The persistent attractions of low-tech: Challenging the efficiency paradigm of forensic technology

Abstract: The promise of efficiency is a catalyst for technological development in forensic science and crime scene investigation practices. Crime scene investigators aim to locate and document every potential trace for evidence and investigate crime scenes as quickly, accurately and objectively as possible. This narrative suggests that performance leads the police to adopt new efficient technologies. But to what extent is this narrative reflective of actual practices? Through examining how crime scene investigators use existing technologies in practice, i.e. traditional or ‘low-tech’ methods and tools such as DNA detection dogs and manual documentation tools, this article shows that the use of low-tech persists even under the pressure to perform and achieve. The study finds that low-tech remains attractive as a less-restraining alternative to digital and high-tech solutions. It is also contrast to, integrated with or necessary in co-existence with high-tech for crime scene investigations practices to function. The study draws on fieldwork conducted at police stations in Norway, including interviews and participatory observation of investigations in practice, to discuss the persistent attractions of low-tech and what the implications are for research into policing and technology.


Toward Redefining Library Research Support Services in Australia and Aotearoa New Zealand: An Evidence-Based Practice Approach

Abstract: An increasingly complex and demanding research landscape has seen university libraries rapidly evolve their services. While research data management, bibliometrics, and research impact services have predominantly featured in the literature to date, the full scope of support libraries are currently providing to their institutions is unknown. This paper aims to present an up-to-date view of the scope and extent of research support services by university libraries across Australia and Aotearoa New Zealand. A coding process analyzed content data from university library websites. Eleven research support areas were identified. Service delivery is split between synchronous and asynchronous modes. This paper describes a lived experience of an evidence-based library and information practice approach to improving research support services at two Australian university libraries, and while it highlights continued maturation of research support services, more research is needed to better understand influences on service development.


Addressing quantum’s “fine print” with efficient state preparation and information extraction for quantum algorithms and geologic fracture networks

Abstract: Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come without difficulty. Quantum algorithms require that problems satisfy not only algorithm-specific constraints, but also application-specific ones. Otherwise, the quantum advantage carefully attained through algorithmic ingenuity can be entirely negated. Previous work addressing quantum algorithms for geologic fracture flow has illustrated core algorithmic approaches while incrementally removing assumptions. This work addresses two further requirements for solving geologic fracture flow systems with quantum algorithms: efficient system state preparation and efficient information extraction. Our approach to addressing each is consistent with an overall exponential speed-up.


Effective Combination of Oral Reading and Retrieval Practice Strategies: The Moderating Effect of Reading Model Development

Abstract: The complementary view of strategy combinations points to the fact that a combination of strategies that promote memory encoding and retrieval practice strategies can be effective in improving memory retention. This study examines the effectiveness of the combination of oral reading and retrieval practice strategies and the effect of the development of reading patterns on their effectiveness. This study designed a mixed experimental design of 3 (grades: Grade three, Grade five, college students) × 2 (reading mode: oral reading and silent reading) × 2 (learning strategies: re-learning and retrieval practice). Among them, reading mode is the within-subject variable, and learning strategy and grade are the between-subjects variables. The dependent variable is the ratio of specific words recalled in the final test. The results show that the oral and retrieval practice is a significant and effective combination of memory strategies for primary school students who are mainly oral reading or in the transition from oral reading to silent reading. It is significantly better than the three strategy combinations of oral reading combined with re-leaning, silent reading combined with re-learning, and silent reading combined with retrieval practice. For adult students who dominated silent reading, silent reading combined with retrieval practice is the best memory strategy.


Do redundant graphical attributes reduce decision-making efficiency?

Abstract: Multiple time-series graphs are commonly used for data visualisation, but few scholars have investigated the impact of graphical attributes on decision-making efficiency. This study explores the effects of graphical attributes of varying redundancy conditions on decision-making efficiency. Two experimental conditions were developed for the experiment: non-redundant (independent graphical attributes: colour, linear and marker) and redundant (combinations of two and more graphical attributes: colour and linear, colour and marker, etc.). A total of 60 people took part in both experiments and performed two tasks: maximisation and discrimination. The experiments revealed that the addition of attributes, such as colour, marker or linear, decreased response time (RT), but the combination of colour & linear & marker increased RT. This is more significant in discrimination tasks. We provide empirical evidence for the design of time-series data visualisations and encourage the combination of two of these graphical attributes, such as colour & linear, colour & marker or linear & marker, when conditions allow, to improve decision-making efficiency.Few scholars have studied the impact of graphical attributes on decision-making efficiency in data visualisation. This study explores the effect of graphical attributes with different redundancy levels on decision-making efficiency through behavioural experiments. It has been found that moderately redundant graphical attributes in difficult tasks can significantly improve decision-making efficiency.


Application of multi-scale feature fusion algorithm based on motion wearable sensors in feature extraction of sports images

Abstract: The utilization of moving image feature extraction in sports teaching has garnered increasing attention. However, traditional feature extraction algorithms often struggle to meet the diverse and complex demands of moving images. To address this challenge, this paper proposes a multi-scale feature fusion algorithm aimed at improving feature extraction in moving images. The algorithm begins by decomposing the moving image into multiple scales, followed by extracting features from each scale using a feature extraction network. To obtain a more comprehensive and accurate feature representation, feature fusion technology is employed to merge the features from different scales. The proposed algorithm, based on multi-scale feature fusion, exhibits a significant improvement in both accuracy and stability when compared to traditional feature extraction algorithms. Byaccurately extracting and representing the crucial features within moving images, the algorithm contributes to an improved understanding of athletes' movements, enabling instructors to provide more targeted and insightful feedback. This algorithm effectively captures key features within the moving images, providing robust support for tasks such as movement analysis and skill evaluation in sports teaching.


Regulating speech: harm, norms, and discrimination

Abstract: Mary Kate McGowan's Just Words offers an interesting account of exercitives. On McGowan's view, one of the things we do with words is change what's permitted, and we do this ubiquitously, without any special authority or specific intention. McGowan's account of exercitives is meant to identify a mechanism by which ordinary speech is harmful, and which justifies the regulation of such speech. It is here that I part ways. I make three main arguments. First, McGowan's focus on harm is misguided; that ordinary speech is harmful is harder to support, and in turn does not less to support conclusions about whether it is wrong and warrants regulation, than Just Words suggests. Second, if the speech is harmful, McGowan's account of exercitives is ill-suited to explain why; the relevant instances of ordinary speech seem to express speakers' views about moral norms, not change social norms. Third, McGowan's argument for why ordinary speech should be regulated because it is a form of discrimination presupposes a great deal that is controversial about what makes discrimination wrong and legally actionable, and even if it is right it may license the regulation of more speech than many, including McGowan, would deem acceptable.


PAC: A monitoring framework for performance analysis of compression algorithms in Spark

Abstract: In Spark, a massive amount of immediate data inevitably leads to excessive I/O overhead. To mitigate this issue, Spark incorporates four compression algorithms to reduce the size of the data for better performance. However, compression and decompression only constitute a portion of the overall logical flows of Spark applications. This indicates a potential considerable interaction between compression algorithms and Spark applications regarding performance. Consequently, identifying factors that significantly impact the performance of compression algorithms in Spark, and subsequently, determining the actual performance benefits these algorithms provide to Spark applications, remains a significant challenge. To address the challenge, this paper presents a monitoring framework, named PAC, for conducting in-depth and systematic performance analysis of compression algorithms in Spark. As the pioneer of such monitoring frameworks, PAC is built on top of Spark core and collaborates with multiple monitors to collect various types of performance metrics of compressors, correlates and integrates them into structured tuples by the data transformer in PAC. This makes it easier to diagnosis of factors that have a significant influence on the performance of compression algorithms in Spark. Upon utilizing PAC, our experiments reveal that new determinants include the input/output data sizes and types of compression/decompression invocations, CPU consumption for compressing a massive amount of data, and hardware utilization, besides traditional determinants. Moreover, these experiments demonstrate that ZSTD is more susceptible to performance issues when compressing and decompressing small data, despite the overall input and output data being huge. In terms of performance, LZ4 serves as a viable alternative to ZSTD. These findings not only benefit researchers and developers in making more informed decisions in terms of configuring and tuning Spark execution environments but also sustainably boost the optimization of compression algorithms for Spark.


Level evaluation of concrete dam fractures based on game theory combination weighting-normal cloud model

Abstract: Hazard evaluation of concrete dam fractures is vital for safe operations. The width (S 1 ), length (S 2 ), and depth (S 3 ) of fractures are adopted as the assessment index, and the game theory combination weighting-normal cloud model is introduced. The normal cloud model of certain dam fractures is subsequently established. The weight coefficients of each index are calculated using game theory combination weighting, and the certainty degree of each index is determined using the cloud model. Finally, the hazard levels of the concrete dam fractures are judged. The proposed model solves the fuzziness and randomness of different indexes; the conclusions demonstrate that the model is feasible for the hazard assessment of concrete dam fractures, and its accuracy is very high; therefore, a new approach can be provided for future hazard-level assessments of concrete dam fractures.


