Reviewerly/exHarmony huggingface dataset:
Factors Influencing K-12 STEM Teachers' Interdisciplinary Teaching Competency

Interdisciplinary teaching is the essential characteristic of STEM education. STEM teachers need superior interdisciplinary teaching competency to gain professional success and development. Through grounded theory, this study tries to reveal the influencing factors and improvement strategies for interdisciplinary teaching. Through open coding, spindle coding, and selective coding, the factors system and its working mechanism were discovered. The personal-psychological factors group, functioning as innovation consciousness and cognition of occupational value, is the endogenous power source for interdisciplinary teaching. The instructional-procedural factors group, including multidisciplinary knowledge, STEM pedagogy, and team cooperation ability, is the methodological basis of integrative and problem-based teaching in STEM. The organizational-environmental factors group, covering organizational environment and culture and social support, is the policy and culture basis of STEM interdisciplinary teaching and also has a mutual effect on personal-psychological factors and instructional-procedural factors. Based on the working mechanism, the strategies are finally proposed from four key points of further work. There is the development plan, the community building, the incentive policy, and the evaluation system that aim to improve the interdisciplinary teaching competency of STEM teachers.
Self-Organizing and Self-Explaining Pervasive Environments by Connecting Smart Objects and Applications

In the past decade, pervasive environments have progressed from promising research concepts to available products present in our everyday lives. By connecting multiple smart objects, device ensembles can be formed to assist users in performing tasks. Furthermore, smart objects can be used to control applications, that, in turn, can be used to control other smart objects. As manual configuration is often time-consuming, an automatic connection of the components may present a useful tool, which should take various aspects into account. While dynamically connecting these components allows for solutions tailored to the needs and respective tasks of a user, it obfuscates the handling and ultimately may decrease usability. Self-descriptions have been proposed to overcome this issue for ensembles of smart objects. For a more extensive approach, descriptions of applications in pervasive environments need to be addressed as well. Based on previous research in the context of self-explainability of smart objects, we propose a description language as well as a framework to support self-explaining ambient applications (applications that are used within smart environments). The framework can be used to manually or automatically connect smart objects as well as ambient applications and to realize self-explainability for these interconnected device and application ensembles.
PERCEPÇÕES DE ALUNOS DO CURSO DE SISTEMAS DE INFORMAÇÃO EM RELAÇÃO AO PLÁGIO ACADÊMICO E AÇÕES PARA SUA REDUÇÃO

Plagiarism is the act of presenting as an idea or work of another author without mentioning it as a source. This practice has been common in academia, especially with the ease offered by the use of the internet. This study aims to verify how students of the Information Systems course understand plagiarism and what possible actions to reduce this practice in academic work. The research was carried out through semi-structured questionnaires, using research and descriptive analysis. The results showed that students do not have full knowledge of plagiarism, not being able to identify specific types of plagiarism, the correct use of citations and the need to place direct and indirect citations of the sources used in their texts.
Cybercrime Experiences, Handling and Practices: A Reality Check in the Perspective of Urban Bangladesh

Cybercrime regarding access to the user’s personal information and mishandling can place the individual or a community in a vulnerable position. Users frequently share personal data online and recent cybercrimes of users' data handling represent that users' online data is not secure, and anytime their online data might get unwanted access from any sources. In this research, we looked at n =23 young adult online users in urban Bangladesh through qualitative studies in eight focus groups. The goal of this study is to understand their current online experiences and the practice of dealing with cybercrimes they have experienced. We also have explored two stakeholders qualitatively from cybercrime divisions in Bangladesh to understand the users' behavior, policy-level challenges, and practices of dealing with cybercrimes. The study revealed that the participants often faced cybercrimes, do not report cybercrime division, where they personally dealt with cybercrimes. From stakeholders' point of view, they have additional infrastructural challenges at cybercrime divisions. Based on these findings, we conclude this paper with some recommendations at users and policy levels that will help users to get better online experiences.
Recurrent Neural Network

Convolutional neural network uses the local correlation of data and the idea of weight sharing to greatly reduce the amount of network parameters. It is very suitable for pictures with spatial and local correlation. It has been successfully applied to a series of tasks in the field of computer vision. In addition to the spatial dimension, natural signals also have a temporal dimension. Signals with a time dimension are very common, such as the text we are reading, the speech signal emitted when we speak, and the stock market that changes over time. This type of data does not necessarily have local relevance, and the length of the data in the time dimension is also variable. Convolutional neural networks are not good at processing such data.
PREDICTION OF YOUTH DEPRESSION RISK CLUSTERS IN THAILAND DURING COVID-19

