Reviewerly/exHarmony huggingface dataset:
Detecting Neighborhood Gentrification at Scale via Street Views and POIs (Student Abstract)

Neighborhood gentrification plays a significant role in shaping the social and economic status of both individuals and communities. While some efforts have been made to detect gentrification in cities, existing approaches mainly relies on estimated measures from survey data and requires substantial work of human labeling yet fails to characterize the physical appearance of neighborhoods. To this end, we introduce a novel approach to incorporate data like street view images and POI features to represent urban neighborhoods comprehensively at each timestamp. We show the effectiveness of the proposed methods with previous research on gentrification measures: each neighborhood representation we trained not only indicates its gentrification status, but also could become supplementary parts for the current measures and valid resource for researchers and policy makers.
Questions and deception: How to ask better questions and elicit the truth

Questions are important tools for uncovering information, but to avoid deception, question askers must be strategic in what and how they ask—and consider the social implications of their questions. Askers should consider that in addition to soliciting information, questions also signal information about expected answers, askers' own knowledge, and the parties' relationship. We review literature on deception, conversations, and impression management to discuss signals embedded in question phrasing, and how these signals affect the truthfulness of respondents’ disclosures. Askers can increase truthful disclosure by remaining neutral about the desirability of possible responses, conveying knowledge of the topic, and signaling trust. We identify how asking better questions requires being more cognizant of the informational and relational signals that questions send.
A Simulation-Based Evaluation Framework for Interactive AI Systems and Its Application

Interactive AI (IAI) systems are increasingly popular as the human-centered AI design paradigm is gaining strong traction. However, evaluating IAI systems, a key step in building such systems, is particularly challenging, as their output highly depends on the performed user actions. Developers often have to rely on limited and mostly qualitative data from ad-hoc user testing to assess and improve their systems. In this paper, we present InteractEva; a systematic evaluation framework for IAI systems. We also describe how we have applied InteractEva to evaluate a commercial IAI system, leading to both quality improvements and better data-driven design decisions.
Relevance of Gandhian Thought of Education in Present Day Context

Abstract: Mohandas Karam Chand Gandhi, better known as Mahatma Gandhi was a freedom fighter, social reformer and a well known educationist of India. Gandhi said, “An all-round drawing out of the best in the child and man—body, mind and spirit.” So, he emphasised the holistic or all round development of a human being. He not only thought for the spiritual change in man but also given importance on social transformation. His educational thought is alive in every part of education. Gandhi prescribed an education scheme known as "Nayi Talim" (Basic Education) in which every child should be educated and trained for the realization of his ethical goal in life and also the goal of a just social order. Gandhiji has given a scientific argument for the educational system. He says that the goal of education is the development of spiritual character strength. Education is the body's response to the mind. Even today, educational psychologists have identified the teacher as a means of integral development of the individual. He spoke of teaching through the mother tongue. He spoke of harmony between the school and home environment and making education unpaid. All of these are currently in force. Now the government is trying to get all children into school through universal education, which was once Gandhiji's goal. The present paper will highlight the thought of Mahatma Gandhi on education which has an impact on every strata of life anywhere in this world. It’s relevance is realized by everybody to build a holistic education system for child and man
Do We Need a New Large-Scale Quality Assessment Database for Generative Inpainting Based 3D View Synthesis? (Student Abstract)

The advancement in Image-to-Image translation techniques using generative Deep Learning-based approaches has shown promising results for the challenging task of inpainting-based 3D view synthesis. At the same time, even the current 3D view synthesis methods often create distorted structures or blurry textures inconsistent with surrounding areas. We analyzed the recently proposed algorithms for inpainting-based 3D view synthesis and observed that these algorithms no longer produce stretching and black holes. However, the existing databases such as IETR, IRCCyN, and IVY have 3D-generated views with these artifacts. This observation suggests that the existing 3D view synthesis quality assessment algorithms can not judge the quality of most recent 3D synthesized views. With this view, through this abstract, we analyze the need for a new large-scale database and a new perceptual quality metric oriented for 3D views using a test dataset.
IMPLEMENTASI ALGORITMA LEAST SQUARE UNTUK FORECASTING PADA TOKO MABEL

