Reviewerly/exHarmony huggingface dataset:
Comparison of Visual Features for Image-Based Visibility Detection

Abstract The image-based visibility detection methods have been one of the active research issues in surface meteorological observation. The visual feature extraction is the basis of these methods, and its effectiveness has become a key factor in accurately estimating visibility. In this study, we compare and analyze the effectiveness of various visual features in visibility detection from three aspects, namely, visibility sensitivity, environmental variables robustness, and object depth sensitivity in multiscene, including three traditional visual features such as local binary patterns (LBP), histograms of oriented gradients (HOG), and contrast as well as three deep learned features extracted from the Neural Image Assessment (NIMA) and VGG-16 networks. Then the support vector regression (SVR) models, which are used to map visual features to visibility, are also trained, respectively based on the region of interest (ROI) and the whole image of each scene. The experiment results show that compared to traditional visual features, deep learned features exhibit better performance in both feature analysis and model training. In particular, NIMA, with lower dimensionality, achieves the best fitting effect and therefore is found to show good application prospects in visibility detection. Significance Statement The visual feature extraction is a basic step for image-based visibility detection and significantly affects the detection performance. In this paper, we compare six candidate visual features, including traditional and deep learned features, from visibility sensitivity, environmental variables robustness, and object depth sensitivity in multiscene. Then the SVR models are also trained to construct the mapping relations between different kinds of features and the visibility of each scene. The experiment results show that the deep learned features exhibit better performance in both feature analysis and model training, especially NIMA achieves the best fitting performance with fewer feature dimensions.
Common-path and synchronous phase shifting of lateral shearing interferometry based on micro-polarizer array

Based on birefringence theory and polarization-synchronized phase-shifting lateral shearing interferometry technology, we proposed a compact and simple polarization phase-shifting lateral shearing interferometer using a polarization camera integrated with a phase-shifting array of micro-polarizers. This interferometer is designed to realize a common optical path synchronous phase-shifting lateral shearing interference for aspheric surface testing system. This interferometer is composed of a polarization beam displacer (PBD), a quarter-wave plate (QWP), polarization camera (PCam). With the polarization birefringent crystal used as the PBD to realize the lateral shearing, and the parallel beam is divided into linearly polarized light whose polarization directions are orthogonal to each other. With the QWP changed the shear beam formed by the interference of the measuring beam and the self-copy beam into circularly polarized light with the opposite rotation direction. Next, with the micro-polarizer phase-shifting array on the PCam obtained four shearing interferograms with a phase-shifting difference is &pi;/2, which allows for the implement of aplanatic and common optical path shearing interferometer. Therefore, the system possesses significant advantages of simple structure, compact and strong antiinterference ability. Then, by the four-step phase-shifting algorithm demodulated the measurement light field distribution on the shearing interferograms. Finally, using the Zernike polynomial fitting method realized the surface shape reconstruction of the interferogram wavefront. The effectiveness of the interferometer is demonstrated by simulations and experiments, which research results showed that the optical measurement method can extract interferogram information in real time and directly measure the wavefront phase distribution.
Five Cells and Tilepaint are NP-Complete

Five Cells and Tilepaint are Nikoli's pencil puzzles. We study the computational complexity of Five Cells and Tilepaint puzzles. It is shown that deciding whether a given instance of each puzzle has a solution is NP-complete.
Build conflicts in the wild

Abstract When collaborating, developers often create and change software artifacts without being fully aware of team members' work. While such independence is essential for increasing development productivity, it might also result in conflicts when integrating developers' contributions. To better understand the conflicts revealed by failures when building integrated code, we investigate their frequency and structure and adopted resolution patterns in 451 open‐source Java projects. To detect such build conflicts, we select merge scenarios from git repositories, parse their Travis build logs, and check whether the build error messages are related to the merged changes. We find and classify 239 build conflicts and their resolution patterns. Most build conflicts are caused by missing declarations removed or renamed by one developer but referenced by another developer. Conflicts caused by renaming are often resolved by updating the missing reference, whereas removed declarations are often reintroduced. Most fix commits are authored by one of the merge scenario contributors. Based on our catalogue of conflict causes, awareness tools could alert developers about the risk of conflict situations. Program repair tools could benefit from our catalogue of resolution patterns to automatically fix conflicts; we illustrate that with a proof of concept implementation of a tool that fixes conflicts.
Practical attacks on a class of secret image sharing schemes based on Chinese Remainder Theorem