Youth depression is a silent threat threatening students around the world. Therefore, the purpose of the research was (1) to cluster students' risk of adolescent depression during COVID-19 and (2) to compare the predictive cluster to the standard depression rating scale. The samples used in the analysis were 687 samples from three institutions with two levels of education. It was 470 samples (68.41%) from Rajabhat Maha Sarakham University (RMU) at the university level, 33 samples (4.80%) from Mahasarakham University (MSU) at the university level, and 184 samples (26.78%) from Phadungnaree School (PS) at the high school level. The research tool is a data mining analysis technique. It consists of k-Means clustering and k-Determination. The results of the data mining analysis showed that the cluster analyzed by data mining was a little dissimilar from the normal process. It discovered that there were 120 different data samples (17.47%). Therefore, it can be concluded that the models studied by the researchers are consistent with the 5th Diagnostic and Statistical Manual of Mental Disorders (Dsm5). For future work, the researchers aim to develop forecasting prototypes and develop mobile applications to facilitate further work.
Procedural Aspects of Handling the Electronic Evidence: the Ukrainian Context

The article analyzes the procedural aspects of the seizure, recording and analysis of electronic traces of the crime. Some statistical data on persons convicted under Art. 361-363-1 of the Criminal Code of Ukraine. The history of formation of the institute of electronic proofs is considered. The theoretical basis for understanding the essence of electronic evidence is outlined. It is noted the lack of regulation of the issue of working with electronic evidence in criminal proceedings. Some procedural aspects of electronic document review are revealed and some examples are given. The authors believe that the nature of electronic data, the mechanism of their formation allow us to consider them as a separate type of evidence, and the forms of their fixation defined in the current Ukrainian legislation are not perfect at present. It is proved that when working on the network, law enforcement agencies can record only the projection of the original electronic document as a separate case of electronic evidence, and such a projection can not be considered the original. The method of recording electronic evidence in the framework of covert investigative (search) action to remove information from electronic information systems is analyzed. Some bills aimed at normalizing the peculiarities of working with electronic traces of crimes in criminal proceedings are studied.
A research of digital forensic method based on the Checkm8 heap vulnerability

With the increasing popularity of mobile phones, mobile phone forensics is becoming increasingly important to the area of government activities. It is considerable difficult to conduct digital investigation work as the mobile phone manufacturer's user data is relatively well secured. In particular, forensic staff have difficulty in obtaining more information about the current situation when the phone holder refuses to give out the password. As the Checkm8 vulnerability requires physical access to the iPhone and the establishment of USB connections, this Apple chip vulnerability, which was revealed in 2019, is a technique that is more conducive to iPhone forensics. If paired with the libmobiledevice environment, forensics workers could gain high access to semi-encrypted data on some iPhone models through jailbreak while on the locked screen. Depending on the available decryption techniques, forensic workers can gain greater access to information about the phone's owner.
A Joint Model of Adaptive Clustering and Multi-kernel Learning for Entity Alignment

research-article Share on A Joint Model of Adaptive Clustering and Multi-kernel Learning for Entity Alignment Authors: Fang Wang Institute of Systems Engineering,Academy of Military Sciences, China Institute of Systems Engineering,Academy of Military Sciences, ChinaView Profile , Yongqiang Xie Institute of Systems Engineering,Academy of Military Sciences, China Institute of Systems Engineering,Academy of Military Sciences, ChinaView Profile , Kai Zhang Institute of Systems Engineering,Academy of Military Sciences, China Institute of Systems Engineering,Academy of Military Sciences, ChinaView Profile , Rui Xia Institute of Systems Engineering,Academy of Military Sciences, China Institute of Systems Engineering,Academy of Military Sciences, ChinaView Profile Authors Info & Claims BDSIC 2021: 2021 3rd International Conference on Big-data Service and Intelligent ComputationNovember 2021 Pages 97–104https://doi.org/10.1145/3502300.3502313Online:27 January 2022Publication History 0citation14DownloadsMetricsTotal Citations0Total Downloads14Last 12 Months14Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The Emergent Learning Model

