Reviewerly/exHarmony huggingface dataset:
AI : Job Creator or Jobless Future

It is said that, "Necessity is the mother of Invention"; and this is absolutely true for Homo sapiens. Living in 21st century, man has made everything to his comfort. Today we have so many machines around us in our service. Just one click or tap to a button and your task is done. Such is the level of ease, we are having today for most of our routine lifestyle work. Man had continuously make efforts to improve what they are currently. From easy job to critical task, we had made machines to do all our operations. Today we had such a breakthrough in technology, that we had made machines to learn, think and act like humans. This is when the Artificial Intelligence, come into the picture. In this research paper, we will be analyzing the fact that there are equal positive aspects too, when we talk about Artificial intelligence. Through this paper, we want to highlight the fact that, AI is not purely meant for human replacement. Our research with try to break the myths and negative opinion of the readers about AI as Job snatcher. Amidst discussing the favorable aspects with respect to AI, we will also through light on potential challenges from AI. Our research is based on secondary data, and our data sources are like-minded journals, websites and magazines.
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution Networks

Quantization is a promising approach to reduce the high computational complexity of image super-resolution (SR) networks. However, compared to high-level tasks like image classification, low-bit quantization leads to severe accuracy loss in SR networks. This is because feature distributions of SR networks are significantly divergent for each channel or input image, and is thus difficult to determine a quantization range. Existing SR quantization works approach this distribution mismatch problem by dynamically adapting quantization ranges to the variant distributions during test time. However, such dynamic adaptation incurs additional computational costs that limit the benefits of quantization. Instead, we propose a new quantization-aware training framework that effectively Overcomes the Distribution Mismatch problem in SR networks without the need for dynamic adaptation. Intuitively, the mismatch can be reduced by directly regularizing the variance in features during training. However, we observe that variance regularization can collide with the reconstruction loss during training and adversely impact SR accuracy. Thus, we avoid the conflict between two losses by regularizing the variance only when the gradients of variance regularization are cooperative with that of reconstruction. Additionally, to further reduce the distribution mismatch, we introduce distribution offsets to layers with a significant mismatch, which either scales or shifts channel-wise features. Our proposed algorithm, called ODM, effectively reduces the mismatch in distributions with minimal computational overhead. Experimental results show that ODM effectively outperforms existing SR quantization approaches with similar or fewer computations, demonstrating the importance of reducing the distribution mismatch problem. Our code is available at https://github.com/Cheeun/ODM.
Readability Analysis of Privacy Policies for Large-Scale Websites: A Perspective from Deep Learning and Linguistics

Privacy policy statements are an essential approach to self-regulation by website operators in the area of personal privacy protection. However, these policies are often lengthy and difficult to understand, with users appearing to actually read the privacy policy in only a few cases. To address these obstacles, we propose a framework, Privacy Policy Analysis Framework for Automatic Annotation and User Interaction (PPAI) that stores, classifies, and categorizes queries on natural language privacy policies. At the core of PPAI is a privacy-centric language model that consists of a smaller fine-grained dataset of privacy policies and a new hierarchy of neural network classifiers that take into account privacy practices with high-level aspects and finegrained details. Our experimental results show that the eight readability metrics of the dataset exhibit a strong correlation. Furthermore, PPAI’s neural network classifier achieves an accuracy of 0.78 in the multi-classification task. The robustness experiments reached higher accuracy than the baseline and remained robust even with a small amount of labeled data.
A Practical Blockchain-based Regulatory Framework for Charitable Funds

The collection and use of charitable funds need to be transparent enough, which may cause a huge crisis of trust if they are not used properly. The current regulation of charitable funds mainly relies on the audit of third-party organizations, which has a lag, and donors cannot verify whether the funds are used in compliance. At the same time, donors are unable to track the use of single donation funds. In this paper, we propose a scheme to assist in regulation through blockchain, using blockchain technology to track the use of charitable funds, speed up the auditing process of funds, and enhance the transparency of the use of charitable funds. At the same time, we consider the privacy protection issues involved in the process of charitable funds regulation, we use confidential computing technology to implement a data privacy protection strategy that allows data owners to flexibly manage the access authorization of data. We implemented a prototype system using Hyperledger Fabric and verified the usability of the system using existing donation data.
Exploring the Sharpened Cosine Similarity

