Reviewerly/exHarmony huggingface dataset:
Principled Design and Implementation of Steerable Detectors

We provide a complete pipeline for the detection of patterns of interest in an image. In our approach, the patterns are assumed to be adequately modeled by a known template, and are located at unknown positions and orientations that we aim at retrieving. We propose a continuous-domain additive image model, where the analyzed image is the sum of the patterns to localize and a background with self-similar isotropic power-spectrum. We are then able to compute the optimal filter fulfilling the SNR criterion based on one single template and background pair: it strongly responds to the template while being optimally decoupled from the background model. In addition, we constrain our filter to be steerable, which allows for a fast template detection together with orientation estimation. In practice, the implementation requires to discretize a continuous-domain formulation on polar grids, which is performed using quadratic radial B-splines. We demonstrate the practical usefulness of our method on a variety of template approximation and pattern detection experiments. We show that the detection performance drastically improves when we exploit the statistics of the background via its power-spectrum decay, which we refer to as spectral-shaping. The proposed scheme outperforms state-of-the-art steerable methods by up to 50% of absolute detection performance.
Critical Thinking Development of Primary School Pupils by Means of Inventive Problems

The purpose of the research is to determine the effective tools for the critical thinking development for primary school pupils.&#x0D; Methodology. The retrospective analysis of the primary education systems development in Ukraine and in highly developed countries was used, the primary school is determined to be the initial stage of the pupils’ critical thinking development. The material of the study is primary school educational programmes, branches of education, and existing academic subjects, it indicates the necessity to develop and implement the particular subject, and its aim is to develop critical thinking and the abilities to express one’s own opinion, assess risks and solve problems. According to the State Standard of Primary Education the key competencies require the following skills: creativity; initiative; the abilities to justify one’s position logically, to manage emotions constructively, to assess risks, to make decisions, to solve problems, to cooperate with others. &#x0D; Results. The article clarifies that critical thinking is a type of thinking aimed at solving problems, namely studying the argument line (hypotheses, criteria, definitions, arguments, facts, etc.), analyzing alternative solutions; forecasting and assessing consequences. It is proved in practice that inventive problems serve as an effective way to develop critical thinking. The basis of such problems is the theory of inventive problem solving (TIPS). It is determined that an inventive problem contains a cognitive contradiction, its solving promotes the pupils’ critical thinking development. The authors’ subject ‘Eureka’ for pupils of 2-4 classes is offered. Its aim is to develop pupils’ critical thinking, creative and inventive abilities, and also primary skills to carry out natural science researches. The results of the subject approbation are given. &#x0D; Conclusions. Primary school pupils’ critical thinking development can be achieved through the introduction of a range of new subjects at the initial stages of education. The aim of such subjects is to teach pupils to solve inventive problems.
IMPROVING WRITING SKILL OF NARRATIVE TEXT BY USING SHORT VIDEO AT SMA NEGERI 1 SILAU KAHEAN

Video technology in ELT has been increasingly used to help students to improve their language skills. Various videos are designed to present ELT materials. This study aims to investigate the use of short videos to improve students’ writing skills of narrative texts at the senior high school. This study is categorized as classroom action research. The data consist of qualitative and quantitative data. The instruments used to attain the qualitative data are observation and documentation, while the quantitative data are collected using writing tests. This study is carried out in two cycles. The use of short videos helps students understand the generic structures of narrative texts. During learning processes, students are asked to analyze the narrative structures from the introductory to the conclusion paragraph. The improvement of students’ narrative texts is made in the second cycle. The students’ writing achievement is 76.25 in the second cycle. The students’ achievement achieves the passing grade. Therefore, the use of short videos is able to improve students’ writing skills of narrative texts at the senior high school.
PREDICTION FOR COOPERATIVE CREDIT ELIGIBILITY USING DATA MINING CLASSIFICATION WITH C4.5 ALGORITHM

BMT Artha Mandiri is a cooperative that provides savings and loans services. In providing credit, BMT Artha Mandiri still uses the manual method, namely by looking at the ledger and history of each customer, to find out whether the applicant is worthy or not worthy of credit so that it is not effective and efficient. The purpose of this research is to make an application that can predict whether a prospective customer is eligible or not to be given credit. Predictions are made using the data mining classification method, namely the C4.5 algorithm based on the supporting data each customer has to classify which factors have the most influence on the level of credit payments in the cooperative. In a built application, the C4.5 algorithm produces a decision tree that is easy to interpret based on the existing variables. In the application, there are features that can be used to make decisions about customers who will apply for credit at the cooperative. The blackbox test results on the application show that the application has been able to run as expected, while the results of the algorithm test also show that the application has been able to implement the C4.5 algorithm correctly. In addition, the results of testing for accuracy show that the maximum average value of Accuracy is 79.19%.
Discrete Reasoning Templates for Natural Language Understanding

