Reviewerly/exHarmony huggingface dataset:
An Analysis of Unsupervised Active Data Drift Techniques

Concept drift occurs when the data used for developing a model in a big data analytics pipeline becomes unrepresentative because of an evolution of the underlying data distribution. Concept drift can cause a dramatic degradation in machine learning models' performance over time, therefore it must be rapidly detected so that adequate actions can be taken. There are several approaches to detect concept drift. Most of them are supervised and assume that the true values of the predicted variable is always available---an assumption rarely fulfilled in practice. Accordingly, there has been in increasing interest in unsupervised methods that can work without such a strong assumption. In this paper, we selected four of the most widely used unsupervised drift detection methods namely, Hellinger Distance Drift Detection Method (HDDDM), Dynamic Adapting Window Independence Drift Detection (DAWIDD), Discriminative Drift Detector (D3), and the Student-Teacher approach (ST). We designed an evaluation framework and performed a series of experiments to compare their performance using real-world data. Our results suggest that HDDDM may provide the best trade-off as it has a coherent performance over all the datasets used in the testbed and it is the most widely applicable.
C++ Implementation of Intelligent Information Platform (IIP) for Tax Planning of SMEs under the New Accounting Standards of Abnormal Data Mining

C++ implementation of the intelligent information platform for tax planning of SMEs under the new accounting standards of abnormal data mining is studied in the paper. No matter what architecture the program is based on, what hardware platform it runs on, or what compiler it uses, there are some general optimization principles and methods, which are valid for all real-time embedded system c/c++ codes. Hence, an enhanced structure is used to design the proposed novel C++ framework for the analysis. Loop optimization is often the focus of the performance optimization. On the one hand, loops with a large number of loops are often very time-consuming; on the other hand, loops have room for some optimization and optimization methods. Furthermore, the intelligent information platform (IIP) for tax planning of SMEs is tested.
The Luenberger sliding innovation filter for linear systems

In this paper, the newly developed sliding innovation filter (SIF) is reformulated to accommodate the ability of extracting the hidden states. This is accomplished by using the well-known Luenberger technique, which is commonly used by observers. In this paper, the SIF is applied to a linear system, which has fewer measurements than states. The results show that the proposed filter extracts the hidden state with small RMSE, as low as 0.1, and small MAE, as low as 1.
Decentralized Blockchain Based IoT System to Enhance Security in Smart Homes

Smart houses are more about being environmentally conscious and offering security. Smart home technologies can make homes more energy-efficient and allow users to save money. This concept of making the life of the common people easier has increased the demand for smart homes. However, the rising demand also calls for security concerns. Many approaches have been taken to enhance IoT installation security, but most of the issues concerned with its protection are still left to deal with. On the other hand, the rise of the popularity of Blockchain, because of the security of data it ensures, has made it a potential component to pair up with IoT. Along with the many benefits of Blockchain integration, it also ensures the decentralization of data. This paper aims to discuss the need for Blockchain-based IoT and its limitations. Further, along with the paper, we consider the past implementations. To support our work, we proposed a model that enhances security in smart homes using blockchain by verifying the incoming requests and giving access to only authorized users.
Automated Training and Deployment of Machine-Learning Models for Anomaly Detection in Telemetry

The past decade has seen scores of academic studies investigating the use of machine learning to automatically find anomalies in spacecraft telemetry streams. Despite the tremendous progress in this area, use of machine-learning at actual ground stations is not widespread. Much of this lack of adoption is due to the fact that training machine-learning models requires subject-matter expertise. Moreover, once the models have been trained, there exists no accepted and straightforward protocol for (a) integrating these models into existing workflows at ground stations and (b) ensuring that spacecraft operators are able to configure and deploy those models with ease. In an attempt to help ameliorate some of these issues, this paper introduces the AMBASSADOR framework and analysis tool. AMBASSADOR allows ground-operations personnel to train semi-supervised novelty-detection algorithms without the help of machine-learning experts. An arbitrary number of models may be trained at the same time and each of those models can be dependent on an arbitrary number of telemetry streams. Once the models have been trained, the AMBASSADOR framework allows them to be used for inference - either on spans of stored telemetry or on live telemetry as it arrives in real time. In order to ensure scalable deployment and ease-of-use, the AMBASSADOR framework has been implemented within a microservices architecture using a containerized Python API. This API is capable for retrieving data from actual telemetry archives such as COSMOS DART. User interaction with the API is facilitated via a modern, intuitive, web-based frontend. Taken together, this means that multiple users can simultaneously train custom anomaly-detection models and use them to analyze actual telemetry streams without ever having to interact with the underlying machine-learning code.
Cognitive Load Measurement in the Impact of VR Intervention in Learning

