Reviewerly/exHarmony huggingface dataset:
Reusable Component Retrieval: A Semantic Search Approach for Low-Resource Languages

A common practice among programmers is to reuse existing code, accomplished by performing natural language queries through search engines. The main aim of code retrieval is to search for the most relevant snippet from a corpus of code snippets. However, code retrieval frameworks for low-resource languages are insufficient. Retrieving the most relevant code snippet efficiently can be accomplished only by eliminating the semantic gap between the code snippets residing in the repository and the user’s query (natural language description). The primary objective of the research is to contribute to this field by providing a code search framework that can be extended for low-resource languages. The secondary objective is to provide a code retrieval mechanism that is semantically relevant to the user query and provide programmers with the ability to locate source code that they want to use when developing new applications. The proposed approach is implemented using a web platform to search for source code. As code retrieval is a sophisticated task, the proposed approach incorporates a semantic search mechanism. This research uses a semantic model for code retrieval, which generates meanings or synonyms of words. The proposed model integrates ontologies and Natural Language Processing. System performance measures and classification accuracy are computed using precision, recall, and F1-score. We also compare the proposed approach with state-of-the-art baseline models. The retrieved results are ranked, showing that our approach significantly outperforms robust code matching. Our evaluation shows that semantic matching leads to improved source code retrieval. This study marks a substantial advancement in integrating programming expertise with code retrieval techniques. Moreover, our system lets users know when and how it is used for successful semantic searching.
Survey of Authorship Identification Tasks on Arabic Texts

Authorship identification is the process of extracting and analysing the writing styles of authors to identify the authorship. From the writing style, the author and his/her different characteristics can be recognised, which is very useful in digital forensics and cyber investigations. In the literature, authorship identification tasks were addressed on both long and short documents and performed on different languages, such as English, Arabic, Chinese, and Greek. This survey has reviewed the authorship identification tasks for the Arabic language to contribute to this area of research by exploring Arabic language performance and challenges. A total of 27 prominent Arabic studies of each authorship identification domain were reviewed considering the used data, selected features, utilised methods, and results. After a review of the various studies, it was concluded that the results of authorship identification tasks vary based on mostly the selected features and used dataset. Furthermore, the effective features differ from one dataset to another based on the various types of the Arabic language. However, all authorship identification tasks involving the Arabic language face considerable challenges with data pre-processing due to the challenging Arabic concatenative morphology.
Framing Effects

Abstract This chapter introduces two kinds of one-place TSIMs representing, respectively, belief activated in working memory and belief left passively stored in long-term memory. The distinction between the two sorts of belief is shown to help with the modelling of a typical form of the well-known framing effect, whereby people can have different attitudes towards logically or necessarily equivalent propositions. The chapter introduces a semantics for active and passive topic-sensitive belief to represent and reason about, agents whose belief states can be subject to framing effects. The analysis of framing calls for a precise characterization of the sense in which framed agents are logically non-omniscient given that they can believe exactly one of two intensionally equivalent propositions even when they are fully on top of the relevant subject matters and, in a ‘dormant’ sense, they are even aware of the equivalence. The formal framework here combines topic-sensitivity with ideas from subset space semantics.
Comparison of Feature Extraction Algorithms for Prediction of Quality Characteristics

Data acquired during production processes often contain redundant and irrelevant features. Thus, to predict quality characteristics from process data, precise feature extraction is essential to sustain a low prediction error and to limit the computational complexity of the deployed machine learning models. Hence, we compare two feature extraction methods: principal component analysis and an autoencoder. Based on an industrial use case, we highlight the advantages of the methods and provide guidance in creating an automated data analysis pipeline for the prediction of quality characteristics. This pipeline is fundamental for other predictive quality applications such as smart experts. Our results favor the principal component analysis for feature extraction, even though it is less expressive than autoencoders.
Information Model for Hybrid Prototyping in Design Reviews of Assembly Stations

Human-centered system design is one way of ensuring required flexibility in assembly through cognitive and motor capabilities of humans. Human factors and ergonomics must be considered at an early stage in system development to improve productivity, quality and to secure well-being of workers. The paper presents a model-based approach for design reviews to ensure the ergonomics of assembly systems at an early stage enabled by different models of mixed reality. An information model is proposed to enable standardized review reports for the combined use of physical, digital and virtual models. System designers are supported by actionable knowledge gathered in MR enabled design reviews. Thus, efforts limiting the economic applicability of Hybrid Prototyping can be significantly reduced.
Test-Driven Reward Function for Reinforcement Learning: A Contribution towards Applicable Machine Learning Algorithms for Production Systems

