Reviewerly/exHarmony huggingface dataset:
Active Anomaly Detection Technology Based on Ensemble Learning

Anomaly detection is an important problem in various research and application fields. Researchers design reliable schemes to provide solutions for effectively detecting anomaly points. Most of the existing anomaly detection schemes are unsupervised methods, such as anomaly detection methods based on density, distance and clustering. In total, unsupervised anomaly detection methods have many limitations. For example, they cannot be well combined with prior knowledge in some anomaly detection tasks. For some nonlinear anomaly detection tasks, the modeling is complex and faces dimensional disasters, which are greatly affected by noise. Sometimes it is difficult to find abnormal events that users are interested in, and users need to customize model parameters before detection. With the wide application of deep learning technology, it has a good modeling ability to solve linear and nonlinear data relationships, but the application of deep learning technology in the field of anomaly detection has many challenges. If we regard exceptions as a supervised problem, exceptions are a few, and we usually face the problem of too few labels. To obtain a model that performs well in the anomaly detection task, it requires a high initial training set. Therefore, to solve the above problems, this paper proposes a supervised learning method with manual participation. We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology. In addition, this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling. In the experimental link, we will show that our method is better than some traditional anomaly detection algorithms.
M-ISFCM: A Semisupervised Method for Anomaly Detection of MOOC Learning Behavior

Massive online courses (MOOCs) are becoming increasingly vital in the modern era, yet tools to track and detect MOOC learners' progress are inadequate. In reality, labeled MOOC data are difficult to acquire, whereas unlabeled data make up the majority of the data, and these massive unlabeled data are difficult to analyze, resulting in data waste. This paper tackles this issue by presenting a MOOC learning behavior anomaly detection model (M-ISFCM) for the supervision and inspection of MOOC learners' learning that combines semisupervised fuzzy C-mean clustering (SFCM) and an isolated forest algorithm. To optimize MOOC data usage, the model leverages unlabeled and labeled MOOC data as prior assumptions. The MOOC detection runtimes are enhanced by integrating the outliers of the isolated forest approach in SFCM. The results show that the model has a higher precision rate, recall rate, and AUC than the traditional anomaly models in MOOC data. Therefore, the model is effective for recognizing anomalous MOOC learning behaviors.
BMSC: A Novel Anonymous Trading Scheme Based on Zero-Knowledge Proof in Ethereum

Blockchains are widely used because of their openness, transparency, nontampering and decentralization. However, there is a high risk of information leakage when trading on blockchain, and the existing anonymous trading schemes still have some problems. To meet the high requirement of anonymity, the cost of proof submitted by the user is too large, which does not apply to blockchain storage. Meanwhile, transaction verification takes too long to ensure the legitimacy of the transaction. To solve these problems, this paper presents a novel anonymous trading scheme named Block Maze Smart Contract (BMSC) based on the zero-knowledge proof system zk-SNARKs to propose efficiency. This scheme can hide account balances, transaction amounts, and the transfer relationships between transaction parties while preventing overspending attacks and double-spending attacks. Compared with other anonymous schemes, this scheme has less cost of proof and takes less time for transaction verification while meeting the high requirements of anonymity and security.
AM-PSPNet: Pyramid Scene Parsing Network Based on Attentional Mechanism for Image Semantic Segmentation

In this paper, AM-PSPNet is proposed for image semantic segmentation. AM-PSPNet embeds the efficient channel attention (ECA) module in the feature extraction stage of the convolutional network and makes the network pay more attention to the channels with obvious classification characteristics through end-to-end learning. To recognize the edges of objects and small objects more effectively, AM-PSPNet proposes a deep guidance fusion (DGF) module to generate global contextual attention maps to guide the expression of shallow information. The average crossover ratio of the proposed algorithm on the Pascal VOC 2012 dataset and Cityscapes dataset reaches 78.8% and 69.1%, respectively. Compared with the other four network models, the accuracy and average crossover ratio of AM-PSPNet are improved.
Blockchain Access Control Scheme Based on Multi-authority Attribute-Based Encryption

