Reviewerly/exHarmony huggingface dataset:
Multi-Task Distillation: Towards Mitigating the Negative Transfer in Multi-Task Learning

In this paper, we propose a novel mechanism for alleviating the negative transfer in multi-task learning. Multi-task learning aims to learn the general meta-knowledge via sharing inductive bias for improving generalization ability. However, there exists a negative transfer problem in MTL, in which the performance improvement of a specific task leads to performance degradation of other tasks. Multi-task learning is essentially a multi-objective problem because of the task competition, necessitating a trade-off in individual optimality. Inspired by knowledge distillation, a set of task-specific teacher models can transfer the knowledge to a student multi-task model, without signicifantly loss in performance. In other words, the individual optimality can be achieved via multi-teacher knowledge distillation, where each teacher model is optimal and contains sufficient general meta-knowledge. Therefore, we propose multi-task distillation to couple with the negative transfer, turning the multi-objective optimization problem into a multi-teacher knowledge distillation problem. In detail, we firstly collect the optimal task-specific teacher models, and then achieve the individual optimality of the student model by knowledge distillation. Extensive experiment results on different benchmark datasets demonstrate the effectiveness of our method over current state-of-the-art multi-task learning formulations and single-task training.
Adversarially Robust Multi-Sensor Fusion Model Training Via Random Feature Fusion For Semantic Segmentation

Multi-sensor data fusion model aims to improve the model performance by fusing multiple types of sensor data. Although multi-sensor data fusion models have been developed for remarkable performance, there is a lack of studies on the adversarial vulnerability of the multi-sensor data fusion models. In this paper, we propose a robust multi-sensor data fusion method that is not vulnerable to adversarial attacks. To this end, we devise a random feature fusion method to preserve multi-sensor fusion features. Through the random feature fusion, we could explicitly hide the information about which features are being used for the fusion. In experiments, we verify that our proposed random feature fusion method shows the adversarial robustness considerably under diverse adversarial settings.
Hiding privacy and clinical information in medical images using QR code

This study aims to hide patient's privacy details of DICOM files using the QR code images with the same size using steganographic technique. The proposed method is based on the properties of the discrete cosine transform (DCT) of the DICOM images to embed a QR code image. The proposed approach includes two main parts: the embedding of data and the extraction procedure. Moreover, the embedded QR code will be reconstructed blindly from the Stego DICOM without the presence of the original DICOM file. The performances of proposed approach were tested using TCIA COVID-19 Dataset and the terms of the Peak Signal to Noise Ratio (PSNR), the Structural Similarity Index (SSIM) and the Bit Error Rate (BER) values. The simulation results achieved high PSNR values ranged between 63.47 dB and 81.97 dB after the embedding procedure by using a QR code image within the DICOM image of the same size.
The Mind’s Eye: Visualizing Class-Agnostic Features of CNNS

Visual interpretability of Convolutional Neural Networks (CNNs) has gained significant popularity because of the great challenges that CNN complexity imposes to understanding their inner workings. Although many techniques have been proposed to visualize class features of CNNs, most of them do not provide a correspondence between inputs and the extracted features in specific layers. This prevents the discovery of stimuli that each layer responds better to. We propose an approach to visually interpret CNN features given a set of images by creating corresponding images that depict the most informative features of a specific layer. Exploring features in this class-agnostic manner allows for a greater focus on the feature extractor of CNNs. Our method uses a dual-objective activation maximization and distance minimization loss, without requiring a generator network nor modifications to the original model. This limits the number of FLOPs to that of the original network. We demonstrate the visualization quality on widely-used architectures. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at https://git.io/JL9Wg and our demo video: https://youtu.be/Au3jaUdnPKM
A Novel Video Processing Algorithm for Accurately Vehicle Detection and Tracking

In this paper, a modified vehicle detection algorithm has been presented according to three based frame difference background subtraction followed by Kalman Filter (KF) to track moving vehicles. The presented vehicles detection and recognition methodology addresses the following issues: obtaining accurate vehicles detection with appropriate frame noise restoration, solving incomplete detection problems for multiple moving vehicles, increasing recognition performance, and reducing calculation time. Furthermore, the trajectory of each moving vehicle in the successive video frames has been measured and allocated. The proposed methodology has been tested on videos which are discriminated with respect to lighting conditions, camera resolution, and vehicle density. The average absolute error between predicted and detected vehicles has been utilized in order to estimate both detection and tracking accuracy of interest. Experimental results for multiple moving vehicle detection and tracking in three separate surveillance videos have achieved high accuracy (95.51%, 94.714%, and 95.719%) for test videos 1, 2, and 3 respectively.
Mapping Hydrothermal Alteration at the Fentale-Dofan Magmatic Segment of the Main Ethiopian Rift