Reinforcement Learning algorithms find more and more application in fields where complex tasks need to be solved. The automation of production systems is one of those fields. Normally, programming a control system defines the automation strategy. Previous contributions by the authors have shown that a so-called agent can learn automation strategies for production systems using a Reinforcement Learning setup. However, the development of the reward function for the agent can be challenging and needs Reinforcement Learning domain knowledge. This paper introduces a novel approach in combining Test-Driven Development with Reinforcement Learning in order to solve the problem of a suitable reward function. In the presented approach predefined test cases are used to derive rewards for the agent. The use of an automated test framework allows for continuous learning sequences until all test cases are passed. An application example of a robot cell is used to demonstrate the novel approach and verify its suitability and usability. The first application shows promising results for further examination towards more application scenarios.
Delay Domain-Based Signal Processing for Intelligent Manufacturing Systems

From a signal, x(i)∈ℜ, i = 0,1,…, a delay map consisting of the points {x(i),x(i+d)}, where d (a non-zero integer) is called the delay, can be constructed. These maps are highly informative when the signal is chaotic. In this study, a set of delay maps of cutting force signals of milling operations is constructed for different values of delay and cutting scenarios. It is found that for some specific values of delay, the delay maps exhibit distinguished patterns. The patterns can serve as the signature of cutting scenarios and, thus, can be used to construct intelligent monitoring systems for cyber-physical systems.
5GMEC-DP: Differentially private protection of trajectory data based on 5G-based mobile edge computing

Machine learning has become a core technology in areas such as big data, Internet of Things, and cloud computing. Training machine learning models requires a large amount of data, How to protect these data with low cost and high efficiency is an important issue. This paper proposes a trajectory data collection model based on the 5G-based mobile edge computing, and uses the service characteristics of the MEC server to propose a differentially private protection scheme of trajectory data based on 5G-based mobile edge computing (5GMEC-DP). If the encrypted location information does not belong to the service range of the current MEC server, it will be projected to the service edge of the MEC server, thereby limiting the amount of noise of the Geo-Indistinguishability algorithm, making it suitable for the protection of trajectory data, and it also proves that the 5GMEC-DP algorithm still satisfies the definition of ε-Geo-Indistinguishability. Finally, experiments on the real data set prove that 5GMEC-DP can maintain the high practicability of the data when the degree of privacy protection is high. At ε=0.001 and ε=0.0005, Average Qloss of each coordinate in the trajectory decreases by 64% and 81%, respectively, compared with Geo-Indistinguishability algorithm. The overall database availability loss decreases by 64% and 82%, respectively.
Blockchain in IoT Networks for Precision Agriculture

With the increase in research and development in communication technology, it is predicted that more and more number of sensing devices will be added in various sectors by application of IoT. Therefore, there is an immediate need of replacing the traditional methods of storing, sorting and sharing of data that has been collected from various sensing devices (Chiang and Zhang in IEEE Internet Things J 3:854–864, 2016), (Lee et al. in Comput Electron Agric 74:2–33, 2010). This will help in making data more transparent, reliable, decentralised and immutable. This has led to the integration of blockchain into IoT systems. The upcoming section gives a vivid picture about the basic concept and feature of blockchain technology and thereby detecting various advantages of integration of blockchain into IoT.
Abstractive-Extractive Combined Text Summarization of Youtube Videos

Automatic text summarization is a data-driven technique for coping with today's huge amounts of textual material. The purpose of this study is to look into a novel way for generating abstractive summaries of huge documents. The lack of precision gained by very intricate models seeking to provide abstractive summaries necessitates the development of a new strategy. In this paper, we looked into combining extractive and abstractive summaries in a two-step process to get more accurate final summaries. The purpose of this research is to make the process of writing a summary as simple as feasible while still maintaining accuracy.
An efficient Industrial Internet of Things video data processing system for protocol identification and quality enhancement

