Reviewerly/exHarmony huggingface dataset:
Quality Monitoring of Manufacturing Processes based on Full Data Utilization

Monitoring of manufacturing processes through dedicated sensors can generate large volumes of data due to high sampling rates and concurrent retrieval of multiple values. Consequently, proper function of engaged systems towards real-time identification of defects may be hindered. This study investigates feasibility of welding processes quality monitoring under the prism of full data utilization in parallel to maintaining the manufacturing efficiency of the whole process-monitoring system. This is performed through analyzing computational times, storage needs and the cost of the alternative scenarios defined by the policy-hardware-software combination. A roadmap is created towards selecting the most appropriate quality monitoring workflow.
Efficient ordering policy for secret key assignment in quantum key distribution-secured optical networks

Quantum key distribution (QKD) is a promising solution to protect data transmission in optical networks against security breaches. Hence, several studies have paid attention on integration of QKD with the existing optical networks. Meanwhile, blocking is a challenging issue for QKD lightpath requests (QLRs) due to limited number of network resources (wavelengths and time-slots) in existing optical networks. In QKD-secured optical networks, the blocking increases with increase in the number of QLRs as well as with the modifications of secret keys for enhancing the security level of QLRs. Hence, the blocking affects the QLRs of different security levels, especially the QLRs of high and moderate security levels. Thus, the prioritization of QLRs based on the security level is essential for reducing the impact of blocking in such networks. In this paper, we propose a secret key assignment priority ordering policy (SKA-POP) for routing, wavelength and time-slot assignment (RWTA) to improve the success probability of QLRs. In the proposed SKA-POP, the resources during assignment and re-assignment are allocated based on the proposed priority criteria. The performance of the proposed SKA-POP is analyzed in terms of the success probability and the probability of secret key update failure (PSKUF). Simulations performed on two different network topologies, namely, NSFNET and UBN24, indicate that the proposed SKA-POP performs better than the non-priority based RWTA (NP-RWTA), priority order-based RWTA (POB-RWTA), partial-priority based RWTA (PP-RWTA), and a version of SKA-POP, i.e., SKA-POP with the longest route first (SKA-POP-LRF) schemes, when the number of QLRs (traffic load) increase in the network. .
Performance Bounds for QC-MDPC Codes Decoders

Quasi-Cyclic Moderate-Density Parity-Check (QC-MDPC) codes are receiving increasing attention for their advantages in the context of post-quantum asymmetric cryptography based on codes. However, a fundamentally open question concerns modeling the performance of their decoders in the region of a low decoding failure rate (DFR). We provide two approaches for bounding the performance of these decoders, and study their asymptotic behavior. We first consider the well-known Maximum Likelihood (ML) decoder, which achieves optimal performance and thus provides a lower bound on the performance of any sub-optimal decoder. We provide lower and upper bounds on the performance of ML decoding of QC-MDPC codes and show that the DFR of the ML decoder decays polynomially in the QC-MDPC code length when all other parameters are fixed. Secondly, we analyze some hard to decode error patterns for Bit-Flipping (BF) decoding algorithms, from which we derive some lower bounds on the DFR of BF decoders applied to QC-MDPC codes.
A quantum walk control plane for distributed quantum computing in quantum networks

Quantum networks are complex systems formed by the interaction among quantum processors through quantum channels. Analogous to classical computer networks, quantum networks allow for the distribution of quantum computation among quantum computers. In this work, we describe a quantum walk protocol to perform distributed quantum computing in a quantum network. The protocol uses a quantum walk as a quantum control signal to perform distributed quantum operations. We consider a generalization of the discrete-time coined quantum walk model that accounts for the interaction between a quantum walker system in the network graph with quantum registers inside the network nodes. The protocol logically captures distributed quantum computing, abstracting hardware implementation and the transmission of quantum information through channels. Control signal transmission is mapped to the propagation of the walker system across the network, while interactions between the control layer and the quantum registers are embedded into the application of coin operators. We demonstrate how to use the quantum walker system to perform a distributed CNOT operation, which shows the universality of the protocol for distributed quantum computing. Furthermore, we apply the protocol to the task of entanglement distribution in a quantum network.
Pre-locate net for object detection in high-resolution images

