Reviewerly/exHarmony huggingface dataset:
Performance Evaluation on CXL-enabled Hybrid Memory Pool

The emerging cache coherent Compute Express Link (CXL) interconnect provides a practical way to disaggregate cloud memory resources from monolithic servers into memory pools with DRAM-level access latency. While DRAM-only memory pool improves the resource utilization and reduces the Total Cost of Ownership (TCO) for cloud providers, we investigate the possibility of applying cheaper SSDs to a memory pooling system to further reduce the cost of cloud servers without sacrificing the application's performance. In this study, we build a simulated CXL-enabled DRAM-SSD hybrid memory pool based on Linux and commodity hardware, and conduct performance evaluation by running representative cloud workloads which cover deep learning training, database, data analytics and video processing on the testbed. The evaluation results show that a hybrid memory pool can potentially reduce memory cost while maintaining the same level of application performance for computation-intensive applications. For example, with memory overcommit ratio of 2, the performance degradation of training ResNet50 on ImageNet dataset is only 2.68%.
Comparison of CPU Scheduling Algorithms: FCFS, SJF, SRTF, Round Robin, Priority Based, and Multilevel Queuing

In this article, we are discussing various aspects of CPU scheduling. We first introduce the concept of CPU scheduling, different types of schedulers and the typical terminology used in relation to processes. Scheduling criteria, the optimization of which is the ultimate goal of a CPU scheduling algorithm, are also discussed. We then discuss various types of research studies that have been carried out with respect to CPU scheduling algorithms. Different CPU scheduling algorithms are examined with examples to highlight their characteristics. Advantages and disadvantages of each of these algorithms are also explored. The scheduling algorithms discussed are, first come first served, shortest job first, shortest remaining time first, priority based, round robin, multilevel queue, and multilevel feedback queue.
ABF de-hazing algorithm based on deep learning CNN for single I-Haze detection

Haze is a kind of natural event which is formed by smoke, dust, or other dry particles. It acts as an overshot-on scene and degrades the visibility of environments and damages the image quality of indoor pictures due to the haze changing colors. Therefore, the importance of removing the haze from the images is increased in the field of computer graphics or vision. Because of its obscurity in mathematical formation, the removal process of haze becomes very complex and it is also made more difficult when the image is in a form of a purely single image. Here, the single-image haze removal process is the most complicated operation because of its ill-posed behavior. In this paper, the authors proposed a powerful single-image haze removal and recognition process. An Adaptive Bilateral Filter (ABF) is designed using an optimal selection of spatial weight parameters for de-hazing the scene depth of the hazy image. It is essential to classify both the haze and non-haze images because, without performing detection tasks, the non-haze images are also fed into the de-hazing process and lead to system complexity. Henceforth for detection purposes, a deep Convolution Neural Network is proposed to categorize the haze and non-haze images. Here deep CNN is proposed for recovering the depth information in an input image using a network training and testing model. The simulation results are demonstrating that the presented technique performed very well in the identification of the de-hazing effect and efficiency of haze removal algorithms.
Region-based feature enhancement using channel-wise attention for classification of breast histopathological images

Abstract Breast histopathological image analysis at 400x magnification is essential for the determination of malignant breast tumours. But manual analysis of these images is tedious, subjective, error-prone and requires domain knowledge. To this end, computer-aided tools are gaining much attention in the recent past as it aids pathologists and save time. Furthermore, advances in computational power have leveraged the usage of computer tools. Yet, usage of computer-aided tools to analyse these images is challenging due to various reasons such as heterogeneity of malignant tumours, colour variations and presence of artefacts. Moreover, these images are captured at high resolutions which pose a major challenge to designing deep learning models as it demands high computational requirements. In this context, the present work proposes a new approach to efficiently and effectively extract features from these high-resolution images. In addition, at 400x magnification, the characteristics and structure of nuclei play a prominent role in the decision of malignancy. In this regard, the study introduces a novel CNN architecture called as CWA-Net that uses a colour channel attention module to enhance the features of the potential regions of interest such as nuclei. The developed model is qualitatively and quantitatively evaluated on private and public datasets and achieved an accuracy of 0.95% and 0.96%, respectively. The experimental evaluation demonstrates that the proposed method outperforms state-of-the-art methods on both datasets.
Dynamic walking of multi-humanoid robots using BFGS Quasi-Newton method aided artificial potential field approach for uneven terrain

