Reviewerly/exHarmony huggingface dataset:
On the Modified Transparency Order of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> <mo>,</mo> <mi>m</mi> </mrow> </mfenced> </math>-Functions

The concept of transparency order is introduced to measure the resistance of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> <mo>,</mo> <mi>m</mi> </mrow> </mfenced> </math> -functions against multi-bit differential power analysis in the Hamming weight model, including the original transparency order (denoted by <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi mathvariant="script">TO</mi> </math> ), redefined transparency order (denoted by <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi mathvariant="script">RTO</mi> </math> ), and modified transparency order (denoted by <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi mathvariant="script">MTO</mi> </math> ). In this paper, we firstly give a relationship between <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi mathvariant="script">MTO</mi> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi mathvariant="script">RTO</mi> </math> and show that <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mi mathvariant="script">RTO</mi> </math> is less than or equal to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mi mathvariant="script">MTO</mi> </math> for any <math xmlns="http://www.w3.org/1998/Math/MathML" id="M10"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> <mo>,</mo> <mi>m</mi> </mrow> </mfenced> </math> -functions. We also give a tight upper bound and a tight lower bound on <math xmlns="http://www.w3.org/1998/Math/MathML" id="M11"> <mi mathvariant="script">MTO</mi> </math> for balanced <math xmlns="http://www.w3.org/1998/Math/MathML" id="M12"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> <mo>,</mo> <mi>m</mi> </mrow> </mfenced> </math> -functions. Secondly, some relationships between <math xmlns="http://www.w3.org/1998/Math/MathML" id="M13"> <mi mathvariant="script">MTO</mi> </math> and the maximal absolute value of the Walsh transform (or the sum-of-squares indicator, algebraic immunity, and the nonlinearity of its coordinates) for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M14"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> <mo>,</mo> <mi>m</mi> </mrow> </mfenced> </math> -functions are obtained, respectively. Finally, we give <math xmlns="http://www.w3.org/1998/Math/MathML" id="M15"> <mi mathvariant="script">MTO</mi> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M16"> <mi mathvariant="script">RTO</mi> </math> for (4,4) S-boxes which are commonly used in the design of lightweight block ciphers, respectively.
A Software Diversity-Based Lab in Operating System for Cyber Security Students

The course of operating system's labs usually fall behind the state of art technology. In this paper, we propose a Software Diversity-Assisted Defense (SDAD) lab based on software diversity, mainly targeting for students majoring in cyber security and computer science. This lab is consisted of multiple modules and covers most of the important concepts and principles in operating systems. Thus, the knowledge learned from the theoretical course will be deepened with the lab. For students majoring in cyber security, they can learn this new software diversity-based defense technology and understand how an exploit works from the attacker's side. The experiment is also quite stretchable, which can fit all level students.
An Empirical Study for the Deep Learning Models

Abstract Deep Learning (DL) models have tested to be very powerful in solving many hard problems. Especially, those are related to computer vision, text, speech, and classification. However, the blueprint of such models requires large space and elaboration that needs to be examined. Convolutional Neural Network (CNN) is the most popular neural network that can extract the features automatically as compared to conventional machine learning algorithms (CMLA). Our aim in this paper is to lessen the human attempt required to layout architectures by the use of a gadget architecture development process that allows the exploration of huge design space by automating sure version construction, alternative generation, and assessment. The main operations in CNN are Convolution, Pooling, Flattening, Full Connection between the input and output layer. The dataset taken as CIFAR 10 having 60,000 color images of 10 different classes is considered for the study where, various classes represent the images of cars, trucks, frogs, horses, trucks, cats, cars, airplanes, ships, and deer. It is expected that the performance of the CNN model can be further improved by using the deeper network architecture, or by an increasing number of epochs or data augmentation. In this paper, an attempt has been made to explain simple and deeper CNN models on the CIFAR 10 dataset and the comparison has been carried out to check the accuracy achieved from both the models.
Deep Reinforcement Learning for Continuous Docking Control of Autonomous Underwater Vehicles: A Benchmarking Study

Docking control of an autonomous underwater vehicle (AUV) is a task that is integral to achieving persistent long term autonomy. This work explores the application of state-of-the-art model-free deep reinforcement learning (DRL) approaches to the task of AUV docking in the continuous domain. We provide a detailed formulation of the reward function, utilized to successfully dock the AUV onto a fixed docking platform. A major contribution that distinguishes our work from the previous approaches is the usage of a physics simulator to define and simulate the underwater environment as well as the DeepLeng AUV. We propose a new reward function formulation for the docking task, incorporating several components, that outperforms previous reward formulations. We evaluate proximal policy optimization (PPO), twin delayed deep deterministic policy gradients (TD3) and soft actor-critic (SAC) in combination with our reward function. Our evaluation yielded results that conclusively show the TD3 agent to be most efficient and consistent in terms of docking the AUV, over multiple evaluation runs it achieved a 100% success rate and episode return of $10667.1\pm 688.8$. We also show how our reward function formulation improves over the state of the art.
A Novel Approach to Detect Concept Drift Using Machine Learning

