Reviewerly/exHarmony huggingface dataset:
An Effective Concept Drift Detection Technique with Kernel Extreme Learning Machine for Email Spam Filtering

The increase in the number of undesirable emails named spam has posed a major requirement to develop a highly dependent and robust antispam filters. This paper presents a novel email spam filtering technique with the capability of adapting with the dynamic environment. Concept drift detector attempts to determine the position of the concept drift in large data stream for replacing the baseline learner next to the modifications in the data distribution and therefore enhances accuracy. The proposed method detects the concept drift depending upon the computation of variation in the email content distribution using Statistical Test of Equal Proportions (STEPD) technique. The STEPD is a simpler commonly available model that identifies the concept drift with respect to a hypothesis test among two proportions. The SPEPD technique is used to determine the criteria of the concept drift for all unknown emails that assist the filtering technique in the recognition of the occurrence of the spam. In addition, the kernel extreme learning machine (KELM) based classification model is applied to classify the instances into two class labels namely spam and non-spam correspondingly. The experimental results of the STEPD-KELM model are tested against Enron dataset and the results are examined interms of distinct aspects. The experimental values indicated that the STEPD-KELM model has resulted to a maximum precision of 93.78%, recall of 96.54%, and accuracy of 95.33%.
Monitoring the Performance of a Multi-UAV Operator Through Eye Tracking

Monitoring the performance and workload of a multi-UAV operator is a challenge because of the complexity of the multi-UAV task and the difficulty to acquire the cognitive state of the operator. This paper proposes a novel approach to detect the operator's mistakes, which can be caused by abnormal condition, fatigue or overload. First, the behaviors of saccade and gaze are classified using support vector machines (SVMs). Then, four kinds of complex eye movement patterns are proposed according to the multi-UAV collaboration task under human supervision, i.e. content reading, aimless scanning, content searching and comparative observation. Furthermore, features such as saccade time ratio, special action state analysis and pupil closure time ratio are used to evaluate the levels of cognitive burden and fatigue or to detect abnormal states caused by the tunnel effect. Finally, the effectiveness of the proposed method has been verified in a multi-UAV surveillance task.
A Novel Edge-pattern-based Just Noticeable Difference Model for Screen Content Images

Just noticeable difference (JND) reflecting visual redundancy of human visual system (HVS) has been widely adopted in image/video processing. In this work, we introduce a novel JND model in pixel domain for screen content (SC) images. Due to the obvious different impacts on HVS between pictorial region and computer-generated texture, we decompose a SC image into screen content set (SCS) and non-screen content set (non-SCS) for JND modeling. For SCS, edge masking effect is mainly considered by using a parametric model with an adaptive edge representation. For non-SCS, both the edge masking effect and pattern masking effect are taken into account to improve distortion tolerance by injecting more noise to pattern complexity region without perceptual quality degradation. Furthermore, visual saliency is employed to adjust pattern masking effect in the view of HVS. Experimental results show that compared with state-of-the-arts, the proposed JND model can tolerate more distortion, and provide better perceptual quality at the same noise level.
Reliable abnormal event detection from IoT surveillance systems

Surveillance systems are widely used in airports, streets, banks, military areas, borders, hospitals, and schools. There are two types of surveillance systems which are real-time systems and offline surveillance systems. Usually, security people track videos on time in monitoring rooms to find out abnormal human activities. Real-time human tracking from videos is very expensive especially in airports, borders, and streets due to the huge number of surveillance cameras. There are a lot of research works have been done for automated surveillance systems. In this paper, we presented a new surveillance system to recognize human activities from several cameras using machine learning algorithms. Sequences of images are collected from cameras using the internet of things technology from indoor or outdoor areas. A feature vector is created for each recognized moving object, then machine learning algorithms are applied to extract moving object activities. The proposed abnormal event detection system gives very promising results which are more than 96% accuracy in Multilayer Perceptron, Iterative Classifier Optimizer, and Random Forest algorithms.
Advancing Flood Disaster Mitigation in Indonesia Using Machine Learning Methods

The fourth industrial revolution essential components which include cloud computing technology, artificial intelligence, big data, and the internet of things has also been affecting the flood disaster mitigation strategy worldwide. This is also true for Indonesia where the flood disaster event has increased almost three times during the past fifteen years. In this literature review, the advancement of the application of artificial intelligence, in particular, machine learning in flood mitigation in Indonesia is studied. Based on this study, the future possible improvement of flood mitigation has also been given. The study revealed that machine learning has not yet been applied extensively in flood disaster mitigation in Indonesia. Some applications are for rainfall, river water level, and discharge level predictions, and to a lesser degree for flood forecasting and early warning. The future prospective advancement of flood disaster mitigation is the application of machine learning methods in an integrated flood prediction and early warning that covers rainfall, river water level, river discharge, and flood predictions as well as estimated spatial flooding area, and early warning dissemination using the internet of things.
Blockchain and Artificial Intelligence Managing a Secure and Sustainable Supply Chain