Abstract Video has become an essential medium to monitoring, identification and knowledge sharing. For industrial applications, especially Industrial Internet of Things (IIoT), videos encoded with specific protocols are transferred to smart gateways. In a typical IIoT scenario, the protocol of the video is firstly recognised, which prepares for subsequent video tasks. Due to the constrained resources in such scenarios, the video quality can be deteriorated during encoding and compression processes, which is challenging for IIoT. Recently, there have been extensive works focussing on the protocol identification (PI) and video quality enhancement (VQE) tasks on IIoT edge devices using deep neural networks (DNNs). Since DNNs often require high computational resources, complex networks can hardly be deployed on edge devices. An IIoT system which can efficiently identify the stream protocol and enhance the video quality is proposed in this study. The light‐weighted network designs and inference optimisation techniques have been proposed for PI and VQE to realise efficient deployments. Our proposed system employed on an IIoT edge device can achieve an accuracy of higher than 97.52% with fast inference speed for PI. For the VQE task, our system has demonstrated superior performance (15.230 FPS, 0.773 FPS/W) in comparison with the state‐of‐the‐art methods.
Comparing Visual Encodings for the Task of Anomaly Detection

Empirical evaluations of visual encodings for analytical tasks inform the design of automatic presentation systems. This study compares anomaly detection effectiveness, efficiency, and user satisfaction using tabular visualization and graphical representation of position, size, and color saturation visualizations and the visualizations’ relative ranking. In our user study analysts used the visualizations to detect anomalies in bivariate quantitative, ordinal, and nominal real data, before and after training. Consistent with Mackinlay ranking of the visual encodings’ effectiveness, the results showed that for all data types, position visualization outperformed size and color saturation visualizations on all measures, before and after training. The use of position visualization after training was as effective as using the table visualization for all data types but significantly easier to use and preferable. Furthermore, the average anomaly detection time was at least three times shorter when using position visualization compared to table visualization for ordinal and quantitative data.
Attribute-Sentiment-Guided Summarization of User Opinions From Online Reviews

Abstract Eliciting informative user opinions from online reviews is a key success factor for innovative product design and development. The unstructured, noisy, and verbose nature of user reviews, however, often complicate large-scale need finding in a format useful for designers without losing important information. Recent advances in abstractive text summarization have created the opportunity to systematically generate opinion summaries from online reviews to inform the early stages of product design and development. However, two knowledge gaps hinder the applicability of opinion summarization methods in practice. First, there is a lack of formal mechanisms to guide the generative process with respect to different categories of product attributes and user sentiments. Second, the annotated training datasets needed for supervised training of abstractive summarization models are often difficult and costly to create. This article addresses these gaps by (1) devising an efficient computational framework for abstractive opinion summarization guided by specific product attributes and sentiment polarities, and (2) automatically generating a synthetic training dataset that captures various degrees of granularity and polarity. A hierarchical multi-instance attribute-sentiment inference model is developed for assembling a high-quality synthetic dataset, which is utilized to fine-tune a pretrained language model for abstractive summary generation. Numerical experiments conducted on a large dataset scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, feasibility, and potentials of the developed framework. Several directions are provided for future exploration in the area of automated opinion summarization for user-centered design.
Apply an optimized NN model to low-dimensional format speech recognition and exploring the performance with restricted factors

The SCD (speech control detection) have received a lot of attention in recent years. A framework established by employing DNN-LSTM (deep neural network-long-short term memory) model for speech and text recognition is implemented in the current article. The performance of the build framework is analyzed with many different merits which consider many features, such as (with and without) noise, track number of speeches (ST (single track) and DT (double track)), and dropout ratio of data training. On the other hand, the speech discriminator model is developed and implemented with the DNN-LSTM framework, and the data sets are collected by four different persons. The adopted model performance is evaluated using the four different datasets, and each with 400–5000 training times. There are three parameters considered as the dominators for the performance evaluation of the completed speech platform. The results from the experiment with DT channel case clearly show that it outperforms the case with ST channel. It can see that the accuracy of the DNN-LSTM model increases from 0.3339 to 0.9696 and the loss rate decreases from 1.09984 to 0.19298 after adjusting the dropout ratio during the training step. This shows that the dropout ratio also dominates the accuracy and loss rate. Eventually, the results indicate that the used model compared to other similar methods, Bi-LSTM (bi-directional LSTM), achieves a more efficient preserving a high accuracy level.
Understanding the effects of situational crime prevention and personality factors on insider compliance

