Reviewerly/exHarmony huggingface dataset:
Unsupervised Low-Light Image Enhancement via Virtual Diffraction Information in Frequency Domain

With the advent of deep learning, significant progress has been made in low-light image enhancement methods. However, deep learning requires enormous paired training data, which is challenging to capture in real-world scenarios. To address this limitation, this paper presents a novel unsupervised low-light image enhancement method, which first introduces the frequency-domain features of images in low-light image enhancement tasks. Our work is inspired by imagining a digital image as a spatially varying metaphoric “field of light”, then subjecting the influence of physical processes such as diffraction and coherent detection back onto the original image space via a frequency-domain to spatial-domain transformation (inverse Fourier transform). However, the mathematical model created by this physical process still requires complex manual tuning of the parameters for different scene conditions to achieve the best adjustment. Therefore, we proposed a dual-branch convolution network to estimate pixel-wise and high-order spatial interactions for dynamic range adjustment of the frequency feature of the given low-light image. Guided by the frequency feature from the “field of light” and parameter estimation networks, our method enables dynamic enhancement of low-light images. Extensive experiments have shown that our method performs well compared to state-of-the-art unsupervised methods, and its performance approximates the level of the state-of-the-art supervised methods qualitatively and quantitatively. At the same time, the light network structure design allows the proposed method to have extremely fast inference speed (near 150 FPS on an NVIDIA 3090 Ti GPU for an image of size 600×400×3). Furthermore, the potential benefits of our method to object detection in the dark are discussed.
How efficient are replay attacks against vote privacy? A formal quantitative analysis1

Replay attacks are among the most well-known attacks against vote privacy. Many e-voting systems have been proven vulnerable to replay attacks, including systems like Helios that are used in real practical elections. Despite their popularity, it is commonly believed that replay attacks are inefficient but the actual threat that they pose to vote privacy has never been studied formally. Therefore, in this paper, we precisely analyze for the first time how efficient replay attacks really are. We study this question from commonly used and complementary perspectives on vote privacy, showing as an independent contribution that a simple extension of a popular game-based privacy definition corresponds to a strong entropy-based notion. Our results demonstrate that replay attacks can be devastating for a voter’s privacy even when an adversary’s resources are very limited. We illustrate our formal findings by applying them to a number of real-world elections, showing that a modest number of replays can result in significant privacy loss. Overall, our work reveals that, contrary to a common belief, replay attacks can be very efficient and must therefore be considered a serious threat.
An Analysis of Students’ Grammatical Errors in Writing Narrative Texts

Senior high school students still make a large number of errors related to grammar when they have writing narrative text. This study aimed to analyze the types of grammatical errors, identifying the sources of errors, and knowing the writing difficulties commonly faced by the tenth grade students of senior high school in their narrative texts. Descriptive qualitative method was used in this study. There were two kinds of data being used namely quantitative data and qualitative data. There were three different instruments applied such as, the teacher, writing test, and questionnaires. There were only 23 narrative texts collected and analyzed. Based on the error analysis, there were 308 grammatical errors classified into four types, namely ‘omission’, ‘misformation’, ‘addition’, and ‘misordering errors’ in students’ narrative texts. ‘Misformation errors’ are the biggest number of errors there were 131, the most dominant errors are ‘misformation of alternating form errors’. The errors occured because of the students’ interlingual and intralingual transfer. There were cognitive problem that made the students feel difficult in writing narrative texts because of their lack of knowledge of English grammar and vocabularies.
Template matching-based connected constraint mapping optimization method for quantum circuits

In order to reduce the number of gates in quantum circuit to reduce the error rate running on NISQ device, it is demonstrated that the template satisfies the relevant conditions for mapping to the linear topology structure in the quantum system, and a quantum circuit connectivity constraint mapping optimization method based on template matching is proposed. This method reconstructs the templates by adding SWAP gates or replacing bridge gates to make them meet the connectivity constraints of linear topology. Aiming at the linear subcircuits in quantum circuits that satisfy the connectivity constraints of quantum topological structure, the template matching optimization method is used to replace the gates in the circuit that match the template with a few remaining unmatched gates in the template. Finally, the optimized circuit also satisfies the connectivity constraint of quantum topology. Using a set of NISQ benchmark circuits, compared with the t|ket⟩ compiler, the test results show that the number of CNOT gates can be further reduced by an average of 28.71 %, up to 40 %.
Opportunities for the critical thinking formation in the digital age