Supply chain management is often the most challenging part of any business that manufactures, sells goods, or provides services nowadays. Regardless of whether the operations are mostly physical or online, managing supply chains relies entirely on being able to manage shared information securely, efficiently and effectively. Managing the information within the context of a closely-knit supply chain offers the benefits of extra resilience and ability to recover quickly from major disturbances. The authors propose here the development of a blockchain enabled and Intelligent Agent supported supply chain community that will provide a secure, intelligent, responsive and sustainable operational partnership.
A Scheme for Controlled Cyclic Asymmetric Remote State Preparation in Noisy Environment

As research on quantum computers and quantum information transmission deepens, the multi-particle and multi-mode quantum information transmission has been attracting increasing attention. For scenarios where multi-parties transmit sequentially increasing qubits, we put forward a novel (N + 1)-party cyclic remote state preparation (RSP) protocol among an arbitrary number of players and a controller. Specifically, we employ a four-party scheme in the case of a cyclic asymmetric remote state preparation scheme and demonstrate the feasibility of the scheme on the IBM Quantum Experience platform. Furthermore, we present a general quantum channel expression under different circulation directions based on the n-party. In addition, considering the impact of the actual environment in the scheme, we discuss the feasibility of the scheme affected by different noises.
Exploring the State-of-the-Art of Persuasive Design for Smart Personal Assistants

Driven by technological advances, smart personal assistants (SPA) have gained importance in human–computer interaction. SPA can influence user behavior and persuade users to reach a specific outcome. However, users often lack the motivation to interact with SPA. One way to support this interaction is persuasive system design – considering concepts as gamification and nudging. Although SPA research has increased recently, there is still no shared knowledge about persuasive designs. Therefore, we aim to identify the current state-of-the-art Design for persuasive SPA to understand how interactions and designs can be improved. Thus, we conduct a systematic literature analysis to represent how gamification and digital nudging are used to design SPA and conclude if and how those concepts can support SPA interactions. Consequently, we contribute to theory, providing better understanding about SPA interaction and design to make SPA more engaging and entertaining. Practitioners can use this contribution for persuasive SPA designs.
Dijkstra and Bidirectional Dijkstra on Determining Evacuation Routes

Abstract Determination of the best path or often called the shortest path finding is a method that has many benefits and can be applied in various cases and fields of science. In previous studies, authors have developed the shortest path finding program using various algorithms, such as held-karp, iterative deepening search, bidirectional depth first search and depth limited search. In this research author implements shortest path finding on information system for determining fire disaster evacuation route using Dijkstra and bidirectional Dijkstra. The program was developed based on web using vuetify.js framework. Both algorithms were tested on several datasets and the results will be compared to find out the strengths and weaknesses. Based on the research, the algorithm was successfully developed based on the web using the vuetify.js framework and successfully tested on several datasets, it is known that 40% of the test results show that bidirectional Dijkstra is better than Dijkstra, 10% vice versa and 50% get the same results from both algorithms. From this comparison it can be concluded that the bidirectional Dijkstra algorithm is better than Dijkstra. This research succeeded in proving that the bidirectional Dijkstra algorithm can produce a better route than the standard or classic Dijkstra algorithm.
Online education and monitoring of quality indicators of e-learning use

Abstract This article analyses the results of the proposed approach to quality assessment of the use of online courses in the system of Ukrainian Engineering Pedagogics Academy e-learning and to assess its effectiveness in solving the problem of improving the quality of education. The courses were inspected after the Spring 2020 semester, according to the following criteria: activity of the process participants; interactivity of the process when using an online course; the intensity of the process by the elements of a course; dynamics of students’ educational achievements and management of the process of using an online course. This article shows that without painstaking daily systematic work regarding supporting the use of each online course, one should not hope for the effective impact of this form of studying. This should be done in every educational institution because the COVID-19 pandemic is not over and a significant part of educational institutions will spend the 2020/2021 academic year under the strict quarantine.
Decentralized Deterministic Multi-Agent Reinforcement Learning

We provide a provably-convergent decentralized actor-critic algorithm for learning deterministic policies on continuous action spaces. Deterministic policies are important in real-world settings. To handle the lack of exploration inherent in deterministic policies, we consider both off-policy and on-policy settings. We give the expression of a local deterministic policy gradient, decentralized deterministic actor-critic algorithms and convergence guarantees for linearly-approximated value functions. This work will help enable decentralized MARL in high-dimensional action spaces and pave the way for more widespread use of MARL.
ALBFL: A Novel Neural Ranking Model for Software Fault Localization via Combining Static and Dynamic Features

