Reviewerly/exHarmony huggingface dataset:
Ontological modeling of manufacturing resources

Standards and ontologies for manufacturing understand resources differently. Because of this heterogeneity, misunderstandings arise concerning the basic features that characterize them. The purpose of the paper is to investigate how to ontologically model resources with the goal of facilitating the development of knowledge representation models for manufacturing. By reviewing the literature, we discuss and compare three approaches for the representation of resources depending on whether they are conceived in connection to either processes, plans or goals. By addressing the advantages and shortcomings of each view, we present a unifying perspective to enable the modeling of resources in an integrated manner. In this way, the intended meanings of the used notions are harmonized and, as a result, one can facilitate multiple experts to interact e.g., via data sharing and/or data integration procedures. Differently, by keeping three separated views, there is no guarantee that data coming from different parties will share common meanings even if the same terms are used. By the end of the paper, we present a case study to show the application of our approach and to compare it with an existing ontology for manufacturing.
Improved Markov‐chain‐based ultra‐short‐term PV forecasting method for enhancing power system resilience

The awareness capability of output power for renewable resources is essential for enhancing the resilience of power systems. Photovoltaic (PV) forecasting technology is an essential technology for increasing the operation efficiency and controllable resources for power systems after extreme natural events. Conventional Markov chain (MC) methods often ignore the time characteristics and the actual distribution of the PV output power sequence when making PV forecasts. This article proposes improved MC methods of equal quantity and clustering-based division methods. The methods can consider the interval distributions of the PV output power time series and select an hour as the time interval. As a sequence, the predicted power at the next moment can be closer to the expectation of the output power distributions. Such a method is combined with a similar day algorithm to calculate the forecast result. Case studies were conducted with one-year operation data from a 25-MW PV station. The results indicate that the proposed methods can effectively improve the accuracy of prediction results compared with traditional methods.
Exploring the architectural impact of possible dependencies in Python software

Dependencies among software entities are the basis for many software analytic research and architecture analysis tools. Dynamically typed languages, such as Python, JavaScript and Ruby, tolerate the lack of explicit type references, making certain syntactic dependencies indiscernible in source code. We call these possible dependencies, in contrast with the explicit dependencies that are directly referenced in source code. Type inference techniques have been widely studied and applied, but existing architecture analytic research and tools have not taken possible dependencies into consideration. The fundamental question is, to what extent will these missing possible dependencies impact the architecture analysis? To answer this question, we conducted an empirical study with 105 Python projects, using type inference techniques to manifest possible dependencies. Our study revealed that the architectural impact of possible dependencies is substantial---higher than that of explicit dependencies: (1) file-level possible dependencies account for at least 27.93% of all file-level dependencies, and create different dependency structures than that of explicit dependencies only, with an average difference of 30.71%; (2) adding possible dependencies significantly improves the precision (0.52%~14.18%), recall(31.73%~39.12%), and F1 scores (22.13%~32.09%) of capturing co-change relations; (3) on average, a file involved in possible dependencies influences 28% more files and 42% more dependencies within architectural sub-spaces than a file involved in just explicit dependencies; (4) on average, a file involved in possible dependencies consumes 32% more maintenance effort. Consequently, maintainability scores reported by existing tools make a system written in these dynamic languages appear to be better modularized than it actually is. This evidence strongly suggests that possible dependencies have a more significant impact than explicit dependencies on architecture quality, that architecture analysis and tools should assess and even emphasize the architectural impact of possible dependencies due to dynamic typing.
Chain-Net: Learning Deep Model for Modulation Classification Under Synthetic Channel Impairment

Modulation classification, an intermediate process between signal detection and demodulation in a physical layer, is now attracting more interest to the cognitive radio field, wherein the performance is powered by artificial intelligence algorithms. However, most existing conventional approaches pose the obstacle of effectively learning weakly discriminative modulation patterns. This paper proposes a robust modulation classification method by taking advantage of deep learning to capture the meaningful information of modulation signal at multi-scale feature representations. To this end, a novel architecture of convolutional neural network, namely Chain-Net, is developed with various asymmetric kernels organized in two processing flows and associated via depth-wise concatenation and element-wise addition for optimizing feature utilization. The network is evaluated on a big dataset of 14 challenging modulation formats, including analog and high-order digital techniques. The simulation results demonstrate that Chain-Net robustly classifies the modulation of radio signals suffering from a synthetic channel deterioration and further performs better than other deep networks.
L4Net: An anchor‐free generic object detector with attention mechanism for autonomous driving

