Reviewerly/exHarmony huggingface dataset:
Decision Support System For Selection of Prospective Members of BLM Polytechnic Caltex Riau Using The Weighted Product Method

The Caltex Riau Polytechnic pupil Legislative frame (BLM) is a pupil organization that includes out the capabilities of budgeting, regulation and supervision. the selection of prospective PCR BLM contributors is still conventional, such as selecting scholar documents one after the other which results in problems in organizing scholar documents. To help the manner of choosing BLM PCR members in figuring out the selected BLM PCR individuals, a choice-making gadget is wanted that may be used as an opportunity consideration between the selection outcomes acquired manually and the outcomes received from the gadget. in addition to being a device to help the pinnacle of BLM in making selections the usage of the Weighted Product approach. based totally at the effects of blackbox testing, it could be concluded that the BLM member selection system works in step with user needs. as well as the consequences of the usability testing, the test consequences obtained with a total percent of ninety two.35% (Strongly Agree). And the outcomes of checking out the accuracy of manual calculations with the system display that the accuracy stage is a hundred%. From the effects of this take a look at it became concluded that the gadget is acceptable to users in order that.
Correcting the Failure of the State

This chapter expands the debate from informational rights to look at non-cyber, non-information related threats, including when the state and its representatives fail to, first, provide and enact good laws equally and fairly, including the failure to apply fair processes, equal treatment, misapplying laws, and lacking the ability and political will to enforce the good laws; or second, when the state develops unjustifiably harmful laws, policies, procedures or institutional cultures. It will argue that in both instances, given the failure of the state and the subsequent threat these failures represent, hackers can use political violence to defend people from harm, though the type of response must be matched to the threat posed. This chapter will look at police brutality; the failure of due and fair process; the development of laws that seek to directly discriminate and foster hatred and violence against members of the LGBTQ+ community; and the locating and unmasking of online paedophiles.
Policy analysis and government capacity

A preliminary feasibility study (PFS) was adopted to support rational decision making in large fiscal investments, such as railroad and large infrastructure in Korea. This PFS is perceived as a cost-effective tool ensuring fiscal integrity in public programmes and enhancing bureaucratic capacity, which is a social benefit greater than the additional costs incurred from the administration of PFSs and related preparations. However, there is a concern that too many resources are being poured into the PFS process, given the limited reflection of the PFS on the budget formulation by the policy decision makers. Moreover, even though the results of a PFS have no legal binding, many people believe that the result can be used as an arbitrary policy tool increasing uncertainty for the stakeholders and public.
Introduction: Fantasies, Hope and Compelling Narratives

This introduction offers an alternative means for understanding smart cities in Africa by surfacing the contradictions and problematic discourses that often inform them. It introduces the structuring themes of the book: mobility, food security, social mobilization and public culture. This chapter is also central to establishing the ethos and conceptual lens that inform its narrative.
A Noise Convolution Network for Tampering Detection

The vulnerability of digital images to tampering is an ongoing information security issue in the multimedia field. Thus, identifying tampered digital images and locating the tampered regions in the images can help improve the security of information dissemination. A deep fusion neural network named NC-Net is designed in this paper, introducing pattern noise as assistance to fully exploit the tampered features present on the tampered image. The incorporation of noise texture information enabled NC-Net to acquire deeper tampered image features during the training phase. The extracted noise is incorporated as a crucial component within the convolutional structure of the model, serving as a potent activation signal for the tampered region. The performance of NC-Net is confirmed through relevant experiments on publicly available tampered datasets, and outstanding results are achieved in comparison to other methods.
Classification-Based and Lightweight Networks for Fast Image Super Resolution

Lightweight image super-resolution (SR) networks are of great significance for practical applications. Presently, there are several SR methods based on deep learning with excellent performance, but their memory and computation costs hinder practical applications. In this paper, we propose a down-up sampling continuous mutual affine super-resolution network (DUSCMAnet) to solve above problems. Moreover, we propose a classification-based SR algorithm based on image statistical features (TSClassSR-DUSCMAnet) for accelerating SR networks on large images (2K–8K). The proposed algorithm first decomposes the large images into small sub-images, then uses a Class-Module to classify sub-images into different classes according to the difficulty of reconstruction, then use a SR-Module to perform SR for different classes. The Class-Module is composed of a support vector machine (SVM) based on image statistical features, and the SR-Module is composed of our proposed DUSCMAnet, a lightweight SR network. After classifying, a majority of sub-images will pass through lighter networks, thus the computational cost can be significantly reduced. Experiments show that our DUSCMAnet is superior to the existing lightweight SR models in terms of time performance and also has competitive SR performance. Our TSClassSR-DUSCMAnet can help DUSCMAnet save up to 63% FLOPs on DIV8K datasets.
Cross Classroom Domain Adaptive Object Detector for Student’s Heads

