Reviewerly/exHarmony huggingface dataset:
An Efficient Approach for Phishing Detection using Machine Learning

The increasing number of phishing attacks is one of the major concerns of security researchers today. The traditional tools for identifying phishing websites use signature-based approaches which are not able to detect newly created phishing webpages. Thus, researchers are coming up with machine learning-based methods which are capable to detect and classify the phishing webpages with high accuracy if a large and variety of features are considered. However, building a classification model using a large number of features takes time which hampers the timely detection of phishing websites. Therefore, it is pertinent to shortlist a set of features using a feature selection method so that high-performance classification models can be developed in less time. In this chapter, we study the role of feature selection methods in detecting phishing webpages efficiently and effectively. A comparative analysis of machine learning algorithms is carried out on the basis of their performance without and with feature selection. Experiments are conducted on a phishing dataset with 30 features containing 4898 phishing and 6157 benign webpages. Several machine learning algorithms are used for obtaining the best results. Afterward, a feature selection method is employed to improve the efficiency of the models. The best accuracy is obtained by random forest both before and after feature selection with a significant improvement in model building time. The experiments demonstrate that employing a feature selection method along with machine learning algorithms can improve the build time of classification models for phishing detection without compromising their accuracy.
Image compression-encryption method based on two-dimensional sparse recovery and chaotic system

In this paper, we propose an image compression-encryption method based on two-dimensional (2D) sparse representation and chaotic system. In the first step of this method, the input image is extended in a transform domain to obtain a sparse representation. To achieve better performance of image compression by 2D sparse recovery, the sparse representation is scrambled via a chaotic confusion. This step helps the satisfaction of the uniqueness conditions for sparse recovery, and the security level of encryption is increased. Then, two orthogonal measurement matrices are generated using the chaotic time series. The singular value decomposition is used to compress the sparse scrambled representation in two dimensions. Finally, to reduce the correlation between adjacent pixels in the compressed matrix, and obtain a uniform distribution in the encrypted image, a compressed scrambling matrix based on chaotic confusion is used. Then, XOR operation is applied for final encryption. In the decryption process, to improve the compression efficiency, the total variation constraint is added to the 2D sparse recovery problem based on the smoothed norm. The simulation results demonstrate the satisfying performance of the proposed method for different compression ratios. Security analysis describes the effectiveness of the proposed encryption approach.
A Fuzzy Evaluation Model of College English Teaching Quality Based on Analytic Hierarchy Process

The evaluation of English teaching quality in colleges mainly faces two problems: the evaluation index system (EIS) is incomplete, and the evaluation model cannot easily handle complex fuzzy indices. To solve the problems, this paper explores deep into the fuzzy evaluation of college English teaching quality, and establishes a fuzzy evaluation model of college English teaching quality based on analytic hierarchy process (AHP). Firstly, theoretical analysis was combined with model calculation to create an improved multi-angle EIS for college English teaching quality. From both quantitative and qualitative analyses, a college English teaching quality evaluation model was constructed to handle fuzzy indices. Implementation results show that the proposed EIS and fuzzy evaluation model can effectively assess the quality of English teaching in colleges. The research results greatly promote the quality improvement of college English teaching.
Clip thinking of generation Z: methods of developing students’ creative potential

The article is devoted to actual problem of clip thinking of generation Z. The goal of research is to determine the most effective methods of working with clip thinking taking into account the peculiarities of information perception by modern students. It covers phenomenon of clip thinking, its characteristics and origins. Positive and negative features of clip thinking are highlighted, as well as effective methods of university educational process applying having the trend of clip-based consumption of info by students. The authors conclude that clip thinking is a natural process: it is necessary to work competently with this phenomenon of generation Z. Its shown, that vast selection of digital tools is changing lifestyle of young generation, but multitasking, responsiveness, high degree of adaptability characterize modern students. The authors concluded that its necessary to form students' analytical and conceptual thinking in combination with clip thinking. Its not worth changing students or fight against clip thinking as well as to act according to old methodology of educational process. Modern students need in interesting cases of thinking efficiency, multitasking that requires active action. Specificity of case method application of teaching was substantiated empirically (including observation, experiment, survey), it develops creativity, increases the level of analytical and critical thinking, allows to work with a large amount of information, helps to study proposed business situations, analyzes data and make the respective decision. Its important to practice educational and search methods in a dialogue mode of learning process, construct situations, interactivity, mobility, frequent changes, multitasking. Clip thinking should be managed competently, by using its positive characteristics and preparing effective studying programs engaging relevant online and offline technologies.
Perspective on COVID-19 Pandemic Factors Impacting Organizational Leadership

