Reviewerly/exHarmony huggingface dataset:
Bridging National Policies with Practical Rural Construction and Development: Research on a Decision Support System Based on Multi-Source Big Data and Integrated Algorithms

While national policies play a crucial role in shaping local development, effective governance is essential for rural revitalization. However, the successful implementation and impact of these policies in rural areas can vary due to unique local circumstances, limited information, and a lack of sophisticated decision making tools. Closing the divide between overarching national policies and practical rural development is an immediate necessity. This study begins by creating a comprehensive five-dimensional evaluation system encompassing industrial economy, public utilities, transportation and logistics, policy and institutions, and resources and the environment. It then summarizes four typical development modes—the suburban fusion mode, the characteristic industry-oriented mode, the humanistic and ecological resource-based mode, and the balanced development mode with less distinct characteristics—through an analysis of the Chinese government’s policy framework for rural construction. Subsequently, it introduces a decision support system for rural construction and development founded on multi-source heterogeneous big data and integrated algorithms. This system was tested using 782 townships as samples for classification, evaluation, and decision support. The results leverages insights into current rural development trends to efficiently align with national policies and provide customized implementation recommendations tailored to local resource characteristics. This contributes to the practical execution of rural revitalization strategies and the advancement of scientific rural decision making.
Generic Construction of Key-Aggregate Searchable Encryption

A key-aggregate searchable encryption (KASE) scheme is an encryption scheme that enables a user to search plaintexts under their ciphertexts and control access to the ciphertexts for other users. This paper proposes a generic construction of KASE, which is an open problem for the design of KASE. Our generic construction consists of two common cryptographic primitives, i.e., digital signatures and public-key authenticated encryption with keyword search (PAEKS). We also prove that our proposed KASE scheme is secure if its underlying digital signature scheme and PAEKS scheme are secure.
On the Effects of Filtering Methods on Adversarial Timeseries Data

Adversarial machine learning is very well studied in image classification. On the other hand, other domains such as deep timeseries classification have not received similar levels of attention, leaving them disproportionately vulnerable. Specifically, adversarial defenses for deep timeseries classifiers have only been investigated in the context of attack detection. However, the proposed methods do not perform well and fail to generalize across attacks, affecting their real-world applicability. In this work we investigate adversarial defense via input data purification for deep timeseries classifiers. We subject clean and adversarially-perturbed univariate timeseries data to 4 simple filtering methods with a view to establishing whether such methods may potentially be used as purification-based adversarial defenses. In experiments involving 5 publicly-available datasets, we identify and compare the benefits of various filtering techniques. Thereafter we discuss our results and provide directions for further investigation.
GLPS: A Geohash-Based Location Privacy Protection Scheme

With the development of mobile applications, location-based services (LBSs) have been incorporated into people's daily lives and created huge commercial revenues. However, when using these services, people also face the risk of personal privacy breaches due to the release of location and query content. Many existing location privacy protection schemes with centralized architectures assume that anonymous servers are secure and trustworthy. This assumption is difficult to guarantee in real applications. To solve the problem of relying on the security and trustworthiness of anonymous servers, we propose a Geohash-based location privacy protection scheme for snapshot queries. It is named GLPS. On the user side, GLPS uses Geohash encoding technology to convert the user's location coordinates into a string code representing a rectangular geographic area. GLPS uses the code as the privacy location to send check-ins and queries to the anonymous server and to avoid the anonymous server gaining the user's exact location. On the anonymous server side, the scheme takes advantage of Geohash codes' geospatial gridding capabilities and GL-Tree's effective location retrieval performance to generate a k-anonymous query set based on user-defined minimum and maximum hidden cells, making it harder for adversaries to pinpoint the user's location. We experimentally tested the performance of GLPS and compared it with three schemes: Casper, GCasper, and DLS. The experimental results and analyses demonstrate that GLPS has a good performance and privacy protection capability, which resolves the reliance on the security and trustworthiness of anonymous servers. It also resists attacks involving background knowledge, regional centers, homogenization, distribution density, and identity association.
Codesigning community networking literacies with rural/remote Northern Indigenous communities in Northwest Territories, Canada

