Reviewerly/exHarmony huggingface dataset:
Hypotheses Generation and Verification Based Framework for Crowd Anomaly Detection in Single-Scene Surveillance Videos

A two-stage framework for crowd anomaly detection in single-scene or scene-dependent surveillance videos is proposed in this article.The first stage generates several hypotheses corresponding to potential anomalous regions in a video frame and the second stage verifies them to reduce false alarms and identifies crowd anomalies.In the hypotheses generation stage, spatial and temporal derivatives are computed for each video frame and a saliency detector employing Hypercomplex Fourier Transform (HFT) is used to generate a saliency map.A threshold is applied to the saliency map to generate potential anomalous regions in the form of connected components.For each connected component, a set of 4 statistical features are computed and fed to the second stage which employs a Gaussian Mixture Model (GMM) as a verification method to yield the final crowd anomalies in the frame.The effectiveness of the proposed framework has been shown through results obtained on the UCSD anomaly detection benchmark dataset which contains two subsets namely Ped1 and Ped2 with a total of 48 test videos (9210 frames).Both frame-level and pixel-level anomaly detection results are provided using the widely recognized evaluation criterion in the domain and compared with the state-of-the-art methods.The experimental results show that the proposed framework obtains comparable results against the state-of-the-art methods.
Uncertainty-aware photovoltaic generation estimation through fusion of physics with harmonics information using Bayesian neural networks

We develop an aggregate photovoltaic generation estimation methodology that uses diverse inputs and can reason on its current input-dependent predictive uncertainty. Named PV-PHEst, for PhotoVoltaic Physics- & Harmonics-driven Estimator, the resulting tool is intelligently weighing and fusing information carried by the output of physics models, harmonics, and line sensors, using Bayesian neural networks and related techniques aimed at solving machine learning problems with intrinsic uncertainty quantification. As each of the three input classes carries heterogeneous information that only sheds light on one facet of the estimation problem but its value can diminish in the face of diverse grid phenomena, PV-PHEst with its estimation and uncertainty reasoning capabilities perform a nontrivial and potentially mission-critical task of value to grid operators.
LOW-COMPLEXITY CHANNEL ESTIMATION AND MULTI-USER DETECTION IN MIMO-ENABLED UAV-ASSISTED MASSIVE IoT ACCESS, 231-240.

Unmanned aerial vehicle (UAV) could be seen as an importanttechnique to eﬃciently support wide-area massive Internet of Things(IoT) access because of inherent properties, such as ﬂexibility,mobility, and adaptive altitude.
Integraphy of media education in geography lessons at the modern school

New educational standards imply the active use of new pedagogical technologies.The article shows the role of media education in geography lessons.Media education prepares students to understand the role of the media in culture and perception of the world, develops the ability to process information received from various sources.The integration of media education into geography lessons allows students to learn how to explain environmental phenomena, as well as analyze media reports.The formation of such thinking in students is influenced by the ability to evaluate, interpret and accept the position of the author.This work is dedicated to the introduction of media education in geography lessons.The complexity of tasks for finding information and working with it, the analysis of text headings and much more affects the student's acquisition of skills to use the media.The use of active teaching methods at school has a number of significant advantages: awareness of the learning process by schoolchildren; the possibility of active inclusion in it; planning their activities; the ability to build the learning process at different levels of complexity and widely use non-traditional forms of learning.In general, the use of active methods of teaching at school is not only possible, but also necessary, they allow to qualitatively improve the educational process, increase the strength of the acquired knowledge, significantly develop the personal qualities of the child.Active methods of teaching geography allow you to form the qualities of an active participant in any process, learn to find and make decisions; develop abilities that can be found in other conditions and situations; learn competitiveness, extraordinary behavior, the ability to adapt to changing conditions; learn the ability to communicate, establish contacts; enjoy communicating with partners; learn to create a special emotional environment that is attractive to students..
Deep Reinforcement Learning based Mobile Video Concurrent Multipath Transmission over Heterogeneous Wireless Networks

Video concurrent multipath transmission (VCMT) has more enormous challenges in solving the multi-objective, multi-parameter optimization problem over time-varying channels. To solve this issue, we first propose a deep reinforcement learning based algorithm (VCMT-RL) for real-time VCMT systems. We establish a system reward model of deep reinforcement learning, where the real-time networks status of heterogeneous wireless network is taken as input and the multi-objective quality of experience metric is the system reward. Compared with traditional algorithms, the proposed algorithm can maintain long-term rewards under the multi-objective complex optimization goals. We evaluate our learning-based VCMT-RL algorithm on a semi-physical test platform, which consists of actual transceiver hosts and a HoloWannetwork simulator over WIFI and LTE networks. Evaluation results demonstrate that the proposed VCMT-RL achieves appreciable improvements over the reference schemes in terms of perceptual video quality, delay performance, and loss rate.
An Intelligent Security Operation Method for Cloud-network Integration Services in Multi-cloud Scenes