&lt;p&gt;The people's purchasing power for wood-processed products has increased significantly, especially for home accessories such as doors, windows, tables, chairs, and so on. UD Jatisubur is a business selling various types of furniture. The purpose of this study is to apply the Least Square method to predict the sale of goods at the UD Jatisubur store. Prediction is a guess or estimate about the occurrence of events in the future. Prediction is the ability in a business to expect the purchase and use of a product until the product can be made in the correct quantity. Prediction is the process of estimating future demand. In sales activities, predictions are made to determine the number of sales on a product. In this application the method used is Least Square. The Least Square method is used to calculate the number of sales of goods in the following year, namely 2021. Data on sales of goods during 2016 to 2020 with items such as doors, windows, tables, chairs and cabinets. The results of the prediction of sales of goods in 2021, obtained as many as 103 doors with MAPE 14.247%, windows as many as 93 with MAPE 8.4755%, chairs as many as 21 with MAPE 6.7767%, tables as many as 12 with MAPE 3.8283%, and cupboards as many as 15 with a MAPE of 16.00413%.&lt;/p&gt;
Modern archaeological education in Russia: solving the problems of humanitarians with the involvement of experience in the training of higher technical personnel

Introduction. The relevance of the study is conditioned by the crisis state of Russian archaeological education and the need to find a way out of the current situation. The research objective: proposing ways out of the crisis of archaeological education based on the experience of training technical specialists. Materials and methods. Comparative-historical, classification and descriptive methods were used as a methodological basis that made it possible to compare the training systems of humanities and technical specialists, to classify the features of both systems and describe the current situation and ways out of it. The paper analyzes the researchers’ works of this problem, including the staff of the Moscow Polytechnic University, and uses the observations of the authors of the article. Results. An analysis of the experience of the Russian Empire showed that in the pre-Soviet period there was no unified system for the formation of archaeological personnel. The training history of the archaeologists in the Soviet Union was ambiguous, but as a result, a system of training highly qualified specialists was created that is now in a state of crisis. The training of archaeologists abroad is carried out according to two models, which is of concern to the foreign scientific community. Russian education needs to look for its own ways to change the situation in the formation of archaeological personnel. Conclusion. The crisis of archaeological education in Russia has administrative (formation of the higher education system), financial (low level of university science funding), scientific (spread of “black archaeology”), psychological (perception peculiarities of the current situation by Russian students) and a number of other reasons disclosed by us. This can be overcome considering the experience of creating professionals in technical specialties accumulated, in particular, at the Moscow Polytechnic University that we reveal on specific examples. This conclusion does not seem paradoxical in the context of the multidisciplinary nature of modern science and the growing economic demand for highly professional archaeological research.
Contrastive Personalization Approach to Suspect Identification (Student Abstract)

Targeted image retrieval has long been a challenging problem since each person has a different perception of different features leading to inconsistency among users in describing the details of a particular image. Due to this, each user needs a system personalized according to the way they have structured the image in their mind. One important application of this task is suspect identification in forensic investigations where a witness needs to identify the suspect from an existing criminal database. Existing methods require the attributes for each image or suffer from poor latency during training and inference. We propose a new approach to tackle this problem through explicit relevance feedback by introducing a novel loss function and a corresponding scoring function. For this, we leverage contrastive learning on the user feedback to generate the next set of suggested images while improving the level of personalization with each user feedback iteration.
Transformation of Emotions in Images Using Poisson Blended Generative Adversarial Networks (Student Abstract)

We propose a novel method for transforming the emotional content in an image to a specified target emotion. Existing techniques such as a single generative adversarial network (GAN) struggle to perform well on unconstrained images, especially when data is limited. Our method addresses this limitation by blending the outputs from two networks to better transform fine details (e.g., faces) while still operating on the broader styles of the full image. We demonstrate our method's potential through a proof-of-concept implementation.
Awareness on Elementary Schools English Handbook: Teaching Material Appropriateness Survey Research

This study aims to investigate teachers’ and students’ awareness on elementary schools students’ English handbook in terms of the students’ need and interest. It was a survey research involving 5 teachers of English and also 120 fifth grade students at Madrasah Ibtidaiyah Negeri II Palembang. A likert scale questionnaire was distributed as the instrument which was validated and tried out to determine the rigor of the instrument. The data was analyzed by using descriptive analysis. The finding revealed that dealing with elementary schools students English handbook, the teachers tended to disagree that that it was designed based on childrens’ need and interest at the aspect of teaching material, but they agreed that the English handbook fulfilled students’ need and interest in terms of teaching and learning process. Then, students tended to agree that their English handbook fulfil their need and interest. Teachers tended to agree that they need to do some adaptation when using the handbook. Dealing with expectation, teachers tended to strongly disagree that there should be both evaluation and improvement given by the government related to the use of the elementary schools students’ English handbook. In contrast, children tended to agree that government should evaluate and improve the use of elementary schools students English handbook.
Generating Information of URL Based on Web Scraping Using YOLOv3 Face Recognition Technology