Data concept drift is reported as one of the critical performance degradation phenomena in Machine Learning, especially for volumetric data. Besides, the concept drift annotation is also one of the major research problems in the said domain. In this paper, a novel approach for data concept drift detection is presented. Moreover, the performance after removing the instances with concept drift is also compared with the original dataset on various machine learning algorithms. Specifically, the concept using Euclidean distance in clusters and the mutual information of an instance refer to the degree of concept drift of the instance. The said approach has been employed on the SEA dataset
Multi-agent Deep Reinforcement Learning based on Maximum Entropy

Deep reinforcement learning at the same time combines the perception of deep learning and the decision-making of reinforcement learning, is currently a hot research topic in the field of artificial intelligence. Multi-agent deep reinforcement learning applies the idea and algorithm of deep reinforcement learning to the learning and control of multi-agent system, which is an important method to develop multi-agent system with swarm agent. Multi-agent deep deterministic policy gradient(MADDPG) is the most popular model-free multi-agent reinforcement learning algorithm. To solve the problem of low learning and training efficiency and slow convergence speed of MADDPG due to the deterministic single action output of policy network, this paper combines the maximum reinforcement learning soft actor -critic algorithm to make each agent’s policy network output action with a random strategy and propose a multi-agent deep reinforcement learning algorithm MASAC based on maximum entropy. The experimental results show that the training speed of MASAC is better than that of MADDPG. At the same time, the learning agent has good performance, stable performance and strong anti-interference ability.
Achieving Multi-Objective Scheduling of Heterogeneous Workflows in Cloud through a Genetic Programming Based Approach

Traditional human-designed heuristics-based algorithms are commonly employed to address workflow scheduling problems. Such as Heterogeneous Earliest Finish Time (HEFT) and CriticalPath are two best-known list based heuristics. Generally, heuristics-based approaches can generate a single heuristic by making decisions based on the current status of the tasks and available resources and map the unscheduled tasks to the available resources. However, traditional heuristics can easily cause unbalancing load problem. For example, with the objective of minimizing the makespan, traditional heuristics prefer to use computation resources with high computation capacity. Usually, such resources are expensive, which will lead to a high cost as a result. Therefore it is hard for single traditional heuristics to do a trade-off; thus, traditional heuristics are hard to apply for multi-objective workflow scheduling. In this paper, we develop a novel algorithm for workflow scheduling by considering both minimizing cost and makespan. Experiments show that the MOSGP approach can effectively minimize makespan and cost simultaneously.
How to design quantum-jump trajectories via distinct master equation representations

Every open-system dynamics can be associated to infinitely many stochastic pictures, called unravelings, which have proved to be extremely useful in several contexts, both from the conceptual and the practical point of view. Here, focusing on quantum-jump unravelings, we demonstrate that there exists inherent freedom in how to assign the terms of the underlying master equation to the deterministic and jump parts of the stochastic description, which leads to a number of qualitatively different unravelings. As relevant examples, we show that a fixed basis of post-jump states can be selected under some definite conditions, or that the deterministic evolution can be set by a chosen time-independent non-Hermitian Hamiltonian, even in the presence of external driving. Our approach relies on the definition of rate operators, whose positivity equips each unraveling with a continuous-measurement scheme and is related to a long known but so far not widely used property to classify quantum dynamics, known as dissipativity. Starting from formal mathematical concepts, our results allow us to get fundamental insights into open quantum system dynamics and to enrich their numerical simulations.
Research on SDN-based Dynamic Management of Energy Consumption of Cloud Data Center

With the rapid growth of the number and scale of cloud data centers, their high energy consumption and low energy efficiency have become a social problem that has a significant impact on technology, economy and environment. This paper studies the energy consumption components of IT equipment and main supporting infrastructure, and summarizes the current situation and difficulties of energy consumption management. An SDN-based dynamic management method for energy consumption is proposed on this basis. Four equipment layers in the data center are centralized control by building a programmable unified management platform. It centrally controls the four equipment levels in the data center, monitors the status of the equipment in real time, and intelligently manages the energy consumption of IT equipment and main supporting infrastructure through a global optimization strategy, and ultimately reduces the energy consumption of the cloud data center.
Dispersive estimates for quantum walks on 1D lattice

