Reviewerly/exHarmony huggingface dataset:
MespaConfig: Memory-Sparing Configuration Auto-Tuning for Co-Located In-Memory Cluster Computing Jobs

Distributed in-memory computing frameworks usually have lots of parameters (e.g., the buffer size of shuffle) to form a configuration for each execution. A well-tuned configuration can bring large improvements of performance. However, to improve resource utilization, jobs are often share the same cluster, which causes dynamic cluster load conditions. According to our observation, the variation of cluster load reduces effectiveness of configuration tuning. Besides, as a common problem of cluster computing jobs, overestimation of resources also occurs during configuration tuning. It is challenging to efficiently find the optimal configuration in a shared cluster with the consideration of memory-sparing. In this article, we introduce MespaConfig, a job-level configuration optimizer for distributed in-memory computing jobs. Advancements of MespaConfig over previous work are features including memory-sparing and load-sensitive. We evaluate MespaConfig by 6 typical Spark programs under different load conditions. The evaluation results show that MespaConfig improves the performance of six typical programs by up to 12× compared with default configurations. MespaConfig also achieves at most 41 percent reduction of configuration memory usage and reduces the optimization time overhead by 10.8× compared with the state-of-the-art approach.
Blockchain in manufacturing quality control: A computer simulation study

Blockchain has been applied to quality control in manufacturing, but the problems of false defect detections and lack of data transparency remain. This paper proposes a framework, Blockchain Quality Controller (BCQC), to overcome these limitations while fortifying data security. BCQC utilizes blockchain and Internet-of-Things to form a peer-to-peer supervision network. This paper also proposes a consensus algorithm, Quality Defect Tolerance (QDT), to adopt blockchain for during-production quality control. Simulation results show that BCQC enhances data security and improves defect detections. Although the time taken for the quality control process increases with the number of nodes in blockchain, the application of QDT allows multiple inspections on a workpiece to be consolidated at a faster pace, effectively speeding up the entire quality control process. The BCQC and QDT can improve the quality of parts produced for mass personalization manufacturing.
A fast saddle-point dynamical system approach to robust deep learning

Recent focus on robustness to adversarial attacks for deep neural networks produced a large variety of algorithms for training robust models. Most of the effective algorithms involve solving the min–max optimization problem for training robust models (min step) under worst-case attacks (max step). However, they often suffer from high computational cost from running several inner maximization iterations (to find an optimal attack) inside every outer minimization iteration. Therefore, it becomes difficult to readily apply such algorithms for moderate to large size real world data sets. To alleviate this, we explore the effectiveness of iterative descent–ascent algorithms where the maximization and minimization steps are executed in an alternate fashion to simultaneously obtain the worst-case attack and the corresponding robust model. Specifically, we propose a novel discrete-time dynamical system-based algorithm that aims to find the saddle point of a min–max optimization problem in the presence of uncertainties. Under the assumptions that the cost function is convex and uncertainties enter concavely in the robust learning problem, we analytically show that our algorithm converges asymptotically to the robust optimal solution under a general adversarial budget constraints as induced by ℓp norm, for 1≤p≤∞. Based on our proposed analysis, we devise a fast robust training algorithm for deep neural networks. Although such training involves highly non-convex robust optimization problems, empirical results show that the algorithm can achieve significant robustness compared to other state-of-the-art robust models on benchmark data sets.
Moroccan Arabic vocabulary generation using a rule-based approach

NLP resources play a crucial role in the building of many NLP applications. The importance of these resources depends not only on their size and coverage but also on the richness and the precision of the annotated information they provide. In the case of resource-scarce languages such as Moroccan Arabic, the building of NLP applications is limited due to the lack of these resources. To overcome this problem, we follow a rule-based approach to generate a Moroccan morphological vocabulary (MORV) which constitutes the first step addressing the problem of Moroccan morphological generation. MORV is designed and implemented based on two main components: On one hand, an MA lexicon and a list of fully annotated affixes and clitics that we have created specifically to ensure the generation process. On the other hand, a set of rules covering the concatenation and the orthographic adjustments of the generated words. Moreover, given a base form, MORV outputs more than 4.5 M Moroccan words with rich morphological features such as tense, gender, number, state, etc. We tested the coverage of MORV on texts collected from Moroccan social media and realized that it reaches a vocabulary coverage of 84% and a precision of 94%. This system is a benefit for building other NLP applications such as spell checking, morphological analysis, and machine translation.
An efficient methodology for aspect-based sentiment analysis using BERT through refined aspect extraction