Training on a label-rich dataset and test on another label-scarce dataset usually leads to a poor performance because of the domain shift. Unsupervised domain adaptation is proved to be effective on this problem in recent researches. Unsupervised domain adaptive object detection of students’ heads between different classrooms has becoming an important task with the development of Smart Classroom. However, few cross-classroom models for students’ heads have been proposed despite the rapid development of domain adaptive object detection. In this paper, we propose two adaptations which focus on the challenges of domain adaptive object detection of students’ heads between different classrooms, including the adaptation based on the numbers of students and the adaptation based on the locations of students. Based on Unbiased Mean Teacher framework, our Cross Classroom Domain Adaptive Object Detector achieves an average precision of 50.2% on the cross-classroom students’ heads dataset called SCUT_HEAD, which outperforms the existing state-of-the-art methods.
The Italian Response to COVID-19

Almost three years after the outbreak of the pandemic, it seems to be possible to identify some trends and draw some concluding remarks concerning the legal response to the pandemic crisis in Italy. While the constitutional system was probably successful in safeguarding the core content of the rule of law, the question to be asked might well be another one. Namely, did the legal framework, based on the combination of the use of decree-laws and prime ministerial decrees, and greatly diverged from the Civil Protection Code, really prove to be the most suitable response, or, on deeper analysis, did it end up generating more problems than it solved?
Generation 'Z' skills for knowledge-based ecosystemic change: a systematic literature review on Industrial 4.0

The gap in technology innovation that exists across nations is a significant problem that needs to be overcome by cooperation and collaboration throughout the countries. The aim of this paper is thus two-fold. First study focuses on systematic and comprehensive literature review to understand the present status of research done on this area. Secondly, a roadmap of growth is the need of the hour to achieve inclusive sustainable development. The next generation belongs to individuals and mostly groups who work as a team thinking creatively, developing new products and services with the latest methods and technologies, keeping in mind the social requirements. A framework has been proposed to understand the overall scenario of the stakeholders like Gen Z and current requirements. The skill imparted to the graduates is not in line with the industry requirement, at times creating huge problems like unemployment and needs to be imparted.
VR therapy - mental therapy tool based on virtual reality

Mental health is a healthcare issue. Awareness for taking care and treatment of one's mental health is rarely taken care of. When people face mental issues, they do not go for treatment in the initial phase because of high consultancy fee and shyness to reach to doctors for mental problems due to societal pressure. Hence, this paper offers VR technology based application or tool to target mental health issues. In this application, mental health issues such as anger, phobia, and fear of public speaking are taken as an input and solution is suggested depending on the problem. Using this application, it is possible to create therapist like a game so that user can interact with it and user can tell their problems to the therapist sitting in a virtual room by speaking to the therapist.
Accuracy analysis of university authority evaluation based on Chinese university data + neural algorithm

This paper is mainly based on the social evaluation data of the quality of higher education personnel training, and uses text sentiment analysis and text classification technology to conduct research on the data, so as to explore the attitudes and suggestions of the network society towards the quality of higher education personnel training, and to dig deeper Provide relevant universities and relevant departments with public opinion monitoring and decision-making support on improving the quality of personnel training. This has important research value and significance for the improvement of the quality of higher education personnel training and the study of high-performance text analysis methods.
Enhancing motion trajectory segmentation of rigid bodies using a novel screw-based trajectory-shape representation

Trajectory segmentation refers to dividing a trajectory into meaningful consecutive sub-trajectories. This paper focuses on trajectory segmentation for 3D rigid-body motions. Most segmentation approaches in the literature represent the body's trajectory as a point trajectory, considering only its translation and neglecting its rotation. We propose a novel trajectory representation for rigid-body motions that incorporates both translation and rotation, and additionally exhibits several invariant properties. This representation consists of a geometric progress rate and a third-order trajectory-shape descriptor. Concepts from screw theory were used to make this representation time-invariant and also invariant to the choice of body reference point. This new representation is validated for a self-supervised segmentation approach, both in simulation and using real recordings of human-demonstrated pouring motions. The results show a more robust detection of consecutive submotions with distinct features and a more consistent segmentation compared to conventional representations. We believe that other existing segmentation methods may benefit from using this trajectory representation to improve their invariance.
Fed-LSAE: Thwarting Poisoning Attacks against Federated Cyber Threat Detection System via Autoencoder-based Latent Space Inspection