Automatic fault localization plays a significant role in assisting developers to fix software bugs efficiently. Although existing approaches, e.g., static methods and dynamic ones, have greatly alleviated this problem by analyzing static features in source code and diagnosing dynamic behaviors in software running state respectively, the fault localization accuracy still does not meet user requirements. To improve the fault locating ability with statement granularity, this paper proposes ALBFL, a novel neural ranking model that involves the attention mechanism and the LambdaRank model, which can integrate the static and dynamic features and achieve very high accuracy for identifying software faults. ALBFL first introduces a transformer encoder to learn the semantic features from software source code. Also, it leverages other static statistical features and dynamic features, i.e., eleven Spectrum-Based Fault Localization (SBFL) features, three mutation features, to evaluate software together. Specially, the two types of features are integrated through a self-attention layer, and fed into the LambdaRank model so as to rank a list of possible fault statements. Finally, thorough experiments are conducted on 5 open-source projects with 357 faulty programs in Defects4J. The results show that ALBFL outperforms 11 traditional SBFL methods (by three times) and 2 state-of-the-art approaches (by 13%) on ranking faulty statements in the first position.
KylinX

Unikernel specializes a minimalistic LibOS and a target application into a standalone single-purpose virtual machine (VM) running on a hypervisor, which is referred to as (virtual) appliance . Compared to traditional VMs, Unikernel appliances have smaller memory footprint and lower overhead while guaranteeing the same level of isolation. On the downside, Unikernel strips off the process abstraction from its monolithic appliance and thus sacrifices flexibility, efficiency, and applicability. In this article, we examine whether there is a balance embracing the best of both Unikernel appliances (strong isolation) and processes (high flexibility/efficiency). We present KylinX, a dynamic library operating system for simplified and efficient cloud virtualization by providing the pVM (process-like VM) abstraction. A pVM takes the hypervisor as an OS and the Unikernel appliance as a process allowing both page-level and library-level dynamic mapping. At the page level, KylinX supports pVM fork plus a set of API for inter-pVM communication (IpC, which is compatible with conventional UNIX IPC). At the library level, KylinX supports shared libraries to be linked to a Unikernel appliance at runtime. KylinX enforces mapping restrictions against potential threats. We implement a prototype of KylinX by modifying MiniOS and Xen tools. Extensive experimental results show that KylinX achieves similar performance both in micro benchmarks (fork, IpC, library update, etc.) and in applications (Redis, web server, and DNS server) compared to conventional processes, while retaining the strong isolation benefit of VMs/Unikernels.
Dothraki: Tracking Tangibles Atop Tabletops Through De-Bruijn Tori

Tangibles are small, graspable objects that act as input devices or physical representations of digital data. Oftentimes, it is desirable to track the position of tangibles on a surface and their relation to each other. However, outside-in tracking techniques - such as capacitive touchscreens or cameras - require setting up elaborate infrastructure and are prone to occlusion or interference. We propose Dothraki, an inside-out tracking technique for tangibles on flat surfaces. An optical mouse sensor embedded in the tangible captures a small (36×36 pixel / 1×1 mm), unique section of a black-and-white De-Bruijn dot pattern printed on the surface. Our system efficiently searches the pattern space in order to determine the precise location of the tangible with sub-millimeter accuracy. Our proof-of-concept implementation offers a recognition rate of up to 95%, robust error detection, an update rate of 14 Hz, and a low-latency relative tracking mode.
An Experimental Performance Analysis on Robotics Process Automation (RPA) With Open Source OCR Engines: Microsoft Ocr And Google Tesseract OCR

Abstract Robotic Process Automation (RPA) plays an important role in the solution for automating human digital interaction in various software domains like IT, Business Strategy and so on. Optical Character Recognition(OCR) superimposes subtitled characters on an image.Here we use two Open source OCR engines, Google Tesseract OCR - It literally makes use of the open source Tesseract OCR Engine[1]. Microsoft OCR - This is another open source OCR engine accessible in the Robotics Process Automation tool, UiPath[1]. The technique of optical character recognition (OCR) has been used to transform printed text into editable text. In various applications, OCR is a very useful and common technique. OCR precision can be dependent on algorithms for text preprocessing and segmentation. Often because of different scale, design, orientation, complex picture context, etc., it is difficult to retrieve text from the image. We begin this paper with an introduction to the Optical Character Recognition (OCR) process, in this paper, we propose a research analysis to estimate the accuracy of two open source OCR engines with Robotics Process Automation(RPA). These RPA processes are created using the UiPath tool and our results compare the OCR engines performance during the execution with the workflow and save the results to our local storage or on cloud storage if cloud support enabled and here we used local storage for purpose, and the result analysis and error analysis was evaluated by manual process which also included excel formulas to calculate the accuracy of the extracted string with the original string.
Action Units: Exploring the Use of Directorial Cues for Effective Storytelling with Swivel-chair Virtual Reality