This study investigated the moderating effects of situational factors and personality factors on an insider's attitude towards insider compliance within the domain of information security. The situational factors consider the manipulation of the environment via measures of Situational Crime Prevention. There is a dearth of studies that consider the moderating effects of situational factors and personality factors on the information security domain. Studies of this nature could be significant for organizations grappling with the insider threat problem – a challenging and multifaceted domain that requires a multipronged mitigation strategy with technical, organizational, socio-technical, and sociological effects. This study considers the socio-technical influence of the human element on the information security domain.
Design and optimization of a quad-directional stereo vision sensor with wide field of view based on single camera

Miniaturized multi-directional stereo vision sensor with a wide field of view (FOV) and high measurement precision is highly desirable for internal inspection in confined spaces. However, the current analysis of the design and optimization strategies for the sensor layout are not quite comprehensive. To make optimal use of the sensor and achieve high measurement accuracy, a thorough understanding of this strategy is required. In this work, we present a quad-directional stereo vision sensor based on a single camera and a dual mirror pyramid with no constraint on the placement of the devices. Geometrical analysis of the sensor is conducted by ray-tracing to examine effects of the parameters on the measurement precision, such as model accuracy, depth of field, and optical path difference. On this basis, we have investigated the method to optimize the structural parameters to maximize the overall axial and radial FOV and the utilization efficiency of the sensor. The design and optimization strategies are summarized to optimally set up the structural parameters and a miniaturized prototype of the sensor is developed and verified by both simulations and experiments.
Average steered coherence in correlated amplitude damping channel

The combined effects of memory effects due to correlated actions of the amplitude damping channel on two qubits and temporal correlations in the time evolution of each qubit on the average steered coherence (ASC) are explored. For the input Bell states, it is shown that the two kinds of memory effects are beneficial for enhancing ASC of the output states in a wide time region. In particular, the ASC is immune to decoherence for some input states if the actions of the amplitude damping channel are fully correlated. Moreover, in the infinite-time limit, the positive role of the non-Markovian effect on the steady-state ASC disappears, whereas the correlated actions of the channel are still beneficial to certain input states. • Two kinds of memory effects for the correlated amplitude damping channel (ADC). • Correlated actions and non-Markovianity of ADC on average steered coherence (ASC). • The ASC is immune to decoherence for some inputs in the fully correlated ADC. • The correlated actions of the ADC are useful for enhancing the steady-state ASC.
Dual-discriminator adversarial framework for data-free quantization

Thanks to the potential to address the privacy and security issues, data-free quantization that generates samples based on the prior information in the model has recently been widely investigated. However, existing methods failed to adequately utilize the prior information and thus cannot fully restore the real-data characteristics and provide effective supervision to the quantized model, resulting in poor performance. In this paper, we propose Dual-Discriminator Adversarial Quantization (DDAQ), a novel data-free quantization framework with an adversarial learning style that enables effective sample generation and learning of the quantized model. Specifically, we employ a generator to produce meaningful and diverse samples directed by two discriminators, aiming to facilitate the matching of the batch normalization (BN) distribution and maximizing the discrepancy between the full-precision model and the quantized model, respectively. Moreover, inspired by mixed-precision quantization, i.e., the importance of each layer is different, we introduce layer importance prior to both discriminators, allowing us to make better use of the information in the model. Subsequently, the quantized model is trained with the generated samples under the supervision of the full-precision model. We evaluate DDAQ on various network structures for different vision tasks, including image classification and object detection, and the experimental results show that DDAQ outperforms all baseline methods with good generality.
Interpretable meta-score for model performance

Benchmarks for the evaluation of model performance play an important role in machine learning. However, there is no established way to describe and create new benchmarks. What is more, the most common benchmarks use performance measures that share several limitations. For example, the difference in performance for two models has no probabilistic interpretation, there is no reference point to indicate whether they represent a significant improvement, and it makes no sense to compare such differences between data sets. We introduce a new meta-score assessment named Elo-based Predictive Power (EPP) that is built on top of other performance measures and allows for interpretable comparisons of models. The differences in EPP scores have a probabilistic interpretation and can be directly compared between data sets, furthermore, the logistic regression-based design allows for an assessment of ranking fitness based on a deviance statistic. We prove the mathematical properties of EPP and support them with empirical results of a large scale benchmark on 30 classification data sets and a real-world benchmark for visual data. Additionally, we propose a Unified Benchmark Ontology that is used to give a uniform description of benchmarks.