Reviewerly/exHarmony huggingface dataset:
An online real-time face recognition system for police purposes

In general, face recognition algorithms are often utilized in order to identify or authenticate a human through a camera feed. However, face recognition algorithms must adapt to real-life uncertainties and noisy environments. This work targets upon extending the existing work of facial recognition algorithms by proposing an online platform that can be used from the police forces towards effective real-time human recognition. The approach on facial recognition is to use and extend the Haar Cascade algorithm for real-time purposes, which is widely considered one for the most efficient and used algorithms for that cause. As for the criminal identification, it will made possible through image pattern recognition between the provided criminal’s image and snapshots of identified faces from the livestream feed. The platform will include a live feed section with different options for video filters, enabling the user to select the best filter, depending on relevant situation of the physical surroundings for better recognition.
A checklist for the evaluation of software process line approaches

A Software Process Line (SPrL) can help organisations to construct bespoke software development processes for specific project situations by reusing core assets. However, as there are diverse approaches for SPrL Engineering (SPrLE), this necessitates proper assistance to organisations in selecting the SPrL approach best suited to their needs. This paper aims to identify an evaluation checklist that can be used for evaluating SPrLs. The checklist was constructed in five stages: first, relevant aspects for managing process variability in the context of SPrLs were identified; based on these, research questions were then formed in the second stage. In the third stage, to answer the research questions, a literature review was conducted that focused on analysing 39 primary studies. In the fourth stage, the checklist was built by synthesising the literature results. In the fifth stage, the checklist was applied to two SPrL approaches as a proof of concept. The checklist includes seven main aspects, including the modelling language used, the type of the approach based on the number of artefacts produced, the language constructs provided for variability modelling, the process perspectives covered, the tool used for supporting the SPrL approach, the variability-specific features provided to support process variability throughout the SPrL lifecycle, and the empirical evaluation conducted to evaluate the approach. The checklist can be used by organisations to compare SPrLs and then select the most suitable SPrL approach; furthermore, it can be used by researchers to propose novel SPrL approaches that consider important aspects for variability management throughout the SPrL lifecycle. Although we have provided an example of the use of the checklist to compare SPrLs, an empirical evaluation of the checklist is required to get feedback from the organisations regarding the strengths and weaknesses of the checklist.
Defeating Misclassification Attacks Against Transfer Learning

Transfer learning is prevalent as a technique to efficiently generate new models (Student models) based on the knowledge transferred from a pre-trained model (Teacher model). However, Teacher models are often publicly available for sharing and reuse, which inevitably introduces vulnerability to trigger severe attacks against transfer learning systems. In this article, we take a first step towards mitigating one of the most advanced misclassification attacks in transfer learning. We design a distilled <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">differentiator</i> via activation-based network pruning to enervate the attack transferability while retaining accuracy. We adopt an ensemble structure from variant differentiators to improve the defence robustness. To avoid the bloated ensemble size during inference, we propose a two-phase defence, in which inference from the Student model is first performed to narrow down the candidate differentiators to be assembled, and later only a small, fixed number of them can be chosen to validate clean or reject adversarial inputs effectively. Our comprehensive evaluations on both large and small image recognition tasks confirm that the Student models with our defence of only 5 differentiators are immune to over 90% of the adversarial inputs with an accuracy loss of less than 10%. Our comparison also demonstrates that our design outperforms prior problematic defences.
Observability and its impact on differential bias for clinical prediction models

Electronic health records have incomplete capture of patient outcomes. We consider the case when observability is differential across a predictor. Including such a predictor (sensitive variable) can lead to algorithmic bias, potentially exacerbating health inequities.We define bias for a clinical prediction model (CPM) as the difference between the true and estimated risk, and differential bias as bias that differs across a sensitive variable. We illustrate the genesis of differential bias via a 2-stage process, where conditional on having the outcome of interest, the outcome is differentially observed. We use simulations and a real-data example to demonstrate the possible impact of including a sensitive variable in a CPM.If there is differential observability based on a sensitive variable, including it in a CPM can induce differential bias. However, if the sensitive variable impacts the outcome but not observability, it is better to include it. When a sensitive variable impacts both observability and the outcome no simple recommendation can be provided. We show that one cannot use observed data to detect differential bias.Our study furthers the literature on observability, showing that differential observability can lead to algorithmic bias. This highlights the importance of considering whether to include sensitive variables in CPMs.Including a sensitive variable in a CPM depends on whether it truly affects the outcome or just the observability of the outcome. Since this cannot be distinguished with observed data, observability is an implicit assumption of CPMs.
What is the price for joining securely?