The humanoid robot garners paramount interest because of its ability to mimic human-like behavior in real-time environments. In this paper, a hybridized Artificial Potential Field (APF)-Broyden Fletcher Goldfarb Shanno (BFGS) Quasi-Newton technique is being proposed for the trajectory planning of the humanoid robot. The proposed methodology combines the faster local search BFGS Quasi-Newton method with the global search APF method for an efficient and faster-hybridized trajectory planning technique. The humanoid robot herein is being tested in a multi-humanoid working space having uneven surfaces. The data obtained from the sensors concerning the location of obstacles and the target are at first imparted to the APF method, which supplies an intermediate turning angle dependent on the predesigned training model of the APF method. The obtained turning angle is then fed into the BFGS quasi-newton method to produce an optimum turning angle, which inevitably guides the humanoid robots to the target by keeping a minimum safe distance from the obstacles. Multi-humanoid robots are employed in an environment having random static obstacles, with unique targets for them to reach. The proposal of using a multi-humanoid system arises the chance of inter-collision among the humanoids. To get rid of this situation, the dining philosophers controller is being implemented. The simulations and experiments are carried using the proposed technique to ratify the efficacy of the proposed technique. The experimental and simulation results yield a suitable acceptance rate under 5%. In comparison with the previously used navigational technique, it proves to be comfortably skillful and robust for the trajectory planning of the humanoid robot.
Multibranch Attention Mechanism Based on Channel and Spatial Attention Fusion

Recently, it has been demonstrated that the performance of an object detection network can be improved by embedding an attention module into it. In this work, we propose a lightweight and effective attention mechanism named multibranch attention (M3Att). For the input feature map, our M3Att first uses the grouped convolutional layer with a pyramid structure for feature extraction, and then calculates channel attention and spatial attention simultaneously and fuses them to obtain more complementary features. It is a “plug and play” module that can be easily added to the object detection network and significantly improves the performance of the object detection network with a small increase in parameters. We demonstrate the effectiveness of M3Att on various challenging object detection tasks, including PASCAL VOC2007, PASCAL VOC2012, KITTI, and Zhanjiang Underwater Robot Competition. The experimental results show that this method dramatically improves the object detection effect, especially for the PASCAL VOC2007, and the mapping index of the original network increased by 4.93% when embedded in the YOLOV4 (You Only Look Once v4) network.
Moth Search: Variants, Hybrids, and Applications

Moth search (MS) is a nature-inspired metaheuristic optimization algorithm based on the most representative characteristics of moths, Lévy flights and phototaxis. Phototaxis signifies a movement which organism towards or away from a source of light, which is the representative features for moths. The best moth individual is seen as the light source in Moth search. The moths that have a smaller distance from the best one will fly around the best individual by Lévy flights. For reasons of phototaxis, the moths, far from the fittest one, will fly towards the best one with a big step. These two features, Lévy flights and phototaxis, correspond to the processes of exploitation and exploration for metaheuristic optimization. The superiority of the moth search has been demonstrated in many benchmark problems and various application areas. A comprehensive survey of the moth search was conducted in this paper, which included the three sections: statistical research studies about moth search, different variants of moth search, and engineering optimization/applications. The future insights and development direction in the area of moth search are also discussed.
An analogical inductive solution to the grounding problem

The Causal Cognitive Architecture 5, a brain inspired cognitive architecture, is presented. The navigation map which holds features, procedures, and linkages in its spatially mapped cells, is the basic data structure of the architecture. By enhancing the feedback processing of intermediate results not only can causal abilities emerge but as well inductive analogical processing readily emerges as a core mechanism of the architecture. It is shown that abstract or poor features or concepts which cannot readily be grounded in sensorimotor interactions with the environment, can effectively be grounded via the core inductive analogical processing of the architecture, and provides a performance advantage in solving problems. As well, it is shown that relatively small changes in architecture can allow analogical reasoning and a solution to the grounding problem to emerge in a brain-inspired navigation map-based architecture.
Robust multi-view learning with the bounded LINEX loss