SSA‐UNet: Whole brain segmentation by U‐Net with squeeze‐and‐excitation block and self‐attention block from the 2.5D slice image

Abstract: Abstract Whole brain segmentation from magnetic resonance images (MRI) is crucial in diagnosing brain diseases and analyzing neuroimaging data. Despite advances through deep learning, challenges such as uneven gray distribution and the presence of artifacts still present hurdles in medical image processing. These limitations are often a result of insufficient spatial contextual information and lack of attention to important regions within existing models. To address these issues, this paper presents SSA‐UNet (Squeeze‐and‐Excitation and Self‐Attention UNet), a uniquely designed deep convolutional neural network that integrates spatial constraints by converting three consecutive 2D MRI slices into a single 2.5D image. This facilitates capturing inter‐slice dependencies effectively. Additionally, the newly formulated SSA block, which sequentially incorporates channel attention and Self‐Attention mechanisms, is placed before the decoders in the conventional U‐Net architecture. This enables the network to automatically weight different feature maps and focus more effectively on regions requiring precise segmentation. Rigorous evaluations on LPBA40 and IBSR18 datasets substantiate the remarkable improvements in accuracy and stability achieved by SSA‐UNet. Results indicate Dice coefficients of 98.38% and 97.47%, specificity of 99.69% and 99.57%, and sensitivity of 98.5% and 97.98% for the respective datasets. Compared to other existing models, SSA‐UNet shows significant improvements on both the LPBA40 and IBSR18 datasets. On the LPBA40 dataset, SSA‐UNet's Dice coefficient improved by 0.33% compared to the sub‐optimal model, while on the IBSR18 dataset, the improvement reached 1.78%. These empirical findings demonstrate SSA‐UNet's heightened capability in addressing the long‐standing challenges in MRI‐based whole‐brain segmentation.


Diverse branch feature refinement network for efficient multi‐scale super‐resolution

Abstract: Abstract Despite the existence of various super‐resolution (SR) methods, most of them focus on designing models for specific upscaling factors rather than fully exploiting inter‐scale correlation to improve efficiency. In contrast, multi‐scale SR methods can effectively reduce the redundancy of network parameters by aggregating the feature extraction processes corresponding to multiple scales into a unified process. The aim of this study is to enhance the compactness and efficiency of the SR model. Thus, an efficient multi‐scale SR method called the diverse branch feature refinement network (DBFRN) is proposed. By decoupling the training process and inference process based on the idea of structural re‐parameterization, multi‐branch topology is adopted to enrich multi‐scale learning and merge branches to achieve efficient inference with equivalent effects. Specifically, two re‐parameterization strategies are designed and two corresponding feature refinement blocks for different feature levels in multi‐scale SR network. Extensive experiments demonstrate that the proposed multi‐scale SR method is effective and efficient, and it can outperform advanced single‐scale methods in terms of quantity and quality.


Blockchain and agricultural sustainability in South America: a systematic review

Abstract: In its fundamental role for food security in South America, sustainable agriculture faces the challenge of addressing the current and future needs of the region while ensuring profitability, environmental health, and social and economic equity. Currently, as support for sustainable agriculture, a significant transformation is observed in the agricultural landscape due to the development of advanced information systems. Technologies such as Artificial Intelligence, Machine Learning, and Blockchain have emerged as crucial tools to document and support sustainable agricultural processes. Blockchain technology has proven to be highly beneficial for sustainable agriculture, effectively addressing a significant issue in the agricultural supply chain by providing solutions for transparent and traceable processes. This technology solves the problem by establishing a permanent and open record of all transactions and activities in the supply chain, allowing consumers and stakeholders to track the origin and quality of agricultural products, thereby fostering trust and fair trade. For this reason, this article conducted a review of the current state of blockchain technology in sustainable agriculture, aimed at researchers and farmers in South America. The advantages and disadvantages of blockchain technology were identified, focusing on technologies developed and tested during the design and pilot phases. The PRISMA methodology was used in this review, and documents were searched in Scopus and Web of Science databases. Six hundred and fifty-six articles were identified and selected (2018–2023 period), but only 104 met the eligibility and inclusion criteria. The findings indicate a 30% increase in the adoption of decentralized applications (DAPs) powered by blockchain in the agribusiness sector compared to the previous year. After a thorough analysis, it has been determined that smart contracts, non-fungible tokens for digital assets, and blockchain oracles will provide promising solutions for sustainable agricultural technology in the future.


Modalities combining two negations

Abstract: Abstract In the context of positive logic expanded with the dual of intuitionistic negation, obtaining intuitionistic negation itself as a consequence, those two connectives allow to introduce modal concepts such as necessity and possibility. We study the resulting modal logic, paying attention to different extensions of it. We provide a syntactic approach and both algebraic and Kripke semantics.


Epidemiological contemplation for a currently pragmatic COVID-19 health passport: a perspective

Abstract: The coronavirus disease 2019 (COVID-19) has caused a global pandemic that has wreaked havoc on the lives of millions of people around the world. Confinement measures aim to reduce the epidemic's spread and minimize the burden of morbidity and mortality. In response to the challenges caused by the pandemic, digital health passports have been developed exponentially. We highlight the latent epidemiological barriers to health passports to achieve standardized digital care platforms. This review paper not only highlights the epidemiological barriers but also articulates the possible infrastructure required to make the International Standard for a multi-factor authenticated and validated health passport.


Designing ICT integrated lesson activities: an intervention to bolster tutors’ pedagogical use of ICT competences in Tanzania teacher education

Abstract: Purpose The study aims to assess the effectiveness of engaging tutors in designing and using ICT integrated lesson activities in strengthening their pedagogical use of ICT competences. Design/methodology/approach Survey data from an intervention group of 70 tutors from two teachers colleges (TCs) were used to compare their level of ICT competences and domains of professional practice before and after the intervention. Document analysis, lesson observations and feedback from the learning management system (LMS) were used to describe tutors’ experiences from the intervention. Findings There was a statistically significant increase in tutors’ level of pedagogical use of ICT competences and domains of professional practice associated with hands-on practice in designing and implementing the intervention. Research limitations/implications The intervention focus on hands-on practice, actual teaching and learning needs, and the use of active learning strategies like flipped classroom and the LMS, were useful means for tutors to make sense of pedagogical use of ICT competences. Practical implications The results offer useful insights to teacher education institutions and policymakers on how to prepare professional learning and supportive policies to enhance teaching and learning with ICT for addressing the learning needs of the subject matter. Originality/value Creating 16 ICT integrated lesson activities helped tutors to learn pedagogical use of ICT competences by doing. Use of such intervention could be a useful strategy in teacher education institutions to reposition ICT competence development from reproducing technological competences toward developing knowledge creators who could innovate their pedagogical practice with support from mentors, digital learning resources and networks.


Structure similarity virtual map generation network for optical and SAR image matching

Abstract: Introduction: Optical and SAR image matching is one of the fields within multi-sensor imaging and fusion. It is crucial for various applications such as disaster response, environmental monitoring, and urban planning, as it enables comprehensive and accurate analysis by combining the visual information of optical images with the penetrating capability of SAR images. However, the differences in imaging mechanisms between optical and SAR images result in significant nonlinear radiation distortion. Especially for SAR images, which are affected by speckle noises, resulting in low resolution and blurry edge structures, making optical and SAR image matching difficult and challenging. The key to successful matching lies in reducing modal differences and extracting similarity information from the images. Method: In light of this, we propose a structure similarity virtual map generation network (SVGNet) to address the task of optical and SAR image matching. The core innovation of this paper is that we take inspiration from the concept of image generation, to handle the predicament of image matching between different modalities. Firstly, we introduce the Attention U-Net as a generator to decouple and characterize optical images. And then, SAR images are consistently converted into optical images with similar textures and structures. At the same time, using the structural similarity (SSIM) to constrain structural spatial information to improve the quality of generated images. Secondly, a conditional generative adversarial network is employed to further guide the image generation process. By combining synthesized SAR images and their corresponding optical images in a dual channel, we can enhance prior information. This combined data is then fed into the discriminator to determine whether the images are true or false, guiding the generator to optimize feature learning. Finally, we employ least squares loss (LSGAN) to stabilize the training of the generative adversarial network. Results and Discussion: Experiments have demonstrated that the SVGNet proposed in this paper is capable of effectively reducing modal differences, and it increases the matching success rate. Compared to direct image matching, using image generation ideas results in a matching accuracy improvement of more than twice.


Foreign Direct Investment and Financial Development: Evidence from Selected Arab League Countries

Abstract: This study examined the impact of foreign direct investment on financial development in selected Arab League countries (Algeria, Comoros, Egypt, Jordan, Kuwait, Lebanon, Mauritania, Oman, and Qatar) for the period from 2010 to 2021. The study used one explanatory variable — financial development — and one explained variable — foreign direct investment. To obtain reliable and valid results, panel data were analyzed, and various tests were carried out, including the Chow test, Breusch-Pagan Lagrange multiplier test, Hausman test, Jarque-Bera normality test, Wooldridge test, generalized least squares, and ordinary least squares. The robust model of the study revealed a positive and significant relationship between foreign direct investment and the financial development index, human development index, and interest rate. Also, there is a negative and significant relationship between foreign direct investment and the consumer price index and domestic credit to private sector. Based on these findings , the study recommends that Arab League countries’ policies and strategies should attract foreign investors to maintain and sustain economic developmental goals for healthy, literate, and wealthy lives.