Aspect-Based Sentiment Analysis (ABSA) has become a trending research domain due to its ability to transform lives as well as the technical challenges involved in it. In this paper, a unique set of rules has been formulated to extract aspect-opinion phrases. It helps to reduce the average sentence length by 84% and the complexity of the text by 50%. A modified rank-based version of Term-Frequency - Inverse-Document-Frequency (TF-IDF) has been proposed to identify significant aspects. An innovative word representation technique has been applied for aspect categorization which identifies both local as well as global context of a word. For sentiment classification, pre-trained Bidirectional Encoder Representations from Transformers (BERT) has been applied as it helps to capture long-term dependencies and reduce the overhead of training the model from scratch. However, BERT has drawbacks like quadratic drop in efficiency with an increase in sequence length which is limited to 512 tokens. The proposed methodology mitigates these drawbacks of a typical BERT classifier accompanied by a rise in efficiency along with an improvement of 8% in its accuracy. Furthermore, it yields enhanced performance and efficiency compared to other state-of-the-art methods. The assertions have been established through extensive analysis upon movie reviews and Sentihood data-sets.
Deep learning methods for data classification

Deep learning is the key aspect of machine learning and artificial intelligence. In the past decades the methods introduced from the research of deep learning concepts impact an extensive range of information and signal processing task. The hierarchical models in the deep learning have the facility to learn various levels of data representation corresponding to different abstraction levels that enable the concept of representation in a dense way. Hence, the deep learning methods are extensively used in the last decades in various automatic classification processes. Various deep learning methods developed to perform the data classification process in the data mining activity are discussed in this chapter. Data classification is a data mining technique, where the training samples or database tuples are effectively analyzed to generate a generalized data. However, the classification scheme is used to sort out the future data samples and to provide superior understanding with the contents in the database.
Trans-Media Continuous-Variable Quantum Key Distribution via Untrusted Entanglement Source

We propose a continuous-variable quantum key distribution (CVQKD) scheme based on the atmosphere-to-seawater channel. In particular, an untrusted entanglement source (two modes of the compressed vacuum state, TMSV) is deployed on the surface of the seawater, each mode of TMSV is sent to one of the two legitimate parts, respectively. In this way, we can establish a trans-media CVQKD link between the atmosphere and the underwater. Meanwhile, a suitable non-Gaussian operation, namely photon subtraction, is introduced to enhance the performance of the trans-media CVQKD scheme. Security analysis shows that the proposed scheme can establish a trans-media quantum communication system on the atmosphere-to-seawater channel. Moreover, the maximum transmission distance can be further extended by using proper photon subtraction operation. Our scheme provides a guidance for applying CVQKD to trans-media channels.
Accurate Differentially Private Deep Learning on the Edge

Deep learning (DL) models are increasingly built on federated edge participants holding local data. To enable insight extractions without the risk of information leakage, DL training is usually combined with differential privacy (DP). The core theme is to tradeoff learning accuracy by adding statistically calibrated noises, particularly to local gradients of edge learners, during model training. However, this privacy guarantee unfortunately degrades model accuracy due to edge learners' local noises, and the global noise aggregated at the central server. Existing DP frameworks for edge focus on local noise calibration via gradient clipping techniques, overlooking the heterogeneity and dynamic changes of local gradients, and their aggregated impact on accuracy. In this article, we present a systematical analysis that unveils the influential factors capable of mitigating local and aggregated noises, and design PrivateDL to leverage these factors in noise calibration so as to improve model accuracy while fulfilling privacy guarantee. PrivateDL features on: (i) sampling-based sensitivity estimation for local noise calibration and (ii) combining large batch sizes and critical data identification in global training. We implement PrivateDL on the popular Laplace/Gaussian DP mechanisms and demonstrate its effectiveness using Intel BigDL workloads, i.e., considerably improving model accuracy by up to 5X when comparing against existing DP frameworks.
Unknown Traffic Identification Based on Deep Adaptation Networks

Network traffic classification has become an important basis for computer networks. However, the emergence of new applications, which generate unknown traffic constantly, has brought new challenges. The most critical challenge is how to divide the mixed unknown traffic into clusters containing only one category. In this paper, we propose a transfer learning approach using Deep Adaptation Network (DAN). This approach utilizes a few labeled samples from known traffic to improve the clustering purity of unknown traffic. We first trained a Convolutional Neural Network (CNN) model on unlabeled dataset with sampled time-series features. Then the model was extended to an adaptation model, co-trained on labeled and unlabeled samples. We evaluated our model using two publicly available datasets, achieving a purity of 98.23%. Our results demonstrate the effectiveness of DAN model in unknown traffic clustering. Moreover, we studied three sampling techniques and five clustering algorithms in our model for better clustering performance.
Collision-free kinematics for hyper-redundant manipulators in dynamic scenes using optimal velocity obstacles

