Reviewerly/exHarmony huggingface dataset:
Informational risks

The information age, or the age of information revolution, is the result of a leap in communications and a breakthrough in information technology, resulting in traditional criminal activities taking a new form in terms of the illegal exploitation of this technology, which is termed as the information crimes, and this became a significant threat especially when it comes to children. The special nature of these new crimes and the difficulty of determining their legal nature makes it the subject of special attention directed at protecting children from the threat of this type of crime. The recent criminal phenomena, which poses a significant threat to children, especially in its sophisticated methods, became a security concern not only in Western countries but also in Arab countries. This has necessitated a scientific research about this phenomenon, its causes and how to address it to save this special segment of society. Therefore, the rapid and dramatic development of communication technology has led to the production of modern means of social communication, which have greatly affected the category of children, due to the universal attraction of the Internet, which comes at the forefront of these developments without a competitor.
Interaction with the Information Environment and Contemporary Educational Approaches in Higher Education (Educational Level “Master”)

A new look at the professional training of a higher school teacher, in particular, a teacher of general technical disciplines and teaching methods for technology, the disclosure of their creative potential is based on contemporary methodological approaches. Let’s consider in more detail each of the indicated approaches. The emergence of the information society is one of the signs of the transition of civilization into the noosphere. With the introduction of integrated communication networks, the possibilities of direct communication of individuals on a global scale are expanding. The introduction of computer and communication technologies into the daily life of people and into production makes a great socio-economic impact on the post-industrial society and causes such powerful social changes, it (society) turns into a completely new type – an information society, the structure and dynamics of which is based on the production of information, and not material values, as it was before. Information in such a society becomes a social and productive value. Hence the new features of society: a certain hierarchy of prestige, a specific type of education, information technology that provides a balance of dynamics and social statics, a fundamentally different manage-ment system, etc. Analysis of the introduction of degree training in higher pedagogical educational institutions of Ukraine allows us to identify general approaches in the implementation of master’s programs, in particular: their focus on the bachelor’s degree; variability, flexibility and mobility of master’s education, taking into account current socio-economic trends and regional needs; scientific and practical pedagogical direction and individual approaches in the organization of the educational process.
Prompt Injection: Parameterization of Fixed Inputs

Recent works have shown that attaching prompts to the input is effective at conditioning Language Models (LM) to perform specific tasks. However, prompts are always included in the input text during inference, thus incurring substantial computational and memory overhead. Also, there is currently no straightforward method of utilizing prompts that are longer than the maximum input length of the LMs without incurring additional costs during inference. We propose Prompt Injection (PI), a novel formulation of injecting the prompt into the parameters of an LM to be an efficient alternative to attaching fixed prompts to the input. We show that in scenarios with long fixed prompts, PI can be up to 280 times more efficient in terms of total FLOPs than previous approaches. We further explore methodologies for PI and show promising results in persona-dependent conversation, semantic parsing, and zero-shot learning with task instructions. Through these explorations, we show that PI can be a promising direction for conditioning language models, especially in scenarios with long and fixed prompts.
English Writing Correction Based on Intelligent Text Semantic Analysis

In order to improve the teaching effect of English writing, this paper combines the intelligent text semantic analysis algorithm to construct an English writing correction model. Moreover, this paper deduces the relationship formula between the range of English semantic information cloud drop and the evaluation word through the contribution of cloud drop group to qualitative concepts, the association between cloud drop central area and evaluation words, and the relationship between cloud drop ring area and evaluation word. In addition, this paper uses the word selection probability results obtained from the preference evaluation. The simulation results show that the English writing correction model based on intelligent text semantic analysis proposed in this paper can play an important role in the intelligent teaching of English writing.
Note: Picking Sides: The influencer-driven #HijabBan discourse on Twitter

Social Media is increasingly central to culture wars, and in recent years has been at the center of debates around the weaponization of peoples’ opinions in polarized situations. We examine one such issue, an attempt to allow schools and junior colleges to ban girls from wearing Hijab in India in the southern state of Karnataka, India, by studying Twitter messaging related to the issue in early 2022, when it was in the news. We find that the narrative supporting the ban of the Hijab on Twitter is primarily driven by a minority of highly-influential individuals who are predominantly male and polarised in favour of the ruling party, while the discourse against the ban relies largely on influencers from within the Muslim community. Our findings show that social media can be a useful tool to craft the contours of politically-motivated escalation in the Global South.
DESAIN DAN ANALISIS MEKANISME BENDUNG GERAK DENGAN RADIAL GATE UNTUK PENANGANAN BANJIR ROB