Small-object detection has long been a challenge. High-megapixel cameras are used to solve this problem in industries. However, current detectors are inefficient for high-resolution images. In this work, we propose a new module called Pre-Locate Net, which is a plug-and-play structure that can be combined with most popular detectors. We inspire the use of classification ideas to obtain candidate regions in images, greatly reducing the amount of calculation, and thus achieving rapid detection in high-resolution images. Pre-Locate Net mainly includes two parts, candidate region classification and behavior classification. Candidate region classification is used to obtain a candidate region, and behavior classification is used to estimate the scale of an object. Different follow-up processing is adopted according to different scales to balance the variance of the network input. Different from the popular candidate region generation method, we abandon the idea of regression of a bounding box and adopt the concept of classification, so as to realize the prediction of a candidate region in the shallow network. We build a high-resolution dataset of aircraft and landing gears covering complex scenes to verify the effectiveness of our method. Compared to state-of-the-art detectors (e.g., Guided Anchoring, Libra-RCNN, and FASF), our method achieves the best mAP of 94.5 on 1920 × 1080 images at 16.7 FPS.
Medical image segmentation with generative adversarial semi-supervised network

Recent medical image segmentation methods heavily rely on large-scale training data and high-quality annotations. However, these resources are hard to obtain due to the limitation of medical images and professional annotators. How to utilize limited annotations and maintain the performance is an essential yet challenging problem. In this paper, we try to tackle this problem in a self-learning manner by proposing a generative adversarial semi-supervised network. We use limited annotated images as main supervision signals, and the unlabeled images are manipulated as extra auxiliary information to improve the performance. More specifically, we modulate a segmentation network as a generator to produce pseudo labels for unlabeled images. To make the generator robust, we train an uncertainty discriminator with generative adversarial learning to determine the reliability of the pseudo labels. To further ensure dependability, we apply feature mapping loss to obtain statistic distribution consistency between the generated labels and the real labels. Then the verified pseudo labels are used to optimize the generator in a self-learning manner. We validate the effectiveness of the proposed method on right ventricle dataset, Sunnybrook dataset, STACOM, ISIC dataset, and Kaggle lung dataset. We obtain 0.8402-0.9121, 0.8103-0.9094, 0.9435-0.9724, 0.8635-0.886, and 0.9697-0.9885 dice coefficient with 1/8 to 1/2 proportion of densely annotated labels, respectively. The improvements are up to 28.6 points higher than the corresponding fully supervised baseline.
What is Scientific Progress?

The article examines the understanding of scientific progress by scientists and representatives of the philosophy of science, in particular, W. Whewell, E. Mach, M. Weber, logical positivism. It is noted that until the middle of the twentieth century, scientists and philosophers were convinced that science is constantly progressing, developing more and more complete and accurate knowledge of the world. Only in the second half of the twentieth century K. Popper and T. Kuhn questioned this belief, proposing their conceptions of the development of sci­ence. However, the belief in the progressive nature of the development of sci­ence received a new justification in a three layer model of the structure of sci­ence, which added an engineering level to the theoretical and empirical levels. By the end of the twentieth century it was realized that the experimental science of modern times initially saw its main task in the improvement of technology, the search for truth was 0nly a means of solving its main social task. In the sec­ond half of the twentieth century technoscience has fulfilled its historical mission, creating a technical basis for the formation of a consumer society. Ap­parently, its development is over. In place of science, occupied with the improve­ment of technology, there are sciences, the subject of which is man and his im­provement.
Resource efficient activation functions for neural network accelerators

Implementations of machine learning models in resource-limited embedded systems are becoming highly desired. This has led to a need for resource-efficient building blocks for computing the mathematical operations required for neural network training and inferencing. Efficient activation functions for low-end hardware devices with limited hardware capabilities are important. In this work, we present a method for generating symmetric and asymmetric activation functions for deep and convolution neural networks. Furthermore, we propose a solution that simultaneously computes a symmetric activation function with an integrated scaling functionality for Long Short Term Memory (LSTM) models. This effectively eliminates two of the three element-wise multipliers in an LSTM cell. Also, this built-in scaling requires no additional computation time because it is integrated within the computation of the symmetric non-linear mapping. This approach replaces the need to compute several Tanh activation functions and element-wise multipliers separately. A resource-efficient approximate multiplier is also proposed to eliminate the third element-wise multiplier and potentially replace all the resource-hungry multipliers. The digital implementation of the proposed method is highly amenable to parallelization and is extremely resource-efficient. We record an area-saving on field-programmable gate arrays with different precision. Our proposal's formulaic equivalents are also computationally fast on CPU-based engines. On an embedded ARM processor, our method achieves a speedup of at least 4.37× for the proposed functions. We show that LSTMs with our method can achieve up to 3.5× resource footprint saving when compared to the hard activation implementation. We demonstrate that our method achieves competitive results with negligible loss of performance.
Synthesis of Three-Layer Dynamic Binary Neural Networks for Control of Hexapod Walking Robots