Multiple Factors Drive the Development of Scientific Thinking in Urban Primary School Students of China: FsQCA Analysis Based on the Ecological Systems Theory

Abstract: With the introduction and implementation of core literacy, scientific thinking (ST) has become an essential goal and key dimension of science teaching. At present, there is no agreement on how to cultivate students’ ST. This study took 238 sixth grade students from a public primary school in urban China as research sample, built a theoretical model of scientific thinking development based on the theory of the Bronfenbrenner’ ecological systems theory, and used multiple data to explore and analyze the impact path of ST development of primary school students using the fuzzy sets of qualitative comparative analysis method (fsQCA). The development of urban primary school students’ ST is the result of multiple factors at the level of individual drive, family environment, school teaching, and social resources. The result of data analysis showed that the influence path of ST development of urban primary school students driven by multiple factors includes three paths: parent participation leading, scientific practice leading, and home-school-community integration. We have interpreted the influence mechanism of each path in detail and put forward the enlightenment for science education policy and practice.


Real human touch: performer-facilitated touch enhances presence and embodiment in immersive performance

Abstract: Despite being an embodied medium, virtual reality (VR) prioritizes vision and sound over the other senses. While touch has been demonstrated to foster a sense of presence and embodiment, most haptic research in VR focuses on uncanny vibration motors or limited experiences of touch with simple props. Meanwhile, immersive performances such as Eve 3.0 incorporate performer-facilitated touch in novel ways to evoke a complete and social experience of human touch in VR. In response, we conducted a mixed-methods study to investigate the experience of performer-facilitated touch in a 360° video segment from the immersive performance Eve 3.0 . Using a 3 × 2 factorial design, we compared touch from a diary prop and performer in festival and laboratory settings. We found that performer-facilitated touch increased realistic behaviours and questionnaire measures of social presence, embodiment, and tactile realism. The setting also had a significant effect with festival participants demonstrating significantly more behaviours indicating presence, particularly in the no-touch condition. Participant descriptions reveal that in addition to touch, a rich narrative and vivid visuals of social interaction were just as important in immersing participants in the experience and making them feel present. We find that participant experiences are a co-creation situated at the intersection of artefact and context that require a willing suspension of disbelief. The authentic setting and performance artefact afforded a deep understanding of the rich and complex experience of human touch in immersive performance.


Assisted design of data science pipelines

Abstract: Abstract When designing data science (DS) pipelines, end-users can get overwhelmed by the large and growing set of available data preprocessing and modeling techniques. Intelligent discovery assistants (IDAs) and automated machine learning (AutoML) solutions aim to facilitate end-users by (semi-)automating the process. However, they are expensive to compute and yield limited applicability for a wide range of real-world use cases and application domains. This is due to (a) their need to execute thousands of pipelines to get the optimal one, (b) their limited support of DS tasks, e.g., supervised classification or regression only, and a small, static set of available data preprocessing and ML algorithms; and (c) their restriction to quantifiable evaluation processes and metrics, e.g., tenfold cross-validation using the ROC AUC score for classification. To overcome these limitations, we propose a human-in-the-loop approach for the assisted design of data science pipelines using previously executed pipelines. Based on a user query, i.e., data and a DS task, our framework outputs a ranked list of pipeline candidates from which the user can choose to execute or modify in real time. To recommend pipelines, it first identifies relevant datasets and pipelines utilizing efficient similarity search. It then ranks the candidate pipelines using multi-objective sorting and takes user interactions into account to improve suggestions over time. In our experimental evaluation, the proposed framework significantly outperforms the state-of-the-art IDA tool and achieves similar predictive performance with state-of-the-art long-running AutoML solutions while being real-time, generic to any evaluation processes and DS tasks, and extensible to new operators.


Reunion helper: an edge matcher for sibling fragment identification of the Dunhuang manuscript

Abstract: Abstract The Dunhuang ancient manuscripts are an excellent and precious cultural heritage of humanity. However, due to their age, the vast majority of these treasures are damaged and fragmented. Faced with a wide range of sources and numerous fragments, the process of restoration generally involves two core elements: sibling fragments identification and fragment assembly. Currently, fragment restoration still heavily relies on manual labor. During the long practice, a consensus has been reached on the importance of edge features for not only assembly but also for identification. However, accurate extraction of edge features and their use for efficient identification requires extensive knowledge and strong memory. This is a challenge for the human brain. So that in previous studies, fragment edge features have been used for assembly validation but rarely for identification. Therefore, an edge matcher is proposed, working like a bloodhound, capable of “sniffing out” specific “flavors” in edge features and performing efficient sibling fragment identification accordingly, providing guidance when experts perform entity assembly subsequently. Firstly, the fragmented images are standardized. Secondly, traditional methods are used to compress the representation of fragment edges and obtain paired local edge images. Finally, these images are fed into the edge matcher for classification discrimination, which is a CNN-based pairwise similarity metric model proposed in this paper, introducing residual blocks and depthwise separable convolutions, and adding multi-scale convolutional layers. With the edge matcher, a complex matching problem is successfully transformed into a simple classification problem. In the absence of a standard public dataset, a Dunhuang manuscript fragment edge dataset is constructed. Experiments are conducted on that dataset, and the accuracy, precision, recall, and F1 scores of the edge matcher all exceeded 97%. The effectiveness of the edge matcher is demonstrated by comparative experiments, and the rationality of the method design is verified by ablation experiments. The method combines traditional methods and deep learning methods to creatively use the edge geometric features of fragments for sibling fragment identification in a natural rather than coded way, making full use of the computer’s computational and memory capabilities. The edge matcher can significantly reduce the time and scope of searching, matching, and inferring fragments, and assist in the reconstruction of Dunhuang ancient manuscript fragments.


Multiple degrees‐of‐freedom programmable soft‐matter‐photonics: Configuration, manipulation, and advanced applications

Abstract: Abstract For centuries, humans have never stopped exploring the nature of light and manipulating it, since light carries multiple information through its intrinsic wave‐particle dualism, including wavelength, amplitude, phase, polarization, spin/orbital angular momentum, etc., which determines the physical language and basic manners we perceive the objective world. Conventional optical devices, such as lenses, prisms, and lasers, are composed of solid elements that are bulky, making it difficult to manipulate light dynamically with multiple degrees‐of‐freedom. Comparatively, some responsive soft matters, especially represented by liquid crystals (LCs), possess distinctive orientational order and spontaneous self‐assembled superstructures, enabling the digital programming of microstructures and multiple degrees‐of‐freedom manipulation of their optical characteristics. The optical manipulation based on these soft superstructures, that is, the “soft‐matter‐photonics”, is playing an impressive role in integrated functional devices, especially in the present age of information explosion. Herein, we review the latest advances, respectively, in the microstructure configurations, multiple degrees‐of‐freedom manipulations, and the relevant prospective applications. Additionally, scientific issues and technical challenges that hinder the programing operation and optical manipulations are discussed. Toward a four‐dimensional optical manipulation of soft condensed matter, this review may have wide implications on a variety of applications, including the integrated fabrication of compact elements, multi‐channel information processing and high‐capacity optical communications.


The case for open research in entomology: Reducing harm, refining reproducibility and advancing insect science

Abstract: Abstract Open research is an increasingly developed and crucial framework for the advancement of science and has seen successful adoption across a broad range of disciplines. Entomology has, however, been slow to adopt these practices compared to many adjacent fields despite ethical and practical imperatives to do so. The grand challenges facing entomology in the 21st century require the synthesis of evidence at global scales, necessitating open sharing of data and research at a pace and scale incompatible with the slow adoption of open research practices. Open science also plays a vital role in fostering trust in research and maximizing use of research outputs, which is ethically crucial for reducing harms to insects. We outline these imperatives and how open research practices can enhance entomological research across a range of contexts. We also highlight the holistic nature of open science across the full research lifecycle through several specific examples of open research practices, which can be adopted easily by individual entomologists. We do, however, argue that the responsibility of promoting, integrating and encouraging open research is most crucially held by publishers, including scholarly societies, which have leveraged widespread adoption in adjacent fields. Entomology must advance quickly to become a leading discipline in the open research transition.


Open‐domain event schema induction via weighted attentive hypergraph neural network

Abstract: Summary Event schema refers to the use of a template to depict similar events, and it is a necessary prerequisite for event causality extractions. The induction of event schemas is a difficult task, especially for texts in the open domain, due to the complex and diverse manifestations of events. Previous models considered participants in event mentions are independent or compositional, ignoring the high‐order correlations among participants, which limit their capability of induce event schema. To remedy this, we propose constructing an Event Structure Hypergraph (ESH) to better utilizes the event structural information for event schema induction. In particular, we first extract event mentions from the open‐domain corpus. and then construct an ESH by representing event mentions as a hyperedges. ESH contains high‐order information between participants in event mention. To, learn event mentions representation based on ESH, we propose a weighted attentive hypergraph neural network (WHGNN) to model event high‐order correlations and then integrate node‐category weight matrix into the training of network by improving event representation. By applying jointly cluster algorithm on the event mentions representation, we can induce reliable event schemas. Experimental results on three datasets demonstrate that our approach can induce salient and high‐quality event schemas on open‐domain corpus.


