Reviewerly/exHarmony huggingface dataset:
Cybersecurity breaches and investors’ interest in the firm as an investment

Cybersecurity breaches pose a significant risk to firms. To combat these risks, many firms engage in strategic cybersecurity risk management initiatives. While these efforts may reduce the likelihood of a cybersecurity breach, they do not eliminate the risk of a breach. In the event of a cybersecurity breach, firms may issue an apology to investors. This study uses an experiment to examine whether a firm indicates cybersecurity risk management is a strategic initiative and whether a post-cybersecurity breach apology by the CEO impacts nonprofessional investors’ investment interest in the firm. Results show that, in response to a cybersecurity breach, the presence of a CEO apology positively impacts investors’ investment impression and their perceptions of CEO affective and CEO cognitive trust. We find that investors’ investment interest is lowest for a firm that previously indicates cybersecurity risk management is a strategic initiative and where the CEO does not issue an apology. The CEO apology, however, does not significantly impact investment amount, a secondary measure of investor interest. Results from this study have implications for managers, investors, and regulators.
Reciprocal normalization for domain adaptation

Batch normalization (BN) is widely used in modern deep neural networks, which has been shown to represent the domain-related knowledge, and thus is ineffective for cross-domain tasks like unsupervised domain adaptation (UDA). Existing BN variant methods aggregate source and target domain knowledge in the same channel in normalization module. However, the misalignment between the features of corresponding channels across domains often leads to a sub-optimal transferability. In this paper, we exploit the cross-domain relation and propose a novel normalization method, Reciprocal Normalization (RN). Specifically, RN first presents a Reciprocal Compensation (RC) module to acquire the compensatory for each channel in both domains based on the cross-domain channel-wise correlation. Then RN develops a Reciprocal Aggregation (RA) module to adaptively aggregate the feature with its cross-domain compensatory components. As an alternative to BN, RN is more suitable for UDA problems and can be easily integrated into popular domain adaptation methods. Experiments show that the proposed RN outperforms existing normalization counterparts by a large margin and helps state-of-the-art adaptation approaches achieve better results. The source code is available on https://github.com/Openning07/reciprocal-normalization-for-DA.
Multi-Featured Anomaly Detection for Mobile Edge Computing Based UAV Delivery Systems

With the rapid development of communication infrastructure and the widespread of IoT computing facilities such as Mobile Edge Computing (MEC), the application of Unmanned Aerial Vehicles (UAVs) are increasingly growing especially in last-mile deliveries in smart logistics. However, the anomaly detection in the MEC environment is still one yet unresolved issue. In recent years, the methods of UAV anomaly detection mainly focus on detections of specific types of UAVs or a certain feature of UAVs, and it is not as certain to achieve efficient and reliable results, in other words, there is currently a lack of better ways to address security issues in UAV networks. In this paper, we propose a Multi-Featured Anomaly Detection (MFAD) method for MEC-based UAV delivery systems to detect malicious attacks in the network through the abnormal state of UAV. Firstly, we develop deep learning model to recognize the correct UAV behaviors using several common UAV data in normal states, including altitude and speed. Secondly, we apply attacks in the data to simulate anomalous UAV behaviors under cyberattacks. Thirdly, the abnormal data is merged into the selected normal behavior model to detect the Normalized Root Mean Square Error (NRMSE) between the input data and the data provided by the normal behavior model. Finally, experiments with UAV sensor data demonstrate that our method is effective in detecting anomalies, but also reduce the vital energy consumption in UAV delivery systems.
Experimental higher-order interference in a nonlinear triple slit

Interference between two waves is a well-known concept in physics, and its generalization to more than two waves is straightforward. The order of interference is defined as the number of paths that interfere in a manner that cannot be reduced to patterns of a lower order. In practice, second-order interference means that in, say, a triple-slit experiment, the interference pattern when all three slits are open can be predicted from the interference patterns between all possible pairs of slits. Quantum mechanics is often said to only exhibit second-order interference. However, this is only true under specific assumptions, typically single particles undergoing linear evolution. Here we experimentally show that nonlinear evolution can in fact lead to higher-order interference. The higher-order interference in our experiment can be understood using a simple classical or quantum description, namely optical coherent states interacting in a nonlinear medium. Our work shows that nonlinear evolution could open a loophole for experiments attempting to verify Born's rule by ruling out higher-order interference.
Ethics of Adversarial Machine Learning and Data Poisoning