The significant rise of security concerns in conventional centralized learning has promoted federated learning (FL) adoption in building intelligent applications without privacy breaches. In cybersecurity, the sensitive data along with the contextual information and high-quality labeling in each enterprise organization play an essential role in constructing high-performance machine learning (ML) models for detecting cyber threats. Nonetheless, the risks coming from poisoning internal adversaries against FL systems have raised discussions about designing robust anti-poisoning frameworks. Whereas defensive mechanisms in the past were based on outlier detection, recent approaches tend to be more concerned with latent space representation. In this paper, we investigate a novel robust aggregation method for FL, namely Fed-LSAE, which takes advantage of latent space representation via the penultimate layer and Autoencoder to exclude malicious clients from the training process. The experimental results on the CIC-ToN-IoT and N-BaIoT datasets confirm the feasibility of our defensive mechanism against cutting-edge poisoning attacks for developing a robust FL-based threat detector in the context of IoT. More specifically, the FL evaluation witnesses an upward trend of approximately 98% across all metrics when integrating with our Fed-LSAE defense.
Pengaruh Discovery Learning Berbasis Augmented Reality terhadap Hasil Belajar Siswa

Abstrak: : Tujuan penelitian ini untuk mengetahui pengaruh model pembelajaran discovery learning berbasis augmented reality terhadap hasil belajar kognitif materi sistem ekskresi manusia pada siswa Kelas VIII SMPN 2 Raman Utara. Jenis penelitian ini adalah eksperimen semu dengan menggunakan Posttest Only Control Design, peneliti menggunakan instrument berupa butir soal tes uraian (essay) yang digunakan peneliti untuk mengukur kemampuan peserta didik setelah melakukan proses belajar dengan menggunakan model pembelajaran Discovey Learning Berbasis Augmented Reality. Penelitian ini menggunakan 2 kelas yaitu kelas kontrol dengan menggunakan model pembelajaran konvensional dan kelas eksperimen menggunakan model pembelajaran Discovery Learning Berbasis Augmented Reality. Data penelitian diuji hipotesis dengan Independent Sampel t-test. Hasil uji dengan uji normalitas menunjukan Sig &gt; daripada α=0,05 yaitu 0,908 &gt; 0,05, dan untuk uji homogenitas didapatkan nilai 0,715 yang berarti terima H0 kedua populasi mempunyai varians yang homogen (sama), kemudian pada uji Independent Sample t-test didapatkan nilai sig &lt; dari ( = 5%) yaitu 0,001 &lt; 0,05 Sehingga ada pengaruh model pembelajaran discovery learning berbasis augmented reality terhadap hasil belajar kognitif materi sistem ekskresi manusia pada siswa Kelas VIII SMPN 2 Raman Utara.&#x0D; &#x0D; Kata kunci: model Discovery Learning, hasil belajar siswa&#x0D; Abstract: The purpose of this study was to determine the effect of the augmented reality-based discovery learning model on cognitive learning outcomes of the human excretory system material in Class VIII students of SMPN 2 Raman Utara. This type of research is a quasi-experimental using the Posttest Only Control Design, the researcher uses an instrument in the form of essay test items used by researchers to measure the ability of students after carrying out the learning process using the Augmented Reality Based Discovery Learning learning model. This study used 2 classes, namely the control class using conventional learning models and the experimental class using the Augmented Reality Based Discovery Learning model. The research data was tested by the hypothesis with the Independent Sample t-test. The test results with the normality test show Sig &gt; than α = 0.05, namely 0.908 &gt; 0.05, and for the homogeneity test a value of 0.715 is obtained, which means that accept H0, the two populations have homogeneous (same) variance, then test the Independent Sample t-test obtained a sig value &lt; of (α = 5%), namely 0.001 &lt;0.05 So that there is an influence of the augmented reality-based discovery learning model on cognitive learning outcomes of human excretory system material in Class VIII students of SMPN 2 Raman Utara.
China's city-wide quantum network

A network connecting quantum devices in Hefei can allow multiple secure chats at once
COMPARATIVE FACTOR ANALYSIS OF E-LEARNING AND GOOGLE SITES ADOPTION AS FINAL PROJECT INFORMATION PORTALS USING TAM

Final Project is a scientific project that is structured based on the application of science and technology. For now, information about the final project can be accessed on the e-learning website. However, several problems were found in the use of e-learning as a source of information regarding the final project. For example, information regarding the final project must be re-uploaded every year even though there is a course that is similar to the final project course in the previous semester. Departing from the problems above, lecturers at the Faculty of Information Technology, Ciputra University finally made new innovations. The innovation is to build a static website to access information about the final project. The website uses Google Sites. This study uses the TAM (Technology Acceptance Model) analysis method. The author conducted a survey about the website usage to the students in the Program from the class of 2016 to 2019. The sampling method used was purposive sampling. Purposive sampling was used in this study because the researchers needed specific respondents, namely SIFT students who had taken the final assignment course. This study uses the regression model to analyze the relationship between intention to use and other constructs in the adoption of e-learning as an information portal for the Final Project. Researchers use the regression model on the adoption of e-learning and google sites. The model gets a value of 0.852 and 0.905 respectively.
Upaya Meningkatkan Hasil Belajar Matematika Dengan Menggunakan Model Pembelajaran Kooperatif Tipe Example Non Example Pada Siswa Kelas XII SMAS Guppi Samata Gowa

