Reviewerly/exHarmony huggingface dataset:
Use of personas in Requirements Engineering: A systematic mapping study

Requirements Engineering (RE) is one of the crucial activities in software development that requires a high involvement of humans (i.e., stakeholders). The aim of RE-related tasks is to develop the scope of the target software products to ensure they will fulfil its stakeholder needs. In RE, the requirements engineers have to deeply understand the software stakeholders including their needs, motivations, and goals. Attaining this information directly from stakeholders requires regular interaction which needs considerable effort. The persona, as a user representation, is a useful tool that can reduce effort amount by modelling the software users and being the primary source of information. The aim of this work is to systematically review relevant studies that have investigated the use of personas in RE, the benefits of personas, and challenges during the implementation of personas in RE. We conduct a systematic mapping study (SMS) using a formal protocol based on an established guideline. The systematic search result in a total of 904 publications from six databases. After filtering, we select 78 relevant studies for critical appraisal, analysis, synthesis, and reporting. We identify methods to create and validate personas (mostly qualitative), map the benefits of using personas in RE (to ensure stakeholders' satisfaction, support a human-centric RE, and support requirements engineers' tasks and roles in RE), identify methods used with personas, discover challenges during persona incorporation in RE and their respective mitigation strategies, and recommend potential strategies for unaddressed challenges. We also make recommendations for future research directions. The findings of this SMS will help RE researchers and practitioners better understand the use of personas in RE and highlights key research gaps for future research.
Preventing Discriminatory Decision-making in Evolving Data Streams

Bias in machine learning has rightly received significant attention over the past decade. However, most fair machine learning (fair-ML) works to address bias in decision-making systems has focused solely on the offline setting. Despite the wide prevalence of online systems in the real world, work on identifying and correcting bias in the online setting is severely lacking. The unique challenges of the online environment make addressing bias more difficult than in the offline setting. First, Streaming Machine Learning (SML) algorithms must deal with the constantly evolving real-time data stream. Secondly, they need to adapt to changing data distributions (concept drift) to make accurate predictions on new incoming data. Incorporating fairness constraints into this already intricate task is not straightforward. In this work, we focus on the challenges of achieving fairness in biased data streams while accounting for the presence of concept drift, accessing one sample at a time. We present Fair Sampling over Stream (FS2), a novel fair rebalancing approach capable of being integrated with SML classification algorithms. Furthermore, we devise the first unified performance-fairness metric, Fairness Bonded Utility (FBU), to efficiently evaluate and compare the trade-offs between performance and fairness across various bias mitigation methods. FBU simplifies the comparison of fairness-performance trade-offs of multiple techniques through one unified and intuitive evaluation, allowing model designers to easily choose a technique. Overall, extensive evaluations show our measures surpass those of other fair online techniques previously reported in the literature.
A binary chaotic horse herd optimization algorithm for feature selection

One of the most challenging and common problems in machine learning is the Feature Selection (FS) process, which reduces the dataset size by finding optimal subsets of features. The Horse Herd Optimization Algorithm (HOA) is a new metaheuristic algorithm created by modeling the herd behavior of horses and developed for large scale optimization problems. This paper proposes the binary version of the HOA as a wrapper FS method to solve the FS problem. The proposed algorithm is a binary chaotic horse herd optimization algorithm for feature selection (BCHOAFS). The proposed BCHOAFS is applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features. Classifier algorithms from machine learning algorithms were used to test the accuracy of the reduced subsets. The proposed method was named binary horse herd optimization for feature selection (BHOAFS) before adding chaotic maps; the k-nearest neighbor (k-NN) and Support Vector Machine (SVM) were tested as separate classifiers. It has been seen that k-NN classification accuracy gives better results than SVM. The BHOAFS-kNN method using the k-NN classification was combined with five chaotic maps and named as BCHOAFS-Logistics, BCHOAFS-Piecewise, BCHOAFS-Singer, BCHOAFS-Sinusoidal, BCHOAFS-Tent. The BCHOAFS versions were run on datasets consisting of 18 different sizes and quality datasets (i.e., low scale, medium scale, and large scale) taken from the UCI repository and compared with state-of-the-art algorithms in previous studies. The results prove that the proposed version, especially with the BCHOAFS-Piecewise and the BCHOAFS-Singer chaotic map outperforms or competes with well-known methods. The proof of the proposed approach's statistical significance has been validated using the Friedman Signed Rank test and post hoc Wilcoxon test. The novelty of BCHOAFS is that HOA, which is an optimization algorithm specially designed for large scale data, is the first binary chaotic-based algorithm developed for feature selection problems. It also proposes a new local search strategy called Similarity Measurement Function (SMF). As a result, versions of the proposed algorithm BCHOAFS can be used for the FS problem.
Mobile App Crowdsourced Test Report Consistency Detection via Deep Image-and-Text Fusion Understanding