This paper investigates the ethical implications of using adversarial machine learning for the purpose of obfuscation. We suggest that adversarial attacks can be justified by privacy considerations but that they can also cause collateral damage. To clarify the matter, we employ two use cases—facial recognition and medical machine learning—to evaluate the collateral damage counterarguments to privacy-induced adversarial attacks. We conclude that obfuscation by data poisoning can be justified in facial recognition but not in the medical case. We motivate our conclusion by employing psychological arguments about change, privacy considerations, and purpose limitations on machine learning applications.
Path planning of patrol robot based on modified grey wolf optimizer

Abstract The grey wolf optimizer (GWO) as a new intelligent optimization algorithm has been successfully applied in many fields because of its simple structure, few adjustment parameters and easy implementation. This paper mainly aims at the defects of GWO in path planning application, such as easily falling into local optimization, poor convergence and poor accuracy, and turn point grey wolf optimization (TPGWO) algorithm is proposed. First, the idea of cross-mutation and roulette is used to increase the initial population of GWO and improve the search range. At the same time, the convergence factor function is improved to become a nonlinear update. In the early stage, the search range is expanded, and in the later stage, the convergence speed is increased, while the parameters in the convergence factor function can be adjusted according to the number of obstacles and the map area to change the turning point of the function to improve the convergence speed and accuracy of the algorithm. The turning times and turning angles of the obtained path are added to the fitness function as penalty values to improve the path accuracy. The optimization test is carried out through 16 test functions, and the test results prove the convergence and robustness of TPGWO algorithm. Finally, the TPGWO algorithm is applied to the path planning of patrol robot for simulation experiments. Compared with the GWO algorithm and Particle Swarm Optimization, the simulation results show that the TPGWO algorithm has better convergence, stability and accuracy in the path planning of patrol robot.
Local Bidirection Recurrent Network for Efficient Video Deblurring with the Fused Temporal Merge Module

Video deblurring methods exploit the correlation between consecutive blurry inputs to generate sharp frames. However, designing an effective and efficient method is a challenging problem for video deblurring. To guarantee the effectiveness and further improve the deblurring performance, we adopt the recurrent-based method as the baseline and reconsider the recurrent mechanism as well as the temporal feature alignment in the state-of-the-art methods. For the recurrent mechanism, we add the local backward connection to the global forward recurrent backbone to effectively exploit accurate future information. For the temporal alignment, we adopt a fused temporal merge module that exploits the superiority of flow-based and kernel-based methods with progressive correlation volumes estimation. In addition, we evaluate our method with both synthetic datasets (GoPro, DVD) and a realistic dataset (BSD). The experimental results demonstrate that our method achieves significant performance improvement with a slight computational cost increase against the state-of-the-art video deblurring methods. The extended ablation studies verify the effectiveness of our model.
Weak information extraction of gamma spectrum based on a two-dimensional wavelet transform

This paper studies the spectrum of gamma rays based on two-dimensional wavelet transforms using Haar, Daubechies, and Symlets wavelets. A two-level two-dimensional transform is carried out using these three wavelets. The paper focuses on the amplitude and extreme horizontal components of high-frequency components after each stage of transform and extracts the weak information from the gamma-ray spectrum data. This paper analyzes three wavelets, two moments, and two energy levels. The analysis incorporates a small support torque and a two-dimensional transform. The standard traversal comparison peak search approach, the symmetric zero area method, the Gaussian fitting method, and the contemporary popular SNIP algorithms are also compared and analyzed in this work. The outcomes were also contrasted with the NaI(Tl) detector's measured peak levels that were calibrated in a lab. The analysis's findings demonstrate that, in the presence of high radioactive background, standard analysis techniques are unable to assess weak information. The chosen Daubechies and Symlets wavelets were used for the first-level 2D wavelet transform processing, and their peak search results surpassed those obtained from SNIP-processed analysis. Additionally, the standard deviation approach was applied to the outcomes of the 2D wavelet and SNIP transform to identify transform fluctuations in the local peaks of the complete spectrum and assess the algorithm's stability. The outcomes demonstrate that the local peaks of the 2D Symlets wavelet transform and 2D Daubechies transform, as well as the full spectrum fluctuations, are very tiny and steady. This technology can extract weak information from the data and can be applied to other gamma-ray data sets. It lays the foundation for a shielding study of special radiation protection materials.
Nested DWT–Based CNN Architecture for Monocular Depth Estimation