A classification and recognition algorithm of key figures in public opinion integrating multidimensional similarity and K-shell based on supernetwork

Abstract: Abstract In online public opinion events, key figures are crucial to the formation and diffusion of public opinion, to the evolution and dissemination of topics, and to the guidance and transformation of the direction of public opinion. Based on the four-dimensional public opinion communication supernetwork (social-psychology-opinion-convergent), this study proposes a classification and recognition algorithm of key figures in online public opinion that integrates multidimensional similarity and K-shell to identify the key figures with differentiation in online public opinion events. The research finds that the evolutionary process of public opinion events is the joint action of key figures with different roles. The opinion leader is the key figure in the global communication of public opinion. The focus figure is the core figure that promotes the dissemination of public opinion on local subnetworks. The communication figure is the “bridge” node in the cross-regional communication of public opinion. Through the algorithm verification of the case “China Eastern Airlines Passenger Plane Crash Event”, we find that the algorithm proposed in this paper has advantages in feasibility, sensitivity, and effectiveness, compared with traditional algorithms such as CI, forwarding volume, degree centrality, K-shell, and multidimensional similarity. The classification and recognition algorithm proposed in this study can not only identify multirole key figures simultaneously but also improve the recognition granularity and eliminate the interference of core-like nodes.


Decentralized Medical Record Management with Blockchain Technology and Artificial Intelligence

Abstract: The objective of this undertaking is to make a decentralized and straightforward clinical records the executives framework utilizing blockchain innovation. The undertaking intends to work on the respectability, security and openness of clinical records by decentralizing them. The objective is to engage individuals with more command over their medical services data, work with consistent and secure information dividing among approved elements and drive development in the field of medical services examination and investigation. The objective of the venture is to assist with making a cutting edge patient-centered medical services environment that puts protection, effectiveness and the smooth trade of wellbeing data at the focal point of all that we do. This theoretical exemplifies the quintessence of the undertaking, framing its goals, philosophy, and expected influence on medical services frameworks. We will likewise give an outline of what is right now had some significant awareness of decentralized clinical records the board utilizing blockchain innovation, as well as certain thoughts for additional exploration and execution.


A cooperative stochastic configuration network based on differential evolutionary sparrow search algorithm for prediction

Abstract: Stochastic configuration network (SCN) is a powerful prediction model whose performance is significantly influenced by the configuration of the network parameters. To improve the prediction accuracy of the network, a cooperative stochastic configuration network (CSCN) based on a novel differential evolutionary sparrow search algorithm (DESSA), termed as DESSA-CSCN, is proposed. In the CSCN, the number of hidden layer nodes is adaptively adjusted according to the number of iterations, and the parameters of hidden nodes are cooperatively optimized by using a population-based metaheuristic algorithm. A sparse matrix is introduced to mitigate parameter overfitting caused by the increased number of hidden layer nodes. During parameter optimization, the fitness function is constructed by using the supervision mechanism of the SCN, and the DESSA is utilized as the metaheuristic algorithm to update the weights and biases. In order to verify the effectiveness of the DESSA-CSCN, several simulation experiments have been conducted. The performance of the DESSA is evaluated by the CEC2017 test suit, and the simulation results show that the DESSA exhibits better convergence accuracy and can jump out of local optima more effectively than other algorithms. The performance of the DESSA-CSCN is evaluated by 4 datasets from KEEL, and the simulation results indicate that the DESSA-CSCN achieves better prediction accuracy and faster prediction speed than other models.


Investigating EFL teacher candidates’ acceptance and self-perceived self-efficacy of augmented reality

Abstract: Abstract The mixed-methods sequential explanatory design study presented here sought to investigate the acceptance and self-perceived self-efficacy of Augmented Reality technology among English as a Foreign Language teacher candidates. This study’s contribution to the field of AR as an instructional technology lies in that research on the using AR technology in language teacher education are particularly limited. To bridge this research gap, one-group pre-test-post-test design was used in this study, in which ten weeks of AR-enhanced English language teacher training treatment was carried out. To answer the research questions, quantitative data were gathered from 50 volunteers by using the Technology Acceptance Model survey and Self-Efficacy Scale. Qualitative data were received via semi-structured interviews carried out with 12 pre-service teachers. Findings revealed that there was a significant increase in EFL teacher candidates’ levels of AR technology acceptance whereas no significant difference was identified in their self-perceived self-efficacy. The interviewees proposed interpersonal, intrapersonal, technological, and technical factors to explain their low self-efficacy levels. Based on the findings, the paper offers implications and suggestions for researchers, teacher educators, and policymakers. The solutions to increase the level of acceptance and self-perceived self-efficacy levels to use AR among EFL Teacher Candidates include courses on Emergent Technologies (ICT, MALL, CALL, AR), training via seminars, workshops, input sessions through (inter)nationally funded projects, real-life examples, field-specific samples, and hands-on experiences.


Out-of-Distribution Detection in Dependent Data for Cyber-Physical Systems with Conformal Guarantees

Abstract: Uncertainty in the predictions of learning-enabled components hinders their deployment in safety-critical cyber-physical systems (CPS). A shift from the training distribution of a learning-enabled component (LEC) is one source of uncertainty in the LEC’s predictions. Detection of this shift or out-of-distribution (OOD) detection on individual datapoints has therefore gained attention recently. But in many applications, inputs to CPS form a temporal sequence. Existing techniques for OOD detection in time-series data for CPS either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We propose using deviation from the in-distribution temporal equivariance as the non-conformity measure in conformal anomaly detection framework for OOD detection in time-series data for CPS. Computing independent predictions from multiple conformal detectors based on the proposed measure and combining these predictions by Fisher’s method leads to the proposed detector CODiT with bounded false alarms. CODiT performs OOD detection on fixed-length windows of consecutive time-series datapoints by using Fisher value of the input window. We further propose performing OOD detection on real-time time-series traces of variable lengths with bounded false alarms. This can be done by using CODiT to compute Fisher values of the sliding windows in the input trace and combining these values by a merging function. Merging functions such as Harmonic Mean, Arithmetic Mean, Geometric Mean, and Bonferroni Method, etc. can be used to combine Fisher values of the sliding windows in the input trace, and the combined value can be used for OOD detection on the trace with bounded false alarm rate guarantees. We illustrate the efficacy of CODiT by achieving state-of-the-art results in two case studies for OOD detection on fixed-length windows. The first one is on an autonomous driving system with perception (or vision) LEC. The second case study is on a medical CPS for walking pattern or GAIT analysis where physiological (non-vision) data is collected with force-sensitive resistors attached to the subject’s body. For OOD detection on variable length traces, we consider the same case studies on the autonomous driving system and medical CPS for GAIT analysis. We report our results with four merging functions on the Fisher values computed by CODiT on the sliding windows of the input trace. We also compare the false alarm rate guarantees by these four merging functions in the autonomous driving system case study. Code, data, and trained models are available at https://github.com/kaustubhsridhar/time-series-OOD.


Reconstructing bifurcation diagrams of chaotic circuits with reservoir computing

Abstract: Model-free reconstruction of bifurcation diagrams of Chua's circuits using the technique of parameter-aware reservoir computing is investigated. We demonstrate that (1) reservoir computer can be utilized as a noise filter to restore the system dynamics from noisy signals; (2) for a single Chua circuit, a machine trained by the noisy time series measured at several sampling states is capable of reconstructing the whole bifurcation diagram of the circuit with a high precision; and (3) for two coupled chaotic Chua circuits with mismatched parameters, the machine trained by the noisy time series measured at several coupling strengths is able to anticipate the variation of the synchronization degree of the coupled circuits with respect to the coupling strength over a wide range. Our studies verify the capability of the technique of parameter-aware reservoir computing in learning the dynamics of chaotic circuits from noisy signals, signifying the potential application of this technique in reconstructing the bifurcation diagram of real-world chaotic systems.


Bidirectional Complementary Correlation-Based Multimodal Aspect-Level Sentiment Analysis

Abstract: Aspect-based sentiment analysis is the key to natural language processing, and it focuses on the polarity of emotions associated with specific text aspects. Traditional models that combine text and visual data tend to ignore the deeper interconnections between patterns. To solve this problem, the authors propose a multimodal sentiment-oriented analysis (BiCCM-ABSA) model based on bidirectional complementary correlation. The model utilizes text-image synergy through a novel cross-modal attention mechanism to align text with image features. With the transformer architecture, it is not only a simple fusion, but also ensures the complex alignment of multi-modal features and gating mechanisms. Experiments were conducted on the Twitter-15 and Twitter-17 datasets, achieving 69.28 accuracy and 67.54% F1 score, respectively. The experimental results demonstrate the advantages of BiCCM-ABSA, the bidirectional approach of the model and the effective cross-modal correlation set a new benchmark in the field of multimodal emotion recognition, providing insights beyond traditional single-modal analysis.