This chapter synthesises the earlier work on modelling learning and tries to create a design toolkit for anyone who wants to design for learning. However, the conceptual starting point for this chapter is the desire expressed in the EU Bologna Process to integrate “informal,” “non-formal,” and “formal” learning. The authors believe that the process the EU carried out, which led to the Horizon 2020 funding programme, was mistaken. The critical dimension of this lies in whether one examines these three dimensions of learning by starting with the existing formal structures of education or if one starts with the largely unexamined processes of learning. Education assumes that learning is an automatic by-product, an epiphenomenon, of the education system and so does not need to be defined separately. As has been seen in the chapters based on an ethnographic study of learning in digital environments and on learner-modelling (Chapters 1 and 2), learning has not been sufficiently discussed or described in much academic literature focused on education.
Two Dimensional Electromagnetic Scattering from Dielectric Objects Using Quantum Lattice Algorithm

A quantum lattice algorithm (QLA) is developed for Maxwell equations in a two- dimensional Cartesian geometry. In particular, the initial value problem of electromagnetic pulse scattering off a localized 2D dielectric object is considered. A matrix formulation of the Maxwell equations, using the Riemann-Silberstein-Weber vectors, forms a basis for the QLA and a possible unitary representation. The electromagnetic fields are discretized using a 16-qubit representation at each grid point. The discretized QLA equations reproduce Maxwell equations to second order in an appropriate expansion parameter ε . The properties of scattered waves depend on the scale length of the transition layer separating the vacuum from the core of the dielectric. The time evolution of the fields gives interesting physical insight into scattering when propagating fields are excited within the dielectric medium. Furthermore, simulations show that the QLA recovers Maxwell equations even when ε ∼ 1.
Research on Light Field Correction Based on LinkNet in HER2 Pathological Image

The assessment of pathological Images plays a crucial role in cancer cure and research. The automatic evaluation method based on artificial intelligence can provide assistance for doctors and effectively improve the efficiency and accuracy of doctors’ diagnosis. However, the current methods based on artificial intelligence have the problem of low quality of collected pathological images, which has a negative impact on the prediction of intelligent algorithms. Due to the influence of the illumination deviation, the brightness distribution of the collected pathological images is uneven, and the noise generated by the uneven illumination will be mixed with the useful signal in the image, and some details are partially blurred or completely obscured. To tackle these problems, this paper proposes a LinkNet-based light field correction method for polarization correction of pathological images. This method takes advantage of Gaussian image characteristics to generate a large number of images with different center points and different brightness diffusion speeds to simulate the light field distribution map, and uses WSI (Whole-slides Image) as a standard unbiased pathological image to train a polarization correction model. Compared with traditional image enhancement methods, the method proposed in this paper is not only the best in terms of visual effect contrast, but also has very desirable effects in image light field correction and detail enhancement. The experimental results show that the method proposed in this article and other evaluation methods we use are the best in optional various index evaluations.
Speaker Independent Accent Based Speech Recognition for Malayalam Isolated Words: An LSTM-RNN Approach

Automatic speech recognition (ASR) has been a very active area of research for the past few decades. Though there are great advancements in ASR in many languages accent-based speech recognition is an area that is yet to be explored in many languages. Speech recognition by humans is an intuitive process and so is a tough process to make the computers automatically recognize human speech. Although speech recognition has achieved promising achievements for many languages; speech recognition for the Malayalam language is still in infancy. The scarcity of the datasets makes it researchers difficult to do the experiments. Here in this paper, we have experimented with Long Short-Term Memory (LSTM) a Recurrent Neural Network (RNN), for recognizing the accent-based isolated words in Malayalam. The datasets we used here have been constructed manually under a natural recording environment. We used Mel Frequency Cepstral Coefficient (MFCC) methods to extract the features from the audio signals. LSTM with RNN is used to train and build the model since this technology significantly outperforms all other feed-forward deep neural networks and other statistical methodologies.
Toward Efficient CNN Compression via Coordinate Descent Structure Search

Despite the impressive performance, convolution neural networks (CNNs) are often pilloried for challenging deployment on edge devices due to huge computation burden and power consumption. As an effective tool to compress CNNs, filter pruning has attracted ever-increasing attention in recent years. However, most methods suffer from complex manual design, huge overhead of structure search and unstable compression rate. To address these problems, this paper proposes a novel end-to-end pruning method based on coordinate descent, termed CDPruner. Specifically, the proposed method models filter pruning as an independent optimization for the pruning rate of each layer. By assigning each layer a pruning sensitivity score, CDPruner selects the least sensitive layer to prune in an iterative manner and dynamically updates the sensitivity queue until the FLOPs reduction reaches the pre-given budget. Extensive experiments on CIFAR-10 and ImageNet-IK dataset demonstrate the superiority of CDPruner over many state-of-the-arts.
Bitcoin and Ethereum: Twins, Cousins or Just Good Friends?