Artificial Intelligence (AI) is a system developed to learn and apply human intelligence. Some technologies produced from the development of Al are face recognition and web scraping. Face recognition is used for identifying or verifying the identity of an individual using their face. The result of a face recognition process can be used to collect information on the internet with a web scraping technique. This paper proposes a face recognition model and web scraping system using You Only Look Once (YOLO) object detection method and Request library written in Python. The face recognition model performed fine-tuning in two hyperparameters, which are learning rate and step training. The proposed model for face recognition is using custom datasets that contain 8000 images divided into 5 classes and evaluated using the Mean Average Precision (mAP) performance parameter, while the web scraping system is evaluated using the precision rate parameter. From the test results, the best configuration was obtained at a learning rate of 0.0001 and step training of 10K. The highest mAP that is achieved is 0.90 with a recall and precision value of 0.75 for each, while the average precision rate is 0.87. The results of this paper are expected to contribute to the development of biometric security technology.
Evolution of Smart Contracts- A Bibliometric Analysis and Review

Abstract: Smart contracts are self-executing, deterministic scripts that run transparently on top of a blockchain. They enable trusted transactions and agreements between anonymous parties without the need for a trusted figure or authority. The smart contract code and agreement between the parties are publicly available on the blockchain, making transactions transparent, traceable, and irreversible. Smart contracts have the potential to revolutionize and disrupt numerous industries' traditional operating procedures. Although research on smart contracts has yielded significant results, their full potential remains untapped. A bibliometric analysis was performed in this paper to obtain a holistic overview and to assist academic researchers in developing a comprehensive understanding of the state of scientific research on smart contracts. 513 articles on smart contracts were extracted and analyzed using biblioshiny and VOSviewer from the core collection of the Web of Science (WOS) database. Influential journals, top authors, most contributing countries, and impactful articles in the smart contract domain are revealed. A three-fields plot is constructed to show the interactions between the most relevant sources, author key words, and countries, providing a deeper understanding of the state of global research on smart contracts. Furthermore, keyword co-occurrence analysis identified four clusters representing underlying research streams: (1) smart contracts technology and its applications in healthcare (2) applications of smart contracts in supply chain management (3) smart contracts for the Internet of Things (IOT) (4) Smart contracts for business ventures on consortium blockchains Finally, future research directions are suggested based on areas of low coverage but high potential impact
Random vs. Best-First: Impact of Sampling Strategies on Decision Making in Model-Based Diagnosis

Statistical samples, in order to be representative, have to be drawn from a population in a random and unbiased way. Nevertheless, it is common practice in the ﬁeld of model-based diagnosis to make estimations from (biased) best-ﬁrst samples. One example is the computation of a few most probable fault explanations for a defective system and the use of these to assess which aspect of the system, if measured, would bring the highest information gain. In this work, we scrutinize whether these statistically not well-founded conventions, that both diagnosis researchers and practitioners have adhered to for decades, are indeed reasonable. To this end, we empirically analyze various sampling methods that generate fault explanations. We study the representativeness of the produced samples in terms of their estimations about fault explanations and how well they guide diagnostic decisions, and we investigate the impact of sample size, the optimal trade-off between sampling efﬁciency and effectivity, and how approximate sampling techniques compare to exact ones.
Incorporating virtual reality into maritime safety training to enhance competency-based learning outcomes

The potential of using immersive technologies like Virtual Reality (VR) in education is endorsed by several research studies indicating an expected increase in its use within the foreseeable future. Owing to its capacity to provide a highly interactive and immersive experience, the adoption and integration of VR into maritime safety training can create new opportunities for competency-based learning and teaching methods. In a safety-critical domain such as the maritime industry, safety training is crucial for ensuring trainees’ competency in emergency response and survival skills. This is accomplished through a range of training programs that were specifically tailored to address emergency events. However, during training exercises, fear and anxiety can have a substantial influence on trainees performance, especially if the activity is stressful as the "Helicopter Under Water Escape Training" (HUET). To address this problem, it is essential to psychologically prepare the trainees before executing the exercise. Driven by this aim, this applied research claims that incorporating VR technology into the classroom prior to practical exercises might contribute to achieving the best feasible results of the preparation by improving skill acquisition while mitigating fear and disorientation.&#x0D; The methodology followed in the study consists of two stages; the first comprises a 3D-Camera recording the training exercises from the trainee’s seats view, and examples of commonly committed faults. The trainees watch these recordings in VR headsets before practicing the exercises. The second stage entails conducting, collecting and analyzing trainees’ questionnaire, as well as observations raised by training instructors to distinguish viable elevations in trainees’ performance. Based on the outcomes of this study, it can be concluded that VR technology considerably enhances learning by enabling trainees to anticipate difficulties of the exercises, hence contributing to improved training performance.
Categorical Neighbour Correlation Coefficient (CnCor) for Detecting Relationships between Categorical Variables

