Reviewerly/exHarmony huggingface dataset:
You shall not pass: Mitigating SQL Injection Attacks on Legacy Web Applications

SQL injection (SQLi) attacks pose a significant threat to the security of web applications. Existing approaches do not support object-oriented programming that renders these approaches unable to protect the real-world web apps such as Wordpress, Joomla, or Drupal against SQLi attacks. We propose a novel hybrid static-dynamic analysis for PHP web applications that limits each PHP function for accessing the database. Our tool, SQLBlock, reduces the attack surface of the vulnerable PHP functions in a web application to a set of query descriptors that demonstrate the benign functionality of the PHP function. We implement SQLBlock as a plugin for MySQL and PHP. Our approach does not require any modification to the web app. We evaluate SQLBlock on 11 SQLi vulnerabilities in Wordpress, Joomla, Drupal, Magento, and their plugins. We demonstrate that SQLBlock successfully prevents all 11 SQLi exploits with negligible performance overhead (i.e., a maximum of 3% on a heavily-loaded web server).
Signatures of many-particle interference

Quantum systems with many constituents give rise to a range of conceptual, analytical and computational challenges, hence, the label 'complex systems'. In the first place, one can think of interactions, described by a many-body Hamiltonian, as the source of such complexity. However, it has gradually become clear that, even in absence of interactions, many-body systems are more than just the sum of their parts. This feature is due to many-body interference. One of the most well-known interference phenomena is the Hong–Ou–Mandel effect, where total destructive interference is observed for a pair of (non-interacting) identical photons. This two-photon interference effect can be generalised to systems of many particles which can be either fermionic or bosonic. The resulting many-particle interference goes beyond quantum statistical effects that are contained in the Bose–Einstein or Fermi–Dirac distributions, and is dynamical in nature. This Tutorial will introduce the mathematical framework for describing systems of identical particles, and explain the notion of indistinguishability. We will then focus our attention on dynamical systems of free particles and formally introduce the concept of many-particle interference. Its impact on many-particle transition probabilities is computationally challenging to evaluate, and it becomes rapidly intractable for systems with large numbers of identical particles. Hence, this Tutorial will build up towards alternative, more efficient methods for observing signatures of many-particle interference. A first type of signatures relies on the detection of a highly sensitive -but also highly fragile- processes of total destructive interference that occurs in interferometers with a high degree of symmetry. A second class of signatures is based on the statistical features that arise when we study the typical behaviour of correlations between a small number of the interferometer's output ports. We will ultimately show how these statistical signatures of many-particle interference lead us to a statistical version of the Hong–Ou–Mandel effect. The work presented in this Tutorial was one of the four shortlisted finalists of the 2018 DPG SAMOP dissertation prize.
Measurement-induced nonlocality based on affinity

Measurement-induced nonlocality (MIN), a quantum correlation measure for the bipartite system, is an indicator of global effects due to locally invariant von Neumann projective measurements. It is well known fact that the correlation measures based on Hilbert-Schmidt norm are not credible measure in capturing nonlocal attributes of a quantum state. In this article, to remedy the local ancilla problem of Hilbert-Schmidt norm based MIN, we propose a new form of MIN-based on affinity. This quantity satisfies all criteria of a bonafide measure of quantum correlation measure. For an arbitrary pure state, it is shown that affinity based MIN equals to other forms of geometric versions of correlation measure. We obtain an upper bound of this measure for m \times n-dimensional arbitrary mixed state. We obtain a closed formula of the proposed version of MIN for 2 \times n dimensional (qubit qudit) mixed state. We apply these results on two-qubit mixed states such as Werner, isotropic and Bell diagonal state. To illustrate the robustness of affinity-based measure against noise, we study the dynamics of MIN under generalized amplitude damping channel.
Enabling Robust State Estimation Through Measurement Error Covariance Adaptation