Chinese Remainder Theorem based secret image sharing schemes provide an efficient way to store secret image files securely in a distributed manner. Two basic properties required for designing such schemes are — availability and confidentiality . The first property ensures the retrieval of secret image from a qualified subset of shares and the latter guarantees the secrecy of the image against a coalition of non-qualified subset of shares. To model practical attacks on such schemes, in this paper, more powerful active adversaries are considered who can tamper with some shares to modify the reconstructed secret image. We show that an active adversary can tamper with one single share to make the honest parties reconstruct a false secret image of her choice. Our mathematical analysis along with the experimental data exhibit the practicality of our attacks for large class of existing schemes. • Examined security of a class of CRT-based (k,n)-threshold SIS against active attacks. • Single cheater can deceive k-1 honest parties to make them reveal a false image. • Mathematical analyses & experiments exhibit the practicality of our proposed attacks.
Algorithmic Analysis on Deer Hunting-based Grey Wolf for Dynamic Time Wrapping-based Hand Gesture Recognition

Nowadays, dynamic hand gesture recognition has become a complicated work in the recognition of pattern and the communities with consideration of computer vision. This paper tempts to frame an algorithmic analysis on proposed &#x201C;static and dynamic-oriented hand gesture recognition&#x201D;. Moreover, the recognition of static and dynamic models is improved by Dynamic Time Warping (DTW) model. In the phase called pre-processing, &#x201C;grey scale conversion and histogram equalization&#x201D; is used, whereas, in segmentation, &#x201C;Active Contour model, and canny edge detection&#x201D; is employed. Moreover, the significant features are extracted depending on the &#x201C;Histogram of Oriented Gradients (HOG), and Edge Oriented Histogram (EOH)&#x201D;, and the feature dimension is reduced by Principle Component Analysis (PCA). The optimal feature selection technique is adopted by the novel &#x201C;Deer Hunting-based Grey Wolf Optimization (DH-GWO)&#x201D;. Moreover, the &#x201C;significant frames in the video&#x201D; are eliminated by the DTW pattern. Finally, the characters and words are exactly recognized by Neural Network (NN), using the DH-GWO training. Here, the analysis is carried out by varying the random number <tex>$e$</tex> from 0.5 to 3.0 of the proposed DH-GWO.
Research on the Propagation Characteristics of Negative News Information Based on Personalized Recommendation Algorithm

This article will focus on how to reduce the negative information in the main theme report. Negative information contains subjectivity, deviation, interference, and cancellation characteristics, which will influence the primary theme report's communication effect, interfere with the audience's interpretation of the report, and cancel out the report's positive energy. The original intention of the theme report is to promote social harmony and safeguard social justice, but the appearance of negative information makes the reported effect fail to reach the expected purpose. The concept of theme reports and negative information is defined in this work. This study examines the primary topic report's qualities, such as The Times’ mainstream, good content, and strong report. In addition, the form and characteristics of negative information are also described. In this paper, a collaborative filtering recommendation method based on non-neighbor user contributions is suggested, which uses a linear fitting formula to apply the responsibilities of both neighbor and non-neighbor users to the recommendation system. The results show that the accuracy and diversity of our algorithm are better than those of traditional collaborative filtering algorithms. The diversity of several common recommendation algorithms is studied. The findings reveal that the diversity of recommendation algorithms is linked to the sparsity of data as well as the algorithm's suggestion mechanism. In general, the more scarce the data, the higher the recommendation algorithm’s variety. At the same time, we also study the diversity of recommendation systems, and the results show that although the overall diversity of the system is gradually decreasing, user behavior is becoming more and more diverse.
Domain Adaptive Semantic Segmentation through Photorealistic Enhancement of Video Game

Unsupervised domain adaptation is considered as an effective technique to reduce the large amount supervised data. In order to solve this problem, unsupervised domain adaptation is considered to be an effective technique. In this work, three types of domain adaptation: image-level domain adaptation, inter-domain adaptation, and intra-domain adaptation are introduced to achieve better semantic segmentation accuracy. The proposed method achieved an mean IoU of 45.0&#x0025;. Furthermore, by combining the proposed method with intra-domain adaptation, an mean IoU improvement of 1.2&#x0025; is achieved compared to previous work.
Post-Quantum Two-Party Adaptor Signature Based on Coding Theory

