Reviewerly/exHarmony huggingface dataset:
Using MLP partial responses to explain in-hospital mortality in ICU

In this paper we propose to use partial responses derived from an initial multilayer perceptron (MLP) to build an explanatory risk prediction model of in-hospital mortality in intensive care units (ICU). Traditionally, MLPs deliver higher performance than linear models such as multivariate logistic regression (MLR). However, MLPs interlink input variables in such a complex way that is not straightforward to explain how the outcome is influenced by inputs and/or input interactions. In this paper, we hypothesized that in some scenarios, such as when the data noise is significant or when the data is just marginally non-linear, we could find slightly more complex associations by obtaining MLP partial responses. That is, by letting change one variable at the time, while keeping constant the rest. Overall, we found that, although the MLR and MLP in-hospital mortality model performances were equivalent, the MLP could explain non-linear associations that otherwise the MLR had considered non-significant. We considered that, although deeming higher-other interactions as disposable noise could be a strong assumption, building explanatory models based on the MLP partial responses could still be more informative than on MLR.
Geometric calibration method based on Euler transformation for a large field of view polarimetric imager

A geometric calibration method based on a two-dimensional turntable and Euler transformation with simple operation, high precision and wide application is proposed in this paper. Instead of precisely adjusting the relative position of the sensor and the turntable, the method introduces rotation matrix and translation vector to effectively eliminate system error and alignment error. The geometric calibration verification experiments of the directional polarimetric camera were carried out in the laboratory with the original method without Euler transformation and the proposed method. The experimental results show the calibration residual of the proposed method is less than 0.2 pixel while the maximum residual of the original method reaches about 0.35 pixel, and the root mean squared error and mean reprojection error are also improved compared with the original method. These facts prove the availability and superiority of the proposed method.
Algorithmic Monoculture and Social Welfare

chapter Share on Algorithmic Monoculture and Social Welfare Author: Manish Raghavan Massachusetts Institute of Technology, Sloan School of Management and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology, Sloan School of Management and Department of Electrical Engineering and Computer ScienceView Profile Authors Info & Claims The Societal Impacts of Algorithmic Decision-MakingSeptember 2023https://doi.org/10.1145/3603195.3603211Published:08 September 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Modeling and Selecting Frameworks in Terms of Patterns, Tactics and System Qualities

Selecting a framework and documenting the rationale for choosing it is an essential task for system architects. Different framework selection approaches have been proposed in the literature. However, none of these connect frameworks to qualities based on their implemented patterns and tactics. In this paper, we propose a way to semi-automatically compare the quality attributes of frameworks by extracting the patterns and tactics from a framework’s source code and documenting them to connect frameworks to requirements upon which a selection can be made. We use a tool called Archie (a tool used to extract tactics from a Java-based system’s code) to extract the patterns/tactics from the implementation code of frameworks. We then document and model these patterns/tactics and their impact on qualities using the Goal-oriented Requirements Language (GRL). After that, we reuse these models of patterns and tactics to model frameworks in terms of their implemented patterns and tactics. The satisfaction level of the quality requirements integrated with other criteria such as the preferences of an architect provide architects with a tool for comparing different frameworks and documenting their rationale for choosing a framework. As a validation of the approach, we apply it to three realistic case studies with promising results.
Research on the slope support technology in civil engineering construction

For the whole process of civil engineering construction, slope support technology is a very key technical point of civil construction. Engineering and technical personnel shall be able to fully ensure the safety and firmness of the foundation structure of the engineering slope by setting up retaining walls or using other slope support treatment techniques, and extend the safe service life of civil engineering. Under this premise, engineering and technical personnel must integrate the technical measures of slope support into the construction process of civil engineering.
Citation Networks Iraqi Universities Case Study

In this paper, the current scientific research status of the main Iraqi universities is deeply investigated. To this end, a Citation Network is generated among them. This kind of network has the ability to reflect the actual scientific research status in Iraq. The approach that is used in this study is based on complex network concepts. For the data collection, a special-purpose program is designed to crawl the Google Scholar repository and retrieve all the required data. This crawler is designed to collect information on the published research articles based on the official educational domains of the selected universities. We also use data from other repositories such as Scopus, Clarivate, and ResearchGate. The main contribution of this work is generating a citation network of the main universities in Iraq and extracting the main facts on scientific research activities. The second contribution is proposing a local rank for these universities based on network measurements and other academic indicators. Another aspect that is investigated in this work is the scientific collaboration among the Iraqi universities and with the worldwide universities.
Using Grey Wolf Hunting Mechanism to Improve Spherical Search