Accurate platform localization is an integral component of most robotic systems.As these robotic systems become more ubiquitous, it is necessary to develop robust state estimation algorithms that are able to withstand novel and non-cooperative environments.When dealing with novel and non-cooperative environments, little is known a priori about the measurement error uncertainty, thus, there is a requirement that the uncertainty models of the localization algorithm be adaptive.Within this paper, we propose the batch covariance estimation technique, which enables robust state estimation through the iterative adaptation of the measurement uncertainty model.The adaptation of the measurement uncertainty model is granted through non-parametric clustering of the residuals, which enables the characterization of the measurement uncertainty via a Gaussian mixture model.The provided Gaussian mixture model can be utilized within any non-linear least squares optimization algorithm by approximately characterizing each observation with the sufficient statistics of the assigned cluster (i.e., each observation's uncertainty model is updated based upon the assignment provided by the nonparametric clustering algorithm).The proposed algorithm is verified on several GNSS collected data sets, where it is shown that the proposed technique exhibits some advantages when compared to other robust estimation techniques when confronted with degraded data quality.
Machine Learning Techniques for Software Bug Prediction: A Systematic Review

The goal of software bug prediction is to identify the software modules that will have the likelihood to get bugs by using some fundamental project resources before the real testing starts.Due to high cost in correcting the detected bugs, it is advisable to start predicting bugs at the early stage of development instead of at the testing phase.There are many techniques and approaches that can be used to build the prediction models, such as machine learning.This technique is widely used nowadays because it can give accurate results and analysis.Therefore, we decided to perform a review of past literature on software bug prediction and machine learning so that we can understand better about the process of constructing the prediction model.Not only we want to see the machine learning techniques that past researchers used, we also assess the datasets, metrics and performance measures that are used during the development of the models.In this study, we have narrowed down to 31 main studies and six types of machine learning techniques have been identified.Two public datasets are found to be frequently used and object-oriented metrics are the highly chosen metrics for the prediction model.As for the performance measure, both graphical and numerical measures are often used to evaluate the performance of the models.From the results, we conclude that the machine learning technique can predict the bug, but there are not many applications in this area that exist nowadays.There are a few challenges in constructing the prediction model.Thus, more studies need to be carried out so that a well-formed result is obtained.We also provide a recommendation for future research based on the results we got from this study.
Language and Visual Entity Relationship Graph for Agent Navigation

Vision-and-Language Navigation (VLN) requires an agent to navigate in a real-world environment following natural language instructions. From both the textual and visual perspectives, we find that the relationships among the scene, its objects,and directional clues are essential for the agent to interpret complex instructions and correctly perceive the environment. To capture and utilize the relationships, we propose a novel Language and Visual Entity Relationship Graph for modelling the inter-modal relationships between text and vision, and the intra-modal relationships among visual entities. We propose a message passing algorithm for propagating information between language elements and visual entities in the graph, which we then combine to determine the next action to take. Experiments show that by taking advantage of the relationships we are able to improve over state-of-the-art. On the Room-to-Room (R2R) benchmark, our method achieves the new best performance on the test unseen split with success rate weighted by path length (SPL) of 52%. On the Room-for-Room (R4R) dataset, our method significantly improves the previous best from 13% to 34% on the success weighted by normalized dynamic time warping (SDTW). Code is available at: https://github.com/YicongHong/Entity-Graph-VLN.
A Message-Passing Interpretation of Adjoint Logic

We present a system of session types based on adjoint logic which generalize standard binary session types [14].Our system allows us to uniformly capture several new behaviors in the space of asynchronous message-passing communication, including multicast, where a process sends a single message to multiple clients, replicable services, which have multiple clients and replicate themselves on-demand to handle requests from those clients, and cancellation, where a process discards a channel without communicating along it.We provide session fidelity and deadlock-freedom results for this system, from which we then derive a logically justified form of garbage collection.
Investigating quantum approximate optimization algorithms under bang-bang protocols

The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices. We analyze the algorithm as a bang-bang protocol with fixed total time and a randomized greedy optimization scheme. We investigate the performance of bang-bang QAOA on MAX-2-SAT, finding the appearance of phase transitions with respect to the total time. As the total time increases, the optimal bang-bang protocol experiences a number of jumps and plateaus in performance, which match up with an increasing number of switches in the standard QAOA formulation. At large times, it becomes more difficult to find a globally optimal bang-bang protocol and performances suffer. We also investigate the effects of changing the initial conditions of the randomized optimization algorithm and see that better local optima can be found by using an adiabatic initialization.
A Mathematics Education Research Agenda in Latin America Motivated by Coronavirus Pandemic