Three-layer dynamic binary neural networks are characterized by binary connection parameters and signum activation function. Depending on the parameters, the networks can generate various periodic orbits. First, as a basic theory, we give a synthesis method that guarantees storage and global stability of desired periodic orbits. Second, as an engineering application, we synthesize networks that generate periodic orbits corresponding to typical walking patterns of hexapod robots. Presenting an FPGA based hardware, switching of the walking patterns is confirmed experimentally.
Beyond @CloudFunction

Simplicity in elastically scalable application development is a key concern addressed by the serverless computing paradigm, in particular the code-level Function-as-a-Service (FaaS). Various FaaSification frameworks demonstrated that marking code methods to streamline their offloading as cloud functions offers a simple bridge to software engineering habits. As application complexity increases, more complex runtime patterns with background activities, such as keeping containerised cloud functions warm to ensure the absence of cold starts, usually require giving up on simplicity and instead investing efforts into orchestrating infrastructure. By bringing infrastructure-as-code concepts into the function source via powerful code annotations, typical orchestration patterns can be simplified again. We evaluate this idea and demonstrate its practical feasibility with FaaS Fusion, an annotations library and transpiler framework for JavaScript.
Hierarchical Semantic Broadcasting Network for Real-Time Semantic Segmentation

Semantic segmentation has been one of the essential tasks in computer vision. More complicated and computationally intensive mechanisms are integrated into segmentation models to get more accurate results, leading to increased processing time and resource usage. Based on the idea that pixels with similar high-level features are more likely to have similar semantic labels, we propose a computationally efficient mechanism named Hierarchical Semantic Broadcasting (HSB), which can infer earlier-stage semantic label maps from lower-level feature maps by referring to semantic label maps of higher-level feature maps. Since lower-level feature maps have higher resolution and richer context, HSB can provide additional details for better semantic segmentation. By integrating HSB into a general-purpose light network, we propose Hierarchical Semantic Broadcasting Network (HSB-Net) for real-time semantic segmentation, which achieves a good trade-off between accuracy and speed. Evaluated by the Cityscapes dataset, HSB-Net can run at 123.7 FPS for a 512 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \boldsymbol{\times }$</tex-math></inline-formula> 1024 input on a single GeForce RTX 2080 Ti card while achieving 73.1% mean IoU.
Turbo Autoencoder with a Trainable Interleaver

A critical aspect of reliable communication involves the design of codes that allow transmissions to be robustly and computationally efficiently decoded under noisy conditions. Advances in the design of reliable codes have been driven by coding theory and have been sporadic. Recently, it is shown that channel codes that are comparable to modern codes can be learned solely via deep learning. In particular, Turbo Autoencoder (TurboAE), introduced by Jiang et al., is shown to achieve the reliability of Turbo codes for Additive White Gaussian Noise channels.In this paper, we focus on applying the idea of TurboAE to various practical channels, such as fading channels and chirp noise channels. We introduce TurboAE-TI, a novel neural architecture that combines TurboAE with a trainable interleaver design. We develop a carefully-designed training procedure and a novel interleaver penalty function that are crucial in learning the interleaver and TurboAE jointly. We demonstrate that TurboAE-TI outperforms TurboAE and LTE Turbo codes for several channels of interest. We also provide interpretation analysis to better understand TurboAE-TI.
Solar Energy Radiation Forecasting Method

After the oil crisis in 1973, the planet has got to believe requirement of alternative source of energy aside from conventional energy resources. Watching out the current situation, it is becoming important to increase the electricity generation using renewable energy sources such as solar, wind, tidal, biomass, hydropower, and wave energy. Solar energy among these energy sources has a lot of potential for producing electricity. As a result, in order to make effective use of energy PV systems, it is important to accurate forecasting of solar energy because it is intermittent, less reliable, and site specific. Accurate solar forecasting is necessary for the proper performance, planning, and managing of electric power produced by stand-alone photo voltaic plants and interconnected solar power plants. Here in this chapter, a detailed analysis of the techniques used to forecast solar irradiance is presented. This method help to choose the correct forecasting methodology as required. All important parameters and finding of models are given in tabular model. It also discusses the effect of climatic variables, forecasting horizons, decomposition technique on the performance of the model. This research founds that neural network-based models perform better than other forecasting techniques in simulating various complex problems, and that their accuracy can be further enhanced by combining the two models or performing preprocessing on the input data. The performance analysis of these methods outperforms the traditional method. This chapter also discusses the metric used to evaluate the efficiency of forecasted model.
Bayesian analysis of two-part nonlinear latent variable model: Semiparametric method