The Main Ethiopian Rift (MER) is characterized by extensional tectonics and volcanism, associated with active hydrothermal systems, hydrothermal alteration and fumarolic deposits. The spatial distribution of these hydrothermal products and their link with faults and rock types provides important clues to what controls fluid flow in the subsurface. However, little is known about this in the East African Rift. We address this issue with a multidisciplinary approach in the Fentale-Dofan magmatic segment of the MER, an area characterized by intense volcanic and tectonic activity and a geothermal prospect. Primarily we conduct mapping of hydrothermal alteration and fumarolic deposits, and rock lithologies using a surface feature classification technique of multispectral satellite images. Then we interpret the map using a new database of faults and active hydrothermal manifestations such as hot-springs and fumaroles. We find that the surface hydrothermal alteration and deposits are mainly focused near Fentale and the Dofan Volcanic Complex (DVC). At DVC the hydrothermal products are focused on rhyolites on the western side of the volcano, in an area of intense NNE striking, rift parallel faults. At Fentale volcano the hydrothermal products are mainly associated with ignimbrite and show a circular pattern around the volcanic edifice, but also in places follow the NNE striking faults. At Fentale, the more complex association of hydrothermal products and active manifestations around the edge of the ignimbrite suggests formation contacts may also localize fluid flow in places. At both volcanoes the association between hydrothermal products with either the rhyolites and ignimbrites is likely due to them being relatively easily altered (in comparison to basalt), and also their brittle nature allows for fracturing through which localized fluid flow can occur (as opposed to the sediments). The general pattern of hydrothermal products suggests a stronger structural influence at the DVC with respect to Fentale. The presence of hydrothermal products and active hydrothermal manifestations, along with other lines of evidence such as locus of subsurface dike intrusion at the volcanic centres, suggest that discrete and localized magma reservoirs beneath Fentale and the DVC are the heat source for hydrothermal circulation. Our study also demonstrates that geology, including hydrothermal deposits, can be successfully mapped using automated remote sensing based classification.
Expert System Design for Web-Based Lecturer Scheduling

In this study contains an expert system design for web-based lecturer scheduling that can facilitate the Chair of the Study Program in scheduling courses for lecturers. This system was built using Notepad ++ software, XAMPP with HTML, CSS, JavaScript, PHP and database programming languages using MYSQL. Subjects in this study are expert system applications as the media schedule lecturer subjects. Data collection in this study uses the interview method and questionnaire method. This system is built with the Waterfall model namely analysis, design, coding and testing. This application in the inference engine used is the forward chaining method. The results of this study are expert system applications to schedule lecturers' subjects according to the questionnaire data. Based on the results of the trial it can be concluded that this expert system application can facilitate the Chair of the Study Program in accurately scheduling courses, classes, days, hours, space, and lecturers
An Energy-Efficient PSO-Based Cloud Scheduling Strategy

Cloud computing provides useful services to users with extensive and scalable resources that virtualized over the internet. It defined as a collection of the communication and computing resources located in the data-center. The service based on on-demand is subject to QoS, the load balance, and certain other constraints with a direct effect on the user's consumption of resources that are controlled by this cloud infrastructure. It is considered a popular method as it has several advantages that have been provided by a cloud infrastructure. The cloud scheduling algorithm's primary goal was to bring down the time taken for completion (the cost of execution) of the task graph. The start time and the finish time for the task node influence the task graph completion completed to the time (the cost). The task node sort order an essential aspect that influences the start time and the finish time for every task node. In a hybrid cloud, efficient dense particle mass-based cloud scheduling is efficient because users need to maintain the security of the hybrid cloud. Different algorithms with different algorithms suggested by researchers in the cloud. This paper proposes particle swarm optimization (PSO)-based cloud optimal scheduling. Effective results obtained in an efficient fuzzy mass-based PSO cloud scheduling.
Theory meets Practice at the Median: A Worst Case Comparison of Relative Error Quantile Algorithms