Crowdsourced testing, as a distinct testing paradigm, has attracted much attention in software testing, especially in mobile application (app) testing field. Compared with in-house testing, crowdsourced testing shows superiority with the diverse testing environments when faced with the mobile testing fragmentation problem. However, crowdsourced testing also encounters the low-quality test report problem caused by unprofessional crowdworkers involved with different expertise. In order to handle the submitted reports of uneven quality, app developers have to distinguish high-quality reports from low-quality ones to help the bug inspection. One kind of typical low-quality test report is inconsistent test reports, which means the textual descriptions are not focusing on the attached bug-occurring screenshots. According to our empirical survey, only 18.07% crowdsourced test reports are consistent. Inconsistent reports cause waste on mobile app testing. To solve the inconsistency problem, we propose ReCoDe to detect the consistency of crowdsourced test reports via deep image-and-text fusion understanding. ReCoDe is a two-stage approach that first classifies the reports based on textual descriptions into different categories according to the bug feature. In the second stage, ReCoDe has a deep understanding of the GUI image features of the app screenshots and then applies different strategies to handle different types of bugs to detect the consistency of the crowdsourced test reports. We conduct an experiment on a dataset with over 22k test reports to evaluate ReCoDe, and the results show the effectiveness of ReCoDe in detecting the consistency of crowdsourced test reports. Besides, a user study is conducted to prove the practical value of ReCoDe in effectively helping app developers improve the efficiency of reviewing the crowdsourced test reports.
Label-Correction Capsule Network for Hierarchical Text Classification

Hierarchical Text Classification (HTC) aims to predict the category of a document in a given label hierarchy. Considering a parent-child relationship among labels at different levels, previous works mainly leverage the parent-level label information to guide the child-level classification and achieve promising results. However, they still suffer from two drawbacks: (1) insufficient for distinguishing similar labels at the same level; (2) fail to consider the error propagation problem caused by the incorrect parent-level predictions. For this reason, we first propose a hierarchical capsule network for the HTC task, due to the ability of capsules to distinguish similar categories. To ease the error propagation problem, we further devise two novel mechanisms in the proposed hierarchical capsule framework, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Label Injection</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Label Re-Routing</i> , to enhance the tolerance of the model to the incorrect parent-level predictions. Experiments on two widely used datasets prove that our model achieves competitive performance. The ablation study further demonstrates the scalability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Label Injection</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Label Re-Routing</i> .
Outlier-Robust Iterative Extended Kalman Filtering

In this paper, we develop OR-IEKF which is a novel outlier-robust iterative extended Kalman filtering (IEKF) framework based on nonlinear regression formulation of update step. A new Kalman-type update step with reweighted prediction covariance and reweighted observation noise covariance are produced under the OR-IEKF framework, which could cut off the large outliers in observations causing by unknown outlier noises. By using various robust cost functions to solve such special nonlinear regression problems, we derive three algorithms. The performances of these new filters are evaluated in a nonlinear system simulation study.
Fast High-Efficiency Photon-Number-Resolving Parallel Superconducting Nanowire Single-Photon Detector

Photon-number-resolving (PNR) single-photon detectors are an enabling technology in many areas, such as photonic quantum computing, nonclassical light-source characterization, and quantum imaging. Here, we demonstrate high-efficiency PNR detectors using a parallel superconducting nanowire single-photon-detector (P-SNSPD) architecture that does not suffer from crosstalk between the pixels and that is free of latching. The behavior of the detector is modeled and used to predict the possible outcomes given a certain number of incoming photons. We apply our model to a four-pixel P-SNSPD with a system detection efficiency of 92.5%. We also demonstrate how this detector allows reconstructing the photon-number statistics of a coherent source of light, which paves the way towards the characterization of the photon statistics of other types of light source using a single detector.
A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data

