Reviewerly/exHarmony huggingface dataset:
Open-Set Black-Box Domain Adaptation for Remote Sensing Image Scene Classification

Domain adaptation (DA) has recently made tremendous progress in remote sensing image scene classification. Particularly, open-set DA (OSDA) has attracted increasing attention, wherein the target domain includes unknown classes. However, existing OSDA methods assume that the source samples or the parameters of the source model are available, which is not practical due to concerns about digital privacy and portability issues. Addressing this, we investigate a more realistic and challenging open-set DA scenario for remote sensing image scene classification, where the unlabeled target domain is only provided with a black-box source predictor (i.e., only model predictions are accessible). To address this problem, we devise an Open-set Knowledge distillation framework with neighboRhood similarity regularization and uncertAinty modeling called OKRA. Specifically, we introduce a neighborhood similarity regularization to facilitate the open-set knowledge distillation (KD) using local neighborhood information. Furthermore, we propose an energy-based uncertainty modeling (UM) strategy for open-set recognition, which can effectively discriminate known and unknown target data without any thresholding. Empirical results on six cross-scene scenarios built from three datasets verify that OKRA is effective and practical for remote sensing image scene classification, outperforming existing data-dependent OSDA methods by a large margin.
Analyzing Security and Privacy issues for Multi-Cloud Service Providers Using Nessus

Cloud computing security is a broad term that covers a wide range of security and privacy issues for businesses that use multi-cloud services. The service providers must take several factors into account when addressing security for their clients, including access and identity control, in-transit, and other security factors such as confidentiality, vulnerability management, threats, and audits. This paper takes a close look at each of these areas of cloud security and shows how multi-cloud service providers can protect themselves and move forward. It also talks about the problems with protecting systems that use more than one cloud and gives ways to deal with these problems. The reader should understand all of the security concerns that come with multi-cloud setups and how to deal with them in light of the latest cyber threats. The sole motivation of this paper is to raise awareness about multi-cloud service security in different aspects and its impact. Nessus, which is a well-known security tool, has been used to find vulnerabilities and other critical security concerns. Overall, experiments have been performed on three different websites: “www.cuchd.in”, “indiapost.gov.in” and “ajaykumar.in”. Despite this, the mitigation approaches for each vulnerability are explained systematically.
Improving Deep Features for Image Retrieval Using Multi-Source Spatial Information

The representational quality of the generated feature vectors for images is essential for image retrieval models to achieve high performance. Spatial information is crucial in obtaining highly representative feature vectors for image retrieval, and deep convolutional neural networks provide an excellent framework to generate such features. Through convolutional operations, deep convolutional neural networks include spatial information in the feature maps. However, most available architectures cannot include adequate spatial details in the feature maps required for high-performance image retrieval. Deep residual networks are deep networks capable of including useful information through residual learning. This paper proposes a novel residual block to generate feature maps by focusing on spatial information. The proposed residual block comprises three modules: a spatial feature extraction module, a hierarchical feature extraction module, and a feature fusion module. The first module includes spatial information in the feature maps at different levels of abstraction, while the second module includes spatial information using conventional convolution hierarchy. The third model fuses the outputs of the first two modules to provide a very rich set of feature maps. The present study tests a deep network employing the proposed residual block. The results indicate that the proposed network performs comparably or is superior to state-of-the-art methods on standard benchmarks, thus showing the effectiveness of the proposed residual block in improving the representational capacity.
Development of contents based on virtual environment of basic physics education

HMD, which is applied with the latest technology, minimizes motion sickness with high-resolution displays and fast motion recognition, and can accurately track location and motion. This can provide an environment where you can immerse yourself in a virtual three-dimensional space, and virtual reality contents such as disaster simulators and high-risk equipment learning spaces are developing using these characteristics. These advantages are also applicable in the field of basic science education. In particular, expanding the concepts of electric and magnetic fields in physics described by existing two-dimensional data into three-dimensional spaces and visualizing them in real time can greatly help improve learning understanding. In this paper, realistic physical education environments and contents based on three-dimensional virtual reality are developed and the developed learning contents are experienced by actual learning subjects to prove their effectiveness. A total of 46 middle school and college students were taught and experienced in real time the electric and magnetic fields expressed in three dimensions in a virtual reality environment. As a result of the survey, more than 85% of positive responses were obtained, and positive results were obtained that three-dimensional virtual space-based physical learning could be effectively applied.
High-Order Masking of Lattice Signatures in Quasilinear Time