We consider quantum walks with position dependent coin on 1D lattice $\mathbb{Z}$. The dispersive estimate $\| U^{t} P_{c} u_0\|_{l^{\infty}} \lesssim (1+|t|)^{-1/3} \|u_0\|_{l^1}$ is shown under $l^{1,1}$ perturbation for the generic case and $l^{1,2}$ perturbation for the exceptional case, where $U$ is the evolution operator of a quantum walk and $P_{c}$ is the projection to the continuous spectrum. This is an analogous result for Schrödinger operators and discrete Schrödinger operators. The proof is based on the estimate of oscillatory integrals expressed by Jost solutions.
Data Analytics in Solar Photovoltaics Power Forecasting for Smart Grid Applications

Solar energy is an endless energy, but it is variable and intermittent based on many meteorological parameters. Due to the significant growth in the penetration of Solar Photovoltaics (PV) into the smart grid, the accurate forecasting of Global Horizontal Irradiance (GHI) and PV power in time and space is very important. This paper discusses the importance of solar energy prediction and the data analysis of solar PV generation using Python. Real plant data is analyzed for 500 kW solar system installed in Xavier University Bhubaneswar (XUB), Odisha, India.
A Two-Step Classification Method Based on Collaborative Representation for Positive and Unlabeled Learning

Positive and Unlabeled learning (PU learning) has drawn plenty of attention among researchers over the last few years, where only labeled positive examples and unlabeled examples are available for training a classifier. Many classic techniques for solving PU learning problems belong to the category of “two-step strategy”. However, quite a number of them cannot extract reliable negative examples accurately and often lead to unsatisfactory classification results. In this paper, we propose a two-step learning scheme based on the collaborative representation (CR) for PU learning. In the first step, to handle the deficiency of negative training data, collaborative representation (CR) technique is utilized to identify reliable negative examples from unlabeled training examples. Subsequently, collaborative representation based classification (CRC) framework with $${l}_{2}$$ -norm regularization term is applied to perform PU classification. Extensive experiments on both benchmark and real-world datasets were conducted to verify the effectiveness of the proposed method, and the results demonstrate that the two-step CR-based approaches can achieve competitive classification accuracy when compared with both traditional and state-of-the-art techniques in dealing with different PU learning issues.
K-Medoids Clustering and Fuzzy Sets for Isolation Forest

Capturing anomalies in data is one of the most important problems in modern data analysis. In recent years, scientists have developed many interesting approaches. One of the leading is the Isolation Forest method, which is based on searching a forest of binary trees. This method is extremely effective, especially in the case of relatively small databases. Despite of that, a lot of work has been done for years to improve it. For instance, variants based on rotation or fuzzy sets were developed. In this paper, we propose a very effective method of building search trees based on grouping data using the K-Medoids method. The results of the conducted experiments suggest a significant improvement in the quality of the method in relation to the original Isolation Forest.
The effect of degradation on compressibility of video

The technology climate for video streaming has vastly changed during 2020. Since the pandemic, video traffic over the internet has increased dramatically. This has clearly put increased interest in the bitrate/quality tradeoff for video compression for applications in video streaming and real time video communications. As far as we know, the impact of different artefacts on that tradeoff has not previously been systematically evaluated. In this paper we propose a methodology for measuring the impact of various degradations (noise, grain, flicker, shake) in a video compression pipeline. We show that noise/grain has the largest impact on codec performance, but that the modern codecs are more robust to the artefact. In addition, we report on the impact of a denoising module deployed as a pre-processor and show that performance metrics change in the context of the pipeline. Denoising would benefit from being treated as part of the processing pipeline both in development and testing.
Binocular Stereo Vision Intelligent Control Framework for the Landing Position of Quadrotor UAV Based on Fuzzy Control

Binocular stereo vision intelligent control common framework for the landing position of quadrotor UAV based on fuzzy control is studied in this paper. The simulation system can truly reflect the changes in the position parameters of the UAV during the flight, and has the good user display and control interface. With the consideration of the vision model, the accuracy landing function is achieved. During autonomous landing, the drone always flies in the fixed altitude mode to maintain its own altitude stability. The altitude controller adopts the cascade PID control form of altitude with speed. Based on the experiment regarding the landing accuracy, it is reflected that the designed system can ensure the UAV intelligent control.
Silver-gold and polymetallic mineralization in the banded iron formations deposit in Kryvyi Rih, Ukraine