Scalable approach to quantum simulation via projection-based embedding

Abstract: Owing to the computational complexity of electronic structure algorithms running on classical digital computers, the range of molecular systems amenable to simulation remains tightly circumscribed even after many decades of work. Many believe quantum computers will transcend such limitations although in the current era the size and noise of these devices militates against significant progress. Here we describe a chemically intuitive approach that permits a subdomain of a molecule's electronic structure to be calculated accurately on a quantum device, while the rest of the molecule is described at a lower level of accuracy using density functional theory running on a classical computer. We demonstrate that this approach produces improved results for molecules that cannot be simulated fully on current quantum computers but which can be resolved classically at a cheaper level of approximation. The algorithm is tunable, so that the size of the quantum simulation can be adjusted to run on available quantum resources. Therefore, as quantum devices become larger, this method will enable increasingly large subdomains to be studied accurately.


On the (relation between) efficiency and secret key rate of QKD

Abstract: The processes of evaluation and comparison play a vital role in the development of a scientific field. In the field of quantum cryptography (especially quantum key distribution, QKD), the so-called secret key rate is used for characterizing the performance of a protocol (scheme). However the current definition of this quantity is incomplete. It does not consider the classical communication process taking place in a QKD protocol. There exists a quantity that involves all the procedures (resources) in a communication process: it is the efficiency (total efficiency). This paper reports a definition of this parameter. Also the relation between the total efficiency and key rate is found. By means of this relation, the total secret key rate of a QKD protocol is expressed. An application of the total key rate is demonstrated: the original twin-field QKD (TF-QKD) is evaluated in terms of this rate. The paper also shows a comparison between the total key rate and the standard key rate of a TF-QKD.


Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways

Abstract: Natural language processing (NLP), a technology that translates human language into machine-readable data, is revolutionizing numerous sectors, including cancer care. This review outlines the evolution of NLP and its potential for crafting personalized treatment pathways for cancer patients. Leveraging NLP's ability to transform unstructured medical data into structured learnable formats, researchers can tap into the potential of big data for clinical and research applications. Significant advancements in NLP have spurred interest in developing tools that automate information extraction from clinical text, potentially transforming medical research and clinical practices in radiation oncology. Applications discussed include symptom and toxicity monitoring, identification of social determinants of health, improving patient-physician communication, patient education, and predictive modeling. However, several challenges impede the full realization of NLP's benefits, such as privacy and security concerns, biases in NLP models, and the interpretability and generalizability of these models. Overcoming these challenges necessitates a collaborative effort between computer scientists and the radiation oncology community. This paper serves as a comprehensive guide to understanding the intricacies of NLP algorithms, their performance assessment, past research contributions, and the future of NLP in radiation oncology research and clinics.


Supervised learning for improving the accuracy of robot-mounted 3D camera applied to human gait analysis.

Abstract: the use of 3D cameras for gait analysis has been highly questioned due to the low accuracy they have demonstrated in the past. The objective of the study presented in this paper is to improve the accuracy of the estimations made by robot-mounted 3D cameras in human gait analysis by applying a supervised learning stage.the 3D camera was mounted in a mobile robot to obtain a longer walking distance. This study shows an improvement in detection of kinematic gait signals and gait descriptors by post-processing the raw estimations of the camera using artificial neural networks trained with the data obtained from a certified Vicon system. To achieve this, 37 healthy participants were recruited and data of 207 gait sequences were collected using an Orbbec Astra 3D camera. There are two basic possible approaches for training and both have been studied in order to see which one achieves a better result. The artificial neural network can be trained either to obtain more accurate kinematic gait signals or to improve the gait descriptors obtained after initial processing. The former seeks to improve the waveforms of kinematic gait signals by reducing the error and increasing the correlation with respect to the Vicon system. The second is a more direct approach, focusing on training the artificial neural networks using gait descriptors directly.the accuracy of the 3D camera to objectify human gait was measured before and after training. In both training approaches, a considerable improvement was observed. Kinematic gait signals showed lower errors and higher correlations with respect to the ground truth. The accuracy of the system to detect gait descriptors also showed a substantial improvement, mostly for kinematic descriptors rather than spatio-temporal. When comparing both training approaches, it was not possible to define which was the absolute best.supervised learning improves the accuracy of 3D cameras but the selection of the training approach will depend on the purpose of the study to be conducted. This study reveals the great potential of 3D cameras and encourages the research community to continue exploring their use in gait analysis.


Design of a dynamic and robust recommender system based on item context, trust, rating matrix and rating time using social networks analysis

Abstract: Collaborative filtering recommender systems type have been increasingly used in e-commerce sites both to facilitate users' decision-making and increase sales. On the one hand, the open and interactive nature of recommender systems makes them vulnerable to shilling attacks, and on the other hand, given that most recommender systems are used in dynamic environments, and this issue generates incremental data over time. One of the main obstacles of this type of recommender systems is the inability to model the dynamic behavior of users and the incremental flow of data, as well as the vulnerability to shilling attacks. Therefore, classic models do not have the necessary ability to provide suitable recommendations to the user and also to detect shilling attacks dynamically. The objective of this study was to address this gap by designing a dynamic and robust recommender system model against shilling attacks. This model was based on item context, trust, users' rating and users' rating time, and it benefits from the social networks analysis of users and items to suggest @N top items to the target user, and had a dynamic and increasing property over time and robust against shilling attacks. Finally, to assess the performance and robustness of the recommender system to shilling attacks, 4850 different tests have been performed without shilling attacks and under three average, random, and bandwagon attacks. To validate the proposed model, the results of the tests have been compared with similar methods such as TRACCF, TOTAR and T&TRS using the evaluation criteria of Precision, Recall, F1, MAE and RMSE.The results depicted that the proposed method, due to finding users and similar items in communities created by social networks, causes a reduction in the number of predicted items that are not liked by the user (FP) and the number of unpredicted items that are liked by the user (FN) and finally the F1 criterion (which is a combination of Precision and Recall criteria) performs better than the comparison methods. Also, this method is robust against the shilling attacks by detecting fake profiles and ignoring them in the recommendation process, and the evaluation criteria before and after shilling attacks show this.


Automatic recognition of second language speech-in-noise

Abstract: Measuring how well human listeners recognize speech under varying environmental conditions (speech intelligibility) is a challenge for theoretical, technological, and clinical approaches to speech communication. The current gold standard-human transcription-is time- and resource-intensive. Recent advances in automatic speech recognition (ASR) systems raise the possibility of automating intelligibility measurement. This study tested 4 state-of-the-art ASR systems with second language speech-in-noise and found that one, whisper, performed at or above human listener accuracy. However, the content of whisper's responses diverged substantially from human responses, especially at lower signal-to-noise ratios, suggesting both opportunities and limitations for ASR--based speech intelligibility modeling.


BERT-Based Arabic Diacritization: A state-of-the-art approach for improving text accuracy and pronunciation

Abstract: In order to accurately represent the meaning and pronunciation of Arabic words and sentences, the presence of diacritics plays a crucial role. Over the years, researchers have dedicated significant efforts to enhancing automated diacritization systems. This paper introduces a novel approach for Arabic diacritization utilizing Bidirectional Encoder representations from Transformers (BERT) models. To evaluate the effectiveness of the proposed approach, two publicly available datasets, namely the Arabic Diacritization (AD) dataset and the Tashkeela Processed (TP) dataset, were employed. The performance of the models was assessed using various error metrics, including Diacritic Error Rate (DER) and Word Error Rate (WER). The findings demonstrate the superior performance of BERT in the diacritization process, surpassing all models employed in other diacritization systems. On the AD dataset, the proposed system achieved state-of-the-art (SOTA) syntactic DER and WER of 1.14% and 3.34%, respectively. For morphological diacritization, the best results yielded a DER of 0.92% and a WER of 1.91%. These outcomes reflect a remarkable relative error reduction of over 30% compared to previous research. Additionally, on the TP dataset, the BERT models exhibited a substantial decrease in DER, reducing the benchmark from 4.0% to 1.11%. Furthermore, this study introduces a real-time diacritization system called SUKOUN, which offers diacritized text through a user-friendly website. A comparison with existing automatic diacritization tools, using six example texts, reveals the superior prediction accuracy and preservation of input format provided by SUKOUN.