Protection of personal data has been raised to be among the top requirements of modern systems. At the same time, it is now frequent that the owner of the data and the owner of the computing infrastructure are two entities with limited trust between them (e. g., volunteer computing or the hybrid-cloud). Recently, trusted execution environments (TEEs) became a viable solution to ensure the security of systems in such environments. However, the performance of relational operators in TEEs remains an open problem. We conduct a comprehensive experimental study to identify the main bottlenecks and challenges when executing relational equi-joins in TEEs. For this, we introduce TEEbench, a framework for unified benchmarking of relational operators in TEEs, and use it for conducting our experimental evaluation. In a nutshell, we perform the following experimental analysis for eight core join algorithms: off-the-shelf performance; the performance implications of data sealing and obliviousness; sensitivity and scalability. The results show that all eight join algorithms significantly suffer from different performance bottlenecks in TEEs. They can be up to three orders of magnitude slower in TEEs than on plain CPUs. Our study also indicates that existing join algorithms need a complete, hardware-aware redesign to be efficient in TEEs, and that, in secure query plans, managing TEE features is equally important to join selection.
Machine learning and control theory

We survey in this chapter the connections between Machine Learning and Control Theory. Control Theory provide useful concepts and tools for Machine Learning. Conversely Machine Learning can be used to solve large control problems. In the first part of the paper, we develop the connections between reinforcement learning and Markov Decision Processes, which are discrete time control problems. In the second part, we review the concept of supervised learning and the relation with static optimization. Deep learning which extends supervised learning, can be viewed as a control problem. In the third part, we present the links between stochastic gradient descent and mean field theory. Conversely, in the fourth and fifth parts, we review machine learning approaches to stochastic control problems, and focus on the deterministic case, to explain, more easily, the numerical algorithms.
SWMQ: Secure wildcard pattern matching with query

Secure wildcard pattern matching (WPM) allows the pattern holder to obtain the matched positions without revealing pattern and text information about both parties. However, standard secure WPM may have limitations in practical applications, as users may prefer to have access to the actual data of the match in many scenarios. Fortunately, secure wildcard pattern matching with query (SWMQ) extends standard secure WPM by allowing the pattern holder to obtain the matched positions and the actual data, which has important applications in many scenarios, such as electronic healthcare and gene matching. This also motivates the research of SWMQ in this paper. In this study, we focus on the efficient construction of SWMQ in the semihonest adversary setting. First, we propose two new primitives, hereafter referred to as shared wildcard pattern matching (Sh-WPM) and choice-sharing oblivious transfer (CSOT). Furthermore, we propose an SWMQ protocol via Shared WPM and CSOT. In addition, we evaluate the performance of SWMQ. More specifically, the running time in local area network and wide area network settings is less than 0.4 and 2 s, respectively, when the text length is 2 16 ${2}^{16}$ and the pattern length is 2 12 ${2}^{12}$ . In fact, our evaluation results suggest that SWMQ is not only more broadly functional, but also comparable in efficiency to state-of-the-art approaches.
Memory-Saving and High-Speed Privacy Amplification Algorithm Using LFSR-Based Hash Function for Key Generation

Privacy amplification is an indispensable procedure for key generation in the quantum key distribution system and the physical layer key distribution system. In this paper, we propose a high-speed privacy amplification algorithm that saves hardware memory and improves the key randomness performance. Based on optimizing the structure of the Toeplitz matrix generated by a linear feedback shift register, the core of our algorithm is a block-iterative structure hash function that is used to generate a secure key of arbitrary length. The proposed algorithm adopts multiple small Toeplitz matrices to compress the negotiation key for convenient implementation. The negotiated key is equally divided into multiple small blocks, and the multiplication operation of the negotiated key with the Toeplitz matrix is converted into a modular addition operation through an accumulator. The analysis results demonstrate that the algorithm has the advantages of saving memory and running quickly. In addition, the NIST randomness test and avalanche effect test on the key sequences indicate that the proposed algorithm has a favorable performance.
Information batteries