Abstract Digital literacy research and practice typically presume certain conditions, such as an urban orientation and adequate, affordable access to connectivity and devices. But these conditions are not universal; for example, people in small, rural/remote Indigenous communities may seek to balance connectivity challenges and digital innovations with land-based living specific to place and community. Drawing on efforts to broaden critical digital literacies to support Indigenous sovereignty, we consider how overlapping contexts of places, communities, and infrastructures intersect in the cocreation of appropriate digital literacy. Specifically, we discuss a series of virtually facilitated, participatory workshops that utilize “hacker literacies” and “infrastructure literacy” to reimagine connectivity infrastructure and demonstrate the potential of community networking in, with, and by rural/remote Indigenous communities. We also reflect on limitations of this work and identify lessons for future projects.
Image capturing and stitching system based on scale-invariant feature transform features for untextured and smooth objects

Many stitching studies employ improved scale-invariant feature transform (SIFT) or speeded up robust features (SURFs) stitching algorithms, owing to their excellent robustness against variations in scale and rotations of input images. Traditional SIFT-based stitching algorithms suffer from the limitation of having insufficient feature points that can be extracted and matched from untextured and smooth objects, resulting in stitching failure. To overcome this issue, we propose a system that uses a proposed laser pattern projection (LASPP)-pasted texture technique before feature point extraction and description. The proposed system can extract and describe feature points based on SIFT feature extraction techniques. The progressive sample consistency (PROSAC) algorithm was used to remove false matching points and enhance the accuracy in matching feature points. The experimental results clearly revealed the effect of the pattern projected using LASPP on the stitching results. Furthermore, the efficiency of the proposed system, which employed the PROSAC algorithm, in matching feature points, was compared with that of the traditional system, which used the random sample consensus algorithm. Additionally, the stitching quality of the proposed system was compared with that of other SIFT- and SURF-based stitching systems.
PDIN: a progressive image rain removal network based on camera imaging principle

Traditional rain models typically focus on modeling images based on the physical characteristics and generation of rain streaks, which often obtain good results in the synthetic dataset. However, in real-world scenarios, the transformation of the camera depth of field (DOF) and the focusing position lead to different degrees of blur in different areas of the image. This variability poses challenges in extracting and processing rain streaks far from the camera DOF. Furthermore, the current image rain removal model has grown increasingly complex, which makes the image rain removal task difficult to make real time for practical applications. To address this challenge, we take a novel approach by considering the camera imaging principle. We explore the potential depth information concealed within regions of image blurriness, introducing a raindrop model and proposing the progressive depth information network (PDIN). To be specific, PDIN concurrently learns both the direction characteristics and depth information of the rain streaks and gradually removes the rain streaks in a cyclic way. Our method innovatively considers the blur problem of rainfall images from the perspective of camera shooting. Importantly, our model demonstrates excellent rain removal performance with a relatively low time complexity. A large number of experiments on synthetic and real datasets show that PDIN has better performance in image rain removal, which also indicates the rationality of our new model.
Dependency-based BERT for Chinese Event Argument Extraction

Existing event extraction methods independently identify and classify each argument role separately, ignoring the interdependence between different parameter roles. Further, these methods rely on simple vectors to represent word embeddings. By embedding explicit syntactic constraints in the attention mechanism, we address these shortcomings by using dependency syntax to guide the text modeling. Specifically, we use dependency syntax to guide the BERT model for Chinese event argument role extraction, which mainly consists of three stages. First, the self-attention method guided by a dependency syntactic parsing tree is embedded in the Transformer computing framework of the BERT model. In addition to obtaining a deep two-way linguistic representation of a word according to its context information, this method also expresses the long-distance syntactic dependency relationships between words based on context information. Secondly, the designed conditional layer normalization method is applied to the event argument extraction model in order to integrate the semantic information of trigger words into the text, thereby improving the accuracy of the argument role extraction. Lastly, conditional random fields (CRFs) are used to determine the optimal sequence of labels at the sentence level based on the dependence relationships between adjacent labels. According to the experimental results, the constructed model outperforms several strong baselines in the Chinese event argument extraction task on the ACE2005 dataset and the iFLYTEKA.I.2020 dataset.
Fast light-field 3D microscopy without-of-distribution detection and adaptationthrough Conditional Normalizing Flows