GAP: A novel Generative context-Aware Prompt-tuning method for relation extraction

Abstract: Prompt-tuning was proposed to bridge the gap between pretraining and downstream tasks, and it has achieved promising results in Relation Extraction (RE) tasks in recent years. Although the existing prompt-based RE methods have outperformed the methods based on the fine-tuning paradigm, these methods require domain experts to design prompt templates, making them hard to generalize. In this paper, we proposed a Generative context-Aware Prompt-tuning method (GAP) to address these limitations. Our method consists of three crucial modules: (1) a pretrained prompt generator module that extracts or generates the relation triggers from the context and embeds them into the prompt tokens, (2) an in-domain adaptive pretraining module that further trains the Pretrained Language Models (PLMs) to promote the adaptability of the model, and (3) a joint contrastive loss that prevents PLMs from generating results unrelated to the relation labels while optimizing our model more effectively. We observed that the context-enhanced prompt tokens generated by GAP can better guide PLMs to make accurate relationship predictions. And the in-domain pretraining can effectively inject domain knowledge to enhance the robustness of the model. We conduct experiments on four public RE datasets under the supervised and few-shot settings. The experimental results have demonstrated the superiority of GAP over existing benchmark methods and GAP shows remarkable improvements in few-shot settings, with average F1 score enhancements of 3.5%, 2.7%, and 3.4% on the TACRED, TACREV, and Re-TACRED datasets, respectively. Furthermore, GAP still achieved state-of-the-art (SOTA) performance in supervised settings.


MTD-YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model

Abstract: Underwater light attenuation leads to decreased image contrast. This reduction in contrast subsequently decreases target visibility. Additionally, marine target detection is challenging due to multi-scale problems from varying target-to-device distances, complex target clustering, and noise from waterborne particulates.To address these issues, we propose MTD-YOLOv5.Initially, we enhance image contrast with grayscale equalization and mitigate color shift issues through color space transformation.We then introduce a novel feature extraction module, PCBR, combining max pooling and convolution layers for more effective target feature extraction from the background.Furthermore, we present the Multi-Scale Perceptual Hybrid Pooling (MHP) module.This module integrates horizontal and vertical receptive fields to establish long-range dependencies, thereby capturing hidden target information in deep network feature maps. In the Labeled Fishes in the Wild test datasets, MTD-YOLOv5 achieves a precision of 88.1% and a mean Average Precision (mAP[0.5:.95]) of 49.6%.These results represent improvements of 2.6% in precision and 0.4% in mAP over the original YOLOv5.


An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images

Abstract: Biomedical image analysis plays a crucial role in enabling high-performing imaging and various clinical applications. For the proper diagnosis of blood diseases related to red blood cells, red blood cells must be accurately identified and categorized. Manual analysis is time-consuming and prone to mistakes. Analyzing multi-label samples, which contain clusters of cells, is challenging due to difficulties in separating individual cells, such as touching or overlapping cells. High-performance biomedical imaging and several medical applications are made possible by advanced biosensors. We develop an intelligent neural network model that can automatically identify and categorize red blood cells from microscopic medical images using region-based convolutional neural networks (RCNN) and cutting-edge biosensors. Our model successfully navigates obstacles like touching or overlapping cells and accurately recognizes various blood structures. Additionally, we utilized data augmentation as a pre-processing method on microscopic images to enhance the model's computational efficiency and expand the sample size. To refine the data and eliminate noise from the dataset, we utilized the Radial Gradient Index filtering algorithm for imaging data equalization. We exhibit improved detection accuracy and a reduced model loss rate when using medical imagery datasets to apply our proposed model in comparison to existing ResNet and GoogleNet models. Our model precisely detected red blood cells in a collection of medical images with 99% training accuracy and 91.21% testing accuracy. Our proposed model outperformed earlier models like ResNet-50 and GoogleNet by 10-15%. Our results demonstrated that Artificial intelligence (AI)-assisted automated red blood cell detection has the potential to revolutionize and speed up blood cell analysis, minimizing human error and enabling early illness diagnosis.


Identifying Brexit voting patterns in the British house of commons: an analysis based on Bayesian mixture models with flexible concomitant covariate effects

Abstract: Brexit and its implications are an ongoing topic of interest since the Brexit referendum in 2016. In 2019 the House of commons held a number of "indicative" and "meaningful" votes as part of the Brexit approval process. The voting behaviour of members of the parliament in these votes is investigated to gain insight into the Brexit approval process. In particular, a mixture model with concomitant covariates is developed to identify groups of members of parliament who share similar voting behaviour while also considering characteristics of the members of parliament. The novelty of the method lies in the flexible structure used to model the effect of concomitant covariates on the component weights of the mixture, with the (potentially nonlinear) terms represented as a smooth function of the covariates. Results show this approach allows to quantify the effect of the age of members of parliament, as well as preferences and competitiveness in the constituencies they represent, on their position towards Brexit. This helps grouping the aforementioned politicians into homogeous clusters, whose composition departs sensibly from that of the parties.


Bitcoin forks: What drives the branches?

Abstract: Despite frequent Blockchain splits stemming from Bitcoin, few studies have examined the determinants of Bitcoin fork returns. In this paper, we investigate the relationships between the returns of Bitcoin forks and a range of common risk factors, including Bitcoin, currency, network and equity-based factors. From a statistical perspective, we find consistent and significant associations between fork returns, their Bitcoin counterparts, and equity markets. Other common factors, such as the equity small-minus-big factor and changes in the Japanese Yen, are found to have occasional links with fork returns. From an economic perspective, Bitcoin returns are the predominant driver of fork returns, accounting for essentially all of the explained variation. These findings are confirmed using orthogonalised common factors and with an alternative methodology, quantile regression. This research broadens our understanding of Bitcoin forks, indicating that a change in blockchain protocol is insufficient to sever links with the Bitcoin parent.


Examining perceptions of utilizing augmented reality in hybrid learning environment: instructors and students perspectives

Abstract: Purpose This study aims to explore the perceptions of language learners and instructors on using augmented reality (AR) in a hybrid learning environment. Design/methodology/approach Mixed-method research design was used to elicit information from 62 participants on the study’s objective. Data were collected and analyzed to examine the participants’ views on using AR in language teaching in a hybrid environment. Findings This study unveils that while most studies acknowledge AR as an effective instructional delivery, students and instructors perceive some setbacks. Other novel insights provided by this study reveal necessities to consider before implementing AR in classroom settings. Research limitations/implications This study provides insights into the widely reported effectiveness of AR in the English language-teaching domain. This study suggests that considering the dispositions of learners and instructors toward digitally enhanced learning, using AR without good teaching practices and approaches may not yield expected learning outcomes. Originality/value This study advances scientific knowledge on the use of AR in hybrid learning models by providing empirical evidence to show the perceived effectiveness of AR. It further provides a robust understanding of the pedagogical implications of using AR in classroom settings drawing from diverse lenses. This helps ensure that educational settings that integrate AR consider the novel findings of this study before such implementation.


Gene selection for high dimensional biological datasets using hybrid island binary artificial bee colony with chaos game optimization

Abstract: Abstract Microarray technology, as applied to the fields of bioinformatics, biotechnology, and bioengineering, has made remarkable progress in both the treatment and prediction of many biological problems. However, this technology presents a critical challenge due to the size of the numerous genes present in the high-dimensional biological datasets associated with an experiment, which leads to a curse of dimensionality on biological data. Such high dimensionality of real biological data sets not only increases memory requirements and training costs, but also reduces the ability of learning algorithms to generalise. Consequently, multiple feature selection (FS) methods have been proposed by researchers to choose the most significant and precise subset of classified genes from gene expression datasets while maintaining high classification accuracy. In this research work, a novel binary method called i BABC-CGO based on the island model of the artificial bee colony algorithm, combined with the chaos game optimization algorithm and SVM classifier, is suggested for FS problems using gene expression data. Due to the binary nature of FS problems, two distinct transfer functions are employed for converting the continuous search space into a binary one, thus improving the efficiency of the exploration and exploitation phases. The suggested strategy is tested on a variety of biological datasets with different scales and compared to popular metaheuristic-based, filter-based, and hybrid FS methods. Experimental results supplemented with the statistical measures, box plots, Wilcoxon tests, Friedman tests, and radar plots demonstrate that compared to prior methods, the proposed i BABC-CGO exhibit competitive performance in terms of classification accuracy, selection of the most relevant subset of genes, data variability, and convergence rate. The suggested method is also proven to identify unique sets of informative, relevant genes successfully with the highest overall average accuracy in 15 tested biological datasets. Additionally, the biological interpretations of the selected genes by the proposed method are also provided in our research work.


A Novel Method for 3D Object Detection in Open-Pit Mine Based on Hybrid Solid-State LiDAR Point Cloud

Abstract: In recent years, the mining industry has encountered challenges, such as a shortage of human resources, an ongoing emphasis on safety enhancements, and increased ecological preservation requirements. Autonomous mining trucks have emerged as a novel solution to effectively address these issues within open-pit mining operations. To meet the demanding conditions of open-pit mines, characterized by intense vibrations and extreme temperature variations, hybrid solid-state LiDAR has emerged as the primary choice for perception sensors. Recognizing the distinct data structure and distribution disparities between point clouds obtained through nonrepetitive scanning methods of hybrid solid-state LiDAR and traditional mechanical LiDAR, this paper proposed an innovative LiDAR 3D object detection model, PointPillars-HSL (PointPillars-Hybrid Solid-state LiDAR). This approach harmonizes the unique characteristics of open-pit mining environments and hybrid solid-state LiDAR point clouds. It optimizes the model’s preprocessing methodology, augments the dimensionality of pillar features, fine-tunes the loss function, and employs transfer learning techniques to reduce the reliance on specific datasets. The result is the effective deployment of a 3D object detection algorithm customized for hybrid solid-state LiDAR within the specific operational framework of open-pit mining. This achievement has yielded a noteworthy overall vehicle recognition rate of 89.72%.