Employees are important assets of an organization. Therefore, the employee engagement in teamwork is extremely vital for long-term organizational development. Good managers need excellent leadership skills. However, as the COVID-19 pandemic rapidly spreads around the world, many countries implemented strategies to avoid infection, such as working from home, isolation of infected people from others, and 14 days of self-quarantine. These strategies impact the trust, communal relationships, and social exchange relationships among organizational employees. However, communal and social exchange relationships are necessary for organizational leadership, and they are considered as the basis of social networks. The trust, communal relationships, social exchange relationships, and leadership in an organization are an interesting issue, particularly in the COVID-19 time, since the role of leaders is very crucial for maintaining organizational sustainability. The main objective of this study is to investigate the impact of COVID-19 on leadership in organizations based on trust, communal relationships, and social exchange relationships. The study employed correlation analysis to explore the interrelationships between variables. The 220 samples collected consisted of basic, middle, and high managers of organizations. The findings show that COVID-19 impacted organizational leadership. COVID-19 (F1) and (F2) factors integrated with each variable: (1) trust, (2) communal relationships, and (3) social exchange relationships created a stronger relationship between trust and leadership, communal relationships and leadership, and also social exchange relationship and leadership in organizations. On the contrary, Covid-19 (F3) factors integrated with each variable: (1) trust, (2) communal relationships, and (3) social exchange relationships decreased a relationship between trust and leadership, communal relationships and leadership, and also social exchange relationships and leadership in organizations. These results can help CEOs in organizations to perceive what factors have a positive impact or a negative impact on leadership in organizations in order to improve their leadership skills, even post COVID-19. The managerial implications are that (1) leaders need good communication skills to share true information with empathy and optimism and (2) leaders need to be thoughtful and capable to handle change in uncertain situations ethically.
Statistical mechanical models for quantum codes with correlated noise

We give a broad generalisation of the mapping, originally due to Dennis, Kitaev, Landahl and Preskill, from quantum error correcting codes to statistical mechanical models. We show how the mapping can be extended to arbitrary stabiliser or subsystem codes subject to correlated Pauli noise models, including models of fault tolerance. This mapping connects the error correction threshold of the quantum code to a phase transition in the statistical mechanical model. Thus, any existing method for finding phase transitions, such as Monte Carlo simulations, can be applied to approximate the threshold of any such code, without having to perform optimal decoding. By way of example, we numerically study the threshold of the surface code under mildly correlated bit-flip noise, showing that noise with bunching correlations causes the threshold to drop to $p_{\textrm{corr}}=10.04(6)\%$, from its known iid value of $p_{\text{iid}}=10.917(3)\%$. Complementing this, we show that the mapping also allows us to utilise any algorithm which can calculate/approximate partition functions of classical statistical mechanical models to perform optimal/approximately optimal decoding. Specifically, for 2D codes subject to locally correlated noise, we give a linear-time tensor network-based algorithm for approximate optimal decoding which extends the MPS decoder of Bravyi, Suchara and Vargo.
BIM for Smart Hospital Management during COVID-19 Using MCDM

In context of the recent COVID-19 pandemic, smart hospitals’ contributions to pre-medical, remote diagnosis, and social distancing has been further vetted. Smart hospital management evolves with new technology and knowledge management, which needs an evaluation system to prioritize its associated criteria and sub-criteria. The global effect of the COVID-19 pandemic further necessitates a comprehensive research of smart hospital management. This paper will utilize Analytical Hierarchy Process (AHP) within Multiple Criteria Decision Making (MCDM) to establish a smart hospital evaluation system with evaluation criteria and sub-criteria, which were then further prioritized and mapped to BIM-related alternatives to inform asset information management (AIM) practices. This context of this study included the expert opinions of six professionals in the smart hospital field and collected 113 responses from hospital-related personnel. The results indicated that functionalities connected to end users are critical, in particular IoT’s Network Core Functionalities, AI’s Deep Learning and CPS’s Special Network Technologies. Furthermore, BIM’s capability to contribute to the lifecycle management of assets can relate and contribute to the asset-intensive physical criteria of smart hospitals, in particular IoT, service technology innovations and their sub-criteria.
AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results