In recent years, lattice-based signature schemes have emerged as the most prominent post-quantum solutions, as illustrated by NIST’s selection of Falcon and Dilithium for standardization. Both schemes enjoy good performance characteristics. However, their efficiency dwindles in the presence of side-channel protections, particularly masking – perhaps the strongest generic side-channel countermeasure. Masking at order d-1 requires randomizing all sensitive intermediate variables into d shares. With existing schemes, signature generation complexity grows quadratically with the number of shares, making high-order masking prohibitively slow.In this paper, we turn the problem upside-down: We design a lattice-based signature scheme specifically for side-channel resistance and optimize the masked efficiency as a function of the number of shares. Our design avoids costly operations such as conversions between arithmetic and boolean encodings (A2B/B2A), masked rejection sampling, and does not require a masked SHAKE implementation or other symmetric primitives. The resulting scheme is called Raccoon and belongs to the family of Fiat-Shamir with aborts lattice-based signatures. Raccoon is the first lattice-based signature whose key generation and signing running time has only an O(d log(d)) overhead, with d being the number of shares.Our Reference C implementation confirms that Raccoon’s performance is comparable to other state-of-the-art signature schemes, except that increasing the number of shares has a near-linear effect on its latency. We also present an FPGA implementation and perform a physical leakage assessment to verify its basic security properties.
Hyperbolic Graph Convolutional Networks for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis is a fine-grained sentiment analysis task that aims to predict the sentiment polarity of a specific aspect. Recent work adopts graph convolutional networks over dependency trees to capture the syntactic connections of aspects and opinion words while introducing the BiAffine to jointly refine syntax structures and semantic correlations. However, in the Euclidean space, the neural network models can’t well capture the syntactic connections of aspects and opinion words due to the inaccurate dependency trees representation, and the original structures and correlations are affected due to the BiAffine exchange method. Fortunately, dependency trees can be represented well since hyperbolic space can be viewed as continuous simulations of trees, so we propose a hyperbolic graph convolutional networks (HyperGCN) model to handle these challenges. We employ hyperbolic graph convolution with the dependency tree to model syntactic connections between aspects and opinion words, additionally, we also capture the semantic correlations with a hyperbolic graph convolutional network incorporating self-attention mechanism. Particularly, to exchange the relevant features without original syntax structures and semantic correlations being affected, we leverage an attention mechanism with residual structure to exchange relevant features of syntactic and semantic information. The experimental results on three datasets verify the effectiveness of our model.
Determining the Most Significant Metadata Features to Indicate Defective Software Commits

Defects are largely inevitable in the software development life cycle. Since we cannot avoid them during the development process, we can only desire to fight back with our limited resources in terms of time and monetary investment. Like in many other fields, machine learning models can be of help to mitigate the problem of defects by predicting both bug frequency and defective modules at different granularity levels. However, machine learning models are as good as the quality of the pre-selected set of features under consideration. Therefore, importance must be given while selecting only the necessary features from the original set of features. In this study, we compared various machine learning models with varying feature selection techniques and found the superiority of random forest-based machine learning techniques with wrapper methods. Random forest-based models with the wrapper method were able to detect all the buggy classes successfully on the validation data set.
Reconstruction of Partially Broken Vascular Structures in X-Ray Images via Vesselness-Loss-Based Multi-Scale Generative Adversarial Networks

Coronary artery procedures are primarily performed based on X-ray angiography images. However, coronary arteries in X-ray images are often partially broken, complicating diagnoses and procedures owing to lack of visibility. In this paper, we propose a fully automatic method to restore locally broken parts of coronary arteries in X-ray images without using any external information, such as computed tomography images. To this end, we design a new multi-scale generative adversarial network and a vesselness-loss function. The proposed method is optimized for focus on elongated structures and can be utilized in various clinical applications. The proposed method is evaluated and compared with four other existing methods using the performance metrics, PSNR, MSE, and SSIM, and the result shows 34.3, 0.18, and 0.91 averages, respectively for each metric. Based on the performance result, the blocked regions are plausibly reconstructed into such original shapes of blood vessels, which can aid in image-based guiding catheter manipulation during coronary artery procedures. Eventually, the proposed method can be utilized in various clinical applications, e.g., image-based planning and guidance of coronary procedures and prior simulation of results.
Effectively Modeling Sentence Interactions With Factorization Machines for Fact Verification