The rapid development of VR technology in training and learning is based on the assumption that it is beneficial for skill training within an immersive environment. However, extra cognitive load may be induced due to the additional sensory information and hence learning ability might be affected. In this study, we examined and compared the impact of cognitive load and task performance in real-world and VR environments through an empirical quadrant model. Forty-six participants completed the tasks with and without the secondary task in realworld and VR environments. The detection response task (DRT), as the secondary task, was adopted to estimate cognitive load based on response time and omission rate. No statistically significant differences were found in cognitive load and task performance in the comparison of VR and non-VR environment settings. There was an encouraging trend observed that VR environments have some advantages over the real-world, such as a higher level of immersion, which suggests that VR can benefit trainees with improved concentration levels and task performance. As evidenced by the variation in performance between females and males in our study, it appears that females tend to perform less well in VR environments, with a slightly higher cognitive load.
A Quantitative Assessment of the Detection Performance of Web Vulnerability Scanners

Software developers use web application vulnerability scanners to automatically identify security weaknesses in their web applications. The scanners inspect source code or analyze the running application, and look for specific vulnerability types. While it can be expected that a scanner will not discover every vulnerability, no information is available on the expected efficacy of currently available vulnerability scanners for a given vulnerability type. We present an analysis of 24 web vulnerability scanners and determine their effectiveness on 11 vulnerability types. Our study offers insights into the trade-offs when selecting a specific type of scanner. We show that for some vulnerability types, most vulnerability scanners perform poorly.
Efficient Error-correcting Output Codes for Adversarial Learning Robustness

Despite their many successful applications, Deep Neural Networks (DNNs) are vulnerable to intentionally designed adversarial examples. Adversarial robustness describes the ability of a machine learning model, e.g., a neural network, to defend against such adversarial attacks. In coding theory, codebooks are designed to minimize the impact of errors occurring with transmission through a noisy channel. Motivated by the similarities between passing a codeword through a noisy channel and defending against adversarial attacks, Error-Correcting Output Codes (ECOCs) are used to achieve state-of-the-art adversarial robustness. Research on codebook designs and the association of codewords to classification labels (assignment) is still at the very early stages, with great room for improvement. In this work, we present novel codebook design and assignment procedures in two stages due to the complexity (NP-hardness) of the underlying problem. A rule-based heuristic codebook design method is proposed in the first stage and an optimization problem to assign the codewords to labels is proposed in the second stage. Since this optimization is NP-hard, a greedy algorithm is proposed to provide a sub-optimal solution. We demonstrate the effectiveness of our framework on three benchmark datasets, under different types of adversarial attacks. The experimental results show that our error-correcting output code framework can effectively improve the adversarial robustness of machine learning models, with up to a 10% increase in accuracy.
Access Control and Data Security of IoT Applications Using Blockchain Technology

Almost all data collected today is in digital form and is stored in various places ranging from local databases to the cloud-based storage. However, all this data is useful only when collected and aggregated for further processing and analysis. As such, data sharing among various sources has now become very important. Openly sharing data tends to violate confidentiality and privacy of users. Due to this, sharing of data employs a number of access control methods to ensure that only the authorized and authenticated users are getting access. Most access control methods however, are controlled by a single centralized entity, responsible for writing and enforcing the access policies. Such a system faces the issues as any centralized system includes single point of failure and malicious behavior. With the rising presence of a lot of sensitive and personal-related data such as in healthcare and the Internet of Things (IoT), it has become important to enable fine-grained access control that is in the hands of the owners of the data rather than centralized personnel. It has also become important to ensure that the system is fault-tolerant and secure from attacks as a single breach of data could cause a great loss of sensitive data. Along with this, it is necessary for systems to be able to record access trails for higher security purposes. Blockchain is a public distributed ledger technology that has proven to be tamper-proof and does not require any trusted third party for its usage. With the development of smart contracts on the blockchain, enabling the automatic execution of turing-complete code on the the blockchain network, it has grown to a wide number of applications with one of them being to provide secure and privacy-preserving access control methods. This paper provides a survey of the various access control methods that have been developed in the recent years using blockchain. A study considering the usage of blockchain, smart contracts, methods proposed, and improvised as well as limitations if any of the various papers has been performed and depicted in tabular and descriptive manner. This paper aims to guide readers to gain an in-depth understanding of current research in this field as well as gain insights on future prospects.
Comprehensive Feature Extraction for Cross-Project Software Defect Prediction

