Reviewerly/exHarmony huggingface dataset:
Regulating Algorithms

Despite their profound and growing influence on our lives, algorithms remain a partial “black box.” Keeping the risks that arise from rule-based and learning systems in check is a challenging task for both: society and the legal system. This chapter examines existing and adaptable legal solutions and complements them with further proposals. It designs a regulatory model in four steps along the time axis: preventive regulation instruments; accompanying risk management; ex post facto protection; and an algorithmic responsibility code. Together, these steps form a legislative blueprint to further regulate artificial intelligence applications.
Blockchain: To Improvise Economic Efficiency and Supply Chain Management in Agriculture

As the population of the world is growing at a rapid pace. By 2050, projections show that there will be 9.2 billion people on the planet that needs to be feed. Farmers need more sustainable and productive ways to be employed to seize a prominent place in the market with compelling profits. One effective way to do that is by using Blockchain to eliminate the middleman and scaling the product effectively. A Blockchain is a distributed and immutable ledger that holds a permanent record of transactional data. It operates as a decentralised database managed by the peers in the network eliminating the middleman. Blockchain helps the farmers to secure more profits, achieving them at least 30% more than what they are earning by currently followed conventional techniques. Especially in the Indian market, the price gap is high between producer and retailer due to the intervention of a middleman. Blockchain not only increases the profits but also ensures the authenticity of the product in real time, providing the customers and peers more information about the product as it is easier to trace the details of the product back and forth through the chain. This also enhances and regulates the price variations providing farmers with the best price they can expect for their product. This will gradually increase the involvement of more people and drives more investors towards the agriculture industry, funding more agriculture-based businesses increasing food production that can be able to feed the massive population sooner.
The effect of relational capital on performance: knowledge sharing as mediation variables in supplier and buyer relation

Interactions that occur in cooperation within supplier and buyer relationships create good relationships manifested by mutual trust and respect, and good friendships between suppliers and buyers. Relationships based on good intentions and manifested through a number of attributes are known as relational capital. Relationships built on trust, to the cause of friendship between suppliers and buyers mutual respect and reciprocity are what makes the buyer willing to share knowledge with the supplier. Given this shared knowledge, suppliers can help improve buyer performance. Thus, this study aims to examine the effect of relational capital on performance through knowledge sharing into mediation variables. This research uses a quantitative approach to test several hypotheses through partial least square (PLS) analysis, and examines a sample of 48 buyers within the cement industry. The results of this study indicate that relational capital positively and significantly influences knowledge sharing, which then positively and significantly affects the buyer's performance. However, relational capital has no significant effect on buyer performance. Knowledge sharing mediates in full relational capital relationships to buyer performance.
Cybercrime

The Internet and its affiliated technology currently dominate commerce, education, banking, and so many other facets of our daily lives. However, with this constant use of technology and innovation, it is no surprise that a dark underworld of criminality emerged, otherwise known as cybercrime. This entry will explain the development of cybercrime and its current state online. Each category of cybercrime will be explored, including discussion of the associated behaviors and the potential effects. In addition, this entry will investigate the responses to cybercrime made by the following entities: the criminal justice system, educational programs, and corporations.
A Precision-Scalable Energy-Efficient Bit-Split-and-Combination Vector Systolic Accelerator for NAS-Optimized DNNs on Edge

Optimized model and energy-efficient hardware are both required for deep neural networks (DNNs) in edge-computing area. Neural architecture search (NAS) methods are employed for DNN model optimization with resulted multi-precision networks. Previous works have proposed low-precision-combination (LPC) and high-precision-split (HPS) methods for multi-precision networks, which are not energy-efficient for precision-scalable vector implementation. In this paper, a bit-split-and-combination (BSC) based vector systolic accelerator is developed for a precision-scalable energy-efficient convolution on edge. The maximum energy efficiency of the proposed BSC vector processing element (PE) is up to 1.95× higher in 2-bit, 4-bit and 8-bit operations when compared with LPC and HPS PEs. Further with NAS optimized multi-precision CNN networks, the averaged energy efficiency of the proposed vector systolic BSC PE array achieves up to 2.18× higher in 2-bit, 4-bit and 8-bit operations than that of LPC and HPS PE arrays.
Machine Learning Investigation of Clinopyroxene Compositions to Evaluate and Predict Mantle Metasomatism Worldwide