The pandemic and its disruption of social dynamics, including school dynamics, necessitates a review of both the proposals for education in situations of confinement and the research that must be carried out to account for exceptional educational situations. It is likely that this pandemic is only the first circumstance that has highlighted the need to investigate more decisively areas that have not been previously considered or have been put aside. This document presents the case of three Latin American countries, Mexico, Colombia, and Chile, in relation to educational difficulties generated by the pandemic, with respect to regional actions on the educational problems that have been revealed, and proposes regional research agendas for the Mathematics Education research community.
Cyclic quantum teleportation via GHZ-like state

Following the work of Chen et al. [Quantum Inf. Process. 16, 201 (2017)] and Zhang [Mod. Phys. Lett. A 34, 1950290 (2019)], we propose a scheme for cyclic quantum teleportation (CYQT) in which three participants Alice, Bob and Charlie can teleport three arbitrary single-qubit information states cyclically among themselves by using GHZ-like states. Chen et al. and Zhang proposed schemes for CYQT and bidirectional quantum teleportation (BQT) involving three participants, respectively. In the scheme of Chen et al., the quantum teleportation (QT) can be realized successfully between any two participants without the help of third participants and in Zhang’s scheme, two unknown single-qubit states are teleported bidirectionally between two participants with the help of a third participant. On the other hand, in our proposed scheme, all the three participants are controller as well as sender and receiver. The teleportation processes Alice [Formula: see text] Bob, Bob [Formula: see text] Charlie and Charlie [Formula: see text] Alice are controlled by Charlie, Alice and Bob, respectively, and hence the CYQT could not be realized successfully without the cooperation of all three participants. If any one participant denies to cooperate with other two participants, then the CYQT cannot be realized successfully.
Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting

Cancer is enlisted as the second leading reason for death across the world wherein almost one person out of six dies of cancer. Breast cancer is one of the most common forms of cancer predominant in women having the second highest mortality rate in the world. Various scientific studies have been conducted to combat this disease, and machine learning approaches have been an extremely popular choice. Particle swarm optimization has been identified as one of the most powerful and efficient technique for the diagnosis of breast cancer guiding physicians towards timely and accurate treatment. It is also pertinent to mention that multi-modal prediction methods are used to make decisions depending upon different scenarios and aspects whereas the non-dominating sorting feature is useful to sort different objects based on differing requirements. The main novelty of this work is multi-modal prediction algorithm for breast cancer prediction is proposed. The work encompasses the use of particle swarm optimization, non-dominating sorting and multi-classifier techniques, namely, k-nearest neighbour method, fast decision tree and kernel density estimation. Finally, Bayes’ theorem is implemented for revising the results to achieve optimum accuracy in the breast cancer prediction. The proposed particle swarm optimization and non-domination sorting with classifier technique model helps to select the most significant features relevant to breast cancer predictions. The selected features design the objective of the problem model. The proposed model is implemented on the WBCD and WDBC breast cancer data sets publicly available from the UCI machine learning data repository. The metrics considered are sensitivity, specificity, accuracy and time complexity. The experimental results of the study using measures such as sensitivity, specificity, accuracy and time complexity. The experimental results of the study are evaluated against the state-of-the-art algorithms, namely, genetic algorithm kernel density estimation and particle swarm optimization kernel density estimation wherein the results justify the superiority of the proposed model.
Evaluating the data quality of the Gazepoint GP3 low-cost eye tracker when used independently by study participants

The portability of low-cost eye trackers makes them attractive for research outside of the laboratory. Such research may require independent eye-tracker use. The present work compared the data quality of the Gazepoint GP3 when used independently by research participants with expert eye-tracking users. Twenty participants completed a training and a testing session 1 week apart. At training visits, participants were taught how to set up and use eye-tracking hardware and software and how to complete two tasks: a calibration task to measure accuracy and precision, as well as a visual search task to assess target fixations. At the testing session, participants set up the Gazepoint eye tracker and completed the two tasks without assistance. Participant accuracy and precision and visual search performance were compared to values obtained from two expert eye-tracking users. Additionally, the eye-tracker sampling rate, which is sensitive to factors such as head motion, was assessed in both participants and the expert users. Participant accuracy and precision closely approximated expert user values. Participant target fixations were detected with a 92.5% sensitivity and 76.8% specificity, closely mirroring expert user sensitivity and specificity. The sampling rate distribution was also similar between the participants and expert user (the means of those distributions were 16.99 ± 3.0 ms and 16.43 ± 2.3 ms, respectively). When used independently, data quality obtained from a low-cost, portable eye-tracking setup closely approximated values obtained from an expert user and was adequate enough to be a feasible option for some studies that require independent use by study participants.
RealPi2dDIC: A Low-cost and open-source approach to in situ 2D Digital Image Correlation (DIC) applications