Due to historical legacy or regulatory requirements, many enterprises have built several clouds and are faced with the problem of multi-cloud management. How to effectively integrate and use these different cloud resources is worth exploring. This paper analyzes the boundary and scope of cloud management of cloud-network integration services in the multi-cloud scenario formed by enterprises to realize digital transformation. The enterprise digital platform and core functions to realize multi-cloud management are studied. This paper puts forward an intelligent security operation method and system of cloud-network integration in multi-cloud scenarios, provides the process of cloud-network integration intelligent security operation in multi-cloud scenarios, and points out the role of intelligent security operation based on multi-cloud scenarios in improving the economic benefits of cloud-network resources and helping enterprises' digital transformation.
Research on Deepening Mechanism of University-Enterprise Cooperation and Industry-Education Integration in Undergraduate and Vocational Education

In order to alleviate the current dilemma of undergraduate vocational education cooperation, this paper summarized the research status of deepening mechanism of school-enterprise cooperation at home and abroad, and investigated schools, enterprises, students and other multi-level subjects, then explored the key factors that restricted the construction of school-enterprise cooperation deepening mechanism. Finally, the paper also put forward effective measures from the aspects of system, platform and model, trying to establish a mechanism of industry-education integration and school-enterprise cooperation in undergraduate vocational education to promote the long-term and in-depth development of industry-education integration and school-enterprise cooperation in our vocational education.
A Simulation Study to Compare Classification Performance of Fuzzy Inference System with Machine Learning: Methodological Research

Objective: Classification is one of the most important research topics of machine learning that aims to correctly predict the target class for each case in the data.In this study, classification performances of fuzzy inference systems that learn from experts and machine learning methods that learn from the data were compared in different sample sizes.Material and Methods: This study was planned as a methodological research.The machine learning algorithms used in the comparison are Multilayer Perceptron, Random Forest, Support Vector Machine, which are frequently encountered classifiers in the literature.The dataset were generated for 6 (sex, chest pain type, max heart rate, exercise induced, oldpeak and major vessels) independent variables determined by variable importance, preserving the characteristics of heart disease data in the University of California Irvine database.Sample sizes were determined in four different sizes as 100, 250, 500 and 1,000.The datasets were divided into two separate sets randomly, with 70% training set and 30% test set.In fuzzy inference systems, the fuzzy rules were automatically generated and Chi's technique was used to create the rules.Accuracy, precision, sensitivity and Fmeasure were used as performance metrics for classification to compare four methods.Results: As a result of the study, it had been observed that fuzzy inference systems are affected by the sample size, and the classification performance is better than other methods as the sample size increases.Conclusion: In general, it has been observed that as the sample size increased, the classification performance of the methods increased.
Jiddu Krishnamurti’s method of psychological perception

Jiddu Krishnamurti (1895Krishnamurti ( -1986) ) was a great philosopher of the twentieth century.One of the problems that he is involved in solving for humanity is the spiritual alienation in people.Krishnamurti's teachings have shown valuable instructions in the psychological cognitive activity of each person.He pointed out that the psychological problem of mankind so far is fear.The human nature of fear is caused by sensitivity and the intention to seek.It is the sensitivity and the intention to seek that make the human psychological perception fragmented and dependent on the objects of ideal projection.For psychological awareness to take place smoothly, only all causes can be stopped, that is to "cleanse the mind".The way to clear the mind is to: end knowledge, stop thinking, end prejudice, end tradition, and end time.
Boosting Students' Writing Ability Through Instagram's Caption

ABSTRACT &#x0D; &#x0D; Social media is a tool used by people for communicating to each other in this millennial era. One of them is Instagram. In this pandemic era, people have changed their ways of life and communication that used more online activities. It includes education aspect which becomes online as an instruction of Ministry of Education. Thus, the role of social media is also very important in the online learning. Instagram can be one of means of teaching and learning process. It can help a learning process to build the interaction between teacher and students. So, this research was conducted to see how the implementation of Instagram on students’ writing ability and how to maintain the interactions in online learning. The qualitative method was applied to this study because the data were collected from questionnaire that were distributed through Google Form. The subject were senior high school students. To sum up, the result showed that the students’ writing ability was improved. Most of the students participated well in Instagram. Then, through Instagram, most students interacted in learning process until the end of class. So, it can be concluded that teacher can use Instagram in writing activity and build the interaction well in learning process.
Using Inquiry Based Learning Strategy in Teaching Writing Descriptive Text

