Reviewerly/exHarmony huggingface dataset:
Exploring the relationships between teacher noticing, ambisonic audio, and variance in focus when viewing 360 video

A growing body of research has supported the implementation of innovative and immersive video for teaching and learning across the lifespan. Immersive video, delivered through eXtended Reality (XR) tools like 360 video, provides users with new ways to see real or created environments. Unfortunately, most of the existing research has highlighted immersive video without accompanying immersive audio. This use of monophonic audio can create a disconnect for viewers as they experience close to real world video with sounds that do not match a real-world environment. The purpose of this study was to respond to this gap in the literature by exploring the use of ambisonic audio and its impact on preservice teacher noticing and variability of viewing focus when watching 360 video. Data were collected from undergraduate teacher education students who participated in a self-paced online activity that included watching 360 videos and responding to a questionnaire. A convergent mixed methods design was employed to compare participants’ professional noticing and observed viewing behavior in the context of ambisonic and monophonic audio. Results showed that users in ambisonic audio conditions in 360 video environments were more likely to have higher focus. Moreover, for users who had specific professional knowledge, monophonic audio with immersive video had a negative impact on their variance in focus. The paper concludes with recommendations for future research on the use of audio in virtual and augmented reality environments.
Triple-level relationship enhanced transformer for image captioning

Region features and grid features are often used in the field of image captioning. As they are often extracted by different networks, fusing them for image captioning needs connections between them. However, these connections often rely on simple coordinates, which will lead the captions lacks of precise expression of visual relationships. Meanwhile, the scene graph features contain object relationship information, through the multi-layer calculation, and the extracted object relationship information is of higher level and more complete, which can compensate the shortage of region features and grid features to a certain extent. Therefore, a Triple-Level Relationship Enhanced Transformer (TRET) is proposed in this paper, which can process three features in parallel. TRET can obtain and combine different levels of object relationship features to achieve the advantages of complementarity between different features. Specifically, we obtain high-level object-relational information with the help of Graph Based Attention, and achieve the fusion of low-level relational information and high-level object-relational information with the help of Cross Relationship Enhanced Attention, so as to better align the information of both modalities, visual and text. To validate our model, we conduct comprehensive experiments on the MS-COCO dataset. The results indicate that our method achieves better performance compared with the existing state-of-the-art methods and effectively enhances the ability of describing the representation of object relationships in the generated outcomes.
Near-infrared vascular image segmentation using improved level set method

Near-infrared vascular images are crucial for vascular disease diagnosis and treatment. To solve the problems of difficult segmentation and inaccurate computation issues, we propose a series of image processing methods. First, the images are preprocessed, including background removal, contrast stretching, and noise suppression. After that, the images are enhanced with a two-stage image enhancement method, which successfully combines the benefits of convolutional neural network and traditional image enhancement method. Finally, the images are segmented by an Adaptive Prior Shape Level Set Evolution (APSLSE) method, which effectively improves the common problems of initial contour sensitivity and unidirectional variation of area terms. A shape restriction item is designed to the level set function to improve its suitability for the segmentation of blood vessels. A dataset of 360 images is collected for the research and validation of above algorithms. Extensive experimental results show that the proposed algorithms can effectively process the poor quality near-infrared blood vessel images and segment their vascular shape. Compared the segmentation results with those manually labelled by experts, the False Negative Rate (FNR) is 0.1930 and False Positive Rate (FPR) is 0.04633.
Quantum and Optoelectronic Devices, Circuits and Systems

The fields of quantum electronics (e [...]
VERBAL-LINGUISTICS INTELLIGENCE TOWARDS STUDENT’S SPEAKING ENGLISH SKILLS USING DIGITAL MEDIA (AUDIO VISUAL) IN THE CLASSROOM

Most Indonesian students have a problem speaking English as their foreign language. Most researchers agreed that it is generally caused by the verbal-linguistic Intelligence of the students. This study aims to determine whether there is a relationship between students’ verbal – linguistic intelligence in their ability to speak English using digital media (audio visual) in classroom. The researcher used a systematic literature review (SLR) for the methodology of the study. Ten articles were presented to collect the data related to students’ verbal – linguistic intelligences and speaking skills. This study finds that students verbal – linguistic intelligence is an internal factor that has to be supported by the other internal factors and also some of external factors to determines the mastery of speaking skills
New approaches to forecasting large deposits of strategic metals