&lt;p&gt;Tidal floods are high tides that occur excessively on the mainland. Radial gate weir&lt;br /&gt;is a door building that moves around in a cylindrical shape to regulate the water&lt;br /&gt;level elevation. This study aims to determine the influence factor of the radial gate&lt;br /&gt;movement weir mechanism, analyze the fluid mechanics of the radial gate during&lt;br /&gt;tidal flooding. This research is experimental design research, namely, making a&lt;br /&gt;prototype design of the radial gate motion weir mechanism and analysis of&lt;br /&gt;parameter data in the form of hydrostatic pressure, hydrodynamic pressure, critical&lt;br /&gt;flow, and sub critical flow. The conclusion of this study is, the design is planned to&lt;br /&gt;work when: the automation system runs because the two proximity sensors touch the&lt;br /&gt;30 cm water level, activate the linear actuator to close the radial gate, the water&lt;br /&gt;pump turns on and 15 cm opens the radial gate, the water pump stops. The results of&lt;br /&gt;parameter data analysis: hydrostatic pressure of water level 30 cm = 32.0 N/m2,&lt;br /&gt;hydrodynamic pressure due to earthquake according to (DSI) in Cilacap = 0.1211&lt;br /&gt;tf/m2, BMKG SIG (III/Light)= 33,600 tf/m2, (IV/ Medium)= 96.075 tf/m2,&lt;br /&gt;(V/Weight)= 148.3125 tf/m2. Critical flow rate of water level is 15 cm = 0.0149&lt;br /&gt;m3/second, sub-critical flow rate is 0.0098 m3/second.&lt;/p&gt;
EVALUATION OF THE IMPLEMENTATION OF THE EXTRACURICULAR SCOUTING PROGRAM

The evaluation research on the implementation of extracurricular programs aims to describe the results of research on how the program's achievements from the implementation of the mandatory Scout extracurricular activities. The research in this thesis is motivated by the condition of the lack of efficiency in the mandatory Scout extracurricular activities, and the role of teachers in motivating activities in the implementation of activities that take place every week is felt to be less than optimal and efficient. This study uses qualitative research methods with descriptive research type. The location of this research includes research instruments of parties who have activities in schools, especially students as the main object in the implementation of the mandatory Scout extracurricular activity program as recommended by the Government. Determination of information is done by purposive sampling technique. Meanwhile, the data collection technique was carried out by observation, in-depth interviews with informants, taking documentation as data, and research using triangulation techniques, which included standard utility. Furthermore, the data analysis technique is by means of data reduction, data presentation, methods, discussions with colleagues and drawing conclusions. The results obtained from this study indicate that the process of implementing the program "Evaluation of the Implementation of Scout Extracurricular Programs at MI Al-Khairiyah Pagi Mampang Prapatan, South Jakarta" is less effective and needs improvement and improvement in the implementation of the mandatory Scout extracurricular activity program.
EYE Blinking for Password Authentication

Abstract: Personal identification numbers area unit used for user authentication and security. word verification exploitation PINs requires users to enter a physical PIN, which may be vulnerable to word breakage or hacking via shoulder water sport or thermal chase. PIN authentication with eye blinks entry techniques, doesn't leave any quite physical footprints behind and therefore provide a safer word entry possibility. This project presents a time period application to avoid. The personal identification numbers (PINS) is a common user authentication method for many applications, such as money transaction is online banking application and automatic teller machine(ATMs).unlocking personal devices ,event centers, shopping malls, Medical centers, schools/collages and opening Doors. I
Complete-Coverage Path Planning Algorithm of Multiple Mobile Robots Based on Reliability Functions