Plain Language Summary Clinopyroxene is a major mineral in Earth's upper mantle. Previous studies have attempted to discriminate between reactions modifying the mantle by plotting clinopyroxene major and trace element compositions in two‐dimensional (2‐D) diagrams. However, these 2‐D methods show poor accuracy when applied to global datasets. Therefore, we suggest a machine learning approach to evaluate clinopyroxene compositional data in higher dimensions. Our results demonstrate that machine learning can significantly improve the accuracy of clinopyroxene compositional predictions over classical methods utilizing elemental ratios. Furthermore, the application of our algorithm to a global clinopyroxene dataset suggests that mantle metasomatism is globally widespread.
An Effective Video Summarization Framework Based on the Object of Interest Using Deep Learning

The advancements in digital video technology have empowered video surveillance to play a vital role in ensuring security and safety. Public and private enterprises use surveillance systems to monitor and analyze daily activities. Consequently, a massive volume of data is generated in videos that require further processing to achieve security protocol. Analyzing video content is tedious and a time-consuming task. Moreover, it also requires high-speed computing hardware. The video summarization concept has emerged to overcome these limitations. This paper presents a customized video summarization framework based on deep learning. The proposed framework enables a user to summarize the videos according to the Object of Interest (OoI), for example, person, airplane, mobile phone, bike, and car. Various experiments are conducted to evaluate the performance of the proposed framework on the video summarization (VSUMM) dataset, title-based video summarization (TVSum) dataset, and own dataset. The accuracy of VSUMM, TVSum, and own dataset is 99.6%, 99.9%, and 99.2%, respectively. A desktop application is also developed to help the user summarize the video based on the OoI.
Multi-view data fusion in multi-object tracking with probability density-based ordered weighted aggregation

In this paper, a method is presented for Multi-Object Tracking (MOT) in presence of partial or complete occlusions. This work focuses on improved object detection and data association in a single view, and also fuses data from multiple views using the Ordered Weighted Aggregation (OWA) algorithm. Hence, a deep learning model was proposed to detect objects more accurately in a tracking-by-detection framework. This paper aims to enhance object detection, data association, and the trajectories of the objects in the MOT algorithm respectively by applying Mask R-CNN, Zernike Moments and combination of several similarity metrics, and fusion of multi-camera information by probability density-based OWA (PD-OWA). The inter-frame detected objects are matched based on the appropriate similarity metrics. In the fusion part, the Kernel Density Estimation (KDE) is utilized to assign weight coefficient to each camera view and determine the descending order of data in the OWA algorithm. Finally, the positions of each object coming from different views are weighted and aggregated. The results show that the proposed method improves object detection, association performance, and tracking trajectory in the “PETS09-S2L1″ and the “EPFL Terrace” video sequences and achieved 81.6% and 79.6% multiple objects tracking accuracy (MOTA), respectively.
Statistical-mean double-quantitative K-nearest neighbor classification learning based on neighborhood distance measurement

Neighborhood granulation underlies neighborhood rough sets, and it also induces the basic classifier of K-nearest neighbor (KNN). Based on neighborhood granulation and its distance measurement, a relative-quantitative classifier (KNGR) and an absolute-quantitative classifier (KNGA) already promote the KNN classifier, and a double-quantitative classifier (KNGD) makes a further improvement but still has advancement space. This paper devotes to developing more general and robust double-quantitative classifiers via statistical integration, so two statistical-average double-quantitative distances are systematically constructed to establish arithmetic-mean and geometric-mean double-quantitative classifiers (KNGD-AM and KNGD-GM). Aiming at neighborhood distance measurement, the single-quantitative preference and double-quantitative compromise are first analyzed, and their statistical averages imply the better systematic semantics, simplified mechanism, general adaptability, compromised proportion for information fusion. Then, arithmetic-mean and geometric-mean double-quantitative distances are proposed, and they acquire the essence formula, optimization algorithm, squeeze positioning, metric axiom, and granulation non-monotonicity. Furthermore, the two new neighborhood distances motivate two corresponding classifiers, i.e., KNGD-AM and KNGD-GM. Finally, both the distance property and classifier effectiveness are verified by data experiments with 16 datasets. As shown by experimental results, classifiers KNGD-AM and KNGD-GM generally improve and outperform KNGD as well as KNGR, KNGA, KNN, and they achieve the better applicability and performance of classification learning.
Correlation Study between Rural Human Settlement Health Factors: A Case Study of Xiangxi, China