Synthesis of the obtained materials by sources of metals, agents of their transportation and areas of deposition or accumulation of ores makes it possible to zone the most promising territories to identify new large deposits of strategic metals. As a result of research the main ore-forming systems – sources of strategic metals are identified; a database of deposits and promising ore occurrences of strategic and high-tech metals of Russia has been created; geological and genetic models have been prepared, and on their basis predictive prospecting models of large deposits of strategic metals; new approaches to forecasting large deposits of strategic metals have been developed, including on the basis of data from the GOCE (Gravity Field and Steady-State Ocean Circulation Explorer) spacecraft, which allow using elements of the deep structure of the Earth's crust in predictive prospecting models; a series of predictive metallogenic maps of the territory of various regions of Russia has been compiled.
Background detection affects downstream classification of Camelyon16 whole slide images

Detecting the background is often one of the first tasks when analyzing whole slide images. However, these methods greatly vary and are generally not reported in sufficient detail for subsequent studies to be able to reproduce. Therefore, we sought to determine the effect of varying background detection methods on downstream performance metrics. Specifically, we applied attention-based multiple instance learning, CLAM, and Attention2majority to classify whole slide images from Camelyon16 using four different background detection methods. Our results show that performance metrics (i.e., accuracy and area under the curve - AUC) differ when different background detection methods are utilized. Furthermore, all classification methods perform worse when using hand-drawn tissue annotations for background detection. Finally, we show that depending on the background detection method, the best performing method changes. We conclude that background detection methods must be fully reported so that results can be accurately reproduced and techniques can be fairly compared.
Quantifying soil properties relevant to soil organic carbon biogeochemical cycles by infrared spectroscopy: The importance of compositional data analysis

Oxyhydroxides, soil texture and soil organic carbon (SOC) fractions are key parameters determining organic carbon cycling in soils. Standard laboratory methods to determine these soil properties are, however, time–consuming and expensive. Visible near infrared (Vis–NIR) and mid infrared (MIR) spectroscopy have been recognized as a promising alternative, but previous studies have not explicitly considered the above–mentioned soil properties as compositional data. The fractional components in compositional data are interrelated but their sum should be unity – these features should be represented in the spectral modeling process to minimize the prediction bias. In this study, two unique datasets were used to test these premises. The first one consisted of 655 samples collected from agricultural terraces and lynchets across Europe, which were scanned to acquire MIR spectra, while in the second one 4516 samples from private gardens across Flanders, Belgium were used to acquire Vis–NIR spectra. Memory–based learning models were optimized using both raw data (conventional method) and transformed data of soil properties by additive log–ratio (alr), centered log–ratio (clr), and isometric log–ratio (ilr) transformation methods. Results showed that the log–ratio transformation methods produced predictions as accurate as the conventional method, whilst also added two significant benefits: (1) they ensured the predicted fractions added up to 100% and (2) they reduced the number of samples with extreme prediction errors. We found that for 11 out of 18 investigated soil properties, the three log–ratio transformation methods provided similar model performance, whilst ilr outperformed clr for the prediction of silt and sand content of garden soils, for coarse particulate SOC (>250 µm) and microaggregate–associated SOC (250–53 µm) of terrace soils. For the remaining three properties (Al oxyhydroxides) alr outperformed ilr. Fair to excellent predictive models (RPD from 1.4 to 4.3; R2 from 0.50 to 0.95) were achieved for soil oxyhydroxides (Fe, Al, Mn) and soil texture from MIR spectra. Our approach also enabled accurate predictions of silt and sand content of garden soils using Vis–NIR spectra (RPD = 1.9; R2 = 0.72), although accuracy for clay was lower (RPD = 1.3; R2 = 0.49). This study demonstrates that combining soil infrared spectroscopy with a compositional data analysis is a promising technique that enables cost-effective and reliable quantification of soil properties relevant to SOC stability, thus offering a practical opportunity to assess the role of SOC in global C cycling.
Artificial Neural Network vs. Support Vector Machine For Speech Emotion Recognition

Today, the subject of emotion recognition from speech got the attention of many researchers who are interested in the topic of speech recognition and it has engaged in many applications. Furthermore, Speech Emotion Recognition (SER) a pivotal part of influential human interaction and it has been a modern challenge to speech processing. SER has two basic phases; which are, features extraction and emotion classification.&#x0D; This paper presents a comparison in performances of two popular techniques used for classification that Artificial Neural Network (ANN) as well as Support Vector Machine (SVM). Furthermore, gives a view on the collection of corpus, feature extraction techniques and classification methods, which are regarding to emotion detection from wave speech signal.
On the interplay between the entrepreneurs' challenges and the business performance: empirical study and evidence from emerging economies