GASx: Explainable Artificial Intelligence For Detecting GPS Spoofing Attacks

Abstract: Unmanned Aerial Systems heavily depend on the Global Positioning System (GPS) for navigation. However, the GPS signals are subject to different types of threats including GPS spoofing attacks. While many machine learning methods have been successfully applied to detect spoofing attacks in these unmanned systems, the focus has mainly been on developing accurate prediction models, without delving into the reasons behind the predictions. We believe that understanding the underlying factors leading to a signal being classified as spoofed is crucial for gaining insights and effectively mitigating the effects of spoofing. In this paper, we propose a machine learning approach that incorporates explainable artificial intelligence techniques, specifically Shapley Additive Explanations (SHAP), to analyze why a signal is classified as a spoofed signal. Our approach utilizes a tree-based ensemble model, achieving a high F1 score of 0.956 for three different types of spoofing attacks. By leveraging SHAP, our analysis uncovers distinctive characteristics associated with each type of spoofing, providing valuable insights into the factors contributing to a signal being classified as spoofed.


Hexagon | NovAtel’s Jamming and Spoofing Detection and Classification Performance During the Norwegian JammerTest 2023

Abstract: Global Navigation Satellite Systems (GNSS) are widely used in critical infrastructure and safety-of-life applications. With such widespread use, open signal descriptions, and a crowded RF spectrum, jamming and spoofing are well-known threats to GNSS. Various detection and mitigation methods have been developed to deal with these threats. GNSS Resilience and Integrity Technology (GRIT) is a firmware suite developed for NovAtel’s OEM7 receivers to expand situational awareness and interference detection and mitigation tools across applications and environments to protect against GNSS threats including jamming and spoofing attacks. GRIT includes the Interference Toolkit (ITK) and Spoofing Detection Toolkit (SK) to identify when a GNSS signal is under threat. GRIT relies on different countermeasures ranging from jamming detection and characterization to spoofing detection and mitigation to provide solution integrity and reliability. The Galileo Open Service Navigation Message Authentication (OSNMA) provides message authentication as an additional layer of GNSS protection. JammerTest 2023 was conducted September 18-22, 2023 in Bliek, Norway. Various attacks were generated to interfere with and manipulate over-the-air signals to mislead the participant’s equipment. This paper shows the jamming and spoofing detection performance of NovAtel’s OEM7 receivers with actual data collected during the JammerTest 2023. Spectrum monitoring and jamming characterization on all GNSS bands supports jamming detection. The effectiveness of GRIT anti-jam and anti-spoofing technology is demonstrated using representative complex spoofing and jamming test cases during this event. OSNMA results for various scenarios are provided and discussed to demonstrate it as a complementary layer of protection to be accompanied by other security features and monitoring techniques for full protection.


Conceptualizing Discussions on the Dark Web: An Empirical Topic Modeling Approach

Abstract: Social networks on the Internet have become a home that attracts all types of human thinking to exchange knowledge and ideas and share businesses. On the other hand, it has also become a source for researchers to analyze this knowledge and frame it in patterns that define types of thoughts circulating on these networks and representing the communities around them. In particular, some social networks on the Dark Web attract a special kind of thinking centered around the malicious and illegal activities disseminated on websites and marketplaces on the Dark Web. These networks involve discussions to exchange and discourse information, tips, and advice on performing such business. Studying social networks on the Dark Web is still in its infancy. In this paper, we present a methodology for analyzing the content of social networks on the Dark Web using topic modeling methods. We demonstrate the needed stages for the topic modeling process, beginning with data preprocessing and feature extraction to topic modeling algorithms. We utilize and discuss the following four topic models: LDA, CTM, PAM, and PTM. We discuss the following four topic coherence measures as evaluation metrics: UMass, UCI, CNPMI, and CV, demonstrating the selection of the best number of topics for each model according to the most coherent produced topics. Furthermore, we discuss the limitations, challenges, and future work. Our proposed approach highlights the ability to discover the latent thematic patterns in conversations and messages in the common language used in social networks on the Dark Web, constructing topics as groups of terms and their associations. This paper provides researchers with a leading methodology for analyzing thought patterns on the Dark Web.


Unraveling the crypto market: A journey into decentralized finance transaction network

Abstract: The explosive growth of decentralized finance (DeFi) has revolutionized the accessibility and functionality of financial services. To gain valuable insights into this evolving ecosystem, analyzing transaction data using network analysis methodology proves to be highly effective. Network analysis allows us to uncover intricate relationships, patterns, and communities within the DeFi market by examining participant transactions. We model nodes group representing actors or wallet addresses, and edges represent transactions between wallet addresses. This study focuses on three prominent DeFi token-based Ethereum protocols: DAI, UNI, and WBTC. We analyzed 5,802,742 transaction data spanning from January 2022 to January 2023 of those three tokens. Using network topology metrics, we uncover market size, average transaction of each wallet address, market density, and reachability (diameter). We also detect the presence of transaction clusters and grouping quality using the modularity metric. At last, we employ centrality calculation to identify the most important wallet address and their role in the market. We gain insight by comparing those three tokens and reveal valuable insight into patterns and relationships, network dynamics, decentralized nature, and the presence of intermediaries in the token economy market. Our objective is to uncover hidden dynamics and trace asset flows, which can provide valuable information to market participants, regulators, and innovators seeking to optimize the DeFi infrastructure, ensure stability, and mitigate risks. Leveraging the power of network analysis offers the potential to shape the future of DeFi by enhancing efficiency, security, and accessibility, thereby fostering financial inclusion and empowering individuals and businesses globally.


Slide-block: End-to-end amplified security to improve DevOps resilience through pattern-based authentication

Abstract: DevOps represents the fusion of cultural philosophies, tools, and practices that rapidly enhance an organization's capacity to deploy services and applications. Cloud-based tools, a subset of DevOps services, facilitate collaboration between development and operations teams within an organization. However, persistent challenges such as inadequate security management, substantial leakage of sensitive data, and system/service unavailability pose significant threats to sustainability. We propose an end-to-end enhanced security framework to fortify DevOps resilience by implementing authentication and vulnerability management through the Slide-Block methodology. Our approach comprises four sequential processes: pattern-based authentication, tri-level access control, privacy-focused data storage, and vulnerability management and correction. Initially, we establish candidate legitimacy through pattern-based authentication using the Magnificent Chacha-Poly 1305 algorithm. Subsequently, we devise effective access policies using the Enhanced Deep Deterministic Policy Gradient (EDDPG) algorithm, employing tri-level access control based on trust value, attributes, and roles for optimal user and developer selection via the African Vulture Optimization Algorithm (AVOA). Moreover, we encrypt data in transit and at rest using Mcha-Poly 1305, considering sensitivity, and store it in a blockchain to enhance data privacy. Our approach incorporates a sliding window blockchain for secure data transmission and storage. Finally, we identify and address attack and application-based issues using the Tweak Naive Bayes (Tweak-NB) algorithm and Intruder Vulnerability Scanner (IVS). Our Slide-Block framework demonstrates superior performance in detection rate, authentication time, packet loss, security strengthening, communication overhead, and latency compared to existing models.


Enhancing machine learning-based sentiment analysis through feature extraction techniques

Abstract: A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model's performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we find that the TD-IDF technique demonstrates superior performance, with an accuracy of 99% in the Amazon reviews dataset and 96% in the Twitter US airlines dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.


Global domain adaptation attention with data-dependent regulator for scene segmentation

Abstract: Most semantic segmentation works have obtained accurate segmentation results through exploring the contextual dependencies. However, there are several major limitations that need further investigation. For example, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. Moreover, local convolutions are commonly used in deep learning models to learn attention and capture local patterns in the data. These convolutions operate on a small neighborhood of the input, focusing on nearby information and disregarding global structural patterns. To address these concerns, we propose a Global Domain Adaptation Attention with Data-Dependent Regulator (GDAAR) method to explore the contextual dependencies. Specifically, to effectively capture both the global distribution information and local appearance details, we suggest using a stacked relation approach. This involves incorporating the feature node itself and its pairwise affinities with all other feature nodes within the network, arranged in raster scan order. By doing so, we can learn a global domain adaptation attention mechanism. Meanwhile, to improve the features similarity belonging to the same segment region while keeping the discriminative power of features belonging to different segments, we design a data-dependent regulator to adjust the global domain adaptation attention on the feature map during inference. Extensive ablation studies demonstrate that our GDAAR better captures the global distribution information for the contextual dependencies and achieves the state-of-the-art performance on several popular benchmarks.