The popularity of 360-degree video storytelling has been increasing. However, effectively guiding viewer’s attention remains challenging. In this paper, we propose the notion of Action Units (AU) as a guidance method. It aims to improve the user experience of 360-degree video for seated viewers with swivel chairs, as we call “Swivel-chair VR”. We first conducted a pilot study with four subject-matter experts to verify the practicality of AU. We then conducted a formal user study to compare the AU with two commonly used attention guidance techniques, namely “Pointing Arrow” (PA) and “Angular Shift” (AS), as well as the baseline without any guidance (BK). We applied them to a virtual tour and measured their effects on engagement, enjoyment, memory, viewers behaviors, and cybersickness. The results indicated that AU is an effective guidance as it increased the levels of engagement and enjoyment, reduced the level of cybersickness and helped users focus on target faster. Users also preferred AU for its diegetic aspects.
Non-Invertible Public Key Certificates

Post-quantum public cryptosystems introduced so far do not define a scalable public key infrastructure for the quantum era. We demonstrate here a public certification system based on Lizama’s non-invertible key exchange protocol which can be used to implement a secure, scalable, interoperable and efficient public key infrastructure (PKI). We show functionality of certificates across different certification domains. Finally, we discuss a method that enables non-invertible certificates to exhibit perfect forward secrecy (PFS).
RETRACTED: Research on Innovation and Entrepreneurship of Business Education in Colleges and Universities Based on Computer Big Data Mining Technology

Abstract Researchs on innovation and entrepreneurship education in society are very popular, because the number of students are studying business management in Chinese universities according to statistics from the Ministry of Education. The author found out the problems on it, and elaborated main influence factors to development domestic business education to improve the ability of business administration graduates to meet the needs of the market through the computer technology. The strategies to solve the complicated problem are considered in the paper.
Nested Attention U-Net: A Splicing Detection Method for Satellite Images

Satellite imagery is becoming increasingly available due to a large number of commercial satellite companies. Many fields use satellite images, including meteorology, forestry, natural disaster analysis, and agriculture. These images can be changed or tampered with image manipulation tools causing issues in applications using these images. Manipulation detection techniques designed for images captured by “consumer cameras” tend to fail when used on satellite images. In this paper we propose a supervised method, known as Nested Attention U-Net, to detect spliced areas in the satellite images. We introduce three datasets of manipulated satellite images that contain objects generated by a generative adversarial network (GAN). We test our approach and compare it to existing supervised splicing detection and segmentation techniques and show that our proposed approach performs well in detection and localization.
Simultaneous Estimation of Vehicle Position and Data Delays using Gaussian Process based Moving Horizon Estimation

Automobiles or robots with advanced autonomous systems are equipped with multiple types of sensors to overcome different weather and geographical conditions. These sensors generally have various data delays and sampling rates. Additionally, the communication delays or time synchronization errors between the onboard computers significantly affect the robustness and accuracy of localization for autonomous vehicles. In this paper, the simultaneous estimation of vehicle position and sensor delays using a Gaussian process based moving horizon estimation (GP-MHE) is presented. The GP-MHE can estimate the unknown delays of multiple sensors with the resolution less than that of GP-MHE sampling rate. The localization performance of GP-MHE was confirmed using full-vehicle simulator, then evaluated in a real vehicle experiment on a highway scenario. Experimental result verified the sufficient localization accuracy of sub 0.3m using data that had irregular sampling rate and delay of more than 150ms. The proposed algorithm extends the capability of integrating various data with large unknown delays for vehicles, robots, drones and remote autonomy.
Certifying Findel derivatives for blockchain

Abstract Findel is a composable DSL for financial derivatives that can be executed on the blockchain. The language is simple and expressive enough to express complex derivatives. However, compared to its competitors, Findel does not guarantee that contracts progress properly. There is no default refund mechanism, and the economic enforcement is not handled by the language itself. Given the immutability of the blockchain, more caution should be taken in order to avoid undesired situations. In this paper, we address the formal verification of financial derivatives written in Findel. We define a formal semantics of Findel in Coq and we test it against the existing Findel test suite. We enrich our semantics with means to interactively formalise and prove properties about Findel contracts. The goal is to avoid mistakes in Findel contracts that may have a negative impact (e.g., stuck contract executions, money losses). The derived infrastructure is used to certify non-trivial contract examples that cover the most common types of derivatives (forwards/futures, swaps, options).