Purpose This study aims and tries to identify and examine the effect of the entrepreneurs' challenges factors (i.e. financial, governmental, personal, educational social and cultural, infrastructural, operational, ethical, and Covid-19) on entrepreneurs' business performance in Jordan country as an emerging economy. Further, try to clarify and identify the main critical factors that affect an entrepreneur's business performance. Design/methodology/approach A quantitative methodology was used by conducting web-based survey questionnaires with 178 useful responses out of 443 entrepreneurs listed in the Chamber of the industry of Jordan, representing a 40.2% response rate. Data were analyzed by using Statistical Package for the Social Sciences (SPSS) Sofware. Findings The results of this study confirmed the existence of an inverse relationship between entrepreneurs' challenges and entrepreneurs' business performance. The Covid-19 challenges and governmental challenges were found to be the most affecting on entrepreneurs' business performance, whereby the personal challenges had no impact on entrepreneurs' business performance. Research limitations/implications The study provides a clear classification of entrepreneurs' challenges, which can be useful for both researchers and entrepreneurs. Practical implications This study developed some recommendations that emphasized the government's role in promoting entrepreneurship and supporting entrepreneurs. Originality/value This study attempts to define and clarify the impact of entrepreneurs' challenges on their business performance. In addition, this study can be used to promote entrepreneurship and an innovation-stimulating environment through decision-makers.
LGSNet: A Two-Stream Network for Micro- and Macro-Expression Spotting With Background Modeling

Micro- and macro-expression spotting in an untrimmed video is a challenging task, due to the mass generation of false positive samples. Most existing methods localize higher response areas by extracting hand-crafted features or cropping specific regions from all or some key raw images. However, these methods either neglect the continuous temporal information or model the inherent human motion paradigms (background) as foreground. Consequently, we propose a novel two-stream network, named Local suppression and Global enhancement Spotting Network (LGSNet), which takes segment-level features from optical flow and videos as input. LGSNet adopts anchors to encode expression intervals and selects the encoded deviations as the object of optimization. Furthermore, we introduce a Temporal Multi-Receptive Field Feature Fusion Module (TMRF <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> M) and a Local Suppression and Global Enhancement Module (LSGEM), which help spot short intervals more precisely and suppress background information. To further highlight the differences between positive and negative samples, we set up a large number of random pseudo ground truth intervals (background clips) on some discarded sliding windows to accomplish background clips modeling to counteract the effect of non-expressive face and head movements. Experimental results show that our proposed network achieves state-of-the-art performance on the CAS(ME) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> , CAS(ME) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> and SAMM-LV datasets.
Dual Attribute-Based Auditing Scheme for Fog Computing-Based Data Dynamic Storage with Distributed Collaborative Verification

Compared with cloud computing-based data storage, distributed data storage in fog computing is more vulnerable to malicious attacks. So, it is very necessary to provide a secure distributed auditing mechanism with protecting the identity privacy of data owners and controlling the identities of auditors under fog computing-based data storage. In this paper, we propose a dual attribute-based auditing scheme for fog computing-based data dynamic storage. Our auditing scheme can protect the identity privacy of data owners, and provide an attribute-based access control for corresponding audits with a distributed collaborative verification between related fog servers. In our scheme, a data owner can securely upload his divided and blinded file blocks with corresponding block authenticators (related with his attribute set) to related fog servers. To prevent malicious auditors from consuming system resources by abusing audit requests, the data owner can provide an attribute-based access control for corresponding audits, where the data owner specifies the attribute set of corresponding auditors who have the right to check the integrity of related data. Further, a distributed collaborative verification mechanism between related fog servers is constructed to reduce the disadvantages of centralized verification, where the Shamir’s secret-sharing method is used to decompose the picked blinding factor as the shared sub-secrets sent to each fog server respectively. Compared with cloud computing-based data storage, our collaborative verification mechanism can implement distributed auditing consent of stored data between multiple fog servers. Our auditing scheme can further audit out specific suspicious fog servers. Additionally, we provide a dynamic data operation mechanism to efficiently support the updating of users’ data under fog computing-based data storage. Furthermore, related theoretical analysis and experimental evaluation show our scheme is secure and efficient.
Sustained Harm Over Time and Space Limits the External Function of Online Counterpublics for American Muslims