Coping with the intermittency of renewable power is a fundamental challenge, with load shifting and grid-scale storage as key responses. We propose Information Batteries (IB), in which energy is stored in the form of information---specifically, the results of completed computational tasks. Information Batteries thus provide storage through speculative load shifting, anticipating computation that will be performed in the future. We take a distributed systems perspective, and evaluate the extent to which an IB storage system can be made practical through augmentation of compiler toolchains, key-value stores, and other important elements in modern hyper-scale compute. In particular, we implement one specific IB prototype by augmenting the Rust compiler to enable transparent function-level precomputation and caching. We evaluate the overheads this imposes, along with macro-level job prediction and power prediction. We also evaluate the space of operation for an IB system, to identify the best case efficiency of any IB system for a given power and compute regime.
Quantization-aware sampling set selection for bandlimited graph signals

Abstract We consider a scenario in which nodes of a graph are sampled for bandlimited graph signals which are uniformly quantized with optimal rate and original signals are reconstructed from the quantized signal values residing on the nodes in the sampling set. We seek to construct the best sampling set in a greedy manner that minimizes the average reconstruction error. We manipulate the reconstruction error by using the QR factorization and derive an analytic result stating that the next minimizing node can be iteratively selected by finding the one that minimizes the geometric mean of the row vectors of the inverse upper triangular matrix $$\mathbf {R}^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:math> in the QR factorization. We also compare the complexity of the proposed algorithm with different sampling methods and evaluate the performance of the proposed algorithm by experiments for various graphs, showing the superiority to the existing sampling methods when quantization is involved.
Sentiment Analysis

Sentiment analysis attempts to measure peoples’ feelings such as joy, sadness, anger, and similar, usually expressed in a written text. Since the decision-making process is largely based on knowledge about opinions of others, the sentiment analysis has become central in all areas of business dealing with customers and in many social sciences. While traditionally sentiment analysis was conducted manually by trained raters, the growth of the Big Data, and especially the product reviews in the social media prompted development of the computer sentiment analysis algorithms. Two main approaches to sentiment analysis are lexicon based and machine learning. The lexicon-based approach is based on a pre-existing dictionary of words together with their sentiment orientation and strength; it is fast in implementation yet returns modest performance. In a machine learning approach, a sample of documents from the same domain is manually classified according to the sentiment expressed and then used to train and validate a machine learning algorithm. While this approach requires significant initial expenses in terms of human time and computer resources, it also typically results in a better-performing algorithm. Whichever approach is used, an accurate validation of model performance using appropriate indicators is a must.
Estimating the Coefficients of a System of Ordinary Differential Equations Based on Inaccurate Observations

In this paper, we solve the problem of estimating the parameters of a system of ordinary differential equations from observations on a short interval of argument values. By analogy with linear regression analysis, a sufficiently large number of observations are selected on this segment and the values of the functions on the right side of the system and the values of the derivatives are estimated. According to the obtained estimates, unknown parameters are determined, using the differential equations system. The consistency of the estimates obtained in this way is proved with an increase in the number of observations over a short period of argument values. Here, an algorithm for estimating parameters acts as a system. The error of the obtained estimate is an indicator of its quality. A sequence of inaccurate measurements is a random process. The method of linear regression analysis applied to an almost linear regression function is used as an optimization procedure.
Attention‐Based Machine Vision Models and Techniques for Solar Wind Speed Forecasting Using Solar EUV Images

Abstract Extreme ultraviolet images taken by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory make it possible to use deep vision techniques to forecast solar wind speed—a difficult, high‐impact, and unsolved problem. At a 4 day time horizon, this study uses attention‐based models and a set of methodological improvements to deliver an 11.1% lower RMSE and a 17.4% higher prediction correlation compared to the previous work testing on the period from 2010 to 2018. Our analysis shows that attention‐based models combined with our pipeline consistently outperform convolutional alternatives. Our study shows a large performance improvement by using a 30 min as opposed to a daily sampling frequency. Our model has learned relationships between coronal holes' characteristics and the speed of their associated high‐speed streams, agreeing with empirical results. Our study finds a strong dependence of our best model on the phase of the solar cycle, with the best performance occurring in the declining phase.
Influence of probability distribution initialization methods on the performance of advanced arithmetic optimization algorithm with application to unrelated parallel machine scheduling problem