Blockchain has been widely used in many fields because it can solve the problem of information asymmetry and enable users who do not trust each other to collaborate without the participation of third-party intermediaries. Existing blockchain access control schemes usually use attribute-based encryption, but most of them adopt traditional single-attribute authority for attribute authorization, which has the problem that the authority is overburdened and must be fully credible. This paper proposes a blockchain access control scheme based on multi-authority attribute-based encryption by improving the existing blockchain privacy protection method. Autonomous identity management is performed through the blockchain to complete the initialization of user identity and the issuance of attribute certificates. Attribute authorities are selected using the reputation proof consensus mechanism. The distributed key generation protocol is used to generate keys, and the linear secret sharing scheme is improved. The hierarchical relationship of the access structure is used to encrypt and access control the private data that need to be uploaded to the blockchain. According to the comparison with other blockchain access control schemes, the scheme proposed in this paper has been improved in terms of security and efficiency.
A Facial Size Automatic Measurement and Analysis Technology

Facial measurement and analysis is an important part of anthropometry, which provides data support for the design of facial protective equipment. To overcome the inconveniences, low efficiency and poor measurement accuracy of facial size parameters measured and analyzed by manual contact, a method of automatic measurement and analysis of face size parameters is proposed. First, the automatic marking method of faces based on deep learning can improve the efficiency of measuring facial parameters. Then, facial parameters, including nose middle width, nose width, face width and eye width, can be measured. Finally, the data set of face size parameters is classified and counted based on fuzzy clustering analysis. Sixty-five groups of Han youth facial data are collected for measurement and analysis, and compared with the existing algorithms, the facial morphology analysis system presented in this paper has higher measurement accuracy.
Duration of Support and Financial Health of Business Support Structures in Burkina Faso, Cameroon, and Ghana: A Micro-Econometric Analysis

Access to finance is perceived as one of the major problems facing businesses in Sub-Saharan Africa, as well as the structures that support them in their development. This paper aims to measure the probability that a support structure with given characteristics, specific services to entrepreneurs and some technical capacities will face large-scale financial problems. We estimate a multinomial logistic model using a pool of disaggregated data collected by the Nkafu Policy Institute in a survey of 79 business support structures in Burkina Faso, Cameroon and Ghana in 2021. Our results show that the financial health of a business support structure is not fundamentally dependent on the duration of support, but rather on other factors related to the quality of services offered to entrepreneurs.
Asrpu: A Programmable Accelerator for Low-Power Automatic Speech Recognition

The outstanding accuracy achieved by modern Automatic Speech Recognition (ASR) systems is enabling them to quickly become a mainstream technology. ASR is essential for many applications, such as speech-based assistants, dictation systems and real-time language translation. However, highly accurate ASR systems are computationally expensive, requiring on the order of billions of arithmetic operations to decode each second of audio, which conflicts with a growing interest in deploying ASR on edge devices. On these devices, hardware acceleration is key for achieving acceptable performance. However, ASR is a rich and fast-changing field, and thus, any overly specialized hardware accelerator may quickly become obsolete.
Adversarial Pre-Optimized Graph Representation Learning with Double-Order Sampling for Cross-Modal Retrieval

Cross-modal retrieval aims at retrieving relevant information across different media data, such as image and text. However, inconsistent distribution of different modal data causes a heterogeneity gap, which becomes an intractable issue. Previous researches aim at finding a semantic share space for various modalities, in which the similarity of them can be calculated by a uniform measurement. Graph representation learning has shown excellent performance in many tasks, like information retrieval. However, it is difficult to construct the most proper cross-modal graph which can better present the semantics of multi-modal data. Besides, the projected representations of various modal data optimized by the supervised class labels and unsupervised graph context learning are insufficiently aligned. To address these issues, this paper presents an Adversarial Pre-optimized Graph Representation Learning (AP-GRL) for cross-modal retrieval. Similar to mainstream methods, AP-GRL aims to learn a shared representation for various modalities. We design an adaptive pre-optimization mechanism to optimize the cross-modal data and their relationships, which is different from previous fixed graph. The raw constructed cross-modal graph is optimized by maximum posterior probability estimation. To fully exploit the graph, we proposed a double-order sampling strategy to take the width and depth into representation learning. Besides, we adopt adversarial learning with Wasserstein distance to reduce domain discrepancy in the domain-invariant representation learning. Extensive experiments on five widely used datasets demonstrate that AP-GRL outperforms state-of-the-art cross-modal retrieval methods.
INFORMATION EXTRACTION FROM SCIENTIFIC TEXTS IN RUSSIAN

This article describes methods for automatic term extraction and linking to Wikidata. The advantage of the proposed methods is the potential possibility of their applicability to any field of knowledge where only unmarked texts and small term dictionaries are available. To carry out the experiments, a corpus of scientific texts RuSERRC was collected and marked up. The corpus and models are published on GitHub and may be useful to other research teams.
ENGLISH LEARNING STRATEGIES IN ELEMENTARY SCHOOLS: CHALLENGES AND OPPORTUNITIES IN KAMPUS MENGAJAR PROGRAM