Social media platforms are celebrated for their capacity to empower those with marginalized or disenfranchised identities and support them to create counterpublics. We focus on one such group, Muslim Americans, and ask how visible Muslim Americans, such as journalists, activists, and aspiring politicians, use social media to craft counter-narratives, reclaim control of their stories, and mitigate the harm directed at them. Through a series of 19 interviews, we found that visible Muslim Americans' ability to craft and sustain counter narratives is largely hampered by sustained online harm (e.g. harassment). We found that these public figures were harmed repeatedly over long periods of time and through the weaponization of platform affordances such as replying, tagging, and hashtag takeovers, as well as the weaponization of their gender and identity. Our findings shed light on the serious limitations of social media to provide a safe platform for counterpublics to engage externally with wider publics. Finally, we discuss the limitations of content moderation as the dominant framework for addressing harm online and suggest alternative paths forward based on restorative and transformative justice.
Hiding Data in Binary Images Using Block-Diagonal Partition Pattern

Many pieces of information are exchanged in the form of binary pictures across many domains as a result of the paradigm shift from paper to electronic.This covers banking, economic dealings, military communication, and imaging in the medical field.In some circumstances, it is necessary to communicate sensitive information about photographs in a covert manner.On the other hand, unreadable watermarks are necessary to verify the authenticity and originality of an image.The two most commonly used methods for this purpose are steganography and watermarking, both of which ensure the imperceptibility of hidden information.It is challenging for binary images to hide data and to keep changes unnoticeable.A novel data hiding method for binary images is presented in this paper which uses Block-Diagonal Partition Pattern(BDPP).In this method, 3X3 pixels image blocks are partitioned diagonally.Black and white pixel counts within each partition before and after embedding are used to identify potential blocks.The connectivity between the pixels in each division is also examined in order to determine whether a block is suitable for carrying data bits that account for distortion.The chosen block's central pixel serves as the carrier for data bits.Since every block carrying the data satisfies the embeddability requirements both before and after concealing, the original picture is not required to retrieve the concealed data.Data is twice encrypted using this method to boost data security.The results show that our technique produces little distortion while being safe and having great hiding capabilities.
Smart contract vulnerability detection based on semantic graph and residual graph convolutional networks with edge attention

Smart contracts are becoming the forefront of blockchain technology, allowing the performance of credible transactions without third parties. However, smart contracts on blockchain are not immune to vulnerability exploitation and cannot be modified after being deployed on the blockchain. Therefore, it is imperative to assure the security of smart contracts via intelligent vulnerability detection tools with the exponential increase in the number of smart contracts. The remarkably developing deep learning technology provides a promising way to detect potential smart contract vulnerabilities. Nevertheless, existing deep learning-based approaches fail to effectively capture the rich syntax and semantic information embedded in smart contracts for vulnerability detection. In this paper, we tackle the problem of smart contract vulnerability detection at the function level by constructing a novel semantic graph (SG) for each function and learning the SGs using graph convolutional networks (GCNs) with residual blocks and edge attention. Our proposed method consists of three stages. In the first stage, we create the SG which contains rich syntax and semantic information including the data–data, instruction–instruction and instruction–data relationships, variables, operations, etc., by building an abstract syntax tree (AST) from the code of each function, removing the unimportant nodes in the AST, and adding edges between the nodes to represent the data flows and the execution sequence of the statements. In the second stage, we propose a new graph convolutional network model EA-RGCN to learn the content and semantic features of the code. EA-RGCN contains three parts: node and edge representation via word2vec, content feature extraction with a residual GCN (RGCN) module, and semantic feature extraction using an edge attention (EA) module. In the third stage, we concatenate the code content features and the semantic features to obtain the global code feature and use a classifier to identify whether the function is vulnerable. We conduct experiments on the datasets constructed from real-world smart contracts. Experimental results demonstrate that the proposed semantic graph and the EA-RGCN model can effectively improve the performance in terms of accuracy, precision, recall, and F1-score on smart contract vulnerability detection.
Comments on “MLCM: Multi-Label Confusion Matrix”

In the paper "MLCM: Multi-Label Confusion Matrix" a method for computing the confusion matrix for the multi-label classification problem is proposed.Although the authors state that there is no similar work on computing confusion-matrix for multi-label classification problems, we point out that the method for computing multi-label confusion-matrix was previously proposed in the paper "Multi-Label Classifier Performance evaluation with Confusion Matrix" by Krstinic et al.We will show that both methods are based on the same set of assumptions and scenarios for instances of true and predicted labels, while there are differences in computing the contribution of classification errors to the confusion matrix between these two approaches.