Orthogonal frequency division multiplexing (OFDM) has been widely applied in many wireless communi- cation systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this paper, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to OTA environments and promising for future communication systems. At the end of this paper, we discuss potential challenges and future research inspired by our initial study in this paper.
A comparison of various classical optimizers for a variational quantum linear solver

Variational Hybrid Quantum Classical Algorithms (VHQCAs) are a class of quantum algorithms intended to run on noisy intermediate-scale quantum (NISQ) devices. These algorithms employ a parameterized quantum circuit (ansatz) and a quantum-classical feedback loop. A classical device is used to optimize the parameters in order to minimize a cost function that can be computed far more efficiently on a quantum device. The cost function is constructed such that finding the ansatz parameters that minimize its value, solves some problem of interest. We focus specifically on the Variational Quantum Linear Solver (VQLS), and examine the effect of several gradient-free and gradient-based classical optimizers on performance. We focus on both the average rate of convergence of the classical optimizers studied, as well as the distribution of their average termination cost values, and how these are affected by noise. Our work demonstrates that realistic noise levels on NISQ devices present a challenge to the optimization process. All classical optimizers appear to be very negatively affected by the presence of realistic noise. If noise levels are significantly improved, there may be a good reason for preferring gradient-based methods in the future, which performed better than the gradient-free methods with the only shot-noise present. The gradient-free optimizers, Simultaneous Perturbation Stochastic Approximation (SPSA) and Powell's method, and the gradient-based optimizers, AMSGrad and BFGS performed the best in the noisy simulation, and appear to be less affected by noise than the rest of the methods. SPSA appears to be the best performing method. COBYLA, Nelder-Mead and Conjugate-Gradient methods appear to be the most heavily affected by noise, with even slight noise levels significantly impacting their performance.
Distributed state and fault estimation over sensor networks with probabilistic quantizations: The dynamic event-triggered case

In this paper, the distributed state and fault estimation problem is discussed for a class of nonlinear time-varying systems with probabilistic quantizations and dynamic event-triggered mechanisms. To reduce resource consumption, a dynamic event-triggered strategy is exploited to schedule the data communication among sensor nodes. In addition, the measurement signals are quantized and then transmitted through the network, where the probabilistic quantizations are taken into consideration. Attention is focused on the problem of constructing a distributed estimator such that both the plant state and the fault signal are estimated simultaneously. By using the matrix difference equation method, certain upper bound on the estimation error covariance is first guaranteed and then minimized at each iteration by properly designing the estimator parameters. Subsequently, for the proposed distributed estimation algorithm, the estimator performance is analyzed and a sufficient condition is established to guarantee that the estimation error is exponentially bounded in mean-square sense. Finally, an illustrative example is provided to verify the usefulness of the developed estimation scheme.
Quantum compiling by deep reinforcement learning

Abstract The general problem of quantum compiling is to approximate any unitary transformation that describes the quantum computation as a sequence of elements selected from a finite base of universal quantum gates. The Solovay-Kitaev theorem guarantees the existence of such an approximating sequence. Though, the solutions to the quantum compiling problem suffer from a tradeoff between the length of the sequences, the precompilation time, and the execution time. Traditional approaches are time-consuming, unsuitable to be employed during computation. Here, we propose a deep reinforcement learning method as an alternative strategy, which requires a single precompilation procedure to learn a general strategy to approximate single-qubit unitaries. We show that this approach reduces the overall execution time, improving the tradeoff between the length of the sequence and execution time, potentially allowing real-time operations.
DOLCE: A descriptive ontology for linguistic and cognitive engineering1

DOLCE, the first top-level (foundational) ontology to be axiomatized, has remained stable for twenty years and today is broadly used in a variety of domains. DOLCE is inspired by cognitive and linguistic considerations and aims to model a commonsense view of reality, like the one human beings exploit in everyday life in areas as diverse as socio-technical systems, manufacturing, financial transactions and cultural heritage. DOLCE clearly lists the ontological choices it is based upon, relies on philosophical principles, is richly formalized, and is built according to well-established ontological methodologies, e.g. OntoClean. Because of these features, it has inspired most of the existing top-level ontologies and has been used to develop or improve standards and public domain resources (e.g. CIDOC CRM, DBpedia and WordNet). Being a foundational ontology, DOLCE is not directly concerned with domain knowledge. Its purpose is to provide the general categories and relations needed to give a coherent view of reality, to integrate domain knowledge, and to mediate across domains. In these 20 years DOLCE has shown that applied ontologies can be stable and that interoperability across reference and domain ontologies is a reality. This paper briefly introduces the ontology and shows how to use it on a few modeling cases.
Long Short-Term Relation Transformer With Global Gating for Video Captioning

