Reviewerly/exHarmony huggingface dataset:
A Survey of Key Technologies for Constructing Network Covert Channel

In order to protect user privacy or guarantee free access to the Internet, the network covert channel has become a hot research topic. It refers to an information channel in which the messages are covertly transmitted under the network environment. In recent years, many new construction schemes of network covert channels are proposed. But at the same time, network covert channel has also received the attention of censors, leading to many attacks. The network covert channel refers to an information channel in which the messages are covertly transmitted under the network environment. Many users exploit the network covert channel to protect privacy or guarantee free access to the Internet. Previous construction schemes of the network covert channel are based on information steganography, which can be divided into CTCs and CSCs. In recent years, there are some covert channels constructed by changing the transmission network architecture. On the other side, some research work promises that the characteristics of emerging network may better fit the construction of the network covert channel. In addition, the covert channel can also be constructed by changing the transmission network architecture. The proxy and anonymity communication technology implement this construction scheme. In this paper, we divide the key technologies for constructing network covert channels into two aspects: communication content level (based on information steganography) and transmission network level (based on proxy and anonymity communication technology). We give an comprehensively summary about covert channels at each level. We also introduce work for the three new types of network covert channels (covert channels based on streaming media, covert channels based on blockchain, and covert channels based on IPv6). In addition, we present the attacks against the network covert channel, including elimination, limitation, and detection. Finally, the challenge and future research trend in this field are discussed.
Scrutinising COVIDSafe: Frameworks for evaluating digital contact tracing technologies

Digital technologies are being used to combat the coronavirus disease 2019 (COVID-19) pandemic through a variety of methods, including monitoring compliance with quarantine and contact tracing. These uses of technology are said to promote public health outcomes but risk undermining rights to privacy. In this article we focus on the use of digital technologies for contact tracing, such as the COVIDSafe app used in Australia. We explore the kind of framework that might be used for evaluating the design, deployment and governance of such technologies to ensure they operate in a manner that is proportionate to the ends to be achieved. We conclude that, in addition to issues of privacy, any use of contact tracing technology should address important considerations of efficacy, equity and accountability.
TacticToe: Learning to Prove with Tactics

We implement an automated tactical prover TacticToe on top of the HOL4 interactive theorem prover. TacticToe learns from human proofs which mathematical technique is suitable in each proof situation. This knowledge is then used in a Monte Carlo tree search algorithm to explore promising tactic-level proof paths. On a single CPU, with a time limit of 60 s, TacticToe proves 66.4% of the 7164 theorems in HOL4's standard library, whereas E prover with auto-schedule solves 34.5%. The success rate rises to 69.0% by combining the results of TacticToe and E prover.
Measurement of elastic and rotation fields during irreversible deformation using Heaviside-digital image correlation

The recent development of the high resolution and discontinuity-tolerant digital image correlation technique enables the extraction of discontinuities within a displacement field. The technique provides quantitative analysis of discontinuities arising from slip, shear bands, cracks, and grain boundary sliding in a variety of material systems, including polycrystalline metallic materials. The discontinuity-tolerant digital image correlation method can be implemented to retrieve not only quantitative discontinuity analysis but also the local strain and rotation fields that operate near these discontinuities. The present implementation includes high-resolution digital image correlation (HR-DIC) measurements collected in a scanning electron microscope for analysis of both the plastic and elastic fields that develop during deformation of polycrystalline metallic materials. The combination of the discontinuity-tolerant DIC technique with the computation of internal gradients enables extraction of non-localized strain and rotation fields during plastic deformation of a nickel-based superalloy. Therefore the lattice rotation/expansion and plastic localization that occur during deformation can be determined in a single experiment. This method is validated using synthetic images with preset deformation, and experimental measurements using the electron back scatter diffraction (EBSD) technique.
Key Success Factors of e-Learning Implementation in Higher Education

This study aims to analyse the Key Success Factors (KSFs) of e-learning implementation, so that e-learning can work well and provide maximum benefits for learning activities process in higher education. The method used in this research is begin from investigate the determinant factors based on the results of literature studies and survey techniques. To obtain a more in-depth analysis, then the factors are weighted using the Multi-Attribute Utility Theory (MAUT) method. The results showed that the determinant factors that need to be used as the main consideration in the implementation of e-learning at higher education are organizational factors, technology, and human resources. The dominant factor that needs attention in realizing the effectiveness of the implementation of e-learning in higher education is the organizational aspect which is realized in the form of creating a work culture and setting policies that are binding on the academic community to carry out e-learning.
Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery

Urban rivers are often narrow, and general remote sensing data cannot meet the needs of water quality monitoring.In the process of monitoring of river water quality by remote sensing, the spectral and spatial dimension of satellite-borne images cannot be taken into consideration at the same time, making fine pollution monitoring of urban rivers difficult.Transparency is one of the core indicators for evaluating water quality, and hyperspectral remote sensing data are rich in spectral information and can be used for quantitative transparency estimation.The application of unmanned aerial vehicles (UAV)remote sensing effectively makes up for the deficiencies in satellite remote sensing monitoring.Aiming at this problem, this paper proposed the use of the eXtreme Gradient Boosting (XGBoost) regression algorithm for the quantitative inversion of urban river transparency.The spatial resolution of the collected imagery is 18.5 cm, which is suitable for urban rivers that are almost ten meters wide.Compared with five traditional empirical models, integrated algorithms such as gradient regression and random forest get much better results.Moreover, the accuracy of transparency estimation using the XGBoost regression algorithm was significantly improved, and the inversion model R 2 in both study areas reached over 0.97.Finally, the established transparency inversion models were used to generate transparency distribution maps of the two study areas.The results showed that the distribution of the water transparency was consistent with the results of the field monitoring, indicating that it is feasible to use the XGBoost algorithm for the inversion of urban river transparency in UAV-borne hyperspectral imagery.
Modeling Cost Saving and Innovativeness for Blockchain Technology Adoption by Energy Management

In developed nations, the advent of distributed ledger technology is emerging as a new instrument for improving the traditional system in developing nations. Indeed, adopting blockchain technology is a necessary condition for the coming future of organizations. The distributed ledger technology provides better transparency and visibility. This study investigated the features that may influence the behavioral intention of energy experts to implement the distributed ledger technology for the energy management of developing countries. The proposed model is based on the Technology Acceptance Model construct and the diffusion of the innovation construct. Based on a survey of 178 experts working in the energy sector, the proposed model was tested using structural equation modeling. The findings showed that perceived ease of use, perceived usefulness, attitude, and cost saving had a positive and significant impact during the blockchain technology adoption. However, innovativeness showed a positive effect on the perceived ease of use whereas an insignificant impact on the perceived usefulness. The present study offers a holistic model for the implementation of innovative technologies. For the developers, it suggest rising disruptive technology solutions.
Low-Light Image Enhancement via Pair of Complementary Gamma Functions by Fusion

Enhanced images by the traditional gamma correction (GC) method still have low contrast within high illuminance regions. In order to enhance the visibility in dark regions and simultaneously achieve high contrast in bright regions for low-light images, this paper proposes a novel method via a pair of complementary gamma functions (PCGF) by image fusion. We first define PCGF and then show its outstanding potential for low-light image enhancement by some preliminary experimental results. In order to release its performance and verify its effectiveness, we further design a simple enhancement method for low-light images based on it by an elaborately designed fusion strategy. Two input images for fusion are derived from the enhanced image by PCGF and that by proposed sharpening method, respectively. Experiments show that our proposed method can significantly enhance the detail and improve the contrast of low-light image. The qualitative experiment results show that the proposed method is effective and the comparative quantitative assessment shows that it outperforms other state-of-the-art methods.
Same-different conceptualization: a machine vision perspective

The goal of this review is to bring together material from cognitive psychology with recent machine vision studies to identify plausible neural mechanisms for visual same-different discrimination and relational understanding. We highlight how developments in the study of artificial neural networks provide computational evidence implicating attention and working memory in the ascertaining of visual relations, including same-different relations. We review some recent attempts to incorporate these mechanisms into flexible models of visual reasoning. Particular attention is given to recent models jointly trained on visual and linguistic information. These recent systems are promising, but they still fall short of the biological standard in several ways, which we outline in a final section.
THE USE OF DIGITAL LITERATURE IN TEACHING READING NARRATIVE TEXT