Categorical data is common and, however, special in that its possible values exist only on a nominal scale so that many statistical operations such as mean, variance, and covariance become not applicable. Following the basic idea of the neighbour correlation coefficient (nCor), in this study, we propose a new measure named the categorical nCor (CnCor) to examine the association between categorical variables through using indicator functions to reform the distance metric and product-moment correlation coefficient. The proposed measure is easy to compute, and enables a direct test of statistical dependence without the need of converting the qualitative variables to quantitative ones. Compare to previous approaches, it is much more robust and effective in dealing with multi-categorical target variables especially when highly nonlinear relationships occurs in the multivariate case. We also applied the CnCor to implementing feature selection by the scheme of backward elimination. Finally, extensive experiments performed on both synthetic and real-world datasets are conducted to demonstrate the outstanding performance of the proposed methods, and draw comparisons with state-of-the-art association measures and feature selection algorithms.
Most Used Password Cracking Techniques

Passwords are the most widely used way to secure information and a platform is considered safe to the level of its weakest point users are constantly being educated about making harder or unpredictable passwords as well as one-time passwords or with the introduction of biometrics and password-less account that send a notification to the user’s electronic device the average user habits have not changed but with the introduction of new technology password cracking techniques have changed in this paper we take a look at the widest used password cracking methods Phishing, Malware, Social engineering, Brute force attack, Dictionary attack we also take a look at how these methods have evolved and also how GPU have affected Brute force and Dictionary attack and the number of losses due to password cracking and also where they are used and for what cause are they used and also the problem with using hashing to store password Keywords password cracking, GPU, Brute force attacks, Dictionary attacks, Hashing, link guard algorithm, ben Clark algorithm
FedCC: Federated Learning with Consensus Confirmation for Byzantine Attack Resistance (Student Abstract)

In federated learning (FL), a server determines a global learning model by aggregating the local learning models of clients, and the determined global model is broadcast to all the clients. However, the global learning model can significantly deteriorate if a Byzantine attacker transmits malicious learning models trained with incorrectly labeled data. We propose a Byzantine-robust FL algorithm that, by employing a consensus confirmation method, can reduce the success probability of Byzantine attacks. After aggregating the local models from clients, the proposed FL server validates the global model candidate by sending the global model candidate to a set of randomly selected FL clients and asking them to perform local validation with their local data. If most of the validation is positive, the global model is confirmed and broadcast to all the clients. We compare the performance of the proposed FL against Byzantine attacks with that of existing FL algorithms analytically and empirically.
WAYS TO REDUCE RESONANT SOUND TRANSMISSION THROUGH FRAMED PARTITION WALLS

This article discusses the passage of sound and the reduction of the resonant pas-sage of sound through the frame-sheathed partitions. The types of sound transmission through the frame-sheathed partitions and their differences are given. Conducted an analysis of existing solu-tions to reduce the resonant transmission of sound.
Learning to Evolve on Dynamic Graphs (Student Abstract)

Representation learning in dynamic graphs is a challenging problem because the topology of graph and node features vary at different time. This requires the model to be able to effectively capture both graph topology information and temporal information. Most existing works are built on recurrent neural networks (RNNs), which are used to exact temporal information of dynamic graphs, and thus they inherit the same drawbacks of RNNs. In this paper, we propose Learning to Evolve on Dynamic Graphs (LEDG) - a novel algorithm that jointly learns graph information and time information. Specifically, our approach utilizes gradient-based meta-learning to learn updating strategies that have better generalization ability than RNN on snapshots. It is model-agnostic and thus can train any message passing based graph neural network (GNN) on dynamic graphs. To enhance the representation power, we disentangle the embeddings into time embeddings and graph intrinsic embeddings. We conduct experiments on various datasets and down-stream tasks, and the experimental results validate the effectiveness of our method.