Determinants of Dutch public health professionals’ intention to use digital contact tracing support tools: A cross-sectional online questionnaire study

Abstract: Contact tracing (CT) can be a resource intensive task for public health services. To alleviate their workload and potentially accelerate the CT-process, public health professionals (PHPs) may transfer some tasks in the identification, notification, and monitoring of contacts to cases and their contacts themselves, using 'digital contact tracing support tools' (DCTS-tools). In this study, we aimed to identify determinants of PHPs' intention to use DCTS-tools. Between February and April 2022, we performed a cross-sectional online questionnaire study among PHPs involved in CT for COVID-19 in the Netherlands. We built three random forest models to identify determinants of PHPs' intention to use DCTS-tools for the identification, notification, and monitoring of contacts, respectively. The online questionnaire was completed by 641 PHPs. Most respondents had a positive intention towards using DCTS-tools for the identification (64.5%), notification (58%), and monitoring (55.2%) of contacts. Random forest models were able to correctly predict the intention of 81%, 80%, and 81% of respondents to use DCTS-tools for the identification, notification, and monitoring of contacts, respectively. Top-determinants of having a positive intention are the anticipated effect of DCTS-tools on the feasibility and efficiency of CT (speed, workload, difficulty), the degree to which PHPs anticipated that cases and contacts may find it pleasant and may be willing to participate in CT using DCTS-tools, and the degree to which PHPs anticipated that cases and contacts are sufficiently supported in CT when using DCTS-tools. Most PHPs have a positive intention to involve cases and their contacts in the identification, notification, and monitoring stages of the CT-process through DCTS-tools. The identified top-determinants should be prioritized in the (future) development and implementation of DCTS-tools in public health practice. Citizens' perspectives on the use of DCTS-tools should be investigated in future research.


ChatGPT Is Powerful, but Does It Have Power Distance?

Abstract: Power distance (PD), a cultural value denoting acceptance of asymmetrical power relationships, influences the force of rhetoric used by a writer to address their reader. However, AI technologies such as ChatGPT lack an explicit awareness of PD, which could affect the quality of AI-generated persuasive texts used for language learning. To investigate this issue, 200 persuasive essays written by ChatGPT were compared to 200 essays written by L1-English university learners. Three elements of formulaic language related to PD were examined: stances, modals, and pronoun deixis. Differences in stances (z = -3.411; p = .001) and modals (z = -2.100; p = .036) were both significant according to the Wilcoxon signed ranks formula, whereas differences in pronoun deixis were nearly significant (z = -1.917; p = .055). Overall, language of ChatGPT appears generic and incomplete, suggesting that consistent and uniform expressions are being borrowed from an LLM training corpus to mimic aspects of PD. Limitations of AI highlight a need for pedagogical emphasis of culturally imbued discourse.


Detecting unknown vulnerabilities in smart contracts using opcode sequences

Abstract: Unknown vulnerabilities, also known as zero-day vulnerabilities, are vulnerabilities in software, systems, or networks that have not yet been publicly disclosed or fixed. If these vulnerabilities are ever discovered by hackers, intentionally or unintentionally, they pose a major threat to network security. This is particularly true in the blockchain field, as smart contracts hold a lot of money, and if they are discovered and exploited by hackers, the financial losses to users will be even greater. However, the current research on smart contract vulnerabilities mainly focuses on known vulnerabilities, and the research on unknown vulnerabilities has been limited. Based on this, we introduce a machine learning-based method for detecting unknown vulnerabilities in smart contracts. First, the method obtains the opcode sequences executed by smart contract transactions in the EVM by instrumenting Geth and replaying the Ethereum transactions. Next, we employ an n-gram model and a vector weight penalty mechanism to extract the opcode sequence features. We then use machine learning algorithms to detect unknown vulnerabilities based on the similarity principle. Finally, we test the effectiveness of our method with four machine learning models: the K-Nearest Neighbor algorithm (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). The SVM model performs best at detecting unknown vulnerabilities, with an accuracy of 96%, a precision of 91%, a recall of 100%, and an F1-score of 95%. We also discuss the benefits of the method: timely detection of attacks due to unknown vulnerabilities, thus reducing user losses.


Hybrid Prompt Recommendation Explanation Generation combined with Graph Encoder

Abstract: Abstract Recommendation systems have been effectively utilized in various fields, but their internal decision-making methods are still largely unknown. This opaque decision-making method can greatly affect users’ trust in the recommendation system. Therefore, finding a way to explain the reasons for model decisions has become an urgent task. Previous studies often used LSTM and other models to generate recommendation explanations and explain the reasons for recommendations in text form. However, traditional methods cannot effectively use the ID information of users and items, and the text generated is highly repetitive. To solve this problem, this paper uses the method of prompt learning combined with a graph encoder to design a recommendation explanation generation model. In order to narrow the semantic gap between the ID information of users and items and natural language and capture high-level interaction information, this paper designs a graph encoder based on user similarity to learn the interactive semantic information of user and item IDs, and to construct a continuous prompt. Then, the discrete prompt composed of discrete features of users and items is combined with the continuous prompt to construct a hybrid prompt to input into the pre-trained model to generate the recommended explanation. This paper experiments on three publicly available datasets and compares them with several state-of-the-art methods to demonstrate the personalization and text quality of the generated explanations.


Contour wavelet diffusion – a fast and high-quality facial expression generation model

Abstract: Facial expressions are important for conveying information in human interactions. The diffusion model can generate high-quality images for clearer and more discriminative faces, but its training and inference time is often prolonged, hampering practical application. Latent space diffusion models have shown promise in speeding up training by leveraging feature space parameters, but they require additional network structures. To address these limitations, we propose a contour wavelet diffusion model that accelerates both training and inference speeds. We use a contour wavelet transform to extract components from images and features, achieving substantial acceleration while preserving reconstruction quality. A normalised random channel attention module enhances the quality of generated images by focusing on high-frequency information. We also include a reconstruction loss function to enhance convergence speed. Experimental results demonstrate the effectiveness of our approach in boosting the training and inference speeds of diffusion models without sacrificing image quality. Fast generation of facial expressions can provide a smoother and more natural user experience, which is important for real-time applications. In addition, the increase in inference speed can save the use of computational resources, reduce system cost and improve energy efficiency, which is conducive to promoting the development and application of this technology.


Online Agriculture Management System

Abstract: This paper aims to give a comprehensive overview of Online Agriculture Management System is the web application which helps farmers by providing various kinds Agri related information and Agri services in the website. This website helps farmers by providing them a large online market to sell their produce. labourers and they can be updated with the latest agricultural developments with articles and blogs module. Admin can post latest news and articles and he can sell agriculture machinery products in the website. Workers can upload their resume and they can view work schedules after the login. To provide technology and services to the farmers, seller and farm labourers thus, helping then to expand their business and provide them with a wider market. Hence, improve the present farming processes and to provide knowledge about recent agricultural issues. To provide a helping hand to the farmers and farm labourers in improving their lives through the medium of technology, thereby, improving the Agricultural sector in the Indian economy


ART AS A PSYCHOTHERAPEUTIC INSTRUMENT IN THE PRACTICE OF SCHOOL AND EDUCATIONAL PSYCHOLOGIST

Abstract: School and Educational Psychology is today one of the fields of action within psychology and, just as there has been an evolution in the teaching-learning process in schools, there has also been a transformation in the psychologist's perspective about the student and its development. In this way, not only the theoretical conceptions have changed, but also the methodologies used, requiring from the professionals of education and psychology an adaptation in their practice, which sees the individual in an integral way. In view of this, this article sets out to present the relevance of art as a therapeutic function for the teaching-learning process, seeking as a theoretical basis the work of Nise da Silveira and the images of the unconscious. To do so, they looked for articles referring to the theme in a database such as Google Académico and Scielo, followed by a reading of the summary of the articles found to select those that addressed the proposed theme. It was concluded from this work that art as a tool for education enables processes of awareness of the being about their emotions and conflicts, as well as promotes their identification as active agent in the learning process, emphasizing the importance of an emancipating education.


Research on 3D Face Reconstruction Algorithm Based on ResNet and Transformer

Abstract: In view of the problems of high production cost, scarcity and lack of diversity of 3D face datasets, this paper designs an end-to-end self-supervised learning 3D face reconstruction algorithm with a single 2D face image as input, which only uses 2D face datasets to complete model training. First, the improved ResNet module is introduced to preprocess the input face image. The deep residual neural network has strong feature extraction and characterization ability for the image, which can provide rich high-level semantic feature maps for the subsequent subnetwork. Then, add transformer module completely based on self-attention mechanism to the parameter prediction subnetwork, which can make different parameters of the subnetwork focus on self-related feature map information and avoid interference from invalid feature map information, so as to further improve the parameter prediction accuracy of the subnetwork. Next, training, ablation and comparison experiments were conducted on CelebA, BFM and Photoface datasets, and the combined function of pixel loss function and perceptual loss function was selected as the loss function. The experimental results show that: compared with the historical optimal results of the same network structure, the scale-invariant depth error (SIDE) and mean angle deviation (MAD) are improved by 5.9% and 10.8%, respectively, which strongly proves the effectiveness of the algorithm. Finally, in order to verify the actual effect of the 3D face reconstruction algorithm, examples are selected in this paper for reconstruction. The 3D faces generated by the algorithm all have a good sense of reality, which intuitively and effectively proves the advancement of the algorithm.