This study investigated the use of digital literature in teaching reading narrative text in a State High School in Ciamis. This study is aimed to finding out the teachers’ implementation of using digital literature in teaching reading narrative text and the students’ perception of using digital literature in teaching reading narrative text. Therefore, the writer took one English teacher and one class of eleventh grade students as the purposive sampling. Furthermore, the writer used case study as her research design and conducted the classroom observations to find out how the teacher implemented the digital literature in teaching reading narrative text. The questionnaire was used to figuring the students’ perception of using digital literature in teaching reading narrative text. The finding of the study showed that the teacher implemented well of the use digital literature in teaching and learning process. Besides, there were a lots of students influenced of using digital literature in teaching and learning activity. For example, students felt enjoyable and they became more active in the classroom. It can be concluded that the use of digital literature enabled students to comprehend the narrative text easier. Finally, the writer suggests that the English teacher should implement the digital literature on teaching reading of narrative text during the learning process
Developing an Integrated VR Infrastructure in Architectural Design Education

With the advent of computer technology, Virtual Reality (VR) became an integral part of design studios in architecture education. Researchers have been exploring how VR-enhanced design studios can be assessed from a student-centered perspective. This paper illustrates the role of teaching architectural design for developing a novel and contextual curriculum based on an analysis of student feedback. The background focuses on the development of VR-based architectural design education. The methodology frames two digital design ecosystems which are experimented in four undergraduate courses. With an ecosystem-based approach discussed in this paper, a medium-oriented and a content-oriented curriculum are offered for testing students’ reaction to teaching design in VR. In both ecosystems, students are engaged with advanced digital design methods and techniques, which include 3D form-finding, building information modeling, visual programming, coding, and real-time rendering. The study screens the usage of software solutions for the creation of complex virtual environments, covering Blender, Rhinoceros, Unity, Grasshopper and Revit. The implementation of a User Experience Questionnaire (UEQ) comparatively demonstrates the performative qualities of both digital design ecosystems. Results indicate that the intensity of interaction varied in two incomparable, but connate, levels of qualities. The findings suggest that the perspicuity aspects of student interaction bare the risk of “complicated” and “confusing” software. The results further demonstrate a conflict between task-related qualities and non-task related qualities. Additionally, interacting with VR tools in architecture design education is found attractive, stimulating, and original despite low scores on the pragmatic qualities of perspicuity, efficiency, and dependability. The data and results obtained from this study give insight into the planning of design studios in architecture education based on the use of VR and digital methods. Therefore, this study contributes to future research in the contextualization of the design teaching efforts.
A Deep Hierarchical Network for Packet-Level Malicious Traffic Detection

As an essential part of the network-based intrusion detection systems (IDS), malicious traffic detection using deep learning methods has become a research focus in network intrusion detection. However, even the most advanced IDS available are challenging to satisfy real-time detection because they usually need to accumulate the packets into particular flows and then extract the features, causing processing delays. In this paper, using the deep learning approach, we propose a deep hierarchical network for malicious traffic detection at the packet-level, capable of learning the features of traffic from raw packet data. It used the one-dimensional convolutional layer to extract the spatial features of raw packets and Gated Recurrent Units (GRU) structure to extract the temporal features. To evaluate the performance of our approach, experiments were conducted to examine the efficiency of the proposed deep hierarchical network based on the ISCX2012 dataset, USTC-TFC2016 dataset and CICIDS2017 dataset, respectively. Accuracy (ACC), detection rate (DR) and false alarm rate (FAR) are the metrics for evaluation. In the ISCX2012 dataset, our approach achieved 99.42%, 99.74%, 1.77% on ACC, DR and FAR, respectively. In USTC-TFC2016, there were 99.94%, 99.99%, 0.99%. In CICIDS2017, there were 100%, 100%, 0%. Furthermore, we discussed the impact of data balanced on classification performance and the time efficiency between the Long Short-Term Memory (LSTM) model and the GRU model. Experiments show that our approach can effectively detect malicious traffic and outperform sout s many other state-of-the-art methods in terms of ACC and DR.
Periodic structure of memory function in spintronics reservoir with feedback current

The authors focus on memory function of the physical reservoir computing by spintronics devices with delayed-feedback current. This work reports an achievement of high memory capacities in spintronics reservoir over a wide range of the delay time, whereas a periodic reduction of the memory capacity is also found when the delay time is an integer multiple of the input pulse width.
Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning

Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions.However, the sparse reward signals offered by the KG during traversal are often insufficient to guide a sophisticated walk-based reinforcement learning (RL) model.An alternate approach is to use traditional symbolic methods (e.g., rule induction), which achieve good performance but can be hard to generalize due to the limitation of symbolic representation.In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolicbased methods to provide reward supervision for walk-based agents.Experiments on benchmark datasets show that RuleGuider improves the performance of walk-based models without losing interpretability.
Impact of qubit connectivity on quantum algorithm performance

Quantum computing hardware is undergoing rapid development from proof-of-principle devices to scalable machines that could eventually challenge classical supercomputers on specific tasks. On platforms with local connectivity, the transition from one- to two-dimensional arrays of qubits is seen as a natural technological step to increase the density of computing power and to reduce the routing cost of limited connectivity. Here we map and schedule representative algorithmic workloads—the Quantum Fourier Transform (QFT) relevant to factoring, the Grover diffusion operator relevant to quantum search, and Jordan–Wigner parity rotations relevant to simulations of quantum chemistry and materials science—to qubit arrays with varying connectivity. In particular we investigate the impact of restricting the ideal all-to-all connectivity to a square grid, a ladder and a linear array of qubits. Our schedule for the QFT on a ladder results in running time close to that of a system with all-to-all connectivity. Our results suggest that some common quantum algorithm primitives can be optimized to have execution times on systems with limited connectivities, such as a ladder and linear array, that are competitive with systems that have all-to-all connectivity.
IIRC: A Dataset of Incomplete Information Reading Comprehension Questions

Humans often have to read multiple documents to address their information needs.However, most existing reading comprehension (RC) tasks only focus on questions for which the contexts provide all the information required to answer them, thus not evaluating a system's performance at identifying a potential lack of sufficient information and locating sources for that information.To fill this gap, we present a dataset, IIRC, with more than 13K questions over paragraphs from English Wikipedia that provide only partial information to answer them, with the missing information occurring in one or more linked documents.The questions were written by crowd workers who did not have access to any of the linked documents, leading to questions that have little lexical overlap with the contexts where the answers appear.This process also gave many questions without answers, and those that require discrete reasoning, increasing the difficulty of the task.We follow recent modeling work on various reading comprehension datasets to construct a baseline model for this dataset, finding that it achieves 31.1% F1 on this task, while estimated human performance is 88.4%.The dataset, code for the baseline system, and a leaderboard can be found at https://allennlp.org/iirc.
Development of Augmented Reality Application for Biology Education

In biology education which is an inseparable discipline of medical and veterinary education, it is of great importance that enabling students to make practice in real conditions and gain knowledge and skills related to their professional fields during their education. It is necessary to adapt the new technologies rapidly to education rather than the use of cadavers or laboratory animals, especially when making practice directly related in human and animal anatomy. For this purpose, virtual and augmented reality applications, which have been developed rapidly in recent years, come to the fore as a highly effective technique with the possibility of interacting visually with the objects they give to the user. In this study, the techniques were examined that used in order to increase the learning performance in biology, anatomy, physiology and experimental animals and the process was examined to develop a specific mobile application on Unity3D application. It is envisaged that the difficulties in providing materials for students and ethical debates on the use of experimental animals can be overcome with the dissemination of augmented reality applications and virtual laboratories developed as a result of procedures such as the needs analysis, lesson or course content and scenario writing processes in addition to software development.&nbsp
Efficient Computation of the Waiting Time and Fidelity in Quantum Repeater Chains

Quantum communication enables a host of applications that cannot be achieved by classical communication means, with provably secure communication as one of the prime examples. The distance that quantum communication schemes can cover via direct communication is fundamentally limited by losses on the communication channel. By means of quantum repeaters, the reach of these schemes can be extended and chains of quantum repeaters could in principle cover arbitrarily long distances. In this work, we provide two efficient algorithms for determining the generation time and fidelity of the first generated entangled pair between the end nodes of a quantum repeater chain. The runtime of the algorithms increases polynomially with the number of segments of the chain, which improves upon the exponential runtime of existing algorithms. Our first algorithm is probabilistic and can analyze refined versions of repeater chain protocols which include intermediate entanglement distillation. Our second algorithm computes the waiting time distribution up to a pre-specified truncation time, has faster runtime than the first one and is moreover exact up to machine precision. Using our proof-of-principle implementation, we are able to analyze repeater chains of thousands of segments for some parameter regimes. The algorithms thus serve as useful tools for the analysis of large quantum repeater chain protocols and topologies of the future quantum internet.