With the emergence of the Industry 4.0 era in China, more refined methods are being proposed for healthy living requirements for human settlements. Since the rural human settlements in China are relatively backward, this study aimed to investigate the influencing factors of human health. First, through field surveys and questionnaires conducted with villagers in Xiangxi's traditional villages in Hunan Province, we analyzed the factors affecting human health qualitatively and quantitatively using the SPSS software. We identified three main dimensions affecting human health in rural human settlements including human behavioral activities, physical environment, and natural environment. Then, we used correlation analysis and multiple linear regression analysis methods to analyze the correlation between environmental factors and human health. The results showed that human activities, building physical environment, and natural environment are significantly correlated with human health. Among them, human behavior has the strongest correlation with health. This research contributes to creating healthy human settlements and guiding the creation of a healthy environment in rural China.
FAIR Digital Twins for Data-Intensive Research

Although all the technical components supporting fully orchestrated Digital Twins (DT) currently exist, what remains missing is a conceptual clarification and analysis of a more generalized concept of a DT that is made FAIR, that is, universally machine actionable. This methodological overview is a first step toward this clarification. We present a review of previously developed semantic artifacts and how they may be used to compose a higher-order data model referred to here as a FAIR Digital Twin (FDT). We propose an architectural design to compose, store and reuse FDTs supporting data intensive research, with emphasis on privacy by design and their use in GDPR compliant open science.
Generalizable Task Planning Through Representation Pretraining

The ability to plan for multi-step manipulation tasks in unseen situations is crucial for future home robots. But collecting sufficient experience data for end-to-end learning is often infeasible in the real world, as deploying robots in many environments can be prohibitively expensive. On the other hand, large-scale scene understanding datasets contain diverse and rich semantic and geometric information. But how to leverage such information for manipulation remains an open problem. In this letter, we propose a learning-to-plan method that can generalize to new object instances by leveraging object-level representations extracted from a synthetic scene understanding dataset. We evaluate our method with a suite of challenging multi-step manipulation tasks inspired by household activities (Srivastava, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2022) and show that our model achieves measurably better success rate than state-of-the-art end-to-end approaches.
Weakly-supervised contrastive learning-based implicit degradation modeling for blind image super-resolution

Deep learning-based image super-resolution (SR) methods have attracted growing interest due to their outstanding performance. However, most of these methods assume that the degradations of low-resolution (LR) observations are fixed and known (e.g., bicubic downsampling). This is however not always true in real scenarios. Moreover, the mismatch between the intrinsic degradation of LR images and the assumed degradation generally causes artifacts. Therefore, it is essential to move beyond idealized assumptions and make the SR model adapt to variant degradations. To achieve this goal, we propose a Blind image Super-Resolution approach based on contrastive learning-based Implicit Degradation Modeling (IDMBSR). Since it is challenging to explicitly estimate degradation parameters, a representation is learned from each LR image to model its degradation and differentiate between variant degradations, thereby guiding the subsequent reconstruction to achieve image-specific SR. Since there are no representation labels for LR images, contrastive learning is used to train the attention-enhanced encoder for degradation encoding with the help of degradation parameters for more effective model training. Moreover, to make the reconstruction process adapt to LR images with variant degradations, a degradation-guided SR network is developed, in which the degradation representation adaptively influences the SR process from beginning to end by applying data-driven transformation to middle features. Benefitting from the combination of the degradation representation encoder and the degradation-guided SR network, IDMBSR can adapt to various LR observations without any prior knowledge about their degradations. Experimental results show that IDMBSR outperforms several state-of-the-art blind SR methods with fewer parameters and higher efficiency.
Machine intelligence in dynamical systems: \A state‐of‐art review

Abstract This article is dedicated to study the impact of machine intelligence (MI) methods viz. various types of Neural models for investigating dynamical systems arising in interdisciplinary areas. Different types of artificial neural network (ANN) methods, viz., recurrent neural network, functional‐link neural network, convolutional neural network, symplectic artificial neural network, genetic algorithm neural network, and so on, are addressed by different researchers to investigate these problems. Although various traditional methods have been developed by researchers to solve these dynamical problems but the existing traditional methods may sometimes be problem dependent, require repetitions of the simulations, and fail to solve nonlinearity behavior. In this regard, neural network model based methods are more general and solutions are continuous over the given domain of integration, self‐adaptive and can be used as a black box. As such, in this article, we have reviewed and analyzed different MI methods, which are applied to investigate these problems. This article is categorized under: Technologies &gt; Computational Intelligence Technologies &gt; Machine Learning Application Areas &gt; Science and Technology
A Dense Feature Pyramid Network for Remote Sensing Object Detection