The multi-robot complete coverage problem in unknown area refers to that multi-robots obtain environment information through their sensors in unknown environment, and cooperate with each other to search the specific task area. For the complete-coverage path planning of multiple mobile robots, an algorithm based on grid reliability function was proposed. Firstly, positional reliability function value is calculated according to the information of the environment, the obstacles, covered grids and uncovered grids; Secondly, in order to reduce the path repetition rate and improve the coverage efficiency, the directional reliability function is introduced to adjust the grid function values and guides robots to uncovered grid efficiently; Thirdly, the robots make sure that there will be no excessive proximity between robots while robots cover the area, the density between robots is measured by dense reliability function value. In the final session, by compared with bio-inspired neural network algorithm in two scenarios: box simulation and simulated real environment in gazebo world, the algorithm proposed in this paper was verified to own lower repetition rate and shorter trajectory length.
Urdu News Article Recommendation Model using Natural Language Processing Techniques

There are several online newspapers in urdu but for the users it is difficult to find the content they are looking for because these most of them contain irrelevant data and most users did not get what they want to retrieve. Our proposed framework will help to predict Urdu news in the interests of users and reduce the users searching time for news. For this purpose, NLP techniques are used for pre-processing, and then TF-IDF with cosine similarity is used for gaining the highest similarity and recommended news on user preferences. Moreover, the BERT language model is also used for similarity, and by using the BERT model similarity increases as compared to TF-IDF so the approach works better with the BERT language model and recommends news to the user on their interest. The news is recommended when the similarity of the articles is above 60 percent.
Attention Concatenation Volume for Accurate and Efficient Stereo Matching

Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. To generate reliable attention weights, we propose multi-level adaptive patch matching to improve the distinctiveness of the matching cost at different disparities even for textureless regions. The proposed cost volume is named attention concatenation volume (ACV) which can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy, e.g. using only 1/25 parameters of the aggregation network can achieve higher accuracy for GwcNet. Furthermore, we design a highly accurate network (ACVNet) based on our ACV, which achieves state-of-the-art performance on several benchmarks.
Explanatory causal effects for model agnostic explanations

This paper studies the problem of estimating the contributions of features to the prediction of a specific instance by a machine learning model and the overall contribution of a feature to the model. The causal effect of a feature (variable) on the predicted outcome reflects the contribution of the feature to a prediction very well. A challenge is that most existing causal effects cannot be estimated from data without a known causal graph. In this paper, we define an explanatory causal effect based on a hypothetical ideal experiment. The definition brings several benefits to model agnostic explanations. First, explanations are transparent and have causal meanings. Second, the explanatory causal effect estimation can be data driven. Third, the causal effects provide both a local explanation for a specific prediction and a global explanation showing the overall importance of a feature in a predictive model. We further propose a method using individual and combined variables based on explanatory causal effects for explanations. We show the definition and the method work with experiments on some real-world data sets.
Main Directions of the Organization of Additional Foreign Language Education at the University

The topic of the publication is updated by understanding the importance of the task of creating and functioning of the system of additional education. In accordance with modern trends in the development of society continuous education is developing. Especially in demand today is the acquisition of new skills and experience for life in a world of uncertainty and instability. Lifelong learning offers a unique way to develop the interests of citizens, further progress in life, is seen as a significant step towards career growth. The purpose of the article: analysis and clarification of the basic directions of the functioning of additional education, organized by the department in the educational space of the university. In the era of globalization knowledge of a foreign language is especially in demand. As a result advanced training and retraining courses in the field of foreign language education are being created in the system of additional education. Additional education is considered from the standpoint of a self-fulfilling personality, creating conditions for its competitive development. The article analyzes the experience of the department in the implementation of additional foreign language education. Based on the analysis of scientific literature on this topic, based on the results of a survey of students about their attitude to additional professional education the authors determine the goals, objectives, functions of the main types of programs of additional foreign language education. The article defines the contingent (foreign language teachers, students of philology and students in non-core areas of training, scientific and pedagogical workers of the university, employees of the leading enterprises of the city) and gives a qualitative description of the main areas of retraining and advanced training conducted by the department (methodological, linguistic, in-depth). In conclusion, it is noted that additional foreign language education is in demand by students and contributes to the quality training of specialists.
Hybrid Image Compression technique using Deep Learning model for enhanced reliability and data transmission

Abstract Image compression is the way of data compression which could be applicable to digital form of images for the purpose of reducing the cost of storage or transmission and any algorithm would consider visual perception or statistical properties of an image to establish enhanced results. The compression techniques might be lossy or lossless type. The supreme image quality at a particular compression rate is the major aim of any image compression approach. Objective : In this current research, image compression is carried out with the support of hybrid Convolution Neural Network (CNN) and Random Forest (RF) approach. Data description : The proposed methodology is executed in python environment and the performance metrics is evaluated and outcomes attained are compared with existing research works to verify the effectiveness of suggested concept.
Provably Efficient Model-Free Constrained RL with Linear Function Approximation