Generic object detection is a crucial task for autonomous driving. To devise a safe and efficient object detector, the following aspects are required to be considered: high accuracy, real-time inference speed and small model size. Herein, a simple yet effective anchor-free object detector named L4Net is proposed, which incorporates a keypoint detection backbone and a co-attention scheme into a unified framework, and achieves lower computation cost with higher detection accuracy than prior art across a wide spectrum of resource constrains. Specifically, the backbone utilizes Multi-scale Receptive-fields Enhancement module (MRE) to capture context-wise information, where the features of object scale and shape invariance are simultaneously considered. The co-attention scheme integrates the strength of both Class-agnostic Attention (CA) and Semantic Attention (SA), and explores the valuable features from low-level to high-level to generate more accurate prediction boxes. Compared with previous feature fusion strategy, multi-scale features are selectively integrated by fully exploiting the different characteristics of low-level and high-level features, which leads to a small model size and faster inference speed. Extensive experiments on four well-known datasets demonstrate the effectiveness of our method. For instance, L4Net achieves 71.68% mAP on KITTI test set, with 13.7 M model size at the speed of 149 FPS on NVIDIA TX and 30.7 FPS on Qualcomm-based device, respectively, which is 4x smaller and 2x faster than baseline model.
A Power Efficiency Enhancements of a Multi-Bit Accelerator for Memory Prohibitive Deep Neural Networks

Convolutional Neural Networks (CNN) are widely employed in the contemporary artificial intelligence systems. However these models have millions of connections between the layers, that are both memory prohibitive and computationally expensive. Employing these models on an embedded mobile application is resource limited with high power consumption and significant bandwidth requirement to access the data from the off-chip DRAM. Reducing the data movement between the on-chip and off-chip DRAM is the main criteria to achieve high throughput and overall better energy efficiency. Our proposed multi-bit accelerator achieves these goals by employing the truncation of the partial sum (Psum) results of the preceding layer before feeding it into the next layer. We exhibit the architecture by inferencing 32-bits for the first convolution layers and sequentially truncate the bits on the MSB/LSB of integer and fractional part without any further training on the original network. At the last fully connected layer, the top-1 accuracy is maintained with the reduced bit width of 14 and top-5 accuracy upto 10-bit width. The computation engine consists of an systolic array of 1024 processing elements (PE). Large CNNs such as AlexNet, MobileNet, SqueezeNet and EfficientNet were used as benchmark CNN model and Virtex Ultrascale FPGA was used to test the architecture. The proposed truncation scheme has 49% power reduction and resource utilization was reduced by 73.25% for LUTs (Look-up tables), 68.76% for FFs (Flip-Flops), 74.60% for BRAMs (Block RAMs) and 79.425% for Digital Signal Processors (DSPs) when compared with the 32 bits architecture. The design achieves a performance of 223.69 GOPS on a Virtex Ultrascale FPGA, the design has a overall gain of 3.63 × throughput when compared to other prior FPGA accelerators. In addition, the overall power consumption is 4.5 × lower when compared to other prior architectures. The ASIC version of the accelerator was designed in 22nm FDSOI CMOS process to achieve a overall throughput of 2.03 TOPS/W with a total power consumption of 791 mW and with a area of 1 mm × 1.2 mm.
A Comparative Study of Spatio-Temporal U-Nets for Tissue Segmentation in Surgical Robotics

In surgical robotics, the ability to achieve high levels of autonomy is often limited by the complexity of the surgical scene. Autonomous interaction with soft tissues requires machines able to examine and understand the endoscopic video streams in real-time and identify the features of interest. In this work, we show the first example of spatio-temporal neural networks, based on the U-Net, aimed at segmenting soft tissues in endoscopic images. The networks, equipped with Long Short-Term Memory and Attention Gate cells, can extract the correlation between consecutive frames in an endoscopic video stream, thus enhancing the segmentation's accuracy with respect to the standard U-Net. Initially, three configurations of the spatio-temporal layers are compared to select the best architecture. Afterwards, the parameters of the network are optimised and finally the results are compared with the standard U-Net. An accuracy of 83.77% ± 2.18% and a precision of 78.42% ± 7.38% are achieved by implementing both Long Short Term Memory (LSTM) convolutional layers and Attention Gate blocks. The results, although originated in the context of surgical tissue retraction, could benefit many autonomous tasks such as ablation, suturing and debridement.
Non-classicalities exhibited by the superposition of Schrödinger’s cat state with the vacuum of the optical field

Abstract Superposition of two coherent states, the Schrodinger’s cat state, can exhibit different nonclassical properties having foundational applications in quantum information processing. We consider the ‘superposition of Schrodinger’s cat state with the vacuum state (SCVS)’ of the optical field. We discuss different witness of nonclassicality properties such as lower- and higher-order squeezing (viz., squeezing, Hong &amp; Mandel’s fourth-order squeezing, amplitude-squared squeezing) and sub-Poissonian photon statistics. Further, we discuss the negativity of the Wigner function of SCVS indicating the nonclassicality of the state under investigation. We find that the vacuum state contribution in SCVS exhibits different nonclassicalities under some conditions stronger where the nonclassicalities exhibited by the state without vacuum state contribution are weaker.
Exploring the role of lexis and grammar for the stable identification of register in an unrestricted corpus of web documents