Convolutional layers have long served as the primary workhorse for image classification. Recently, an alternative to convolution was proposed using the Sharpened Cosine Similarity (SCS), which in theory may serve as a better feature detector. While multiple sources report promising results, there has not been to date a full-scale empirical analysis of neural network performance using these new layers. In our work, we explore SCS's parameter behavior and potential as a drop-in replacement for convolutions in multiple CNN architectures benchmarked on CIFAR-10. We find that while SCS may not yield significant increases in accuracy, it may learn more interpretable representations. We also find that, in some circumstances, SCS may confer a slight increase in adversarial robustness.
Centroid-aware feature recalibration for cancer grading in pathology images

Cancer grading is an essential task in pathology. The recent developments of artificial neural networks in computational pathology have shown that these methods hold great potential for improving the accuracy and quality of cancer diagnosis. However, the issues with the robustness and reliability of such methods have not been fully resolved yet. Herein, we propose a centroid-aware feature recalibration network that can conduct cancer grading in an accurate and robust manner. The proposed network maps an input pathology image into an embedding space and adjusts it by using centroids embedding vectors of different cancer grades via attention mechanism. Equipped with the recalibrated embedding vector, the proposed network classifiers the input pathology image into a pertinent class label, i.e., cancer grade. We evaluate the proposed network using colorectal cancer datasets that were collected under different environments. The experimental results confirm that the proposed network is able to conduct cancer grading in pathology images with high accuracy regardless of the environmental changes in the datasets.
Information Seeking Behavior of the Authors Sang Patriot Pierre Tendean’s Book

Information seeking behavior is one of the interesting topics to be studied so far. Everyone has different information seeking behavior, it is influenced by several factors, such as age, profession, gender, education, beliefs, living environment and so on. This article will discuss the results of research on the information seeking behavior of the authors of a book entitled The Patriot Pierre Tendean. The authors of the book consist of 7 (seven) people and they have different professional backgrounds, some are journalists, teachers, employees, doctors, entrepreneurs, housewives, and travelers. The main purpose of this study was to determine the information seeking behavior of the authors of the book. Besides that, it is also to find out the purpose of writing the book, as well as explore various supporting factors and inhibiting factors faced by the authors in finding the information they need. This study used a qualitative method with a phenomenological approach, and the required data were obtained through in-depth interviews with informants. The informants are six of the seven authors of the book The Patriot Pierre Tendean. The results showed that the information seeking behavior of the six authors had different stages, some were in accordance with Ellis' information seeking behavior model, and some used the Khulthau information search model. Meanwhile, the purpose of writing the book Sang Patriot Pierre Tendean is to provide a book that provides complete and accurate information about Pierre Tendean as one of the revolutionary heroes who died in the events of the September 30th Movement/PKI (G30S/PKI) in 1965. The inhibiting factor faced by these writers was related to licensing to obtain the information they needed, while the supporting factor was their high motivation and enthusiasm in seeking information to meet their needs in completing the writing of the book Sang Patriot Pierre Tendean
Students' Needs Analysis: Teaching Palembang Local Culture Procedure Text with Android for Hospitality Industry Program