In the context of healthcare, big data refers to a complex compilation of digital medical data collected from many sources that are difficult to manage with normal technology and software due to its size and complexity. These big data are useful in various aspects of healthcare, such as disease diagnosis, early prevention of diseases, and predicting epidemics. Even though medical big data has many advantages and a lot of potential for revolutionizing healthcare, it also has a lot of drawbacks and problems, of which security and privacy are of the utmost concern, owing to the severity of the complications once the medical data is compromised. On the other hand, it is evident that existing security and privacy safeguards in healthcare organizations are insufficient to protect their massive, big data repositories and ubiquitous environment. Thus, motivated by the synthesizing of the current knowledge pertaining to the security and privacy of medical big data, including the countermeasures, in the study, firstly, we provide a comprehensive review of the security and privacy of medical big data, including countermeasures. Secondly, we propose a novel cloud-enabled hybrid access control framework for securing the medical big data in healthcare organizations, and the result of this research indicates that the proposed access control model can withstand most cyber-attacks, and it is also proven that the proposed framework can be utilized as a primary base to build secure and safe medical big data solutions. Thus, we believe this research would be useful for future researchers to comprehend the knowledge on the security and privacy of medical big data and the development of countermeasures.
Machine learning in computational histopathology: Challenges and opportunities

Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.
Application of artificial neural networks in the classification of classical encryption algorithms

Cryptography, which has a long historical process, has different encryption algorithms. The first step of any cryptanalysis is to try to determine what type of encryption method that ciphertext uses. This cannot be easy in any process because many types of encryption are currently available. This problem can be solved using artificial neural networks. In this study, we developed a model that discriminates the type of Affine, Playfair, and Vigenere encryption algorithms using neural networks. The research software is based on the Python programming language and its TensorFlow library, as well as Keras. This paper presents the current advances in research on the use of artificial neural networks for cypher-type detection. A built-in neural network can correctly classify encrypted texts by about 95%. The article reveals the possibilities of determining the type of encryption based on an artificial neural network. It can be seen that new methods of cryptanalysis can be developed from artificial neural networks.
Federated Learning Over Wireless Networks: Challenges and Solutions

Motivated by ever-increasing computational resources at edge devices and increasing privacy concerns, a new machine learning (ML) framework called federated learning (FL) has been proposed. FL enables user devices, such as mobile and Internet of Things (IoT) devices, to collaboratively train an ML model by only sending the model parameters instead of raw data. FL is considered the key enabling approach for privacy-preserving, distributed ML systems. However, FL requires frequent exchange of learned model updates between multiple user devices and the cloud/edge server, which introduces a significant communication overhead and hence imposes a major challenge in FL over wireless networks that are limited in communication resources. Moreover, FL consumes a considerable amount of energy in the process of transmitting learned model updates, which imposes another challenge in FL over wireless networks that usually include unplugged devices with limited battery resources. Besides, there are still other privacy issues in practical implementations of FL over wireless networks. In this survey, we discuss each of the mentioned challenges and their respective state-of-the-art proposed solutions in an in-depth manner. By illustrating the tradeoff between each of the solutions, we discuss the underlying effect of the wireless network on the performance of FL. Finally, by highlighting the gaps between research and practical implementations, we identify future research directions for engineering FL over wireless networks.
Qubit-Oscillator Concatenated Codes: Decoding Formalism and Code Comparison

Concatenating bosonic error-correcting codes with qubit codes can substantially boost the error-correcting power of the original qubit codes. It is not clear how to concatenate optimally, given there are several bosonic codes and concatenation schemes to choose from, including the recently discovered GKP-stabilizer codes [Phys. Rev. Lett. 125, 080503 (2020)}] that allow protection of a logical bosonic mode from fluctuations of the mode's conjugate variables. We develop efficient maximum-likelihood decoders for and analyze the performance of three different concatenations of codes taken from the following set: qubit stabilizer codes, analog/Gaussian stabilizer codes, GKP codes, and GKP-stabilizer codes. We benchmark decoder performance against additive Gaussian white noise, corroborating our numerics with analytical calculations. We observe that the concatenation involving GKP-stabilizer codes outperforms the more conventional concatenation of a qubit stabilizer code with a GKP code in some cases. We also propose a GKP-stabilizer code that suppresses fluctuations in both conjugate variables without extra quadrature squeezing, and formulate qudit versions of GKP-stabilizer codes.
Utilizing Trusted Lightweight Ciphers to Support Electronic-Commerce Transaction Cryptography