Reasoning about information from multiple parts of a passage to derive an answer is an open challenge for reading-comprehension models.In this paper, we present an approach that reasons about complex questions by decomposing them to simpler subquestions that can take advantage of single-span extraction reading-comprehension models, and derives the final answer according to instructions in a predefined reasoning template.We focus on subtraction based arithmetic questions and evaluate our approach on a subset of the DROP dataset.We show that our approach is competitive with the state of the art while being interpretable and requires little supervision.
Virtual Secure Platform: A Five-Stage Pipeline Processor over TFHE.

We present Virtual Secure Platform (VSP), the first comprehensive platform that implements a multi-opcode general-purpose sequential processor over Fully Homomorphic Encryption (FHE) for Secure Multi-Party Computation (SMPC). VSP protects both the data and functions on which the data are evaluated from the adversary in a secure computation offloading situation like cloud computing. We proposed a complete processor architecture with a five-stage pipeline, which improves the performance of the VSP by providing more parallelism in circuit evaluation. In addition, we also designed a custom Instruction Set Architecture (ISA) to reduce the gate count of our processor, along with an entire set of toolchains to ensure that arbitrary C programs can be compiled into our custom ISA. In order to speed up instruction evaluation over VSP, CMUX Memory based ROM and RAM constructions over FHE are also proposed. Our experiments show that both the pipelined architecture and the CMUX Memory technique are effective in improving the performance of the proposed processor. We provide an open-source implementation of VSP which achieves a per-instruction latency of less than 1 second. We demonstrate that compared to the best existing processor over FHE, our implementation runs nearly 1,600$\times$ faster.
Two Training Strategies for Improving Relation Extraction over Universal Graph

This paper explores how the Distantly Supervised Relation Extraction (DS-RE) can benefit from the use of a Universal Graph (UG), the combination of a Knowledge Graph (KG) and a large-scale text collection.A straightforward extension of a current state-of-the-art neural model for DS-RE with a UG may lead to degradation in performance.We first report that this degradation is associated with the difficulty in learning a UG and then propose two training strategies: (1) Path Type Adaptive Pretraining, which sequentially trains the model with different types of UG paths so as to prevent the reliance on a single type of UG path; and (2) Complexity Ranking Guided Attention mechanism, which restricts the attention span according to the complexity of a UG path so as to force the model to extract features not only from simple UG paths but also from complex ones.Experimental results on both biomedical and NYT10 datasets prove the robustness of our methods and achieve a new state-ofthe-art result on the NYT10 dataset.The code and datasets used in this paper are available at https://github.com/baodaiqin/UGDSRE.
Comparison of Soft and Hard Clustering: A Case Study on Welfare Level in Cities on Java Island

The National Medium Term Development Plan 2020-2024 states that one of the visions of national development is to accelerate the distribution of welfare and justice. Cluster analysis is analysis that grouping of objects into several smaller groups where the objects in one group have similar characteristics. This study was conducted to find the best clustering method and to classify cities based on the level of welfare in Java. In this study, the cluster analysis that used was hard clustering such as K-Means, K-Medoids (PAM and CLARA), and Hierarchical Agglomerative as well as soft clustering such as Fuzzy C Means. This study use elbow method, silhouette method, and gap statistics to determine the optimal number of clusters. From the evaluation results of the silhouette coefficient, dunn index, connectivity coefficient, and Sw/Sb ratio, it was found that the best cluster analysis was Agglomerative Ward Linkage which produced three clusters. The first cluster consists of 27 cities with moderate welfare, the second cluster consists of 16 cities with high welfare, the third cluster consists of 76 cities with low welfare. With the best clustering results, the government of cities in Java shall be able to make a better policies of welfare based on the dominant indicators found in each cluster.
Improving Long-Tailed Classification from Instance Level

Data in the real world tends to exhibit a long-tailed label distribution, which poses great challenges for neural networks in classification. Existing methods tackle this problem mainly from the coarse-grained class level, ignoring the difference among instances, e.g., hard samples vs. easy samples. In this paper, we revisit the long-tailed problem from the instance level and propose two instance-level components to improve long-tailed classification. The first one is an Adaptive Logit Adjustment (ALA) loss, which applies an adaptive adjusting term to the logit. Different from the adjusting terms in existing methods that are class-dependent and only focus on tail classes, we carefully design an instance-specific term and add it on the class-dependent term to make the network pay more attention to not only tailed class, but more importantly hard samples. The second one is a Mixture-of-Experts (MoE) network, which contains a multi-expert module and an instance-aware routing module. The routing module is designed to dynamically integrate the results of multiple experts according to each input instance, and is trained jointly with the experts network in an end-to-end manner.Extensive experiment results show that our method outperforms the state-of-the-art methods by 1% to 5% on common long-tailed benchmarks including ImageNet-LT and iNaturalist.
Quality Metrics measurement for Hybrid Systems (Aspect Oriented Programming – Object Oriented Programming)