Abstract The article investigates the influence of several initialization methods on the performance of the newly proposed advanced arithmetic optimization algorithm, also known as the nAOA. The initialization conditions considered include population sizes, diversity of the population, and the number of iterations. We used 23 different probability distributions with different diversity to test the influence of initialization schemes on the convergence and accuracy of the nAOA. The benchmark classical test functions and the functions defined in the CEC 2020 suite having different properties and modalities were used to compare the possible effects of the initialization methods. The numerical results showed that the nAOA is sensitive to population size and the number of iterations, which must be large for optimal performance. Friedman's and correlation tests were used to gain useful insight into the results we obtained. Findings showed that the performance of the nAOA is not particularly sensitive to the different initialization schemes used for most test functions. The implication is that nAOA is relatively stable and robust. However, the variant of beta distribution, b(3,2), recorded the lowest mean rank for most functions. Hence, it is the best performing initialization distribution scheme for the test problems considered. We can also observe from the experimental results that 47.83% and 100% of the classical and CEC2020 test functions used in this article show significant differences for different initialization methods, respectively. We also investigated the applicability of the nAOA to solve the unrelated parallel machine scheduling problem with sequence‐dependent setup times by using the best‐identified set of initialization methods. The performance of the nAOA is evaluated by comparing its solution to seven other well‐known metaheuristic algorithms, and the results reveal that the nAOA can provide promising results in solving all problem instances of the problem under study using the best initialization schemes.
The Current and Evolving Landscape of Requirements Engineering in Practice

Four exploratory surveys were conducted in 2003, 2008, 2013, and 2020 on the state of practice of requirements engineering. This article creates a panoramic view of the changes to the landscape of industrial practices by analyzing the responses from the four surveys.
Parallel Non-negative Matrix Tri-Factorization for Text Data Co-clustering

As a novel paradigm for data mining and dimensionality reduction, Non-negative Matrix Tri-Factorization (NMTF) has attracted much attention due to its notable performance and elegant mathematical derivation, and it has been applied to a plethora of real-world applications, such as text data co-clustering. However, the existing NMTF-based methods usually involve intensive matrix multiplications, which exhibits a major limitation of high computational complexity. With the explosion at both the size and the feature dimension of texts, there is a growing need to develop a parallel and scalable NMTF-based algorithm for text data co-clustering. To this end, we first show in this paper how to theoretically derive the original optimization problem of NMTF by introducing the Lagrangian multipliers. Then, we propose to solve the Lagrange dual objective function in parallel through an efficient distributed implementation. Extensive experiments on five benchmark corpora validate the effectiveness, efficiency, and scalability of our distributed parallel update algorithm for an NMTF-based text data co-clustering method.
Research and Design of Intelligent Speech Equipment in Smart English Language Lab Based on Internet of Things Technology

With the wide popularization of Internet of Things technology, the design and implementation of intelligent speech equipment has attracted more and more researchers' attention. Speech recognition is one of the core technologies to control intelligent mechanical equipment. In this paper, the English speech recognition function is realized by the Hidden Markov model, which strengthens the processing ability of speech signal, and the intelligent speech equipment is applied to the smart language lab. The test results show that the designed system has comparatively accurate recognition ability to English speech and relatively strong control ability to intelligent device. Users are liberated from the traditional interaction mode and can complete the operation of intelligent language lab by non-contact way of voice signal in a non-fixed place without interrupting the user's current behavior.
On Prosody Modeling for ASR+TTS Based Voice Conversion

In voice conversion (VC), an approach showing promising results in the latest voice conversion challenge (VCC) 2020 is to first use an automatic speech recognition (ASR) model to transcribe the source speech into the underlying linguistic contents; these are then used as input by a text-to-speech (TTS) system to generate the converted speech. Such a paradigm, referred to as ASR+TTS, overlooks the modeling of prosody, which plays an important role in speech naturalness and conversion similarity. Although some researchers have considered transferring prosodic clues from the source speech, there arises a speaker mismatch during training and conversion. To address this issue, in this work, we propose to directly predict prosody from the linguistic representation in a target-speaker-dependent manner, referred to as target text prediction (TTP). We evaluate both methods on the VCC2020 benchmark and consider different linguistic representations. The results demonstrate the effectiveness of TTP in both objective and subjective evaluations.