Bitcoin and Ethereum have been at the top of the cryptocurrency market, in terms of market capitalization, since January 2018. Bitcoin, proposed in 2009, is a decentralized digital currency that enables payments to anyone, anywhere in the world with no central authority. Ethereum, went live in 2015. It can be used as a currency but it also includes a decentralized, open-source blockchain with smart contract functionality. The smart contract function allows for the exchange of NFTs (non-fungible tokens) connected to digital works of art or other real-world items and sold as unique digital property. Ethereum, like Bitcoin, uses a proof-of-work protocol in the validation of new transactions, but unlike Bitcoin, Ethereum has no supply cap.An analysis of the two coins from January 2018 to September 2021 finds they have more in common in the 2018 to 2020 period than in 2021, both in terms of daily price and trading volume movement. By 2021 this consistency begins to falter, particularly in the first six months of the year, when price and trading volume show little or no relationship. The popularity of NFTs does not appear to have any impact on Ethereum’s price or daily trading volume. Although the sale of NFTs grows considerably in 2021, they represent only a small percentage of Ethereum’s daily trading volume. These are two of the most successful cryptocurrencies, there are times when they both react in the same way to market conditions, but this is not always the case. Prices and trading volume move in the same direction around 80% of the time, which implies that for 20% of the time they do not respond in the same way to market conditions. This has implications for any Ethereum automated trading strategy based on the price movement of Bitcoin. They react more as part of the cryptocurrency family. So are they twins, cousins or just good friends? The answer would be cousins.
Adversarial Text-Based CAPTCHA Generation Method Utilizing Spatial Smoothing

The development of deep learning (DL) techniques has enabled to crack traditional text-based CAPTCHA (Com-pletely Automated Public Turing test to tell Computers and Humans Apart), which results in new security issues. As a coun-termeasure against DL based attacks, the adversarial CAPTCHA is well suited since it can increase the difficulty of machine recognition while ensuring human readability. However, spatial smoothing can negate the effectiveness of adversarial CAPTCHAs because adversarial noises in them are subject to averaging pixels. As far as we know, there are no effective counters against spatial smoothing, whereas it is the critical problem which facilitates spreading automated attacks. Therefore, to address the unsolved problem, in this paper, we propose an adversarial text-based CAPTCHA generation method utilizing spatial smoothing. We focus on the fact that when spatial smoothing is applied to an image, the amount of information it carries decreases, making the whole image blurred. Spatial smoothing is only viable as an attack when the mitigation of the adversarial noise has a larger impact than the whole image getting blurred. Thus, when the degree of spatial smoothing exceeds a certain threshold, the impact of the two aspects reverses, and the difficulty of the recognition increase. By utilizing this phenomenon in the generation of CAPTCHAs, the proposed method can indirectly neutralize the intended effect of spatial smoothing by attackers, preventing the recognition rate from increasing. Our evaluation shows the proposed method can reduce the recognition rate by up to 34&#x0025;, compared to the conventional method. Besides, an experiment on human recognition rates marked 73.67&#x0025;, showing that human recognition is maintained at an acceptable level.
Conceptual Intelligent Model for Visual Question Answering Using Attention Mechanism and Relational Reasoning

In recent years, a great deal of interest in research of Visual Question Answering (VQA) has been propounded it as a hot topic in computer vision. Many sub-problems were raised in this regard, and reasonable efforts have been made to solve them. Considering salient elements of different modalities, discovering inter or intra correlation, proper information fusion method, using supplementary information of external knowledge bases, visual reasoning, and accepting correct answers that have not been seen before in the training set are examples of these issues. In this paper, the focus is on reinforcing the model by reasoning about complex questions, applying the attention mechanism, and leveraging knowledge graphs (KG) to improve the generated answers. Moreover, the proposed conceptual model includes a zero-shot learning method to allow unlabeled correct answers by implementing a semantic space mapping approach. The use of the fact-based VQA knowledge base for integrating the scene graph with additional information is suggested in the research. It is expected that based on the proposed approach of the framework, its implementation will lead to better accuracy and improvement in efficiency for predicting the appropriate answers.