This paper explores a new framework for calculating hybrid system metrics using software quality metrics aspect-oriented and object-oriented programming. Software metrics for qualitative and quantitative measurement is a mix of static and dynamic software metrics. It is noticed from the literature survey that to date, most of the architecture considered only the evaluation focused on static metrics for aspect-oriented applications. In our work, we mainly discussed the collection of static parameters , long with AspectJ-specific dynamic software metrics.The structure may provide a new direction for research while predicting software attributes because earlier dynamic metrics were ignored when evaluating quality attributes such as maintainability, reliability, and understandability of Asepect Oriented software. Dynamic metrics based on the fundamentals of software engineering are equally crucial for software analysis as are static metrics. A similar concept is borrowed with the introduction of dynamic software metrics to implement aspect-riented software development.Currently, we only propose a structure and model using static and dynamic parameters to test the aspect-oriented method, but we still need to validate the proposed approach.
AMR-Net: Arbitrary-Oriented Ship Detection Using Attention Module, Multi-Scale Feature Fusion and Rotation Pseudo-Label

Ship detection is significant and full of challenges in the field of remote sensing. The widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented ship objects. Complex backgrounds, small objects, and the dilemma of labeling ship datasets are all obstacles that further limit the successful operation of traditional methods in ship detection. In this paper, we propose a multi-task rotation detector using attention module, multi-scale feature fusion and rotation pseudo-label, namely AMR-Net. AMR-Net adds Deformable Convolution Channel Attention Block (DCCAB) to suppress background noise and highlight the foreground. Feature Pyramid Network (FPN) fuses features from different scales, which is beneficial for ship detection. We present Adaptive FPN (AFPN) to determine the optimal number of prediction layers automatically, which can reduce the disturbance of high-level detection of small objects, decrease the size of the model, and adapt to different ship datasets. To enable the detector to achieve excellent performance even when using fewer labeled data, a semi-supervised pseudo-label module is designed, namely Self-Learning Rotation Pseudo-Label (SRP). SRP allows the detector to iteratively self-learn the optimal thresholds, and use the thresholds to refine high-quality rotation pseudo-labels for retraining the model. SRP is optional. If this module is selected, the detector becomes a semi-supervised detector. Extensive both supervised and semi-supervised experiments on remote sensing public datasets HRSC2016 show the state-of-the-art performance of our detector. Experiments on DOTA further illustrate the effectiveness of AMR-Net.
Avoiding big data pitfalls

Clinical decisions are based on a combination of inductive inference built on experience (ie, statistical models) and on deductions provided by our understanding of the workings of the cardiovascular system (ie, mechanistic models). In a similar way, computers can be used to discover new hidden patterns in the (big) data and to make predictions based on our knowledge of physiology or physics. Surprisingly, unlike humans through history, computers seldom combine inductive and deductive processes. An explosion of expectations surrounds the computer’s inductive method, fueled by the “big data” and popular trends. This article reviews the risks and potential pitfalls of this computer approach, where the lack of generality, selection or confounding biases, overfitting, or spurious correlations are among the commonplace flaws. Recommendations to reduce these risks include an examination of data through the lens of causality, the careful choice and description of statistical techniques, and an open research culture with transparency. Finally, the synergy between mechanistic and statistical models (ie, the digital twin) is discussed as a promising pathway toward precision cardiology that mimics the human experience.
Design and Implementation of Decentralized Swarm Intelligence E-Commerce Model Based on Regional Chain and Edge Computing

In this paper, we investigate the model of decentralized groupwise e-commerce through regional chains combined with edge computing algorithms and design and implement it. A blockchain-based architecture for remote sensing resource and service transactions is proposed. The architecture utilizes the characteristics of nontamperable and traceable data on the blockchain to solve the problem of mutual distrust between the transaction parties, uses MongoDB for data retrieval and filtering to solve the problem that it is difficult to query flexibly on the blockchain, uses the file platform to save public resources to solve the problem that it is very expensive or even infeasible to store images or large texts in the blockchain, and ensures remote sensing resources and services related to the effective storage of images and description texts. The Ethernet features are described and analyzed to assess their suitability as a technical basis for the corresponding solutions. To assess the ability of the Ethernet-based system to solve solvable problems, its practical use cases are analyzed. The results of the analysis show that five main issues need to be addressed when applying Ethernet to e-commerce, namely, low scalability, lack of distributed storage, lack of convenience, reliability, and functional integrity. To exploit the idea of using blockchain technology to solve complex problems and to overcome the challenges identified in the existing Ethernet-based implementation solutions, an optimization idea is proposed by creating a multiblockchain architecture based multimodule solution in the Ethernet framework. Finally, the application prospects of the proposed information system are discussed.
Influence of Mall Shopping Culture on Online Shopping Preferences: An Emerging Economy Perspective using the Technology Acceptance Model