Spherical search algorithm (SS) was a swarm-based meta-heuristic recently proposed to solve the bound-constrained non-linear global optimization problems. It has quite competitive performance with respect to other popular algorithms. Nevertheless, it still has several defects, such as it can't easily get rid of the situation that falls into the local optimal and its convergence speed is slow under the condition that the spherical space is much too large. As grey wolf optimization (GWO) algorithm has good abilities of minimizing the global search space and local area avoidance, the search mechanism of GWO by serial pattern is studied and combined with SS to improve its balance between exploration and exploitation. The new spherical search and grey wolf optimization algorithm algorithm we proposed is called SSGWO, and its superiority is demonstrated with experimental results based on 30 benchmark functions of IEEE CEC2017 in comparison with its component algorithms.
Optimization of Job Scheduling with Dynamic Bees Approach

Cloud-based job scheduling is used to achieve high throughput. The problem of load balancing hampers the performance of the cloud. To end this, the bees life algorithm is used to achieve optimization in job scheduling. This paper proposes unique dynamic scheduling using the bees algorithm with the shortest job first approach to achieve a high degree of load balancing. The effectiveness of cloud computing depends greatly upon the performance of the cloud scheduler. The scheduling algorithm used achieves high performance, since waiting time for jobs is reduced. The overall algorithm is divided into phases. The first phase is the initialized section in which fog and execution nodes are defined. In the second phase, job fetching and uploading are accomplished. In the third phase, jobs are sorted according to the shortest burst time. In the last phase, bees are deployed to locate the best possible processor for job execution. The result of the dynamic bees algorithm shows effective load balancing as compared to the static bees algorithm. Result in terms of execution time, waiting time, execution speed, and memory allocation shows improvement by 2–3%.
Controlling and Modelling Thermal Processes of Wood Drying by Artificial Neural Networks

The paper focuses on neural network control by the channels: "temperature of water vapor — dry-bulb temperature" in periodic kilns. Our approach is based on a linear dynamic model in the state space of the chamber and model predictive control that exploits a multilayered feedforward time-delay neural network, which is able to predict and replicate the behavior of a real system or plant. To manifest the effectiveness of the proposed model, the paper presents an example of controlling the multistage temperature conditions of wood drying. Besides, the paper uses the Jordan artificial neural network to numerically solve the heat equation in two dimensions, whose solution may be necessary to predict the temperature of the wood at a specific time step and to adjust the state vector of the drying chamber simulator. Representative cases of simulating are provided as well.
Task-driven Image Preprocessing Algorithm Evaluation Strategy

The image preprocessing algorithm can effectively improve performance of the advanced visual algorithms under complex conditions. But the evaluation of the image preprocessing algorithm usually uses the traditional PSNR/SSIM indicators, there is a big gap between the evaluation result and the performance of the algorithm in actual vision tasks. This article uses the UE4 virtual engine and referring to the atmospheric scattering model to construct a haze vehicle small object dataset (VSOD). Based on virtual data, a vision task-driven image preprocessing algorithm evaluation strategy is proposed. We take dehazing algorithms and object detection tasks as examples to verify the task-driven evaluation strategy. By analyzing the evaluation results of task-driven evaluation strategy and traditional PSNR/SSIM, we proved that the task-driven evaluation strategy can more accurately evaluate the performance of image preprocessing algorithms in actual vision tasks.
Database Security: An Overview and Analysis of Current Trend

Information is the core and most vital asset these days. The subject which deals with Information is called Information Science. Information Science is responsible for different information related affairs from collection, selection, organization, processing, management and dissemination of information and contents. And for this information related purpose Information Technology plays a leading role. Information Technology has different components viz. Database Technology, Web Technology, Networking Technology, Multimedia Technology and traditional Software Technology. All these technologies are responsible for creating and advancing society. Database Technology is concerned with the Database. It is worthy to note that, Database is concerned with the repository of related data in a container or base. The data, in Database normally stored in different forms and Database Technology play a lead role for dealing with the affairs related to database. The Database is very important in the recent past due to wider applications in different organizations and institutions; not only profit making but also non-profit making. Today most organizations and sectors which deal with sensitive and important data keep them into the database and thus its security becomes an important concern. Large scale database and its security truly depend on different defensive methods. This paper talks about the basics of database including its meaning, characteristics, role etc. with special focus on different security challenges in the database. Moreover, this paper highlights the basics of security management, tools in this regard. Hence different areas of database security have mentioned in this paper in a simple sense.
Research on Moving Target Recognition and Tracking Technology Based on Machine Learning