This research is classroom action research (PTK) carried out at SMAS GUPPI Samata, Gowa Regency which aims to improve mathematics learning outcomes by using the Example Non Example type cooperative learning model. The subjects of this research were class XII students with a total of 26 students. Data collection was carried out using learning outcomes tests. The collected data was analyzed using quantitative and qualitative analysis. The research results showed that the average score of student learning outcomes in cycle I reached an average of 82.38 and in cycle II reached an average of 86.15. The standard deviation in cycle I was 5.721 and in cycle II was 4.315. Based on the category of completeness of learning outcomes used, the score obtained for students' mathematics learning outcomes which were categorized as complete in cycle I was 73.07% and in cycle II it increased to 100%. From these results it can be concluded that there has been an increase in mathematics learning outcomes by using the Example Non Example type cooperative learning model.
أثر الذكاء العاطفي على تطوير الأداء الوظيفي

Emotional intelligence is one of the important topics that the majority of business institutions in general and educational institutions in particular may not focus on, perhaps because of the lack or weakness of awareness of its positive results affecting the performance of the employee. The study aims to address new elements that are different from what is familiar and to know the extent of their relationship and their impact on job performance through a questionnaire prepared for this purpose and analyze the results through descriptive statistical models, correlation and multiple linear regression model, where the study concluded that there is a significant relationship between emotional intelligence and job performance. The university was chosen as an educational institution and a fertile field for this study, which recommends holding workshops that focus on spreading organizational awareness and the need to pay attention to the employee's feelings and status in order to build social awareness to be a basic pillar in interaction with others.
Designing a Framework for Conversational Interfaces

Combining the latest advances in machine learning with earlier approaches.
Using Psychology to Bolster Cybersecurity

Getting into the minds of attackers to protect the enterprise.
The place of Information security in large organizations

The need to organize information security work in organizations is no longer a controversial concept, especially in the age of digitalization. How to organize these jobs, what place in the organization to determine, what are the previous experiences of applying certain models in practice, are issues that should be discussed and result in providing different opportunities for organizations to apply the model that suits them best at the same time. Set business goals, characteristics of the environment in which they operate, as well as the type of security threats they face, and the total resources at their disposal for this purpose.
Link words

Outlines the most commonly used link words, i.e. words that are used to highlight a logical connection between one phrase and the next.
Quantifiers: any, some, much, many, much, each, every etc.

Deals with common quantifiers such as some vs any, few vs a few.
Word order

Provides guidelines on how to construct a sentence with the various parts of speech (subject, verb, object, indirect object) in the best possible order.
Modal verbs: can, may, could, should, must etc.

Differentiates between the subtle differences in meaning the most commonly used modal verbs in a written academic text.
Incidence of acute myocardial infarction in elderly people in northern Minas Gerais, From 2008 to 2018

Introduction: Cardiovascular diseases, including acute myocardial infarction (AMI), represent an important public health problem in Brazil and worldwide, increasing incidence and mortality rates, especially among an elderly population. AMI is a condition resulting from coronary artery obstruction, leading to a clinical imbalance between oxygen supply and consumption, due to insufficient flow to the myocardium, which can lead to the death of cells in the cardiac tissue. Methodology: Epidemiological, descriptive, cross-sectional, and quantitative study, carried out through documentary research at DATASUS and the Hospital Information System (SIH / SUS) with data collection in September 2018. Included tabulation and analysis of data related to care emergency room due to AMI in the elderly in the north of Minas Gerais between 2008 and 2018. Results/Discussion: Acute myocardial infarction contributes considerably to the increase in the number of deaths, especially in the elderly population. In the North of Minas, Montes Claros / Bocaiúva, there was a higher prevalence of hospitalizations, 82.3% of the total. The private service represents many hospitalizations and the number of deaths from heart attacks has been decreasing in a way decreasing in he last two years, of the study, in the north of mines. Conclusion: Therefore, with the dedication of a multidisciplinary team, it is possible to develop resources that lead patients to take an active attitude towards their disease, knowing and controlling the risk factors present in their lifestyle.
Readability

Introduces the concept of readability. The chapter outlines how to reduce the length of your sentences, avoid redundancy, only find synonyms for non-key words, avoiding ambiguity and various other ways to make your writing immediately accessible.