Growth of e-commerce activities are keeping up pace with the rapid progress in the development and availability of internet. However, the availability of internet technology and its penetration does not automatically imply consumer preference of online shopping. This study aims to examine the factors influencing the decisions of consumers for engaging in online shopping in the context of UAE, and inquires whether the prevalence of mall culture has an impact on the online shopping adoption by consumers. The Technology Acceptance Model (Davis 1989) was used to assess consumer perceptions and intentions towards online shopping. Data was collected through online surveys using convenience sampling method. From the analysis of sample data, there is sufficient evidence to conclude that online shopping is still not very popular in the UAE as residents prefer to do shopping in malls. The findings of the study are relevant to the e-commerce retail entities operating in countries where the mall culture is an important influence in the retail scenario, as this will help them in formulating their strategy to accommodate the shopping preferences of both online and in-store customers. The impact of the COVID-19 pandemic is also considered on the objectives and findings of the study.
Reference Architecture and Operation Model for PPP (Public-Private-Partnership) Cloud

The cloud has already become the core infrastructure of information systems, and government institutions are rapidly migrating information systems to the cloud. Government institutions in several countries use private clouds in their closed networks. However, because of the advantages of public clouds over private clouds, the demand for public clouds is increasing, and government institutions are expected to gradually switch to public clouds. When all data from government institutions are managed in the public cloud, the biggest concern for government institutions is the leakage of confidential data. The public-private-partnership (PPP) cloud provides a solution to this problem. PPP cloud is a form participation in a public cloud infrastructure and the building of a closed network data center. The PPP cloud prevents confidential data leakage and leverages the benefits of the public cloud to build a cloud quickly and easily maintain the cloud. In this paper, based on the case of the PPP cloud applied to the Korean government, the concept, architecture, operation model, and contract method of the PPP cloud are presented.
Constitutions and Contagion – European Constitutional Systems and the COVID-19 Pandemic

The COVID-19 pandemic has posed an unprecedented challenge, with governments resorting to different legal strategies to respond to the health emergency. This article offers a cross-cuting comparative analysis of measures taken during first six months of the pandemic (the “first wave”) in four European jurisdictions with significantly different constitutional settlements – namely France, Germany, Italy, and the United Kingdom. It explores the influence of specific constitutional features on the legal responses to the pandemic and how, in turn, these responses have the potential to reconfigure the institutional frameworks in place. The inquiry, which unfolds along the analytical categories of (i) legal basis, (ii) horizontal and (iii) vertical allocation of power, and (iv) the role of the judiciary, shows that both constitutional contexts and legal traditions play a significant role in pandemic times and are, moreover, likely to continue shaping post-pandemic governance patterns.
An Efficient Modification of Grey Wolf Optimization Using Cuckoo Search, Levy Fly and Mantegna Algorithm for Real-Time Image Processing Applications

Optimization methods, frequently used in several image and video processing algorithms for the attainment of optimal solutions, pose severe hurdle in case of real-time processing. For catering to the needs of real-time operations in a cost-effective way, dedicated hardware is inevitable. The huge computational load of any optimization method strikes down its feasibility of being realized in terms of dedicated hardware. The computational complexities of meta-heuristic optimization methods are even more than any other conventional optimization methods. So, in spite of having the capability of providing global solution by dodging local optima, meta-heuristic optimizations are avoided in real-time systems. To overcome the bottleneck, in this article, a modified GWOA (modified grey wolf optimization algorithm)is formulated by blending the advantages of CS (cuckoo search), Levy fly (LV), and MA (Mantegna algorithm). This modified GWOA is articulated to be computationally efficient and precise, so that, it can easily be realized in terms of dedicated VLSI architecture while maintaining the accuracy at a high level. The proposed method helps to diminish the cost and power requirement of high end and costly real-time imaging/ video processing systems while upholding its precision. The proposed method is tested by using MATLAB R2018b. The high-level synthesis (HLS) tool of Xilinx Vivado18.2softwareisusedtosynthesizethisMGWOA, thus establishing the viability of the proposed algorithm to be implemented on FPGA/ ASIC.