Hyper-redundant manipulators have been widely used in the complex and cluttered environment for achieving various kinds of tasks. In this article, we present two contributions. First, we provide a novel algorithm of relating forward and backward reaching inverse kinematic algorithm to velocity obstacles. Our optimization-based algorithm simultaneously handles the task space constraints, the joint limit constraints, and the collision-free constraints for hyper-redundant manipulators based on the generalized framework. Second, we present an extension of our inverse kinematic algorithm to collision avoidance for the hyper-redundant manipulators, where the workspaces may have different types of obstacles. We highlight the performance of our algorithm on hyper-redundant manipulators with various degrees of freedom. The results show that our algorithm has made full use of dexterity of hyper-redundant manipulators in complex environments, enhancing the performance and increasing the flexibility.
The visual psychology of European Upper Palaeolithic figurative art: using Bubbles to understand outline depictions

How have our visual brains evolved, and exactly how did this constrain the specific way that animals were depicted in Upper Palaeolithic art? Here, we test predictions derived from visual neuroscience in this field. Using the example of open-air Upper Palaeolithic rock art of Portugal’s Côa Valley, we point out the frequently recurring outline strategies that past artists utilized to depict the prey animals upon which they were dependent for survival. Their depictional tendency can be mirrored onto the most visually salient anatomical aspects of these species, a finding that results from our use of a visual psychological experimental programme, called Bubbles. We find a remarkable correspondence between the aspects of the anatomy of horses and bison that modern participants found most helpful in successfully discriminating between the two, and those same aspects that are elaborated most in Upper Palaeolithic art. This leads us to conclude that the visual system of Homo sapiens drove the way that important prey species were depicted, and hence, the form of their art.
Real-time per-pixel focusing method for light field rendering

Abstract Light field rendering is an image-based rendering method that does not use 3D models but only images of the scene as input to render new views. Light field approximation, represented as a set of images, suffers from so-called refocusing artifacts due to different depth values of the pixels in the scene. Without information about depths in the scene, proper focusing of the light field scene is limited to a single focusing distance. The correct focusing method is addressed in this work and a real-time solution is proposed for focusing of light field scenes, based on statistical analysis of the pixel values contributing to the final image. Unlike existing techniques, this method does not need precomputed or acquired depth information. Memory requirements and streaming bandwidth are reduced and real-time rendering is possible even for high resolution light field data, yielding visually satisfactory results. Experimental evaluation of the proposed method, implemented on a GPU, is presented in this paper.
Truncated Robust Natural Watermarking With Hungarian Optimization

Over the past two decades, spread spectrum (SS) embedding has been widely used in digital watermarking due to its competitive performance in robustness and security. However, the robustness of existing secure SS embedding methods, such as natural watermarking (NW) and robust-NW (RNW), is still weak. In this article, we propose a new secure SS embedding method named truncated-RNW (TRNW), which improves the robustness of RNW while maintaining the same security level. The main idea of TRNW is to move RNW-watermarked correlations within an origin-centered sphere onto the spherical surface along the radial direction. Moreover, Hungarian algorithm is used to reduce embedding distortion, and the optimized method is called Hungarian-TRNW (HTRNW). A theoretical analysis and extensive experiments are conducted to validate the effectiveness of the proposed method. The results show that HTRNW achieves the same security level as RNW and a significant improvement over existing representative secure SS embeddings in terms of robustness.
Blockchain Technology in the Automotive Industry

Even though the automotive industry was among the key players of the industrial revolution in the last century, striking transformations experienced in other sectors did not have significant repercussions on this industry until a few years ago. However, general advancements in technology and Industry 4.0 have presented new opportunities for the reconfiguration of the business environment. Developments in cryptocurrencies such as bitcoin, in particular, have attracted the attention to what is known as blockchain technology. Several successful examples of blockchain applications in different industries have tempted the automotive industry to be rapidly involved with efforts in this direction. As a consequence, the application of the blockchain technology to highly diverse areas in the automotive industry was set in motion. The purpose of this chapter is to explore the application of blockchain technology in the automotive industry, to analyse its advantages and disadvantages, and to demonstrate its successful in general.
Characterizing Ethereum’s Mining Power Decentralization at a Deeper Level