The Internet offers great possibilities for many scientific disciplines that utilize text data. However, the potential of online data can be limited by the lack of information on the genre or register of the documents, as register-whether a text is, e.g., a news article or a recipe-is arguably the most important predictor of linguistic variation (see Biber in Corpus Linguist Linguist Theory 8:9-37, 2012). Despite having received significant attention in recent years, the modeling of online registers has faced a number of challenges, and previous studies have presented contradictory results. In particular, these have concerned (1) the extent to which registers can be automatically identified in a large, unrestricted corpus of web documents and (2) the stability of the models, specifically the kinds of linguistic features that achieve the best performance while reflecting the registers instead of corpus idiosyncrasies. Furthermore, although the linguistic properties of registers vary importantly in a number of ways that may affect their modeling, this variation is often bypassed. In this article, we tackle these issues. We model online registers in the largest available corpus of online registers, the Corpus of Online Registers of English (CORE). Additionally, we evaluate the stability of the models towards corpus idiosyncrasies, analyze the role of different linguistic features in them, and examine how individual registers differ in these two aspects. We show that (1) competitive classification performance on a large-scale, unrestricted corpus can be achieved through a combination of lexico-grammatical features, (2) the inclusion of grammatical information improves the stability of the model, whereas many of the previously best-performing feature sets are less stable, and that (3) registers can be placed in a continuum based on the discriminative importance of lexis and grammar. These register-specific characteristics can explain the variation observed in previous studies concerning the automatic identification of online registers and the importance of different linguistic features for them. Thus, our results offer explanations for the jungle-likeness of online data and provide essential information on online registers for all studies using online data.
Exploration of text matching methods in Chinese disease Q&amp;A systems: A method using ensemble based on BERT and boosted tree models

Text matching is one of the basic tasks in the field of natural language processing. Owing to the particularity of Chinese language and medical texts, text matching has greater application and research value in the medical field. In 2019, at the China Health Information Processing Conference (CHIP), 30,000 sets of real disease Q&A data in Chinese on diabetes, hypertension, hepatitis B, AIDS, and breast cancer were released for public evaluation. A total of 90 teams participated in the evaluation. To explore the best method of text matching of Chinese medical Q&A data by participating in an evaluation competition. After analyzing the Chinese medical Q&A data provided by the competition, we used the bidirectional encoder representations from transformers (BERT) model and a boosted tree model to compare the effects. At the same time, we analyzed the importance of the features extracted through feature engineering. Finally, we integrated the BERT and boosted tree models, and proved the effectiveness of the ensemble through a correlation analysis. The final F1 score of the ensemble model is 0.90825, ranking first among the 90 participating teams. The highest F1 score of the single BERT model is 0.87443, whereas the highest F1 score of the boosted tree single model is only 0.86915. The F1 score of the BERT multi-model ensemble is 0.87473 (an average increase of 0.756% compared to the single model), and the F1 score of the boosted tree multi-model ensemble is 0.86720 (an average decrease of 0.03% compared to the single model). In the feature importance experiment, the out-degree and in-degree of the Q&A sentence are of utmost importance. In the correlation experiment, the correlation coefficients between models of the same type are all as high as 0.9, which shows a high similarity. The correlation coefficient between different types of models is approximately 0.7, which shows a certain degree of discrimination. With the ensemble of the two types of models, the F1 score reached 0.90825, which is 3.88% higher than that of the optimal single model. In our study, the proposed model ensemble method was shown to effectively improve the performance of a single model. It achieves good results in Chinese medical Q&A tasks and has a good generalization property.
A hybrid analysis to detect Java serialisation vulnerabilities

Serialisation related security vulnerabilities have recently been reported for numerous Java applications. Since serialisation presents both soundness and precision challenges for static analysis, it can be difficult for analyses to precisely pinpoint serialisation vulnerabilities in a Java library. In this paper, we propose a hybrid approach that extends a static analysis with fuzzing to detect serialisation vulnerabilities. The novelty of our approach is in its use of a heap abstraction to direct fuzzing for vulnerabilities in Java libraries. This guides fuzzing to produce results quickly and effectively, and it validates static analysis reports automatically. Our approach shows potential as it can detect known serialisation vulnerabilities in the Apache Commons Collections library.
A Defense Mechanism for Timing-based Side-Channel Attacks on IoT Traffic