Estimating the distribution and quantiles of data is a foundational task in data mining and data science. We study algorithms which provide accurate results for extreme quantile queries using a small amount of space, thus helping to understand the tails of the input distribution. Namely, we focus on two recent state-of-the-art solutions: t-digest and ReqSketch. While t-digest is a popular compact summary which works well in a variety of settings, ReqSketch comes with formal accuracy guarantees at the cost of its size growing as new observations are inserted. In this work, we provide insight into which conditions make one preferable to the other. Namely, we show how to construct inputs for t-digest that induce an almost arbitrarily large error and demonstrate that it fails to provide accurate results even on i.i.d. samples from a highly non-uniform distribution. We propose practical improvements to ReqSketch, making it faster than t-digest, while its error stays bounded on any instance. Still, our results confirm that t-digest remains more accurate on the "non-adversarial" data encountered in practice.
Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches

Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations and adversarial patches. Adversarial patches, often considered more appropriate for a real-world attack, are attached to the target object or its surroundings to deceive the target system. However, most previous research employed adversarial patches that are conspicuous to human vision, making them easy to identify and counter. Previously, the spatially localized perturbation GAN (SLP-GAN) was proposed, in which the perturbation was only added to the most representative area of the input images, creating a spatially localized adversarial camouflage patch that excels in terms of visual fidelity and is, therefore, difficult to detect by human vision. In this study, the use of the method called eSLP-GAN was extended to deceive classifiers and object detection systems. Specifically, the loss function was modified for greater compatibility with an object-detection model attack and to increase robustness in the real world. Furthermore, the applicability of the proposed method was tested on the CARLA simulator for a more authentic real-world attack scenario.
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning

We consider federated learning in tiered communication networks. Our network model consists of a set of silos, each holding a vertical partition of the data. Each silo contains a hub and a set of clients, with the silo’s vertical data shard partitioned horizontally across its clients. We propose Tiered Decentralized Coordinate Descent (TDCD), a communication-efficient decentralized training algorithm for such two-tiered networks. The clients in each silo perform multiple local gradient steps before sharing updates with their hub to reduce communication overhead. Each hub adjusts its coordinates by averaging its workers’ updates, and then hubs exchange intermediate updates with one another. We present a theoretical analysis of our algorithm and show the dependence of the convergence rate on the number of vertical partitions and the number of local updates. We further validate our approach empirically via simulation-based experiments using a variety of datasets and objectives.
Security Framework for Enhancing Security and Privacy in Healthcare Data Using Blockchain Technology

New opportunities for clinical data processing and accessibility for patients to view and share their health data are opening up through advances in electronic medical reports, cloud data storage, and patient data protection regulations. Securing data, processing, transfers, and maintaining their seamless integration are of tremendous importance for any data-driven enterprise. Particularly in healthcare, blockchain technology can robustly and effectively solve these essential problems. Blockchain technology has entered a big boom in the health sector amid its utility in solving the safety concerns of the EHR in healthcare. EHRs are able to improve the delivery of healthcare. The introduction of EHRs offered the potential for information technologies to counteract those efforts. Electronic access to medical records allowed doctors to increase the quality of care significantly. In fact, EHR allows improved control of the condition and increased levels of preventive treatment. The digital record provides better functions to support decision-making and enhanced collaboration among carers. Therefore, its position in the health sector is increasingly recognized. The protection of such digital information is, therefore, given the utmost importance, and blockchain is widely used for safe and stable data on healthcare. Holding data integrity and rising data processing storage costs in blockchain healthcare technology play a significant role. Encrypted in the block chain, the proposed LWKG architecture helps a customer to show the moment that health data is displayed. LWKG verifies the integrity of information and analyzes the results stored in the database.
Modern Web Application for Image Annotation Using Web Technologies

The evolution of web technologies has brought to the fore new solutions for content management and distribution. The development of these new technologies has managed to lay the foundations of a strong web industry and work environments that can have ramifications to any field of work present in modern society. Web developers regularly use and build powerful software modules, based on new technologies and with which they can develop powerful applications. In this paper, we present a web application, built according to the new standards, which aims to develop an accessible platform used for annotating and saving images.
Professional and educational activity of youth in the digital economy

