type results: (1) the scaling factor need not be 1/α, and (2) the limiting distribution need not be Gaussian. Based on the numerical study, we come up with a heuristic formula to determine the right scaling factor, and make insightful connection to the Euler-Maruyama discretization scheme for approximating stochastic differential equations.