Two-part model (TPM) is a widely appreciated statistical method for analyzing semi-continuous data. Semi-continuous data can be viewed as arising from two distinct stochastic processes: one governs the occurrence or binary part of data and the other determines the intensity or continuous part. In the regression setting with the semi-continuous outcome as functions of covariates, the binary part is commonly modelled via logistic regression and the continuous component via a log-normal model. The conventional TPM, still imposes assumptions such as log-normal distribution of the continuous part, with no unobserved heterogeneity among the response, and no collinearity among covariates, which are quite often unrealistic in practical applications. In this article, we develop a two-part nonlinear latent variable model (TPNLVM) with mixed multiple semi-continuous and continuous variables. The semi-continuous variables are treated as indicators of the latent factor analysis along with other manifest variables. This reduces the dimensionality of the regression model and alleviates the potential multicollinearity problems. Our TPNLVM can accommodate the nonlinear relationships among latent variables extracted from the factor analysis. To downweight the influence of distribution deviations and extreme observations, we develop a Bayesian semiparametric analysis procedure. The conventional parametric assumptions on the related distributions are relaxed and the Dirichlet process (DP) prior is used to improve model fitting. By taking advantage of the discreteness of DP, our method is effective in capturing the heterogeneity underlying population. Within the Bayesian paradigm, posterior inferences including parameters estimates and model assessment are carried out through Markov Chains Monte Carlo (MCMC) sampling method. To facilitate posterior sampling, we adapt the Polya-Gamma stochastic representation for the logistic model. Using simulation studies, we examine properties and merits of our proposed methods and illustrate our approach by evaluating the effect of treatment on cocaine use and examining whether the treatment effect is moderated by psychiatric problems.
A Profile on Twitter Shadowban: An AI Ethics Position Paper on Free-Speech

Concerns have been expressed lately about content verification algorithms on social media platforms, resulting in data being presented to users or omitted from them. Twitter is one of those platforms that use this strategy, giving censorship strategies for specific content. The shadowban or practice of limiting content distribution without user acknowledgement is a current practice in online social networks, especially on Twitter. So, this paper is an AI Ethics position paper willing to expand the reflection about the impacts of programming artefacts on individual liberties in enterprise's Online social networks (OSN) to the computer science public. The conclusion to be drawn is that the limits of speech are to be imposed by the state after a properly democratic process took place []. The concern then turns into an international relations issue as it would be a threat for national sovereignty that one state, even inadvertently, regulates the actions of a communication enterprise on foreign states. Finally, the use of smart contracts is suggested as an alternative to be used by the base state for enforcing regulations on foreign OSNs.
Temporal Effects on Pre-trained Models for Language Processing Tasks

Abstract Keeping the performance of language technologies optimal as time passes is of great practical interest. We study temporal effects on model performance on downstream language tasks, establishing a nuanced terminology for such discussion and identifying factors essential to conduct a robust study. We present experiments for several tasks in English where the label correctness is not dependent on time and demonstrate the importance of distinguishing between temporal model deterioration and temporal domain adaptation for systems using pre-trained representations. We find that, depending on the task, temporal model deterioration is not necessarily a concern. Temporal domain adaptation, however, is beneficial in all cases, with better performance for a given time period possible when the system is trained on temporally more recent data. Therefore, we also examine the efficacy of two approaches for temporal domain adaptation without human annotations on new data. Self-labeling shows consistent improvement and notably, for named entity recognition, leads to better temporal adaptation than even human annotations.
Container Workload Characterization Through Host System Tracing

The use of containers within cloud environments has become increasing popular due to their lightweight nature, scalability, and efficiency. However, as containers share their host's resources, advanced resource management techniques are essential to avoid performance impacting resource contention. Coarse measures such as CPU, disk, and network usage collected from internal agents are often considered, yet these methods may be improved upon to garner a more precise view of container workloads. In this paper, we present a container workload characterization method using host system tracing. Features derived from thread execution states are taken from tracing data to reveal container runtime behaviour. A PageRank-based algorithm is then used to identify the most significant threads for further analysis. Once this data is collected and vectorized, a two stage K-Means clustering technique is used to generate groups of containers with similar workloads. This eliminates the need for manual analysis of individual containers, and instead allows administrators to view and address container behaviours collectively. Experimental results show that our methodology can identify a variety of execution behaviours. Administrators may use these results to remove idle containers to free up system resources. Moreover, they may identify clusters of containers that are at risk of resource contention, allowing for more effective resource assignment.