The paper is aimed to rank countries similar in terms of selected diagnostic variables: in terms of digital competencies held and variables related to the youth professional activity. The method of descriptive and statistical analysis, including comparative analysis, is used in the study. The paper also uses cluster analysis. The conducted analysis included the empirical material referring to the youth at the age of 15-29 taken from the Eurostat database. The research period is 2011–2019. The analysis by type of educational and professional activity of young people conducted in the EU member countries allowed the identification of four groups of countries, differing in the levels of both the NEET rate (Neither in employment nor in education or training) and the digital skills. The digital skills have been additionally grouped into competences in the field of information, communication, problem-solving and programming skills. The group of countries with the lowest NEET rate proved to include those where young people presented high levels of digital skills in all the dimensions analyzed. The study shows that acquiring digital competencies enables young people to participate in communities and gives them a better chance for professional activity.
Comprehensive analysis of water carrying capacity based on wireless sensor network and image texture of feature extraction

This article takes the complex uncertainty in the carrying process of water resources system as the research object. Firstly, the water resources carrying capacity system is divided into three sub-systems: bearing support capacity, bearing pressure and bearing regulation capacity. System analysis methods based on wireless sensor network and image texture of feature extraction for correlation analysis, gray correlation analysis, and multi-dimensional conditional cloud algorithm are introduced into the field of water resources carrying capacity evaluation research. Moreover, combine it with the subtraction set opposition, the effect partial bias connection number, and the risk matrix. Finally, three types of models for water resource carrying capacity evaluation and trend analysis were constructed. The evaluation results of water resources carrying capacity are consistent with the actual process, and the overall development trend is gradually improving. The application results of the evaluation model of water resource carrying capacity are established. The calculation results of the image texture feature of extraction model are similar to the overall change trend of the model before the improvement. The trend of change is more obvious. It can not only describe the water resource carrying level as a whole, but also characterize the reliability of the calculation results and the bearing risk caused by the uncertainty of the water resource carrying system. The water resources carrying capacity evaluation and trend analysis of calculation results are reasonable and feasible. The research results can provide a reasonable and effective decision-making basis for the systematic analysis of the bearing boundary issues between regional complex water resources systems and human water activities, and then propose adaptive risk prevention and control strategies for water resources carrying capacity.
Hamming weight enumerators of multi-twisted codes with at most two non-zero constituents

In this paper, we explicitly determine Hamming weight enumerators of several classes of multi-twisted codes over finite fields with at most two non-zero constituents, where each non-zero constituent has dimension 1. Among these classes of multi-twisted codes, we further identify two classes of optimal equidistant linear codes that have nice connections with the theory of combinatorial designs and several other classes of minimal linear codes that are useful in constructing secret sharing schemes with nice access structures. We also illustrate our results with some examples.
Design of Electricity Market Big Data Analysis System Based on Hybrid Encryption and Secure Transmission

With the gradual deepening of the informatization construction of Zhejiang Electric Power Trading Center, massive amounts of data have been accumulated, and it is imperative to build a big data analysis system for Zhejiang Electric Power Market. Zhejiang Electric Power Market Big Data Analysis System has data interaction with Zhejiang Electricity Trading System and Zhejiang Electricity Settlement System. In order to ensure the secure transmission of information between various systems, it is necessary to realize the controllability, confidentiality, availability, integrity and non-repudiation of information interaction. This paper proposes a new hybrid encryption method by combining AES encryption algorithm, RSA encryption algorithm and SHA256 encryption algorithm, and it is used in the information transmission of Zhejiang Electric Power Market Big Data Analysis System. It generates a specific hash value for the ciphertext to verify the integrity of the transmitted data by using SHA256 to generate a fixed-digit hash value. Through the program verification of the hybrid encryption method in this paper, it is proved that the usability, security and efficiency of this method can provide security for the information interaction of Zhejiang Electric Power Market Big Data Analysis System.
Storage-Saving Scheduling Policies for Clusters Running Containers

Container technology plays an important role in the virtualization landscape today. A container uses its own file system consisting of a stack of layers, which are stored on the execution server's disk. Containers running on the same server share the layers they have in common, and this sharing results in valuable savings in server storage space. Many containers can run on a single server, but when their resource demands grow enough, they are distributed across a cluster of nodes/servers by orchestration systems, such as Kubernetes. In this work, we found that for the same amount of containers to run, the storage required is higher for clusters consisting of a larger number of nodes. The severity of this storage overhead depends on the scheduling policy used to select the nodes that run the containers. By comparing different storage-saving scheduling policies that differ from each other in the depth of storage knowledge they leverage to make decisions, our analysis reveals that only deep, layer-level knowledge can effectively counter the growth in storage demand as the cluster size increases. Policies with coarser-grained knowledge achieve limited benefit because they achieve performance that is nearly equal to that of a random, zero-knowledge policy.