Video captioning aims to generate a natural language sentence to describe the main content of a video. Since there are multiple objects in videos, taking full exploration of the spatial and temporal relationships among them is crucial for this task. The previous methods wrap the detected objects as input sequences, and leverage vanilla self-attention or graph neural network to reason about visual relations. This cannot make full use of the spatial and temporal nature of a video, and suffers from the problems of redundant connections, over-smoothing, and relation ambiguity. In order to address the above problems, in this paper we construct a long short-term graph (LSTG) that simultaneously captures short-term spatial semantic relations and long-term transformation dependencies. Further, to perform relational reasoning over the LSTG, we design a global gated graph reasoning module (G3RM), which introduces a global gating based on global context to control information propagation between objects and alleviate relation ambiguity. Finally, by introducing G3RM into Transformer instead of self-attention, we propose the long short-term relation transformer (LSRT) to fully mine objects' relations for caption generation. Experiments on MSVD and MSR-VTT datasets show that the LSRT achieves superior performance compared with state-of-the-art methods. The visualization results indicate that our method alleviates problem of over-smoothing and strengthens the ability of relational reasoning.
Global-and-Local Collaborative Learning for Co-Salient Object Detection

The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effectively extract interimage correspondence is crucial for the CoSOD task. In this article, we propose a global-and-local collaborative learning (GLNet) architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture the comprehensive interimage corresponding relationship among different images from the global and local perspectives. First, we treat different images as different time slices and use 3-D convolution to integrate all intrafeatures intuitively, which can more fully extract the global group semantics. Second, we design a pairwise correlation transformation (PCT) to explore similarity correspondence between pairwise images and combine the multiple local pairwise correspondences to generate the local interimage relationship. Third, the interimage relationships of the GCM and LCM are integrated through a global-and-local correspondence aggregation (GLA) module to explore more comprehensive interimage collaboration cues. Finally, the intra and inter features are adaptively integrated by an intra-and-inter weighting fusion (AEWF) module to learn co-saliency features and predict the co-saliency map. The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets, demonstrating that our model trained on a small dataset (about 3k images) still outperforms 11 state-of-the-art competitors trained on some large datasets (about 8k–200k images).
Measurement-Induced Power-Law Negativity in an Open Monitored Quantum Circuit

Generic many-body systems coupled to an environment lose their quantum entanglement due to decoherence and evolve to a mixed state with only classical correlations. Here, we show that measurements can stabilize quantum entanglement within open quantum systems. Specifically, in random unitary circuits with dephasing at the boundary, we find both numerically and analytically that projective measurements performed at a small nonvanishing rate results in a steady state with an $L^{1/3}$ power-law scaling entanglement negativity within the system. Using an analytical mapping to a statistical mechanics model of directed polymers in a random environment, we show that the power-law negativity scaling can be understood as Kardar-Parisi-Zhang fluctuations due to the random measurement locations. Further increasing the measurement rate leads to a phase transition into an area-law negativity phase, which is of the same universality as the entanglement transition in monitored random circuits without decoherence.
An Empirical Assessment of Global COVID-19 Contact Tracing Applications

The rapid spread of COVID-19 has made manual contact tracing difficult.Thus, various public health authorities have experimented with automatic contact tracing using mobile applications (or "apps").These apps, however, have raised security and privacy concerns.In this paper, we propose an automated security and privacy assessment tool-COVIDGUARDIAN-which combines identification and analysis of Personal Identification Information (PII), static program analysis and data flow analysis, to determine security and privacy weaknesses.Furthermore, in light of our findings, we undertake a user study to investigate concerns regarding contact tracing apps.We hope that COVIDGUARDIAN, and the issues raised through responsible disclosure to vendors, can contribute to the safe deployment of mobile contact tracing.As part of this, we offer concrete guidelines, and highlight gaps between user requirements and app performance.