We study the constrained reinforcement learning problem, in which an agent aims to maximize the expected cumulative reward subject to a constraint on the expected total value of a utility function. In contrast to existing model-based approaches or model-free methods accompanied with a `simulator', we aim to develop the first model-free, simulator-free algorithm that achieves a sublinear regret and a sublinear constraint violation even in large-scale systems. To this end, we consider the episodic constrained Markov decision processes with linear function approximation, where the transition dynamics and the reward function can be represented as a linear function of some known feature mapping. We show that $\tilde{\mathcal{O}}(\sqrt{d^3H^3T})$ regret and $\tilde{\mathcal{O}}(\sqrt{d^3H^3T})$ constraint violation bounds can be achieved, where $d$ is the dimension of the feature mapping, $H$ is the length of the episode, and $T$ is the total number of steps. Our bounds are attained without explicitly estimating the unknown transition model or requiring a simulator, and they depend on the state space only through the dimension of the feature mapping. Hence our bounds hold even when the number of states goes to infinity. Our main results are achieved via novel adaptations of the standard LSVI-UCB algorithms. In particular, we first introduce primal-dual optimization into the LSVI-UCB algorithm to balance between regret and constraint violation. More importantly, we replace the standard greedy selection with respect to the state-action function in LSVI-UCB with a soft-max policy. This turns out to be key in establishing uniform concentration for the constrained case via its approximation-smoothness trade-off. We also show that one can achieve an even zero constraint violation while still maintaining the same order with respect to $T$.
There is a fine Line between Personalization and Surveillance: Semantic User Interest Tracing via Entity-level Analytics

User interest tracing is a common practice in many Web use-cases including, but not limited to, search, recommendation or intelligent assistants. The overall aim is to provide the user a personalized “Web experience” by aggregating and exploiting a plenitude of user data derived from collected logs, accessed contents, and/or mined community context. As such, fairly basic features such as terms and graph structures can be utilized in order to model a user’s interest. While there are clearly positive aspects in the before mentioned application scenarios, the user’s privacy is highly at risk. In order to highlight inherent privacy risks, this paper studies Semantic User Interest Tracing (SUIT in short) by investigating a user’s publishing/editing behavior of Web contents. In contrast to existing approaches, SUIT solely exploits the (semantic) concepts [categories] inherent in documents derived via entity-level analytics. By doing so, we raise Web contents to the entity-level. Thus, we are able to abstract the user interest from plain text strings to “things”. In particular, we utilize the inherited structural relationships present among the concepts derived from a knowledge graph in order to identify the user associated with a specific Web content. Our extensive experiments on Wikipedia show that our approach outperforms state of the art approaches in tracing and predicting user behavior in a single language. In addition, we also demonstrate the viability of our semantic (language-agnostic) approach in multi-lingual experiments. As such, SUIT is capable of revealing the user’s identity, which demonstrates the fine line between personalization and surveillance, raising questions regarding ethical considerations at the same time.
Generative Adversarial Network for Generating Synthetic Infrared Image from Visible Image

One of the most important discoveries in the field of deep learning in recent years is the Generative Adversarial Networks (GAN). It offers great convenience and flexibility for image-to-image conversion processes. This study aims to obtain thermal images from visible band colour images by using Pix2Pix Network, which is a Conditionally Generative Adversarial Network (cGAN). For this purpose, a data set has been prepared by taking facial images at different angles in the visible and infrared bands. By applying image processing methods on this created dataset, pixel-by-pixel matching process was performed. Synthetic thermal face images were obtained thanks to this learning network fed with facial images consisting of visible and long wavelength infrared image (LWIR) pairs. In the generator and discriminator deep networks of the Pix2Pix GAN, Batch Normalization and Instance Normalization methods are applied and their effects on the outputs are examined. The same process has also been tested on the Google Maps dataset and thus its effects on different datasets has been demonstrated. Similarity values between synthetic outputs and real images of both studies has been calculated with several image quality metrics. The performance of this generated model in creating the details in the infrared band is also given in the conclusion section.