The article analyzes the digitalization impact on the subject's thinking ability in general and on the formation of critical thinking skills, in particular. The advantages and disadvantages of informational and postmodern approaches in such an analysis are revealed. It is proved that an adequate analysis of the digitalization impact is possible only if we understand the inclusion of this process in the axiological context of the human existence.
Developing A Course Book of F&amp;B Service for Enhancing Instruction in Tourism Class

The purpose of this research is to develop a product and this research uses the research and development method level I from Sugiyono (2019). The steps in developing this research are (1) Potential and problems (2) Literature study (3) Gathering information (4) Design product (5) validate product (6) Design is assessed. The instruments used in this research were observations sheet, lists of interview, and questionnaires. The observation sheet was used to observe the needs and problem that facing by the vocational school students. The lists of the interview were used to make sure the needs of teachers and students. And the questionnaire was used for product validation/product valuation by experts. Based on the recapitulation of experts’ judgment validation, it showed that the developing course book of food and beverage services obtained the best category with score 116 obtained from the first expert, 120 obtained from the second expert, and 123 obtained from the third expert, so the mean score is 119,6. It can be concluded that the mean score is greater than 104.88 with the best category, so the material developed is valid or gets the best category and suitable for students, but there are some points that must be revised based on comments from experts
Organizational cybersecurity readiness in the ICT sector: a quanti-qualitative assessment

Purpose Cyberattacks are becoming increasingly widespread, and cybersecurity is therefore increasingly important. Although the technological aspects of cybersecurity are its best-known characteristics, the cybersecurity phenomenon goes beyond the detection of technological impacts, and encompasses all the dimensions of an organization. This study thus focusses on an additional set of organizational elements. The key elements of cybersecurity organizational readiness depicted here are cybersecurity awareness, cybersecurity culture and cybersecurity organizational resilience (OR). This study aims to qualitatively assess small and medium enterprises’ (SMEs) overall level of organizational cybersecurity readiness. Design/methodology/approach This study focused on conducting a cybersecurity organizational readiness assessment using a sample of 53 Italian SMEs from the information and communication technology sector. Informed mixed method research, this study was conducted consistent with the principles of the explanatory sequential mixed method design, and adopting a quanti-qualitative methodology. The quantitative data were collected through a questionnaire. Qualitative data were subsequently collected through semi-structured interviews. Findings Although many elements of the technical aspects of cybersecurity OR have yielded very encouraging results, there are still some areas that require improvement. These include those facets that constitute the foundation of cybersecurity awareness, and, thus, a cybersecurity culture. This result highlights that the areas in need of improvement are exactly those that are most important in fighting against cyber threats via organizational cybersecurity readiness. Originality/value Although the importance of SMEs is obvious, evidence of such organizations’ attitudes to cybersecurity are still limited. This research is an attempt to depict the organizational issue related to cybersecurity, i.e. overall cybersecurity organizational readiness.
BCSR: toward arbitrarily oriented text image super-resolution via adaptive Bezier curve network

Abstract Although existing super-resolution networks based on deep learning have obtained good results, it is still challenging to achieve an ideal visual effect for irregular texts, especially spatially deformed ones. In this paper, we propose a robust Bezier Curve-based image super-resolution network (BCSR), which can efficiently handle the degradation caused by deformations. Firstly, the arbitrarily shaped text is adaptively fitted by a parameterized Bezier curve, aiming to convert a curved text box into an annotated text box. Then, we design a BezierAlign layer to calibrate between the extracted features and the input image. By importing the extracted text prior information, the accuracy of the super-resolution network can be significantly improved. It is worth highlighting that we propose a kind of text prior loss that enables the text prior image and the super-resolution text image to achieve cooperation enhancement. Extensive experiments on several standard scene text datasets demonstrate that our proposed model achieves desirable objective evaluation results and further immensely helps downstream tasks related to text recognition, especially in text instances with multi-orientation and curved shapes.
Evolutionary Algorithm with Graph Neural Network Driven Cyberbullying Detection on Low Resource Asian Languages