Multi-view learning, as a promising direction, emphasizes the consensus principle and the complementarity principle to boost the performance. By exploiting view-consistency or view-discrepancy among different views, numerous successful multi-view support vector machine models have been proposed. However, existing methods face two challenges. Firstly, most multi-view support vector machine models only consider the consensus principle, but ignore the complementarity principle. How to build a novel model with both principles has not been fully considered. Secondly, most multi-view support vector machine models neglect the robustness when the multi-view dataset is contaminated by the noisy samples, error-prone samples and view-inconsistent samples. Considering that the bounded linear-exponential (BLINEX) loss function possesses elegant merits, i.e., asymmetry and boundedness, developing a robust BLINEX-based model is worth exploring. Therefore, in this paper, we propose a BLINEX-based multi-view learning method called MVASY-BX, which explores the consensus and complementarity information with a between-view co-regularization term and importance weights of two views respectively. The mixed BLINEX loss is designed to make the model robust to noisy samples, view-inconsistent samples and error-prone samples. We solve linear and nonlinear MVASY-BX through the stochastic sub-gradient descent algorithm and the alternating direction method of multipliers respectively. Furthermore, we analyze the generalization error bound via Rademacher complexity. The comprehensive experiments confirm that our proposed model is more competitive than other benchmark methods.
Automatic social distance estimation for photographic studies: Performance evaluation, test benchmark, and algorithm

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitable benchmark for developing such algorithms. Collecting images with measured ground-truth pair-wise distances using different camera settings is cumbersome. Moreover, performance evaluation for these algorithms is not straightforward, and there is no widely accepted evaluation protocol. In this paper, we provide an image dataset with measured pair-wise social distances under different camera positions and settings. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate such algorithms. We also propose an automatic social distance estimation method that can be applied on general photo collections. Our method is a hybrid method that combines deep learning-based object detection and human pose estimation with projective geometry. The method can be applied on uncalibrated single images with known focal length and sensor size. The results on our benchmark are encouraging with 91% human detection rate and only 38.24% average relative distance estimation error among the detected people.
Impact of fintech over consumer experience and loyalty intentions: an empirical study on Jordanian Islamic Banks

The purpose of the study was to identify the impact of fintech over consumers loyalty intentions among Islamic banks. The study covered six independent variables that were taken as a means for showing fintech services which might have a significant impact over consumer experience. Consumer experience has been operationalized taking three dimensions namely, cognitive, affective, and social, whereas fintech services have been measured using six dimensions namely, ease of use, perceived value, consumer support, assurance, speed, and innovativeness. The results of the study drawn on the basis of a sample of 384 respondents who were the consumers of Islamic banks, using structural equation modeling revealed that all the six independent variables have a significant direct impact over dependent variable. While analyzing the mediating role of consumer experience using three dimensions out of six mediating roles, only affective experience between perceived value and loyalty intentions, social experience between consumer support and loyalty intentions, affective experience between assurance and loyalty intentions, and cognitive experience between assurance and loyalty intentions have shown insignificant impact and all other mediating roles have been found significant which is a major contribution of the study. The study is important for academicians as well as practitioners as it guides the management of Islamic banks about the factors that they should focus while opening horizons for future research.
Review on nontraditional perspectives of synthetic aperture radar image despeckling

Synthetic aperture radar (SAR) image despeckling is a preprocessing method. SAR images are, by default, noisy in nature. The kind of noise found in SAR images is called speckle noise. The effect of this noise on SAR images is highly adverse. It degrades the quality of the SAR image, resulting in the loss of vital information. Since SAR images are inherently speckled in nature, it costs a lot of information loss. The removal of such noise from the SAR image is mandatory and is the first step. The elimination of speckle noise from SAR images is called SAR image despeckling. There are various traditional and nontraditional methods of SAR image despeckling based on Bayesian and non-Bayesian techniques. The SAR image despeckling methods based on Bayesian techniques are further subdivided into spatial and transform domains. This paper presents a comparative review of nontraditional perspectives on SAR image despeckling. The comparison is made based on methodology, objectives, merits, and demerits. Its focus is to do analysis of all the latest research done in the field of SAR image despeckling using nontraditional methods.
Meaningful public engagement in the context of open science: reflections from early and mid-career academics