Abstract In this paper, the SIFT feature, HOG feature and SVM are adopted to design a movement vehicle recognition system, when the vehicle is relatively close to the camera and it backs to the camera, also the vehicle can be implemented to track. In this paper, two feature extraction methods, SIFT feature and HOG feature are respectively used to extract the features from 6000 images of vehicles with back facing the cameras and 6000 images without vehicles. Then SVM is used to train them to obtain a model. Then, we use the moving object recognition algorithm to extract the moving object in the given video, and put each moving object into the trained model to judge. If the model is judged as a vehicle, it will be tracked.
Loss and Energy Tradeoff in Multi-access Edge Computing Enabled Federated Learning

Federated learning (FL) encourages users to train statistical models on their local devices. Since mobile devices have the limited power and computing capabilities, the users are rational in minimizing their energy consumption with the cost of the model's accuracy. Multi-access Edge Computing (MEC) enabled FL is a prominent approach where users can offload a fraction of their dataset to the MEC server where the training of the statistical model is performed with the help of the powerful MEC server in parallel with the local training at the mobile users. With the size of dataset offloaded to the MEC server, both the performance of the model and the energy consumption of the system are varied. We analyze this tradeoff between the performance of the system and the energy consumption at the MEC server and mobile users. The time consumption can also be saved by managing the size of the dataset offloaded to the MEC server. Since the MEC server and mobile users have the conflicting interest in saving the energy consumption with the constraint on the time taken for one computing round where the performance of the model fluctuates across the size of offloaded dataset, we analyze the tradeoff by formulating the resource management problem as a penalized convex optimization problem. We propose a distributed resource management problem for MEC enabled FL system where the global model is responsible for radio resource management and each local model performs a dataset offloading decision. Then, we perform the simulation to show the tradeoff and performance of the proposed algorithm.
Multi-Instance Aware Localization for End-to-End Imitation Learning

Existing architectures for imitation learning using image-to-action policy networks perform poorly when presented with an input image containing multiple instances of the object of interest, especially when the number of expert demonstrations available for training are limited. We show that end-to-end policy networks can be trained in a sample efficient manner by (a) appending the feature map output of the vision layers with an embedding that can indicate instance preference or take advantage of an implicit preference present in the expert demonstrations, and (b) employing an autoregressive action generator network for the control layers. The proposed architecture for localization has improved accuracy and sample efficiency and can generalize to the presence of more instances of objects than seen during training. When used for end-to-end imitation learning to perform reach, push, and pick-and-place tasks on a real robot, training is achieved with as few as 15 expert demonstrations.
Modeling the integrity of course learning using percolation through intra-disciplinary connections

Abstract This article deals with the problem of modeling the assessment of the level of integrity of the physics course content learned by students. The need for such modeling is due to the need to bring education courses to online format. The degree of learning integrity is proposed to be established by modeling the percolation of intra-disciplinary connections established in the course structure. The modeling uses a graph model of intra-disciplinary connections (Gnitetskaya T.) and the principles of percolation theory (P-theory). The article provides some practical solutions for the use of P-theory. An algorithm has been developed that allows forming the topics of the physics course into the structure examined for percolation.
The Optimization Method of Knowledge Distillation Based on Model Pruning

In recent years, Deep Neural Networks have continuously updated their performance in computer vision, speech recognition and other fields, and have been widely used. However, the acquisition of better performance requires the design of more complex networks, and the corresponding model calculation and storage space are also increasing, which is very unfavorable for the deployment and application of models on embedded mobile devices. Knowledge distillation technology based on transfer learning is an effective means to achieve model compression. In our research, the model pruning technology was introduced into the design of the student network of knowledge distillation, and the teacher-student distillation training method was used to improve the accuracy of the student network. In this paper, six pruning methods have been used as student networks and tested on three networks (VGG13, VGG16, VGG19) and two datasets (CIFAR10, CIFAR100). Compared with the network without knowledge distillation method, its accuracy can be increased by up to 5.81%. The results show that compared with the commonly used pruning technology, our method is capable of effectively improving the accuracy of the network without increasement of the network size.
An Optimal Heuristic Model for Greedy and Efficient Consolidation Process in Cloud Datacenters