Applications such as medical diagnosis, navigation, robotics, etc., require 3D images. Recently, deep learning networks have been extensively applied to estimate depth. Depth prediction from 2D images poses a problem that is both ill-posed and non-linear. Such networks are computationally and time-wise expensive as they have dense configurations. Further, the network performance depends on the trained model configuration, the loss functions used, and the dataset applied for training. We propose a moderately dense encoder-decoder network based on discrete wavelet decomposition and trainable coefficients (LL, LH, HL, HH). Our Nested Wavelet-Net (NDWTN) preserves the high-frequency information that is otherwise lost during the downsampling process in the encoder. Furthermore, we study the effect of activation functions, batch normalization, convolution layers, skip, etc., in our models. The network is trained with NYU datasets. Our network trains faster with good results.
A Systematic Review of Literature on Emerging Technologies and Specific Learning Difficulties

Educational research on emerging technologies, particularly virtual reality and augmented reality, is expanding at the moment. The purpose of this contribution is to conduct a systematic review to understand the impact of emerging technologies in the educational and social-health fields. The PRISMA 2020 methodology was used to respond to the objective and research questions, ensuring the fulfillment of the quality criteria in the elaboration of the systematic review. This systematic review analyses the possible benefits that new technologies, specifically virtual reality and augmented reality, can have on learning difficulties such as dyslexia and ADHD disorder, as well as their inclusion in the social sphere. In this study, articles from both the health and educational fields will be reviewed. The research focuses on highlighting the incipient benefits of these new technologies for the aforementioned learning difficulties in the school and health fields. The main conclusions drawn show that research on these emerging technologies is still scarce and incipient in the educational field, with the main results found in the healthcare field.
Analyzing the unrestricted web: The finnish corpus of online registers

Abstract This article introduces the Finnish Corpus of Online Registers (FinCORE) representing the full range of registers – situationally defined text varieties such as news and blogs – on the Finnish Internet. The extreme range of language use found online has challenged the study of registers. It has been unclear what registers the entire Internet includes, and if they can be sufficiently defined to allow for their analysis or classification, previous studies focusing on restricted sets of registers and English. FinCORE features 10,754 texts from the unrestricted web, manually annotated for their register using a scheme originally established for the Corpus of Online Registers of English (CORE). We present the FinCORE registers and compare them to CORE. Finally, we show that the FinCORE registers are sufficiently well-defined to allow for their automatic identification, thus opening novel possibilities for both linguistics and web-as-corpus research. FinCORE is published under an open license.
A Video Splicing Forgery Detection and Localization Algorithm Based on Sensor Pattern Noise

Video splicing forgery is a common object-based intra-frame forgery operation. It refers to copying some regions, usually moving foreground objects, from one video to another. The splicing video usually contains two different modes of camera sensor pattern noise (SPN). Therefore, the SPN, which is called a camera fingerprint, can be used to detect video splicing operations. The paper proposes a video splicing detection and localization scheme based on SPN, which consists of detecting moving objects, estimating reference SPN, and calculating signed peak-to-correlation energy (SPCE). Firstly, foreground objects of the frame are extracted, and then, reference SPN are trained using frames without foreground objects. Finally, the SPCE is calculated at the block level to distinguish forged objects from normal objects. Experimental results demonstrate that the method can accurately locate the tampered area and has higher detection accuracy. In terms of accuracy and F1-score, our method achieves 0.914 and 0.912, respectively.
Relationships among memorable brand experience, brand preference, and behavioral intentions: focusing on the difference between robot servers and human servers

Purpose This study aims to examine the differences among memorable brand experience, brand preference and behavioral intentions based on the type of service providers, such as robot servers and human servers. In addition, this study investigated the relationships among the four concepts. Design/methodology/approach The data was collected from 296 customers who experienced robot servers and from 307 customers who experienced human servers. Findings The data analysis results indicated that there was a statistical difference with the mean value of the five concepts according to the type of employee. The results also revealed that memorable brand experience has a positive influence on brand preference, which in turn positively influences intentions to use, word-of-mouth and willingness to pay more. Originality/value This study attempted to find the difference between robot servers and human servers in memorable brand experience, brand preference and behavioral intentions in the restaurant industry for the first time.
Assessing the effectiveness of machine translation in the Chinese EFL writing context: A replication of Lee (2020)