Quality of software is determined by analyzing the source code metrics and defects. Prediction of pre-and post-release defects and fixing them when they are raised will improve overall quality of software systems. The main aim of this work is to reduce the cost incurred in identifying defects which is a challenging task. Over a decade, the academia and industry addressed the problem of software defect prediction using machine learning. This work emphasizes on the selection of static code metrics and process metrics for defect prediction. Metrics are mined from different open source projects hosted on GitHub. For our experiments, we have chosen projects with at least 10k commits. This paper is establishing a hypothesis that, as the number of commits increase, the bugs are also likely to increase, as our experiments indicate. The software metrics are carefully chosen to identify defects across software projects to help the development and testing teams.
Implementation of Interactive Tutorial for IslayTab by Restricting and Highlighting the DOM Node to Tap

We present how to retrofit an interactive tutorial program onto an already completed web application program by taking the example of IslayTab, a graphical programming environment for children, which can be manipulated only by tapping on a tablet device. The tutorial program prompts the user to tap a single specific DOM node of the original application by highlighting it in front of an additional DOM node in the form of a translucent gray sheet that shades the other DOM nodes. Any user events on wrong parts are caught by the gray sheet and cause repeated prompting. When the user taps the expected DOM node, the process originally assigned to this DOM node is naturally activated. The tutorial program does not have to take any care of processes in the original application and only has to check if the user event took place at the expected DOM node in order to proceed to the next step of the tutorial scenario.
An Implementation of Montgomery Modular Multiplier Based on KO-3 Multiplication

In the public-key cryptographic algorithms like RSA and ECC algorithms, the modular multiplier is the basic and frequently used operation. The core of the modular multiplier is the large integer multiplication. In this paper, we firstly propose a design of multicycle 258-bit KO-3 multiplication based on deduced Karatsuba-Ofman algorithm and further apply this multiplication in the 256-bit pipeline Montgomery modular multiplier. The design is implemented with Verilog hardware description language and we get the synthesis report with Synopsys Design Compiler under SMIC 65nm library. Our design runs a Montgomery modular multiplier in 17 clock cycles and the total latency is 37.4ns. The area of the design is 145281 um2 approximately. Compared with other designs under the same library, our design costs less area and power and can balance the delay-area at the same time.
Soft Fault Detection in Sensor Networks by Using Graph-embedded LSTM Networks

Due to the uncertain working environment and resource constraints, sensor nodes easily produce abnormal responds, resulting in various kinds of soft faults. Existing studies often solely focus on the soft fault diagnosis of individual sensor node, ignoring the possible impact of the spatial relationship between sensor nodes. This paper cooperatively uses the temporal and spatial correlations of sensor data, and proposes a soft fault detection method named as Graph-embedded Long Short-Term Memory AutoEncoder (GLSTM-AE), which is an AE model based on the combination of Graph Attention Network (GAT) and LSTM Network. The experimental results show that the proposed method outperforms several typical traditional methods. On the experimental dataset, both the Area Under Curve (AUC) of the Receiver Operating Characteristic (ROC) and the Average Precision (AP) of the proposed method are greater than 0.95, and the F1 score is best as well as.
La formación de formadores en educación a distancia y virtual

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
Soldering Danger Detection System Using a Line-of-Sight Estimation