Since the beginning of iron mining in Kryvyi Rih in 1881, gold was a target of interest. Exploration for gold began in 1936. As a result, many gold occurrences were discovered. In this paper, regional geology of the gold mineralization and the iron deposit details are also described for better understanding of the position of hydrothermal mineral association. Gold - silver and polymetallic mineralization was found in the iron ore horizon in the Ingulets operating iron open pit. A new association of gold and Sb-bearing sulfosalts is described. Gold occurs in an association with arsenopyrite, pyrite, and pyrrhotite in quartz-arsenopyrite veins type mineralization discovered in the Ingulets quarry. Gold was found as minute inclusions in arsenopyrite and as inclusions in Ag-Sb-, Pb-Sb-, Ag-Pb-Sb- sulfosalts. Based on both ore microscope and EMP measurements, the following minerals have been identified: arsenopyrite, pyrite, pyrrhotite, tetrahedrite, freibergite, sphalerite, chalcopyrite, galena, stibnite, plagonite, zinkenite, fuloppite, miargyrite, chalcostibite, andorite, and native Sb and electrum. It is the first discovery of Ag-Sb-Cu-Pb mineral association in the whole Kryvyi Rich structure. In this paper quantitative EMP measurements of all new minerals of the newly discovered association are presented. The baro-acoustic decrepitation analyses of the massive arsenopyrite and pyrite showed temperature formation ranging between 450 °C and 650 °C with an average of T ~ 585 °C. Based on mineralogical investigations, ore occurrences are considered as the product of high and middle temperature hydrothermal processes.
Twitter Sentimental Analysis based on Ordinal Regression

Abstract For associations and people with a profound social, political, or monetary Sinterest in keeping up and fortifying their clout and notoriety, Twitter has become a goldmine. Sentiment analysis is the way toward characterizing and classifying the considerations and sentiments communicated in a source record. By performing this assessment investigation in a meticulous space, it is feasible to decide the force of area data on notion order. For feeling examination order, the proposed system utilizes the calculations Support Vector Regression (SVR), Decision Trees (DTs), and Random Forest (RF). The real execution of this structure depends on a twitter dataset unveiled by the NLTK corpora devices. The proposed approach will precisely identify ordinal relapse utilizing AI procedures.
Editing like Humans: A Contextual, Multimodal Framework for Automated Video Editing

We propose an automated video editing model, which we term contextual and multimodal video editing (CMVE). The model leverages visual and textual metadata describing videos, integrating essential information from both modalities, and uses a learned editing style from a single example video to coherently combine clips. The editing model is useful for tasks such as generating news clip montages and highlight reels given a text query that describes the video storyline. The model exploits the perceptual similarity between video frames, objects in videos and text descriptions to emulate coherent video editing. Amazon Mechanical Turk participants made judgements comparing CMVE to expert human editing. Experimental results showed no significant difference in the CMVE vs human edited video in terms of matching the text query and the level of interest each generates, suggesting CMVE is able to effectively integrate semantic information across visual and textual modalities and create perceptually coherent quality videos typical of human video editors. We publicly release an online demonstration of our method.
A Framework for Supporting Repetition and Evaluation in the Process of Cloud-Based DBMS Performance Benchmarking

Performance benchmarking of Database Management Systems (DBMS) is an important yet complicated process. We motivate and present two supporting Python packages which help to avoid common pitfalls and in particular improve reproducibility and transparency in heterogeneous systems with hardware accelerators. The first addresses operational aspects by providing dynamic testbeds using Docker images, especially for cloud-based systems. The second helps planning and recurrently running experiments in a predefined setup via JDBC/SQL, and analyzing results with automated reports and an interactive dashboard. The purpose of this is to thoroughly evaluate aspects of performances of DBMS based on real-life measurements, runtime and hardware metrics, depending on various parameters including the hardware, and with high repeatability. We present a series of TPC-H inspired example benchmarks in a Kubernetes cluster for demonstration, and some lessons learned.
A Real-Time Position Monitoring System For Fall Detection and Analysis Using Human Pose Estimation

It has been observed that there was an increase in the afflictions and catastrophes faced by elderly individuals. Concussion and hemorrhage are often a result of a fall and this could prove to be fatal. The normal changes of aging like vision impairment, hearing difficulties, illnesses and physical conditions affect the strength and balance of elderly individuals. Difficult lighting conditions or throw rugs at home can cause elders to trip or slip resulting in a fall. In order to provide the victim of the fall with immediate aid, it is crucial to develop a system to detect falls in real-time. The system proposed in the paper aims to use various object detection algorithms, apply human pose estimation and use various fall detection algorithms on real-time surveillance footage to detect the occurrence of a fall. In addition to the above point, the paper's system proposes to detect the cause of the fall with the help of a wearable device. In case of an emergency, the next of kin gets notified about the occurrence of the fall along with the pulse and BP (Blood Pressure) rate. This helps in not only detecting if a fall has occurred but also helps to determine the cause of the fall.