Today, cloud computing has become an integrated solution for the entrepreneurs to deploy their business applications and to obtain the immense advantages of the cloud. Virtualization increased the cloud productivity and decreased the energy consumption by mapping the physical systems into logical virtual machines. Uneven distribution of load among the hosts may generate the host over subscription and under subscription issues, which impacts negatively on QoS management and energy consumption of cloud. Server consolidation is the convenient solution for handling the host underutilization issues. Although the former researches were proposed several advancements in consolidation, it is still suffering in selection of optimal threshold values and controls the aggressive migrations. This research work has proposed an optimal heuristic model for greedy and efficient consolidation process in cloud datacenters. Experimental results proven that the proposed consolidation model defined an optimal threshold value to save the energy and the greedy consolidation policy controlled the aggressive migrations better than the former consolidation models.
IME-Spell

Intended for reducing manual inspection costs and semantic misunderstandings, Chinese Spelling Check (CSC) has been investigated extensively in natural language processing. However, little work has yet been done on input-method-based CSC in which CSC can make use of Pinyin information to improve spelling correction efficiency. This paper proposes a novel CSC architecture, IME-Spell, based on pre-trained context vectors for input methods, which consists of two parts as follows. The Chinese spelling detection part of the architecture adopts the fusion vectors of character-based pre-trained context vectors and Pinyin vectors, and uses the method of sequence labeling to detect the error characters. The Chinese spelling correction part of the architecture adopts Masked Language Model (MLM) to generate a candidate set of erroneous characters, and uses XGBoost and Pinyin-to-Character conversion models to filter correct characters and correct the error characters for users. IME-Spell has a significant improvement over the benchmark models on the SIGHAN dataset, whose maximum difference of F1 in the spelling detection and correction subtasks reach 48.9% and 27.8%, respectively.
Content-Based Transaction Access From Distributed Ledger of Blockchain Using Average Hash Technique

There are many critical applications working with blockchain-based technology including the financial sector, healthcare, and supply chain management. The fundamental application of blockchain is Bitcoin, which was primarily designed for the financial value transfer. Owing to the feature of decentralized storage structure, immutability, integrity, availability, and reliability of transactions, the blockchain has become the need of the current industry like VANET. However, presently, not much work has been done in order to mitigate the redundancy in the distributed ledger. Hence, the authors arrive at the intelligible conclusion to detect a similar transaction that can mitigate the redundancy of transaction in a distributed ledger. In this chapter, they are addressing two main challenges in blockchain technology: firstly, how to minimize the storage size of blockchain distributed ledger and, secondly, detecting the similar transaction in the distributed ledger to mitigate the redundancy. To detect similar transaction from the distributed ledger they have applied the average hash technique.
A new approximation algorithm for virtual machine placement with resource constraints in multi cloud environment

Virtual Machine placement problem with multiple resources is a complex optimization problem. With large size of instances, the given problems become NP hard problem. In this case techniques like integer programming and linear programming are not suitable. Only option left is to use heuristic-based approach. In this paper we have designed and developed new approximation algorithm to handle such hard instances in multi cloud environment. We have also proved that problem is NP Hard with its complexity by reduction. A new approximation algorithm is proposed and tested on real time multiple cloud providers azure and google with benchmark instances. All empirical result has shown that new proposed algorithm has surpassed all existing state-of-the-art techniques.
Vision-Based Deep Reinforcement Learning For UR5 Robot Motion Control

Recently, Deep Reinforcement Learning (DRL) has been widely used for robot manipulation skill learning. However, learning directly from the high-dimensional sensory observations is always inefficient, which makes it impractical to apply DRL methods to real-world robots. This paper designed a vision-based DRL method based on Deep Deterministic Policy Gradients (DDPG). To improve the learning efficiency, we construct an asymmetric actor-critic structure and add an auxiliary-task branch to the actor network in the method. A reaching-task based UR5 robot is designed to evaluate the performance of the method. The results indicate that the vision-based DRL method proposed in the paper can successfully learn the reaching-task skill and the utilization of asymmetric actor-critic structure and auxiliary-task objective can improve the learning efficiency and the final performance of the DRL method effectively.