In recent years, object detection in remote sensing images has become a popular topic in computer vision research. However, there are various problems in remote sensing object detection, such as complex scenes, small objects in large fields of view, and multi-scale object in different categories. To address these issues, we propose DFPN-YOLO, a dense feature pyramid network for remote sensing object detection. To address difficulties in detecting small objects in large scenes, we add a larger detection layer on top of the three detection layers of YOLOv3, and we propose Dense-FPN, a dense feature pyramid network structure that enables all four detection layers to combine semantic information before sampling and after sampling to improve the performance of object detection at different scales. In addition, we add an attention module in the residual blocks of the backbone to allow the network to quickly extract key feature information in complex scenes. The results show that the mean average precision (mAP) of our method on the RSOD datasets reached 92%, which is 8% higher than the mAP of YOLOv3, and the mAP increased from 62.41% on YOLOv3 to 69.33% with our method on the DIOR datasets, outperforming even YOLOv4.
A reinforced exploration mechanism whale optimization algorithm for continuous optimization problems

Whale optimization algorithm (WOA) is a meta-heuristic algorithm. In process of optimizing single-peak problem and multi-peak problem, it has the problems of weak exploration ability, poor convergence behavior and easy to fall into local optimum (LO). To solve these problems, we analyze WOA from the two aspects of global exploration efficiency and convergence characteristics. Correspondingly, the population redistribution and convergent adaptive weighting strategy are proposed. The population redistribution strategy can improve the space utilization of WOA in search process, enhance its exploration efficiency and prevent it from falling into LO. The purpose of the convergent adaptive weighting strategy is to improve convergence behavior and global exploitation efficiency. Experiment result shows that it can effectively enhance the exploration efficiency of WOA. The paper introduces the two strategies to reconstruct a reinforced exploration mechanism whale optimization algorithm (REM-WOA). In order to verify its performance, 36 well known benchmark functions are selected for experiment from the CEC2017. Compared with other 12 meta-heuristic algorithms, the REM-WOA shows obvious advantages. Compared with 9 WOA and its variant algorithms, the REM-WOA has the best convergence behavior and strong global exploration efficiency. For three real design cases studies, REM-WOA has the best exploration efficiency.
An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications

Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search agents and suggests an algorithm for Evolutionary Field Optimization with Geometric Strategies (EFO-GS) on the basis of the evolutionary field theorem. The proposed EFO-GS algorithm benefits from a field-adapted differential crossover mechanism, a field-aware metamutation process to improve the evolutionary search quality. Secondly, the multiplicative neuron model is modified to develop Power-Weighted Multiplicative (PWM) neural models. The modified PWM neuron model involves the power-weighted multiplicative units similar to dendritic branches of biological neurons, and this neuron model can better represent polynomial nonlinearity and they can operate in the real-valued neuron mode, complex-valued neuron mode, and the mixed-mode. In this study, the EFO-GS algorithm is used for the training of the PWM neuron models to perform an efficient neuroevolutionary computation. Authors implement the proposed PWM neural processing with the EFO-GS in an electronic nose application to accurately estimate Nitrogen Oxides (NOx) pollutant concentrations from low-cost multi-sensor array measurements and demonstrate improvements in estimation performance.
Adversarial malware sample generation method based on the prototype of deep learning detector

The deep learning methods had been proved to be effective for malware detection in the past. However, the recent studies show that deep learning models are vulnerable to adversarial attacks. Thus, the malware detection models based on deep learning face the threat of adversarial examples. As a popular case of adversarial examples, adversarial images are usually generated by adding unrecognizable perturbations to original pictures. When applying the same method to the windows PE (Portable Executable) malware, the original structure cannot be destroyed and the original functions of PE malware need to be preserved. Most existing windows adversarial malware generation works are derived from adversarial image methods with some adaptive modifications such as inserting perturbations in the slack space of the PE file. The both generation methods have some similarities but also many differences. Thus, directly using the methods of adversarial images to create malware effects the efficiency and fooling rate. In this paper, we overcome these issues by proposing a method for generating windows adversarial malware in PE format based on prototype samples of deep learning models. The prototype samples are the most typical ones of a certain category of the classification models. With the characteristic of the prototype samples, the bytes of the prototype samples are added as perturbations to the malware samples. This way can fast generate adversarial malware that could fool the target model. The proposed method is evaluated on a real world dataset of malware. Promising results show that the method can fool the deep learning based malware detection models with a high rate.