An adaptor signature can be viewed as a signature concealed with a secret value and, by design, any two of the trio yield the other. In a multiparty setting, an initial adaptor signature allows each party to create additional adaptor signatures without the original secret. Adaptor signatures help address scalability and interoperability issues in blockchain. They can also bring some important advantages to cryptocurrencies, such as low on-chain cost, improved transaction fungibility, and fewer limitations of a blockchain’s scripting language. In this paper, we propose a new two-party adaptor signature scheme that relies on quantum-safe hard problems in coding theory. The proposed scheme uses a hash-and-sign code-based signature scheme introduced by Debris-Alazard et al. and a code-based hard relation defined from the well-known syndrome decoding problem. To achieve all the basic properties of adaptor signatures formalized by Aumayr et al., we introduce further modifications to the aforementioned signature scheme. We also give a security analysis of our scheme and its application to the atomic swap. After providing a set of parameters for our scheme, we show that it has the smallest pre-signature size compared to existing post-quantum adaptor signatures.
Temporal Features-Based Anomaly Detection from Surveillance Videos using Deep Learning Techniques

Automatic video surveillance is an active research area in recent times to enhance security features. Based on the crowd behavior, the normal and abnormal scenarios can be detected lively which might help someone to get support to safeguard their lives and belongings. Numerous techniques are evolved for anomaly detection from video frames however the accuracy or detection probability lags due to improperly selected features. Optimal feature selection will enhance the accuracy of the system. So, in this research work, a temporal features-based anomaly detection from surveillance video is presented using deep learning techniques. In the proposed model, the keyframes are extracted using the HSV histogram and k-means clustering technique. From the keyframes, the temporal features are extracted using a convolutional neural network and finally a concatenated Convolutional long-short term memory (ConvLSTM) model is presented to learn the sequences to recognize the activities in the video frame. Experiment results using the UCFCrime dataset demonstrate the better performance of the proposed model compared to state of art methods.
Construction and Application of College English Multiple Intelligence Teaching Model Based on Internet of Things

An RFID sensor is installed above the classroom door, and the scanning distance is set to 8 m to identify the composite campus cards of incoming and outgoing teachers and students, so as to realize automatic statistics of teachers’ theoretical teaching workload and automatic attendance of students’ theoretical class hours. The system automatically starts the multimedia equipment just 5 minutes before class and conducts self-checks on all devices in advance, reducing teachers’ preclass operation steps. The multimedia management system of all classrooms is connected to the campus network and is centrally controlled by the cloud system. When the multimedia equipment in a classroom fails, it can directly instruct teachers to change to an idle classroom. Using the relevant theories of educational psychology and teaching theory, the WPBL teaching model based on the theory of multiple intelligences is expounded and demonstrated. It focuses on expounding theories and ideas; relationships and structures; and environment and resources and conducts in-depth discussions on relevant design rationales and architectural levels. Research has found that students are more interested in reading English than before. The reason is that in the English reading class of the experimental class, basically every student can have their own teaching activities, and more students like to participate in these activities. Applying the theory of multiple intelligences to English reading teaching has played a positive role in stimulating students’ interest in reading. Multiple intelligence English reading activities play an important role in improving students’ English reading performance. Through various teaching activities, the students in the experimental class are more interested and confident in the English reading class. Applying the theory of multiple intelligences to the English reading class can improve the students’ English reading performance and reading ability. In order to apply the theory of multiple intelligences to actual English reading teaching, when designing personalized English reading activities, teachers should learn to combine the theory with the purpose and content of English reading teaching.
A General Approach on Privacy and its Implications in the Digital Economy

The information age set off a revolution for organizations and institutions without precedent. Big production of data is a major competitive advantage for companies. Therefore, there are great economic incentives to monetize such data. However, there is evidence that indicates clear vulnerability in terms of user privacy, thereby setting a new paradigm in the search for a balance between privacy and security. This article offers a brief history of privacy to this day, deepens the scope and importance of its standardization in the regulation and future of digital markets.
A Regulatory Readiness Assessment Framework for Blockchain Adoption in Healthcare