For proof-of-work blockchains such as Ethereum, the mining power decentralization is an important discussion point in the community. Previous studies mostly focus on the aggregated power of the mining pools, neglecting the pool participants who are the source of the pools' power. In this paper, we present the first large-scale study of the pool participants in Ethereum's mining pools. Pool participants are not directly observable because they communicate with their pools via private channels. However, they leave "footprints" on chain as they use Ethereum accounts to anonymously receive rewards from mining pools. For this study, we combine several data sources to identify 62,358,646 pool reward transactions sent by 47 pools to their participants over Ethereum's entire near 5-year history. Our analyses about these transactions reveal interesting insights about three aspects of pool participants: the power decentralization at the participant level, their pool-switching behavior, and why they participate in pools. Our results provide a complementary and more balanced view about Ethereum's mining power decentralization at a deeper level.
Statistical Methods in Experimental Pathology

The results of this survey of articles from The American Journal of Pathology indicate that there is room for improvement in statistical methods. Because experimental sample sizes in pathology studies are often small, tests for normality are essential, and nonparametric tests should be used to assess for potential differences between variables. Familiarity with a wider array of statistical techniques, including multisample comparisons, regression and correlation analysis, survival analysis, and high-dimensional genetic data analysis, can help experimental pathologists make the most of their studies to improve research findings and patient care.
Streamlining sustainable design in building information modeling BIM-based PV design and analysis tools

The current BIM-based PV system design process largely depends on transferring a model to special PV simulation software. Critical geometric information is often lost during this process. An automatic PV system design and management tool was developed, which fully integrates the existing and future information of PV systems in a building information modeling (BIM) platform. This tool, PV Link, first analyzes the solar radiation on a building geometry. Then, it automatically places the PV panel based on the solar analysis and user-defined variables like PV size, roof geometry, and rooftop equipment. Finally, the solar radiation on each panel and the electricity produced are calculated and results shown in a building information model. Several case studies show it can design systems efficiently. With the information-rich BIM model, real-time analysis and management of the PVs are feasible to maintain the performance of the PV system in operation.In response to an issue raised by one of the reviewers, there is an additional discussion of the nature of science and applied science.
Medical Entity Recognition in Twitter using Conditional Random Fields

Identifying medical entities such as disease, medication, and treatment in a social media text is a big challenge due to the lack of words and the noisy nature of the text. However, social media portal like Twitter is an interesting source with a broad range of topics, especially health news and events. Therefore, detecting medical entities on Twitter can support public health surveillance and early event detection for health news and trends. Thus, we proposed an approach to identify a medical entity using a statistical modeling method called Conditional Random Field (CRF). With a small amount of labeled data, our proposed NER model outperforms the other machine learning model in annotating seven entity types in Twitter data by the F1-score results of 68%. This score is a relatively good score for such a task with limited training data.
Optimisation of the Largest Annotated Tibetan Corpus Combining Rule-based, Memory-based, and Deep-learning Methods

This article presents a pipeline that converts collections of Tibetan documents in plain text or XML into a fully segmented and POS-tagged corpus. We apply the pipeline to the large extent collection of the Buddhist Digital Resource Center. The semi-supervised methods presented here not only result in a new and improved version of the largest annotated Tibetan corpus to date, the integration of rule-based, memory-based, and neural-network methods also serves as a good example of how to overcome challenges of under-researched languages. The end-to-end accuracy of our entire automatic pipeline of 91.99% is high enough to make the resulting corpus a useful resource for both linguists and scholars of Tibetan studies.
System of Control of the Process of Alkylation of Benzene with Peripene in the Liquid Phase

All alkylation processes with sufficient intensity are possible only in the presence of a catalyst. The impact of changing the state of the catalyst is established. A mathematical model has been developed. The use of the optimal system is impractical due to the existing non-predictable perturbations. It was found that in the presence of aluminum chloride there are adverse reactions. As a result, the value of the pre-exponential multiplier and activation energy changes. The synthesis of H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> regulator is considered.
Handling Imbalanced Dataset Classification in Machine Learning

Real world dataset consists of normal instances with lesser percentage of interesting or abnormal instances. The cost of misclassifying an abnormal instance as normal instance is very high. The majority class is normal class whereas minority class is the abnormal one. Researchers in data mining and machine learning are looking out numerous strategies to resolve issues associated with dataset that is unbalanced and also the challenges featured in way of life. Irregular distribution in the dataset is the motive behind declining performance of classifier. There are mainly two methods, algorithm based and data level based, the utmost widespread methodology associated to the current is hybrid method. The task of decision making and overall classification accuracy is affected due to bias for majority class. Ensemble technique is an effective technique. The objective of study is providing background related to imbalance class issues, way out to confront the disputes and challenges in studying unbalanced data. In support to experimental result accompanied on one of the dataset, ensemble technique in adjacent to different strategies of data-level offers improved outcomes. The fusion of techniques is going to be advantageous for several applications in real-life like intrusion detection, medical diagnosis, software defect prediction, etc.