Abstract With the dramatic improvement in quality, machine translation has emerged as a tool widely adopted by language learners. Its use, however, has been a divisive issue in language education. We conducted an approximate replication of Lee (2020) about the impact of machine translation on EFL writing. This study used a mixed-methods approach with automatic text analyzer Coh-Metrix and human ratings, supplemented with questionnaires, interviews, and screen recordings. The findings obtained support most of the original work, suggesting that machine translation can help language learners improve their EFL writing proficiency, specifically in strengthening lexical expressions. Students generally hold positive attitudes towards machine translation, despite some skeptical views regarding the values of machine translation. Most students express a strong wish to learn how to effectively use machine translation. Machine translation literacy instruction is therefore suggested for incorporation into the curriculum for language students.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si6.svg" display="inline" id="d1e482"><mml:mi mathvariant="script">KA</mml:mi></mml:math>: Keyword-based auditing with frequency hiding and retrieval reliability for smart government

In a keyword-based auditing paradigm, users typically focus on specific parts of a dataset rather than the integrity of the dataset as a whole. However, this approach is subjective and can be exploited by malicious storage servers to analyze audit frequency or reduce backups. This poses a significant risk for government data, where any privacy leakage or corruption could have catastrophic consequences. In this paper, we propose a keyword-based auditing scheme for smart government called KA, which provides both frequency hiding and retrieval reliability. KA leverages a Bloom filter to adjust the false positive rate and audits files based on specified keywords and random files obtained through fuzzy matching. To obtain privacy-preserving fuzzy matching, KA constructs an index table embedded with update times to retrieve a wide range of files to be audited. This approach is secure against replay attacks and supports index table updates through structure iteration instead of recalculation. Additionally, KA uses a relation matrix to detect all challenged storage nodes and ensure honest storage proof. KA provides storage robustness, privacy protection of hidden frequencies, Data security and retrieval reliability. Furthermore, It reduces audit computation overhead by 32.6% compared to probabilistic public auditing.
Markov clustering regularized multi-hop graph neural network

Graph Neural Networks (GNNs) have shown great potential for graph data analysis. In this paper, we focus on multi-hop graph neural networks and aim to extend existing models to a high-order multi-hop form for graph-level representation learning. However, such a directly extending method suffers from two limitations, i.e., computational inefficiency and limited representation ability of the multi-hop neighbor. For the former limitation, we utilize an iteration approach to approximate the power of a complex adjacency matrix to achieve linear computational complexity. For the latter limitation, we introduce the Regularized Markov Clustering (R-MCL) to regularize the flow matrix, i.e., the adjacency matrix, in each iteration step. With these two strategies, we construct Markov Clustering Regularized Multi-hop Graph Neural Network (MCMGN) for graph-level representation learning tasks. Specifically, MCMGN consists of a multi-hop message passing phase and a readout phase, where the multi-hop message passing phase aims to learn multi-hop node embedding, and then the readout phase aggregates multi-hop node representations to generate graph embedding for graph-level representation learning tasks. Extensive experiments on eight graph benchmark datasets strongly demonstrate the effectiveness of Markov Clustering Regularized Multi-hop Graph Neural Network, leading to superior performance on graph classification.
A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method

In digital image processing and steganography, images are often described using edges and local binary pattern (LBP) codes. By combining these two properties, a novel hybrid image steganography method of secret embedding is proposed in this paper. This method only employs edge pixels that influence how well the novel approach embeds data. To increase the quantity of computed edge pixels, several edge detectors are applied and hybridized using a logical OR operation. A morphological dilation procedure in the hybridized edge image is employed to this purpose. The least significant bits (LSB) and all LBP codes are calculated for edge pixels. Afterward, these LBP codes, LSBs, and secret bits using an exclusive-OR operation are merged. These resulting implanted bits are delivered to edge pixels’ LSBs. The experimental results show that the suggested approach outperforms current strategies in terms of measuring perceptual transparency, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI). The embedding capacity per tempered pixel in the proposed approach is also substantial. Its embedding guidelines protect the privacy of implanted data. The entropy, correlation coefficient, cosine similarity, and pixel difference histogram data show that our proposed method is more resistant to various types of cyber-attacks.