In this current work an open-source 2D Digital Image Correlation (DIC) tool, RealPi2dDIC, is presented to monitor in situ full field deformation and strain responses of structures during loading. This software is coded in Python3 and uses Raspberry Pi as the computation and Pi camera as the imaging device. This low-cost approach to in situ DIC analysis enables its application in a broad field of research and industry problems where 2D strain field tensor is one of the principal interests of study. This code is provided with an interactive user interface (UI) making it user friendly and easy-to-use. Furthermore, RealPi2dDIC offers a simple software architecture which can be adopted, modified and extended to suit users’ purpose.
Analysis on the research progress of library and information science since the new century

Purpose Library science and information science, two subdisciplines of library and information science (LIS), are developed independently but interconnectedly. In this information age, LIS is in a special period of transformation and development, which has caused some changes in both library science and information science. By accurately capturing these changes and analyzing them, the authors can effectively map the development of LIS in the new century, thus providing a reference for the evolution and development of the field. The purposes of this paper are to explore the mainstream research fields and frontiers of library science and information science, respectively, since the new century, and to make a comparative analysis of the two subdisciplines. Design/methodology/approach By using CiteSpace to visualize LIS journals, this study draws knowledge maps of the two subdisciplines of LIS through the co-occurrence descriptors network. Using burst detection algorithm, this study detects words of high frequency variation by investigating the time frequency distribution. Findings The results show that the research focus of library science has experienced a change from traditional to digital library while information science has moved from information to data focus. This study also finds the similarities and differences between mainstream areas of library science and information science. Originality/value This study focuses on the evolution of library science and information science, and explores their mainstream research fields and frontiers in the 21st century. These findings will promote the transformation and development of LIS as well as provide research directions for scholars in the field.
A Novel Optimization Method for Constructing Cryptographically Strong Dynamic S-Boxes

The resistance of S-box-based cryptosystems to linear cryptanalysis is often determined by the nonlinearity (NL) and the linear approximation probability (LAP) of the underlying S-box.Constructing dynamic bijective S-boxes with high nonlinearity is a challenging problem.In this paper, we propose a novel S-box construction method based on the concept of constrained optimization.The proposed method uses a random-restart hill-climbing algorithm to construct randomized S-boxes and maximize the nonlinearity of each Boolean function under bijectivity constraints.The proposed algorithm dramatically reduced the S-box construction time.Compared to recent S-box construction methods, the proposed method strikes a better balance among the three design objectives of dynamic S-boxes, namely, cryptographic strength, dynamicity, and speed of construction.On the average, the proposed method constructs a new dynamic 8×8 S-box with NL=112 every 118 ms, whereas a NL=110 S-box can be generated in 5.3 ms, which makes it suitable for real time applications.The proposed method also constructs 8×8 S-boxes with NL=114, which is among the highest reported in literature.Moreover, we demonstrate the extensibility of the proposed constrained optimization formulation to improve other S-box design criteria.Namely, we propose an algorithm to optimize the LAP of an S-box while preserving its NL and bijectivity.
A Novel Approach for Swarm Robotic Target Searches Based on the DPSO Algorithm

Cooperation between individuals plays a very important role when swarm robots search for targets. In this article, we present a novel approach that is based on the distributed particle swarm optimization (DPSO) algorithm to guide swarm robots to search for targets. Both the communication limit and the communication energy consumption (CEC) of the robots are considered. In the proposed approach, robot representatives are selected to represent all of the robots to transfer data to the base stations. The initial deployment and relocation approaches of the base stations are introduced to shorten the transmission distance of the data and to improve the search performance. In addition, a dynamic swarm division method is proposed to efficiently handle cases in which there is more than one target that must be searched for simultaneously. The effectiveness of the proposed approach is verified by some experiments. Simulation results have demonstrated that the proposed approach performs well against other comparative algorithms in various cases.