Real-time 3D fluorescence microscopy is crucial for the spatiotemporal analysis of live organisms, such as neural activity monitoring.The eXtended field-of-view light field microscope (XLFM), also known as Fourier light field microscope, is a straightforward, single snapshot solution to achieve this.The XLFM acquires spatial-angular information in a single camera exposure.In a subsequent step, a 3D volume can be algorithmically reconstructed, making it exceptionally well-suited for real-time 3D acquisition and potential analysis.Unfortunately, traditional reconstruction methods (like deconvolution) require lengthy processing times (0.0220 Hz), hampering the speed advantages of the XLFM.Neural network architectures can overcome the speed constraints but do not automatically provide a way to certify the realism of their reconstructions, which is essential in the biomedical realm.To address these shortcomings, this work proposes a novel architecture to perform fast 3D reconstructions of live immobilized zebrafish neural activity based on a conditional normalizing flow.It reconstructs volumes at 8 Hz spanning 512x512x96 voxels, and it can be trained in under two hours due to the small dataset requirements (50 image-volume pairs).Furthermore, normalizing flows provides a way to compute the exact likelihood of a sample.This allows us to certify whether the predicted output is in-or ood, and retrain the system when a novel sample is detected.We evaluate the proposed method on a cross-validation approach involving multiple in-distribution samples (genetically identical zebrafish) and various out-of-distribution ones.
Resource Description and Access (RDA)-Based Cataloguing in Ghanaian Public University Libraries: State of Play and Potential Strategies for Implementation

This study sets out to determine the extent of adoption and implementation of RDA as the cataloguing standard in Ghanaian public university libraries and explore potential strategies for its successful implementation. Surveys and case study designs were mixed under the tenets of mixed methods approach to address the research problem from the perspective of 80 cataloguing practitioners. The study determined that the RDA standard has not been adopted as the cataloguing benchmark for Ghanaian public university libraries save for one out of the nine libraries studied owing to a variety of reasons. It was also concluded that the phasal mode of implementation was adopted by the sole implementing library and strategically appealed to present RDA non-implementing libraries in lieu of a future implementation alongside collaboraion and partnerships. The study recommends among others, collaboration and partnerships with RDA implementing libraries as well as leadership by Ghana’s academic library consortium as potential strategic initiatives toward the entrenchment of the standard in Ghana.
Aquila optimizer with dynamic group strategy for global optimization tasks

Summary The Aquila optimizer (AO) is an efficient method for solving global optimization problems. However, the evolution of each individual learns from experience in the same group, which can easily fall into local optima. Therefore, this paper adopts the dynamic grouping strategy (DGS) of the population and proposes an improved AO algorithm to solve the global optimization problem. Different from the original AO algorithm, the DGSAO algorithm only evolves the individuals with the worst fitness in each group each time, which increases the diversity of the population. In order to verify the effectiveness of the algorithm, we tested it on 23 benchmark functions, among which the dimensions of to are 30, 100, and 200 dimensions. The experimental results show that the DGSAO algorithm is an effective method for solving global optimization problems. At the same time, we also conduct experiments on two engineering design problems, and the results show that the DGSAO algorithm can obtain a competitive result.
Awareness and use of paraphrasing tools by the research scholars of BBAU and CUSB: A survey

Background The online paraphrasing tool aids in the online rewriting of articles and essays. Users may avoid plagiarism by rephrasing sentences. Scope In this study focus on the awareness and use of paraphrasing tools by the Research scholars of Babasaheb Bhimrao Ambedkar University, situated in Lucknow, Uttar Pradesh, and the Central University of South Bihar, situated in Gaya, Bihar. Method Survey methodology and online questionnaire tools used in this paper. Data collection 200 responses were received from the researchers and scholars at BBAU and CUSB. Finding The finding of this study showed that research scholar of BBAU and CUSB are aware about the paraphrasing tools and they use more than one paraphrasing tool to manage their work. Quillbot and Duplicheaker paraphrasing tools are most used tools by research scholars of BBAU and CUSB. Suggestion The study suggest that need a training program on Paraphrasing tool for proper utilization of tools in best ethical manner.
Public opinion effects of digital state repression: How internet outages shape government evaluation in Africa