Fact verification is a very challenging task that requires retrieving multiple evidence sentences from a reliable corpus to authenticate claims. Many claims require the simultaneous integration and reasoning of several pieces of evidence for verification. Existing models exhibit limitations in two aspects: 1) during the sentence selection stage, they only consider the interaction between the claim and the evidence, disregarding the intersentence information, and 2) most fusion strategies employed in current research, such as addition, concatenation, or simple neural networks, fail to capture the relationships and logical information among the evidence. To alleviate these problems, we propose select and fact verification modeling (SFVM). Our model utilizes a multihead self-attention mechanism combined with a gating mechanism to facilitate sentence interaction and enhance sentence embeddings. Then, we utilize factorization machines to effectively express the compressed alignment vectors, which are then used to expand the representations of the base evidence. To distinguish the importance of features, we use the evidence fusion network to determine the importance of various feature interactions. Results from experiments on the two public datasets showed that SFVM can utilize richer information between the claim and the evidence for fact verification and achieve competitive performance on the FEVER dataset.
Repetition and Template Generalisability for Instance-Based Keystroke Biometric Systems

Keystroke timings can be used as a behavioural biometric, enabling passive and non-intrusive authentication. Fixed-text keystroke biometric systems involve the acquisition of keypress timings when typing a single phrase. They can be used in conjunction with a standard password authentication system to provide an increased level of security. Design decisions need to be made regarding the different technical aspects (e.g., feature sets, matching mechanism, etc.) of the system and there is a wealth of literature to guide this process. However, there is an absence of knowledge available when it comes to understanding how repetitions in user samples and characteristics of the password provided over an extended timeline can impact the system’s accuracy. In this paper, timings are collected from 65 participants, who are required to type the same passwords 4 times per week for 8 weeks, yielding a total of 81,920 timing datasets. A systematic analysis is then performed for each of the 8 weeks, following the same template creation and matching process, to gain an understanding of which week’s timings produce more generalised templates, providing a lower Equal Error Rate when matched against samples from all weeks.
Generative Adversarial Network Based Adaptive Transmitter Modeling

As transmission speed keeps increasing, Serializer/Deserializer (SerDes) complexity grows increasingly with different filters, such as feed-forward equalizers (FFEs) and finite impulse response (FIR) filters. Today, vendors share information about their intellectual property (IP) through IBIS-AMI models, which requires significant engineering time. This paper presents a data-driven approach for transmitter modeling, which, coupled with prior receiver modeling work, enables end-to-end modeling of a SerDes link. The transmitter modeling considers multiple output channel conditions and different FIR configurations and recovers a pulse response for the transmitter. The recovered pulse response strongly correlates to the simulated one and has a root-mean squared error (RMSE) of 0.0080 with the data range between [0, 1]. Moreover, the results are comparable to the setup when additional image transformations are added, which should improve performance. Lastly, the proposed approach can extend the modeling to incorporate the operation at different speeds, showing the flexibility of the approach. This approach reduces design time considerably, from running simulations to the final model deployment in under a week.
Full-Reference Image Quality Assessment via Low-Level and High-Level Feature Fusion

We propose a full-reference image quality assessment (FR-IQA) method by incorporating low-level and high-level image features. First, in contrast to the preexisting deep IQA methods, which only use the features extracted by the deep network, we not only use the image gradient to replace the low-level features in the first two stages of the deep network, but also combine them with the middle-stage features of the deep network to construct the new low-level features. The deep features of shallow layers contain some unwanted noise and further result in a decline in IQA performance. Second, we combine the global features extracted by the self-attention-based model with the semantic features extracted by the convolutional neural network to form the high-level features. Instead of directly using the self-attention-based model trained on the classification task, we first train a no-reference (NR) IQA regression model on a larger dataset and then use the global features of this NR-IQA model. The self-attention-based model can capture the internal connections of the image and is more effective in extracting global information due to its larger perceptual field. In the final pooling stage, we combine the average pooling and the standard deviation pooling to obtain the dispersion and concentration of the similarity maps for a more comprehensive description of quality. Experiments show that our FR-IQA method is able to obtain competitive results on three standard IQA datasets.
Confirmation Method for Flight Operators’ Interaction Based on the Improved Lightweight DenseNet