This work proposes FISHER: a deFense mechanIsm against timing-based Side-channel attack related to response time on the intERnet of things (IoT). IoT connects objects that support important applications, such as electronic health, smart homes, and Industry 4.0. However, timing-based side-channel attacks on IoT network traffic compromise user privacy. Related works present a limited view of side-channel leakages and as a solution, these works try to mask them. However, they ignore that devices have unique behaviors that intensify the problem of privacy leaks through response time. Hence, FISHER follows two modules: (i) vulnerability test and (ii) privacy protection. The vulnerability test module identifies timing-based side-channel leakages and reveals new vulnerabilities associated with the response time. The privacy protection module implements two methods that mask the identified time-based leakages on the network traffic. Results from an experimental scenario show that FISHER identifies precisely the side-channel leakages related to response time and efficiently masks them.
The sensitivity and the measuring range of the typical differential optical flow method for displacement measurement using the fringe pattern

Emerging from motion analysis with the benefits of non-contact, high sensitivity, high precision, and simple operation, and the optical flow method can be utilized to measure micro-displacement. Because only one shot of the deformed fringe pattern is required in measurement, it is suitable for dynamic measurement. As a new tool for fringe analysis, some of its basic characteristics have not been found out, such as the measuring range and sensitivity, which are essential to displacement measurement. In this work, these basic characteristics of typical optical flow algorithms, the global H–S algorithm, and the local L–K algorithm, are discussed by using numerical simulation. The results prove that the minimum of the measurable phase change by both the H–S algorithm and the L–K algorithm is10−13πwith the relative error less than 0.4% under noiseless. The corresponding displacement on the image plane is 1.6×10−12 pixels, which is as good as that of the four-step phase-shifting method. The results also show that when the relative error is less than 2% and the Root-Mean-Square (RMS) error is less than 3%, the measurement range of the H–S and the L–K algorithm is about π/6 and π/2, respectively. But the minimum of the measurable phase change is decreased to 0.01π, which is 0.16 pixels displacement on the image plane, with the relative error less than 0.77% in the H–S algorithm and 1.71% in the L–K algorithm when the fringe patterns are polluted by the Gaussian noise of 40 dB. With increasing noise, the measurable maximum of the displacement will decrease while the minimum increase and the measuring range will lessen.
An Energy Efficient EdgeAI Autoencoder Accelerator for Reinforcement Learning

In EdgeAI embedded devices that exploit reinforcement learning (RL), it is essential to reduce the number of actions taken by the agent in the real world and minimize the compute-intensive policies learning process. Convolutional autoencoders (AEs) has demonstrated great improvement for speeding up the policy learning time when attached to the RL agent, by compressing the high dimensional input data into a small latent representation for feeding the RL agent. Despite reducing the policy learning time, AE adds a significant computational and memory complexity to the model which contributes to the increase in the total computation and the model size. In this article, we propose a model for speeding up the policy learning process of RL agent with the use of AE neural networks, which engages binary and ternary precision to address the high complexity overhead without deteriorating the policy that an RL agent learns. Binary Neural Networks (BNNs) and Ternary Neural Networks (TNNs) compress weights into 1 and 2 bits representations, which result in significant compression of the model size and memory as well as simplifying multiply-accumulate (MAC) operations. We evaluate the performance of our model in three RL environments including DonkeyCar, Miniworld sidewalk, and Miniworld Object Pickup, which emulate various real-world applications with different levels of complexity. With proper hyperparameter optimization and architecture exploration, TNN models achieve near the same average reward, Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) performance as the full-precision model while reducing the model size by 10x compared to full-precision and 3x compared to BNNs. However, in BNN models the average reward drops up to 12% - 25% compared to the full-precision even after increasing its model size by 4x. We designed and implemented a scalable hardware accelerator which is configurable in terms of the number of processing elements (PEs) and memory data width to achieve the best power, performance, and energy efficiency trade-off for EdgeAI embedded devices. The proposed hardware implemented on Artix-7 FPGA dissipates 250 μJ energy while meeting 30 frames per second (FPS) throughput requirements. The hardware is configurable to reach an efficiency of over 1 TOP/J on FPGA implementation. The proposed hardware accelerator is synthesized and placed-and-routed in 14 nm FinFET ASIC technology which brings down the power dissipation to 3.9 μJ and maximum throughput of 1,250 FPS. Compared to the state of the art TNN implementations on the same target platform, our hardware is 5x and 4.4x (2.2x if technology scaled) more energy efficient on FPGA and ASIC, respectively.
Reasoning about monotonicity in separation logic

Reasoning about monotonicity is of key importance in concurrent separation logics. For instance, one needs to reason about monotonicity to show that the value of a concurrent counter with an increment operation only grows over time. Modern concurrent separation logics, such as VST, FCSL, and Iris, are based on resource models defined using partial commutative monoids. For any partial commutative monoid, there is a canonical ordering relation, the so-called extension order, and in a sense the logics are designed to reason about monotonicity wrt. the extension ordering.