This research aimed to look at the effect of inquiry based learning strategy on students’ ability to write a descriptive text. The method of this research was quasi-experiment. The population of this research was eighth-grade students of MTsN Kota Solok. The total population was 65 students. The research results show that the pre-test means score in experimental and control classes was 46,8 and 44,4. The mean score of post-test in the experimental class was 55 and control class 51,6. Students’ ability in writing descriptive text analyzed by using t-test statistical which t calculated= 2,04 and t table (0,975: 38) = 2,02 with significant level t calculated &gt; t table. In conclusion, the students who were taught using an Inquiry based learning strategy have better results in writing descriptive text than those who were taught without inquiry based learning.&#x0D; Keywords: Descriptive text, Inquiry Based Learning, Writing
An Investigative Approach to identify Loop Hole and Data Leak vulnerabilities in a World’s Largest Greenfield VoLTE Mobile Network Operator JIO through Network Packet Dissection and Penetration

Apply Digital forensic technique to diagnose the cyber security threat to the Advanced IP based Mobile telephony Networks. Indian Telecom Market having a huge subscriber base. The Market adapts the new technology Long Term Evolution (LTE), VoLTE, IP Multimedia Subsystem. Hacking the Mobile Network is a major threat to the telecom ecosystem impacts the subscribers and operator seriously. The paper illustrates investigation on the strategy to Dissect and penetrate into JIO’s Network to identify the loop holes and data leaks in the Network pipe. Using the loop holes, the network may subject to attacks, hijacked, data stealth. We have followed out a grey hat technique to an extent, to continue the process and explore network nodal entry gaps to access the systems and flood anonymous traffic in to the network. We may require the Network operator’s permission to dig deeper, investigate further provide an authenticated report. The research and the reports of this paper is carried out by thorough understanding of user limits privileges provided by the network to a normal subscriber. It’s very clear the access is open to every subscriber and not crossed the network boundary limits. The investigation results bring an alarm to telecom network and its stake holders. With recent advancements like 5G, WSN, IOT, IoE, SDN-NFV the stakeholders keep increasing and expanding brings industrial convergence. With this aspect the stakeholder’s groups of telecom imbibes a huge impact across the industries and normal life. This research approach is just a sample on JIO’s Network, this can be carried out on all the Networks in the Indian Market or global Market to ensure no gaps in their network security. The idea is to sniff packets in the network through user agents like Mobile phones and do a deep packet investigation along with the understanding of network interfaces and protocols. Identify the gaps in the Core networks interfaces and the entities in the Network through call flow scenarios PS Mobile originating/ Terminating call and SMS tracing. The paper describes the initial phase of Dissecting and penetrating the Network. By having the operators Permission, we may drill down deeper investigation in this approach and analyses the crucial impacts, damage and Collateral damages to their Network. KEYWORDS Cyber Security, Digital Forensic, Mobile Network security, Network Penetration, Packet sniffing, Deep Packet Inspection.
Covid-19 Literacy as a Modulator of Emotions Linked to the Pandemic

An inquiry-based teacher training proposal concerning covid-19 was implemented during lockdown with Pre-Primary Education Degree students.The objective was to analyse how the workshop changed the students' emotions and their knowledge linked to the pandemic.To this end, the participants responded to an open-ended questionnaire passed out before and after the workshop, and to a Likert-type questionnaire on emotions when they handed in their responses.The results show that, in the case of learning, the students modified their covid-19 literacy to a greater or lesser extent and, in the case of emotions, they felt less fear, anger, concern, and insecurity, and greater tranquillity and optimism.In conclusion, an emotional modulation is perceived in which scientific literacy participates.
Optimism and pessimism in the images of the future in modern Russian cinema

The article is devoted to the analysis of Russian films in order to identify optimism and pessimism about the prospects of the future in them. As a methodological basis, we applied our own methodology for the study of feature films. Its application made it possible to consider not only the content (plot) of films, but also their visual range, to focus on the color, light and auditory characteristics of the film. In conclusion, a conclusion was made about the dominance of pessimism in the images of the future in contemporary Russian cinema.
DATA PROTECTION AND PRIVACY LAWS CURRENTLY IN INDIA WITH CONTRAST TO EUROPE