Some research explores teachers' knowledge and views, proving that knowledge and beliefs play an important role in instructors' classroom decision-making. However, little study has been done to see how teachers' knowledge and opinions about how effective strategies affect implementation. Focus on the challenges and opportunities in Kampus Mengajar Program by implementing the English learning strategies in 16 Elementary School, this study aims to determine which technique is the most beneficial and what barriers participants face when implementing English learning in elementary schools as part of the Kampus Mengajar Program. The participants were English Department students' that participated in Kampus Mengajar Program at Tidar University consisting of 16 students. A questionnaire with open ended questions was used as the instrument in this survey. Playing games, using songs, using interactive media, and repetition were the four most commonly used strategies in teaching English language skills to young learners. These findings may be beneficial to those who teach English or work with young learners.
Latency-Constrained Dnn Architecture Learning for Edge Systems Using Zerorized Batch Normalization

Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding the number of neurons during the design of a deep neural network to maximize performance is not intuitive. Particularly, many application scenarios are real-time and have a strict latency constraint, while conventional neural network optimization methods do not directly change the temporal cost of model inference for latency-critical edge systems. In this work, we propose a latency-oriented neural network learning method to optimize models for high accuracy while fulfilling the latency constraint. For efficiency, we also introduce a universal hardware-customized latency predictor to optimize this procedure to learn a model that satisfies the latency constraint by only a one-shot training process. The experiment results reveal that, compared to state-of-the-art methods, our approach can well-fit the ‘hard’ latency constraint and achieve high accuracy. Under the same training settings as the original model and satisfying a 34ms latency constraint on the ImageNet-100 dataset, we reduce GoogLeNet’s latency from 40.32ms to 34ms with a 0.14% accuracy reduction on the NVIDIA Jetson Nano. When coupled with quantization, our method can be further improved to only 0.04% drop for GoogLeNet. On the NVIDIA Jetson TX2, we compress VGG-19 from 119.98ms to 34ms and even improve its accuracy by 0.5%, and we scale GoogLeNet up from 20.27ms to 34ms and achieve higher accuracy by 0.78%. We also open source this framework at https://github.com/ntuliuteam/ZeroBN .
Interactive feature modeling with background knowledge for validation and configuration

Feature modeling enables a straightforward representation of a product's features, components, and the relations between them. In this way, feature models serve as an excellent approach to diagrammatically model a product design for manufacturing purposes. However, the actual usage of such a feature model to generate suitable designs in the context of real-life industry applications is often limited, as crucial background knowledge cannot be expressed. Moreover, even though expert validation of a feature model is an important aspect of its creation, current tooling often falls short on this aspect. Indeed, although state-of-the-art tools are capable of generating possible configurations, this is not sufficient to completely validate complex applications: instead, we should enable the expert to interactively explore the problem domain. In this paper, we present our feature modeling tool, called FM-IDP, which aims to overcome both of these shortcomings. In FM-IDP, background knowledge can be expressed in FO(·), a rich extension of classical first-order logic. Using an off-the-shelf logical reasoning engine and an integrated interactive configuration interface, modelers can interact with the feature model and its background knowledge to explore the problem space on-the-fly. We motivate our approach using an industrial use case focused on real-life component design.
Defining categorical reasoning of numerical feature models with feature-wise and variant-wise quality attributes

Automatic analysis of variability is an important stage of Software Product Line (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model.
Multidisciplinary variability management for cyber-physical production systems

Cyber-Physical Production Systems (CPPSs) are complex, versatile systems interacting with the environment by sensors and actuators. Specific customer demands and technical requirements lead to high engineering efforts for the control software of CPPSs, especially when following a clone-and-own approach to reuse, as is still common in industry. Utilizing systematic variability management to derive and configure control software variants from a product line could help to reduce the cost of developing and/or maintaining CPPSs. However, modeling CPPS variability is challenging as knowledge from multiple disciplines (e.g., mechanics, electrics, software) is needed, which is either implicit in practice or expressed in multiple heterogeneous engineering artifacts with diverse semantics. Furthermore, techniques commonly used to implement CPPS control software (e.g., graphical programming or modeling languages) do not have any formal mechanism to express variability. In this paper, we report on our ongoing efforts to create a multidisciplinary variability management approach for CPPSs, particularly CPPS control software. We designed our approach as an integrated approach providing configuration options based on related heterogeneous variability models from multiple disciplines. Our integrated approach can generate control software based on related domain-specific implementation artifacts.
Level of Detail Exploration of Electronic Transition Ensembles using Hierarchical Clustering