Electronic-commerce (e-commerce) has become a provider of distinctive services to individuals and companies due to the speed and flexibility of transferring orders and completing commercial deals across far and different places. However, due to the increasing attacks on penetrating transaction information or tampering with e-commerce requests, the interest in protecting this information and hiding it from tamperers has become extremely important. In addition, hacking these deals can cause a huge waste of money and resources. Moreover, large numbers of connected and disconnected networks can cause significant disruption to the built-in security measures. In this paper, we propose to design a protocol to protect transaction information based on ElGamal, advanced encryption standard (AES) and Chinese remainder theorem (CRT) techniques. In addition, our protocol ensures providing scalability with high-performance security measures. We combine these algorithms with a robust methodology that supports the balance of performance and security of the proposed protocol. An analysis of our results proves that our protocol is superior to existing security protocols.
A chip-scale polarization-spatial-momentum quantum SWAP gate in silicon nanophotonics

Recent progress in quantum computing and networking has enabled high-performance, large-scale quantum processors by connecting different quantum modules. Optical quantum systems show advantages in both computing and communications, and integrated quantum photonics further increases the level of scaling and complexity. Here we demonstrate an efficient SWAP gate that deterministically swaps a photon’s polarization qubit with its spatial-momentum qubit on a nanofabricated two-level silicon photonics chip containing three cascaded gates. The on-chip SWAP gate is comprehensively characterized by tomographic measurements with high fidelity for both single-qubit and two-qubit operation. The coherence preservation of the SWAP gate process is verified by single-photon and two-photon quantum interference. The coherent reversible conversion of our SWAP gate facilitates examinations of a quantum interconnect between two chip-scale photonic subsystems with different degrees of freedom, now demonstrated by distributing four Bell states between the two chips. We also elucidate the source of decoherence in the SWAP operation in pursuit of near-unity fidelity. Our deterministic SWAP gate in the silicon platform provides a pathway towards integrated quantum information processing for interconnected modular systems. A deterministic single-photon two-qubit SWAP gate between polarization and spatial-momentum is demonstrated on a silicon chip. A two-qubit swapping process fidelity of 94.9% is obtained. The coherence preservation of the SWAP gate process is verified by two-photon interference.
Target-based image encryption via infinite interval chaotic system with Ill-conditioned parameter and 3DBDM

Most image encryption techniques are proposed to be applied to the whole image, which lacks the focus of information value. In this paper, we propose a target-based image encryption scheme. For remotely sensed airport images, we first take the aircraft considered to be sensitive information as the target and detect its positions. And then perform the proposed three-dimensional bit disorder method (3DBDM) and the arbitrary point dynamic diffusion on these regions. Finally, encrypt the entire image through high-speed index-dynamic diffusion to improve efficiency. To obtain a more stable key stream, a new ill-conditioned parameter infinite-interval chaotic system (IPIS) is introduced. The evaluation of IPIS shows that the introduction of ill-conditioned matrix brings higher sensitivity, randomness, and wider chaotic interval, which potentially improves the security of chaotic image encryption. In particular, the encryption method for aircraft regions can be generalized to ordinary images. Various encryption effect tests verify the security and practicability of the proposed algorithm and target-based encryption scheme.
Globally optimal distributed and sequential state fusion filters for multi-sensor systems with correlated noises

The problem concerning the multi-sensor fusion filtering for systems with correlated system and measurement noises is studied in this paper. Each sensor produces a local predictor by using its own measurements and then sends it to a fusion center. In the fusion center, by applying the projection theory and difference technology, the globally optimal distributed state fusion filter without feedback and sequential state fusion filter with recursive estimators of noises are presented in the sense of linear minimum variance by using all received local predictors, respectively. Under the condition that local prediction gain matrices are of full column rank, the proposed distributed and sequential state fusion filters can achieve the same estimation accuracy as the centralized fusion filter, i.e., they both have global optimality. The optimality is strictly proved. The corresponding steady-state fusion filters are also explored. A sufficient condition for the convergence of the proposed fusion filters is given. A target tracking example verifies the effectiveness of the proposed algorithms.
Lightweight Asymmetric Convolutional Distillation Network for Single Image Super-Resolution

Recent single image super-resolution methods based on various complex deep neural networks have achieved remarkable success. However, these methods require a large amount of computational overhead while improving performance, and thus are difficult to apply to mobile devices in real-world scenarios. In this letter, we design an efficient asymmetric convolutional distillation block (ACDB). Especially in this block, introducing an asymmetric convolution block (ACB) and reusing shallow distillation features can effectively improve the performance of the model and reduce the model complexity. Our model achieves efficient performance while maintaining low complexity.>