Data protection is the process of safeguarding important information from corruption, compromise or loss. It covers three broad categories: traditional data protection, data security, and data privacy. Data privacy defines who has access to data, while data protection provides tools and policies to restrict access. The Personal Data Protection Bill is a controversial draft law that aims to implement similar provisions as the General Data Protection Regulation into data protection law in India. The PDP Bill has been referred to a Joint Parliamentary Committee for further debate and examination which lays down various recommendations and modifications to the report. This paper analyses how India lacks a stand-alone data protection law. Even though the Information Technology (IT) Act of 2000 governs the use of personal data, this has been found to be insufficient for ensuring its protection. A Committee of Experts on Data Protection was established in 2017 to look into matters pertaining to data protection in the nation. The Personal Data Protection Bill, 2019 was presented in Lok Sabha in December 2019 and withdrawn from Parliament in August 2022. The Draft Digital Private Data Protection Law, 2022 was released by the Ministry of Electronics and Information Technology in November 2022 for public comment. These amendments need to be done to protect people from fraud and ensure fair and justice for the people.
Mel frequency spectral domain defenses against adversarial attacks on speech recognition systems

Automatic speech recognition (ASR) systems are vulnerable to adversarial attacks due to their reliance on machine learning models. Many of the defenses explored for defending ASR systems simply adapt defense approaches developed for the image domain. This paper explores speech-specific defenses in the feature domain and introduces a defense method called mel domain noise flooding (MDNF). MDNF injects additive noise to the mel spectrogram speech representation prior to re-synthesizing the audio signal input to ASR. The defense is evaluated against strong white-box threat models and shows competitive robustness.
Verstehen auf den ersten Blick – oder doch nicht? Zur (vermeintlichen) Einfachheit kleiner Texte am Beispiel von Internet-Memes

Small texts are not just short texts. Because of their shortness, they are often also understood as particularly simple texts. This view is further supported by the fact that they are multimodal units that integrate not only language but also images. For this reason alone, small texts represent an exciting object for research on questions of text complexity. This paper makes a usage-based linguistic contribution to that research. The study turns to the question of when and under what circumstances small, structurally short or abbreviated (multimodal) texts can be conceived and described as simple or complex. More clearly stated: this paper attempts to explain why a single small text can usually be conceived as both simple and complex. The research questions are unfolded in this paper on the basis of concrete examples. The theoretical considerations are based on a text corpus of current internet memes (specifically image macros) from the years 2020 and 2021.
Dynamic Ensemble Associative Learning

Associative classifiers have shown competitive performance with state-of-the-art methods for predicting class labels. In addition to accuracy performance, associative classifiers produce human readable rules for classification which provides an easier way to understand the decision process of the model. Early models of associative classifiers suffered from the limitation of selecting proper threshold values which are dataset specific. Recent work on associative classifiers eliminates that restriction by searching for statistically significant rules. However, a high dimensional feature vector in the training data impacts the performance of the model. Ensemble models like Random Forest are also very powerful tools for classification but the decision process of Random Forest is not easily understandable like the associative classifiers. In this study we propose Dynamic Ensemble Associative Learning (DEAL) where we use associative classifiers as base learners on feature sub-spaces. In our approach we select a subset of the feature vector to train each of the base learners. Instead of a random selection, we propose a dynamic feature sampling procedure which automatically defines the number of base learners and ensures diversity and completeness among the subset of feature vectors. We use 10 datasets from the UCI repository and evaluate the performance of the model in terms of accuracy and memory requirement. Our ensemble approach using the proposed sampling method largely decreases the memory requirement in the case of datasets having a large number of features and this without jeopardising accuracy. In fact, accuracy is also improved in most cases. Moreover, the decision process of our DEAL approach remains human interpretable by collecting and ranking the rules generated by the base learners predicting the final class label.
Early Detection of Multilingual Troll Accounts on Twitter

Internet troll farms have recently been employed as a powerful and prevailing weapon of information warfare. Even though different tactics may be utilized by different groups of state-sponsored trolls, our goal is to leverage identified troll data for revealing new emerging trolls generating multilingual content. In this work, we adopt a model agnostic meta-learning framework making use of previously released troll farm datasets for the early detection of newly-emerged troll accounts from identified or unidentified troll farms. The detection earliness of various models is evaluated using variable amounts of the earliest tweets from the tested accounts. To evaluate the proposed meta-model, we compare it to several classification models based on different types of account features. Our experiments demonstrate the effectiveness of the meta-model requiring as few as ten tweets to detect a troll account with an average accuracy of 94%.