Blockchain is now utilized by a diverse spectrum of applications and is proclaimed as a technological innovation that transforms the way that data are stored. This technology has the potential to transform the healthcare sector, especially the prevalent issues of patient’s data-privacy and fragmented healthcare data. However, there is no evidence-based effort to develop a readiness assessment framework for blockchain that combines all the different social and economic factors and involves all stakeholders. Based on a systematic literature review, the proposed framework is applied to Portugal’s healthcare sector and its applicability is outlined. The findings in this paper show the unique importance of regulators and the government in achieving a globally acceptable regulatory framework for the adoption of blockchain technology in healthcare and other sectors. The business entities and solution providers are ready to leverage the opportunities of blockchain, but the absence of a widely acceptable regulatory framework that protect stakeholders’ interests is slowing down the adoption of blockchain. There are several misconceptions regarding blockchain laws and regulations, which has slowed stakeholder readiness. This paper will be useful as a guideline and knowledge base to reinforce blockchain adoption.
Analysis of New Orthogonal Transforms for Digital Watermarking

The paper focuses on the application of new orthogonal transforms in digital watermarking. Novel types of transforms and their characteristics are presented. Potential methods for watermark embedding and recovery are also proposed. They assume embedding hidden information in the transform domains using the luminance channel of the original image. Image spectra are obtained by dividing the original image into smaller blocks that then are further processed by performing the forward transform operation. A watermark is embedded by modifying the spectral coefficients with relatively low values. Since there are various types of transforms, the latter process is realized in an adaptive manner. The proposed solutions were evaluated by measuring the level of visual distortion with respect to the total size of the inserted data. Additionally, the bit error rate (BER) in the recovery phase is also analyzed. The elaborated methods seem to be useful for applications in digital signal and image processing where high imperceptibility and low BER are of great importance. New orthogonal transforms were proved to be useful in watermarking tasks, and in some cases, they can even outperform the classic DCT approach.
Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference

Abstract We introduce a neuro-symbolic natural logic framework based on reinforcement learning with introspective revision. The model samples and rewards specific reasoning paths through policy gradient, in which the introspective revision algorithm modifies intermediate symbolic reasoning steps to discover reward-earning operations as well as leverages external knowledge to alleviate spurious reasoning and training inefficiency. The framework is supported by properly designed local relation models to avoid input entangling, which helps ensure the interpretability of the proof paths. The proposed model has built-in interpretability and shows superior capability in monotonicity inference, systematic generalization, and interpretability, compared with previous models on the existing datasets.
Lore a Red Team Emulation Tool

This article presents the red team emulation tool Lore, which uses boolean logic and trained models to automatically select and execute red team actions. Lore improves the current state of red team automation, and is the first such tool shown to provide a more fun and educational experience than a manual red team during a cyber defence exercise. In addition to the cyber defence exercise, empirical tests are performed to examine the accuracy of Lore's trained models. The results show that application of these models lead to two times more compromised machines than when applying expert-defined models, and five times more compromised machines than when randomly selecting actions.
Talent Cultivation of New Ventures by Seasonal Autoregressive Integrated Moving Average Back Propagation Under Deep Learning

This study combines the discovery methods and training of innovative talents, China's requirements for improving talent training capabilities, and analyses the relationship between the number of professional enrollments in colleges and universities and the demand for skills in specific places. The research learns the characteristics and training models of innovative talents, deep learning (DL), neural networks, and related concepts of the seasonal difference Autoregressive Moving Average (ARMA) Model. These concepts are used to propose seasonal autoregressive integrated moving average back propagation (SARIMA-BP). Firstly, the SARIMA-BP artificially sets the weight parameter values and analyzes the model's convergence speed, superiority, and versatility. Then, particle swarm optimization (PSO) algorithm is used to pre-process the model and test its independence. The accuracy of the model is checked to ensure its proper performance. Secondly, the model analyzes and predicts the relationship between the number of professional enrollments of 10 colleges and universities in a specific place and the talent demand of local related enterprises. Moreover, the established model is optimized and tested by wavelet denoising. Independent testing is done to ensure the best possible performance of the model. Finally, the weight value will not significantly affect the model's versatility obtained by experiments. The prediction results of professional settings and corporate needs reveal that: there is a moderate correlation between professional locations and corporate needs; colleges and universities should train professional talents for local enterprises and eliminate the practical education concepts.
On the Immersive Properties of High Dynamic Range Video

This paper presents the results from two studies which used a dual-task methodology to measure an audience's experience of immersion while watching video under typical television viewing conditions. Immersion was measured while participants watched either a high dynamic range, wide color gamut video or a standard dynamic range, standard color gamut video, in high definition or ultra-high definition. Other video parameters were carefully measured and controlled. The study found that high dynamic range, wide color gamut video is significantly more immersive than standard dynamic range, standard color gamut video in the chosen configuration. However, there was no evidence of significant differences in immersion between high-definition and ultra-high-definition resolutions.