ICT is widely adopted by Asian youth and is utilized by people of all ages across the continent. Despite its many advantages, unethical ICT usage can lead to many complications. A harmful application of ICT for social communication and engagement is cyberbullying. Simply adhering to the generally accepted norms and guidelines for cybersecurity will not protect you from cybercrime. Even well-known social media stages like Twitter are safe from this attack. Natural language processing (NLP) research on cyberbullying detection has become popular recently. Even though old-style NLP procedures have become highly cyberbullying, there are still hurdles to overcome. These include the limited character count allowed by social media platforms, an imbalance among comments, ambiguity, and unnecessary use of slang. Models based on (CNNs), Multilayer Perceptrons (MLPs), and (RNNs), have recently shown encouraging results in a variety of NLP tasks. With this motivation, this research develops an African vulture optimization algorithm with a graph neural network-based cyberbullying detection and classification (AVOAGNN-CBDC) model. The proposed AVOAGNN-CBDC technique mainly intends to detect and classify cyberbullying. The AVOAGNN-CBDC technique undergoes data preprocessing in different stages and a FastText-based word embedding process to achieve this. Besides, the AVOAGNN-CBDC technique employs the GNN model for cyberbullying detection and classification. Finally, the AVOA is used for the optimal parameter selection of the GNN model, which helps achieve improved classification performance. The experimental result investigation of the AVOAGNN-CBDC technique is tested on the cyberbullying dataset, and the outcomes highlighted the supremacy of the AVOAGNN-CBDC technique in terms of several measures.
Data mining and analysis for defects of secondary equipment in power grid

In order to effectively maintain and operate the smart substations, a data mining and analysing method for defects of secondary equipment based on the FP-Growth algorithm is proposed．The basic ideas of association rules and FP-Growth algorithm are introduced firstly, then the defect model and the system framework are proposed to mine and analyze the defect data. Through the analysis of the actual defect data of a substation using the proposed method, it shows that the proposed method can effectively find the relationship among secondary devices, manufactories, defect properties, defect causes and other factors. By data mining, it can provide valuable information for efficient maintaining and operation of the substations. Some auxiliary suggestions are given for operation of secondary device finally．
Opportunities and challenges of using immersive technologies to support students’ spatial ability and 21st-century skills in K-12 education

Abstract Organic chemistry, also known as stereochemistry, is a subject considered to be notably complex for students to understand. Knowledge construction in stereochemistry might demand the ability to imagine or visualise the distribution of atoms. For students with insufficient spatial ability, this could be confusing. This study aimed to explore empirically students and teachers’ experiences regarding the opportunities and challenges they encountered when using virtual reality (VR) and augmented reality (AR) technologies to teach and learn stereochemistry, as well as how these technologies might support students’ spatial ability and 21st-century skills in K-12 education by participating in virtual lab environments. Using design-based research methods, an exploratory study based on the utilization of immersive technologies was designed and carried out with three groups in Grade 8. Two researchers observed the activities that were implemented, after which the participating students answered a qualitative survey about how these technologies and the specific teaching design might support their understanding of molecules in 3D space. The schoolteacher was interviewed afterwards, and she shared her motives and goals (e.g., what she sought to achieve) by using the technologies in her teaching. The results show that the students had a positive experience in their learning of chemistry through immersive realities, increased motivation to learn the subject, and their test results improved slightly. However, it is essential for a teacher to possess technological know-how regarding VR and AR to achieve the intended goals. The findings highlight the added value of these immersive technologies by enhancing students’ learning processes and the central role of the teacher as a designer and technological leader of the group.
Thinking with waste to know the economic

I argue that thinking with `waste' to know the `economic' necessitates making some important conceptual distinctions. Drawing on empirical examples from my research on pollution and waste arising from electronics I first address distinctions between waste, dirt, and discard. Next, I argue that thinking with waste and pollution from electronics justifies a need to carefully distinguish between scale and magnitude. This claim is examined through examples related to chemical pollution arising from manufacturing electronics. I subsequently argue that thinking with waste clarifies the need to think and act in relation to indeterminacy rather than uncertainty. This claim is evidenced through a discussion of the temporal and the spatial magnitudes of waste arising from electronics manufacturing at locations in and beyond Silicon Valley. Finally, I claim that thinking with waste enables broader capacities to think about the ethics of ordering in and of industrial systems. This last claim is examined through a brief discussion of the economies of commercial content moderation on social media platforms. My argument treats wasting as a technique of power that enables social media companies to flourish while wasting the hearts and minds of the people who work to weed out objectionable content for social media users.
Role of Blockchain in Healthcare Sector

The domain of bio-medical engineering is facing significant challenges and issues due to data management, data attribution, data security, data availability and privacy. The mechanism of blockchain corresponds to a time-stamped set of data records that is maintained and managed by a cluster/group of the computer system. The collection of cluster groups is secured and protected by cryptographic values, which are often said to be the chains. The growth of blockchain has progressed in a variety of applications in terms of asset management, IoT, smart devices, supply chains, public data validations, and personal identification. Even though the growth has progressed in different emerging disciplines, the impact of blockchain in technology-based biomedical engineering created a vast difference and incorporation in various fields of operation. In a medical data management system, an old audit trail is mostly needed to perform data operations such as insert, delete, and update. Blockchain is, therefore, suitable for the process concerning fixed ledger to record and update critical information services. This chapter completely focuses on the role of blockchain in biomedical engineering applications.&lt;br&gt;
Generative Adversarial Networks for Deep Learning in Healthcare: Architecture, Applications and Challenges