Soldering is prone to human error due to lack of concentration and corner-cutting caused by simple tasks. Therefore, it is expected that accidents can be reduced by having instructors give indication during dangerous actions or inappropriate postures during soldering iron work. In addition, it is thought that visualization of the line-of-sight can provide real-time assistance at points where mistakes are likely to occur. In this paper, in order to solve these problems, we propose soldering danger detection system and a line-of-sight estimation. The experimental results confirm that the proposed system is effective in detecting hazards during soldering by estimating the line-of-sight.
Artificial Intelligence Technological Revolution in Education and Space for Next Generation

The goal of this research is to discover the various potential for the educational system using artificial intelligence (AI). The world today is dealing with AI in different sectors. This study specifically looked into the prospects for acquiring efficient and high-quality education for each student, automating administrative tasks, including regulating adaptive student support systems. AI has been leveraged and used in the education sector in various formations. AI initially took in the form of computers with the cognitive model, transformed to online learning, together with other technologies, the use of AI provides chatbots to perform instructors. Imagine you can access your classroom from anywhere at any time through an online learning system. These functionalities enable the education system to deal with the curriculum effectively. Using these facilities, teachers instruct the students to desire to achieve their goals efficiently. The primary aim of this article addresses the concepts in AI that serve to regulate and improve the overall quality of academic performance. The secondary aim of this article is to discuss AI involvement in the space domain.
An Analysis on Sentiments Using Deep Learning Approaches

Sentiment analysis is a method for examining public sentiment. It is often referred to as opinion mining. Deep learning (DL) approaches, which contribute to the solution of a broad range of issues at the same time, have acquired appeal among researchers in this area. RNNs are a form of neural network that has been effectively applied to natural language processing (NLP) software. Using a word vector space model and in this research, an expression sentiment analysis system is developed using a recurrent neural network and deep learning. We utilized an RNN algorithm called Long Short-Term Memory to do opinion classification and categorize the output as positive, negative, or neutral. To assess how effectively the deep learning (DL) method is performing, parameters like as accuracy, precision, recall, and F1-score are used.
Scalable Control of Spin Quantum Memories in a Photonic Integrated Circuit

Using magnetic field gradients and optimally shaped microwave pulses, we demonstrate selective control of color center spin qubits in a diamond micro-chiplet coupled to a photonic integrated circuit, yielding a platform for scalable qubit control.
THE EFFECT OF TEXT TWIST GAME ON STUDENTS’ VOCABULARY MASTERY AT MAS MUHAMMADIYAH 13 SEI RAMPAH

The objective of this research was to know whether there is a significant effect of Text Twist Game toward students' English vocabulary mastery at MAS Muhammadiyah 13 Sei Rampah academic year 2020-2021. The research methodology applied was quasi experimental design. In this research, the population was the tenth grade students of MAS Muhammadiyah 13 Sei Rampah. The sample consisted of two classes, namely experimental class consisting of 18 students and control class consisting of 18 students. In experimental class, Text Twist Game was used to teach vocabulary while in control class was applied translation technique. The treatments were held in 3 meetings for each class. In collecting the data, the instrument used was in the form of multiple choice questions which had been tried out before the treatment. The instrument was given in pre-test and post-test. The data obtained were analyzed using SPSS to compute independent sample t-test. Based on the data analysis computed by using SPSS, it was obtained that Sig.= 0.000 and α = 0.05. It means Ha was accepted because Sig.&lt; α = 0.000&lt; 0.05. Therefore, it was concluded that there was a significant effect of Text Twist Game towards students’ vocabulary mastery of English of the tenth grade students at MAS Muhammadiyah 13 Sei Rampah.
Bitcoin as a New Payment Platform

Global e-commerce depends on mediums of exchange that allow users to make secure, quick, and easy transactions without the difficulties of traditional payment methods. We analyze cryptocurrency in general and inspect Bitcoin specifically as a digital-currency option for global e-commerce. Bitcoin can meet the needs of future e-commerce, but it suffers from several types of risks—including social, ethical, and economic—that can pose a threat to online users and must be addressed. Our goal with this research in progress is to review the literature on Bitcoin as a premier future online payment method, Bitcoin proponents must understand how it is being used in e-commerce now and then educate users about the value of digital currency as we move deeper into the digital age. In this work in progress, we define cryptocurrency, note the need for Bitcoin research, and discuss the pros and cons of Bitcoin as a payment platform.