How is public engagement perceived to contribute to open science? This commentary highlights common reflections on this question from interviews with 12 public engagement fellows in Utrecht University’s Open Science Programme in the Netherlands. We identify four reasons why public engagement is an essential enabler of open science. Interaction between academics and society can: (1) better align science with the needs of society; (2) secure a relationship of trust between science and society; (3) increase the quality and impact of science; and (4) support the impact of open access and FAIR data practices (data which meet principles of findability, accessibility, interoperability and reusability). To be successful and sustainable, such public engagement requires support in skills training and a form of institutionalisation in a university-wide system, but, most of all, the fellows express the importance of a formal and informal recognition and rewards system. Our findings suggest that in order to make public engagement an integral part of open science, universities should invest in institutional support, create awareness, and stimulate dialogue among staff members on how to ‘do’ good public engagement.
Using machine learning to guide the application of software refactorings: a preliminary exploration

Refactorings constitute the most direct and comprehensible ap-proach for addressing software quality issues, stemming directly from identified code smells. Nevertheless, despite their popularity in both the research and industrial communities: (a) the effect of a refactoring is not guaranteed to be successful; and (b) the plethora of available refactoring opportunities does not allow their compre-hensive application. Thus, there is a need of guidance, on when to apply a refactoring opportunity, and when the development team shall postpone it. The notion of interest, forms one of the major pil-lars of the Technical Debt metaphor expressing the additional maintenance effort that will be required because of the accumulated debt. To assess the benefits of refactorings and guide when a refac-toring should take place, we first present the results of an empirical study assessing and quantifying the impact of various refactorings on Technical Debt Interest (building a real-world training set) and use machine learning approaches for guiding the application of fu-ture refactorings. To estimate interest, we rely on the FITTED framework, which for each object-oriented class assesses its dis-tance from the best-quality peer; whereas the refactorings that are applied throughout the history of a software project are extracted with the RefactoringMiner tool. The dataset of this study involves 4,166 refactorings applied across 26,058 revisions of 10 Apache projects. The results suggest that the majority of refactorings reduce Technical Debt interest; however, considering all refactoring appli-cations, it cannot be claimed that the mean impact differs from zero, confirming the results of previous studies highlighting mixed ef-fects from the application of refactorings. To alleviate this problem, we have built an adequately accurate (~70%) model for the predic-tion of whether or not a refactoring should take place, in order to reduce Technical Debt interest.
Situational development of low-code applications in manufacturing companies

Companies show an increasing interest in low-code development platforms to facilitate application development by domain experts without sophisticated software development knowledge. Thus, companies aim for a more efficient development of more effective applications since domain experts as so-called citizen developers are no longer limited by the availability and domain knowledge of trained software developers. Nevertheless, efficiency and effectiveness of application development is traditionally also largely influenced by the use of a suitable software development method. Domain experts are, however, not trained in software development methods. This introduces a risk of domain experts creating unusable applications or exceeding the designated time frame of a project (or both). In this paper, we therefore propose an initial version of a situational software development method which supports domain experts in manufacturing companies during the low-code development of applications. The method can be tailored based on situational factors, considering application requirements, features of the used low-code development platform, and characteristics of the development team. We also present feedback corroborating the usefulness of our method and future extension points based on expert interviews.
Microarchitectural Leakage Templates and Their Application to Cache-Based Side Channels

The complexity of modern processor architectures has given rise to sophisticated interactions among their components. Such interactions may result in potential attack vectors in terms of side channels, possibly available to userland exploits to leak secret data. Exploitation and countering of such side channels requires a detailed understanding of the target component. However, such detailed information is commonly unpublished for many CPUs.
Fast Fully Oblivious Compaction and Shuffling

Several privacy-preserving analytics frameworks have been proposed that use trusted execution environments (TEEs) like Intel SGX. Such frameworks often use compaction and shuffling as core primitives. However, due to advances in TEE side-channel attacks, these primitives, and the applications that use them, should be fully oblivious; that is, perform instruction sequences and memory accesses that do not depend on the secret inputs. Such obliviousness would eliminate the threat of leaking private information through memory or timing side channels, but achieving it naively can result in a significant performance cost.