Deep Learning is a new generation of artificial neural networks that have transformed our daily lives and has impacted several industries and scientific disciplines in recent years. Recent development in deep learning provides various significant methods to obtain end-to-end learning models from complex data. Generative Adversarial Networks give the idea of learning the deep representations without extensively interpreting the training data. The generative adversarial network involves the generative modeling approach that uses the deep learning approach. The chapter is broadly divided into the following sections as (1) Insights of deep learning &amp;amp; the generative adversarial networks, (2) GAN’s representative variants, training methods, architecture, and mathematical representation, and (3) Efficacy of GAN in different applications. This chapter will gain the recent work done in GAN and implement this technique in the different deep learning applications for healthcare. Here, we will also analyze some of the future perspectives and trends in the forthcoming time.&lt;br&gt;
Unsupervised Flight Fault Propagation Analysis Using a Variational Autoencoder

Assessing fault propagation is essential for flight safety and for optimal maintenance scheduling of passenger aircrafts. This paper studies such application by way of latent space representation with variational autoencoder. The proposed method learns a topological representation of the dynamics of a normal flight from real industrial data. The method is validated by showcasing that the trained model can separate among different flight modes, e.g., taxiing, landing, and cruise, etc. Then, through case studies of various anomalous flights, it is shown that the dynamics, and as a consequence the latent space representation, of those flights significantly differ from those of the normal flights. The proposed method can be applied subsequently to analyze fault propagation.
Addressing Bias in Fine-Grained Classification Datasets: A Strategy for Reliable Evaluation

The high specificity of classes in fine-grained clas-sification tasks leads to a small number of images per class in the common research datasets. Thus, the intra-class variance, such as differences in vehicle colors for fine-grained vehicle classification, may not be properly represented. Consequently, there can be a heavy bias in regards to certain attributes, such as vehicle colors, leading to unintended information leakage to the classifier which can relate the vehicle color to a vehicle model. This, in turn, can result in misleading accuracy estimations as we show in this study. To address this issue, we propose a method to quantify the bias of a train-test split in regard to a specific attribute, providing a metric for the expressiveness of the results. To prevent the bias from resulting in misleading accuracy estimations, we apply a simple splitting scheme that separates the manifestations of the attribute. This split prevents the model from exploiting features which are unrelated to the actual task at hand, leading to more accurate estimations of the model's real-world performance and generalization ability. We demonstrate the effectiveness of our method by examining the vehicle color bias in fine-grained vehicle classification datasets. Our results show that the strong performance of current methods, which render this task as practically solved, is largely due to the exploitation of this bias. We can cope with it to some degree by transforming the images to grayscale, partly restoring the performance of the original split. However, the accuracies are still far lower than indicated by the original split. Additionally, we demonstrate that the original random train-test splits of datasets may show higher accuracies for poorlier generalizing methods, which renders experimentation to find better methods misleading. Therefore, better splitting schemes, such as our attribute-based splitting scheme, are required to obtain trustworthy results in experiments.
Beam Recognition for Headlamps Aiming

In this paper, a novel approach is introduced to address the issue of vehicle headlamps aiming. Instead of handling it with traditional methods, it is considered as a beam pattern recognition problem. Unlike conventional techniques, which primarily rely on offline measurements of specific reference points, which are then projected onto a wall or panel, our method suggests an automatic aiming process using the Surround View Camera (SVC) via two main steps. First, checking the alignment of headlamps, where it detects whether it is “conform” or not according to the automotive lighting regulations. Second, estimating the deviation from optimal aiming regarding vertical and horizontal angles. A Deep Learning model is proposed to detect whether the headlamp is still within the regulation requirements, and another model for regression with a normalized loss function is designed to predict the errors of both orientations. Our two-step approach, tested with multiple architectures, shows high accuracy. To evaluate the performance of the proposed models, photometrical datasets of three different products are tested for both classification and regression. We computed several metrics on experimental results to evaluate both steps and obtained with the best considered architecture a mean accuracy of 94.46% for the classification part and a normalized error interval of 3.53% for the regression one.