ABSTRACTFootnote1Internet shutdowns have become a popular instrument for repressive regimes to silence dissent in a digitized world. While authorities seek to suppress opponents by imposing Internet outages, we know little about how the public reacts to such incisive measures. The regime might face anger and resentment from the public as a response to Internet deprivation. Why do regimes still use Internet shutdowns when they do not only face economic but also societal losses? In this paper, I argue that Internet shutdowns lower the public’s evaluation of the political leadership as citizens blame the government for the service outages. For the analysis, I combine fine-grained data on Internet outages with survey data from the Afrobarometer and apply an “unexpected event during survey design.” Results show that citizens do not hold the government accountable for Internet disruptions, thus making Internet shutdowns a powerful tool for autocrats to silent dissent digitally.KEYWORDS: Information technologyrepressionAfrica Disclosure statementNo potential conflict of interest was reported by the author.Notes1. This study was preregistered on July 5, 2021 at https://osf.io/d2ewx/. The three-way interaction term of the third hypothesis was added later. Additionally, the set of dependent variables was enlarged beyond the initial selection.2. An exception is the case of Kenya, where I rely on the level of provinces.3. A list of regional identifiers was provided by CAIDA upon request.4. The two key assumptions to UESD, ignorability and excludability, are met. A detailed explanation can be found in the appendix (Section 2).5. The number of individuals living in affected regions varies between these groups. While 953 individuals exposed to Internet outages are to be found within the group of one to 10 days after an outage, the number decreases to 450 and 187 respondents in 11 to 20 and 21 to 30 days, respectively.Additional informationFundingThis study was funded by the Deutsche Forschungsgemeinschaft (DFG), project number 402127652.Notes on contributorsRebecca StrauchRebecca Strauch (PhD, University of Konstanz) is a Postdoctoral Research Fellow at the Cluster of Excellence “The Politics of Inequality” at the University of Konstanz, Germany. Her research focuses on digital adaptation in authoritarian states.
Exploring the impact of graph locality for the resolution of the maximum- independent-set problem with neutral atom devices

In the past years, many quantum algorithms have been proposed to tackle hard combinatorial problems. In particular, the maximum independent set (MIS) is a known NP-hard problem that can be naturally encoded in Rydberg atom arrays. By representing a graph with an ensemble of neutral atoms one can leverage Rydberg dynamics to naturally encode the constraints and the solution to MIS. However, the classes of graphs that can be directly mapped ``vertex-to-atom'' on standard devices with two-dimensional capabilities are currently limited to Unit-Disk graphs. In this setting, the inherent spatial locality of the graphs can be leveraged by classical polynomial-time approximation schemes (PTAS) that guarantee an $\ensuremath{\epsilon}$-approximate solution. In this work, we build upon recent progress made for using three-dimensioanl arrangements of atoms to embed more complex classes of graphs. We report experimental and theoretical results which represent important steps towards tackling combinatorial tasks on quantum computers for which no classical efficient $\ensuremath{\varepsilon}$-approximation scheme exists.
Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable Marketing

In recent years, the interest in recommendation systems (RSs) has dramatically increased, as they have become main components of all online stores. The aims of an RS can be multifaceted, related not only to the increase in sales or the convenience of the customer, but may include the promotion of alternative environmentally friendly products or to strengthen policies and campaigns. In addition to accurate suggestions, important aspects of contemporary RSs are therefore to align with the particular marketing goals of the e-shop and with the stances of the targeted audience, ensuring user acceptance, satisfaction, high impact, and achieving sustained usage by customers. The current review focuses on RS related to retail shopping, highlighting recent research efforts towards enhanced e-shops and more efficient sustainable digital marketing and personalized promotion. The reported research was categorized by main approach, key methods, and specialized e-commerce problems addressed, while technological aspects were linked with marketing aspects. The increasing number of papers in the field showed that it has become particularly popular, following the explosive growth in e-commerce and mobile shopping. The problems addressed have expanded beyond the performance of the core algorithms to the business aspects of recommendation, considering user acceptance and impact maximization techniques. Technologies have also shifted from the improvement of classic filtering techniques to complex deep learning architectures, in order to deal with issues such as contextualization, sequence-based methods, and automatic feature extraction from unstructured data. The upcoming goals seem to be even more intelligent recommendations that more precisely adapt not only to users’ explicit needs and hidden desires but also to their personality and sensitivity for more sustainable choices.
An analysis of blockchain versus relational databases for digitalising information flows in global supply chains using the analytic network process

Global trade is plagued by slow and inefficient manual processes associated with physical documents. Firms are constantly looking for new ways to improve transparency and increase the resilience of their supply chains. This can be solved by the digitalisation of supply chains and the automation of document- and information-sharing processes. Blockchain is touted as a solution to these issues due to its unique combination of features, such as immutability, decentralisation and transparency. A lack of business cases that quantify the costs and benefits causes uncertainty regarding the truth of these claims. This paper explores how the costs and benefits of a blockchain-based solution for digitalising and automating documentation flows in cross-border supply chains compare to a conventional centralised relational database solution. The research described in this paper uses primary data collected through semi-structured interviews with industry experts, as well as secondary data from literature. Two models based on existing services were developed and the costs and benefits compared and then analysed using the Architecture Trade-off Analysis Method (ATAM) and the Analytic Network Process (ANP). Findings from the analysis show that a consortium blockchain solution like TradeLens is the favourable solution for digitalising and automating information flows in cross-border supply chains.