Abstract We present a pipeline for the interactive visual analysis and exploration of molecular electronic transition ensembles. Each ensemble member is specified by a molecular configuration, the charge transfer between two molecular states, and a set of physical properties. The pipeline is targeted towards theoretical chemists, supporting them in comparing and characterizing electronic transitions by combining automatic and interactive visual analysis. A quantitative feature vector characterizing the electron charge transfer serves as the basis for hierarchical clustering as well as for the visual representations. The interface for the visual exploration consists of four components. A dendrogram provides an overview of the ensemble. It is augmented with a level of detail glyph for each cluster. A scatterplot using dimensionality reduction provides a second visualization, highlighting ensemble outliers. Parallel coordinates show the correlation with physical parameters. A spatial representation of selected ensemble members supports an in‐depth inspection of transitions in a form that is familiar to chemists. All views are linked and can be used to filter and select ensemble members. The usefulness of the pipeline is shown in three different case studies.
Synchronizing software variants

Code copying and customization is a common practice to realize variability and serve the demand for custom-tailored software. The clone-and-own-approach is flexible and efficient, but does not scale with the number of variants as developers must transfer changes between clones manually, which is an error-prone and tedious task. This task becomes even more challenging, when developers reuse code not only between variants, but also within variants. As a solution, we propose a novel synchronization technique that supports developers to transfer changes applied to clones to its corresponding clone instances within a variant as well as across other variants. Our technique relies on a common model of clone relationships between and within variants, that can automatically propagate changes two-dimensional into the respective intra- and inter-clone instances. In an empirical evaluation, we demonstrate the need for this two-dimensional clone synchronization and show the usefulness and scalability of our approach using the MobileMedia case study.
A tool for analysing higher-order feature interactions in preprocessor annotations in C and C++ projects

Feature interactions are an intricate phenomenon: they can add value to software systems, but also lead to subtle bugs and complex, emergent behavior. Having a clearer understanding of feature interactions in practice can help practitioners to select appropriate quality assurance techniques for their systems and researchers to guide further research efforts. In this paper, we present pdparser, a Python-based tool for analysing structural feature interactions in software systems developed with C and C++ preprocessor. Our tool relies on a lightweight methodology to quantify the frequency of pairwise and higher-order feature interactions and the percentage of code affected by them. We showcase the individual characteristics brought forward by the automated analysis of one toy example and two open-source text editors: Vim and Emacs. The source code and a demo video are available on GitHub at https://github.com/dkorsman/pdparser.
On the relation of variability modeling languages and non-functional properties

Non-functional properties (NFPs) such as code size (RAM, ROM), performance, and energy consumption are at least as important as functional properties in many software development domains. When configuring a software product line - especially in the area of resource-constrained embedded systems - developers must be aware of the NFPs of the configured product instance. Several NFP-aware variability modeling languages have been proposed to address this in the past. However, it is not clear whether a variability modeling language is the best place for handling NFP-related concerns, or whether separate NFP prediction models should be preferred. We shine light onto this question by discussing limitations of state-of-the-art NFP-aware variability modeling languages, and find that both in terms of the development process and model accuracy a separate NFP model is favorable. Our quantitative analysis is based on six different software product lines, including the widely used busybox multi-call binary and the x264 video encoder. We use classification and regression trees (CART) and our recently proposed Regression Model Trees [8] as separate NFP models. These tree-based models can cover the effects of arbitrary feature interactions and thus easily outperform variability models with static, feature-wise NFP annotations. For example, when estimating the throughput of an embedded AI product line, static annotations come with a mean generalization error of 114.5% while the error of CART is only 9.4 %.
Test case aggregation for efficient feature model testing

The increasing size and complexity of feature models (FM) requires the provision of efficient testing and debugging techniques. Feature models can be tested, for example, with regard to their conformance with a pre-defined set of analysis operations. In this paper, we show how the number of consistency checks for FM testing can be reduced on the basis of test case aggregation. Using a divide-and-conquer based approach, we show how to transform a feature model test suite into a corresponding aggregated representation where individual test cases can be combined if specific consistency criteria are fulfilled. Performance improvements are also analyzed on the basis of a best- and worst-case runtime analysis.