Rapid and accurate confirmation of operational intent can improve the efficiency of flight operators in handling sudden emergencies. A lightweight DenseNet flight operator blink interaction confirmation method based on visual gaze is proposed to address the issues of low accuracy and long-time consumption in traditional methods for confirming operational intentions. After determining whether the visual gaze of the flight operator is located at the confirmation button, this method uses two consecutive blinks to confirm the intention of the operation. When performing blink recognition, an improved lightweight DenseNet network model is used, which only uses one block and introduces attention SEBlocks in its [Formula: see text]1 and [Formula: see text]2 layers. Through testing experiments on our own dataset with open and closed eye images, it has been shown that this method has higher accuracy and lower loss compared to the classical DenseNet for open and closed eye recognition. The interactive experiment of flight operation intention confirmation showed that this method requires a shorter time for operation intention confirmation.
Korean scholarly journal editors’ and publishers’ attitudes towards journal data sharing policies and data papers (2023): a survey-based descriptive study

Purpose: This study aimed to ascertain the attitudes of Korean scholarly journal editors and publishers toward research data sharing policies and the publication of data papers through a survey.Methods: Between May 16 and June 16, 2023, a SurveyMonkey survey link was distributed to 388 societies, including 270 member societies of the Korean Council of Science Editors and 118 societies that used an e-submission system operated by the Korea Institute of Science and Technology Information. A total of 78 societies (20.1%) responded, from which 72 responses (18.6%) were analyzed after excluding invalid responses.Results: Out of the representatives of 72 journals, 20 editors or publishers (27.8%) declared a data sharing policy. Those journals that did not have such a policy often expressed uncertainty about their future plans regarding this issue. A common concern was a potential decrease in manuscript submissions, primarily due to the increased workload this policy might impose on editors and manuscript editors. Four respondents (5.6%) had published data papers, with two of them including this as a publication type in their author guidelines. Concerns about copyright and data licensing were cited as drawbacks to publishing data papers. However, the expansion of publication types and the promotion of data reuse were viewed as benefits.Conclusion: Korean scholarly journal editors’ and publishers’ attitudes toward data sharing policy and publishing data papers are not yet favorable. More training courses are needed to raise awareness of data sharing platforms and emphasize the need for research data sharing and data papers.
Chlorite geochemical vectoring of ore bodies: a natural kind clustering approach

Chlorite has long been considered a mineral group likely to have different trace element chemistry with proximity to mineralization, and therefore can be used to vector towards ore bodies. However, due to their geochemical complexity, it has proven challenging to develop a simple vectoring method based on the variation in abundance of one or a few chemical elements or isotopes. Machine learning, specifically cluster analysis, provides a potential mathematical tool for characterizing multidimensional geochemical correlations with proximity to mineralization. In this contribution we conducted a cluster analysis on 23 elements from 1,679 distinct chlorite sample analyses. The combination of this clustering technique with classification by proximity to the ore body, 1) explores and characterizes the nature of chlorite composition and proximity to ore bodies and 2) tests the efficacy of clustering-classification methods to predict whether a chlorite sample is near to an ore body. We found that chlorite chemistry is more strongly controlled by deposit type than proximity to mineralization and that cluster analysis of chlorite trace element content is likely not a viable way to develop vectors towards porphyry mineralization.
What is next for “transformation, trust, and transparency”?

This year, the 45th SSP Annual Meeting was hosted at the Oregon Convention Center and Hyatt Regency Portland (Portland, OR, USA).The theme of the meeting was "transformation, trust, and transparency." The event spanned 2 days, May 31 and June 1, 2023, and featured numerous concurrent sessions.The meeting was conducted paperlessly, utilizing Whova (Whova Inc, https://whova.com/), an all-in-one platform for hosting and planning virtual conferences.This article is based on the 45th SSP Annual Meeting, and I have incorporated relevant concepts, background, and history where necessary throughout the report.Before the meeting commenced, attendees had the opportunity to leave messages using Whova.These could be simple greetings to other participants, or they could express specific queries or interests, such as a visit to Powell's Books, a breakfast gathering, jogging, and more.This platform allows for interaction with others, including organizers, fellow panelists (if you are a panelist), and poster presenters.While this tool is convenient, without a hardcopy backup and some training in using Whova, I found it challenging to access information quickly, especially during the four parallel sessions.In addition to the talks, I thoroughly enjoyed my brief visit to Powell's Books, reputedly one of the largest bookstores in the world.The meeting kicked off with an "industry session" that showcased publishers and associated businesses.Despite my initial modest expectations, I found this session to be highly informative.Research Square introduced AJE Digital Editing (Research Square, https://www.aje.com/services/digital/), an artificial intelligence (AI)-powered tool that has been trained on millions of edits by professional editors.This tool is designed to expedite the publishing process and make it more equitable.As a non-native English speaker, I, along with many others, have taken a keen interest in this development.We often encounter difficulties in enhancing the quality and clar-