The globalization period is characterized by rapid advances in research, especially in the field of technology. Learning in social contexts and virtual learning must be taken into account to keep up with the needs of the industry era 4.0. With the help of SMK students and teachers, this study attempts to find out the students' needs in Procedure text reading material based on local culture in Palembang. The writer utilized a questionnaire and an interview to gather information. Statistics and qualitative analysis of the data were conducted. The need revealed that: 1) English was required for the students to help them in their future careers, and 2) English language proficiency of the students was still at the preliminary level, 3) the students' interest in reading was low, 4) the topic related to general knowledge was relevant, 5) The local culture of Palembang was crucial as a topic in reading comprehension, 6) The type of texts needed was procedure text, 7) Palembang local culture reading materials were not readily accessible. 8) The purpose of including Palembang culture in reading materials for students was to increase their knowledge of Palembang local cultures and improve their English proficiency in reading skills, especially in comprehension of the text. 9) The Palembang local culture aspect that the students needed was traditional foods, 10) An Android application was required to help the teaching and learning process. DIt was also required to create procedural text reading materials for supplementary with Palembang local culture using an Android application, which complemented the students' English proficiency. The application may benefit their future careers.
Information Seeking Behavior of Darul Qur'an Mulia Islamic Boarding School Students in Determining Higher Education Study Programs

This study aims to determine the level of information-seeking behavior of students in SMA Class XII Pesantren Terpadu Darul Qur'an Mulia in determining the study program in higher education. This type of research is descriptive research with a quantitative approach. The population of this study amounted to 204 students of Class XII Pesantren Terpadu Darul Qur'an Mulia, and the sample was taken by purposive sampling technique using the Slovin formula, so that a sample of 67 students was obtained. This research uses Kuhlthau's 1993 Information Search Process (ISP) model. Kuhlthau's theory has six stages including initiation, selection, exploration, formulation, collection, and presentation. The results showed that the information seeking behavior of students in determining the study program in higher education was classified as good. This can be seen from each stage of the Kuhlthau model, most students experience feelings, thoughts and actions that are in accordance with the Kuhlthau model, namely: Initiation gets a score of 3.27 (Very Good), Selection gets a score of 3.12 (Good), Exploration got a score of 2.92 (Good), Formulation got a score of 3.12 (Good), Collection got a score of 3.06 (Good) and Presentation got a score of 3.22 (Good). Santri experience all the stages that exist in the Kuhlthau model, especially at the Initiation stage, both in feelings, thoughts, and actions taken. Meanwhile, at the exploration stage, students tend not to feel the feeling to stop searching and the inconsistency of information as according to Kuhlthau's model. Based on the results of the study, the information seeking behavior of students in determining the study program in higher education as a whole is at a good level by obtaining a score of 3.11 (Good).
Cooperative Script as A Strategy in Optimizing Listening Learning Outcomes of SMA Negeri 1 Mandastana Students

This classroom action research aims to determine how effective the cooperative script learning model is in improving students' listening skills in class XI.IPS.2 at SMA Negeri 1 Mandastana. The research participants were 36 students, and the study was conducted in two cycles. Each cycle included two face-to-face meetings. The data for this study were acquired utilizing observation sheet instruments, learning accomplishment exams, and student responses to the cooperative script learning model. The data was then analyzed descriptively and qualitatively. The study's findings indicated that using the cooperative script model enhances learning outcomes and student activities in listening learning. Prior to using this learning model, the class average was 59.43. Following the implementation of this model, actions 1 and 2 cycles I increased to 65.43 and 70.29, respectively. Meanwhile, the class average in action 1 cycle 2 increased to 75.14. According to the student replies, 93% of students enjoyed and felt assisted by this learning paradigm, while just 7% did not feel helped. The study's implications include that the cooperative script learning model can be employed as an alternative to maximize learning outcomes and student activities in listening learning. This learning technique can also boost students' learning reactions and make them more interested in the teaching and learning process. As a result, teachers might think about adopting cooperative script learning models to create effective and efficient learning techniques.
Implementation of Data Mining Using K-Means Algorithm for Bicycle Sales Prediction

During the pandemic, to reduce the number of Covid-19 spreads, the government imposed social distancing and work from home (WFH) to reduce community activities outside the home. This caused people to have irregular patterns or lifestyles which less any physical activity . It surely can lower immunity system in which can increase the risk of being infected by the virus. Therefore, during the pandemic, sports or exercises become one of the activities that regularly carried out by the community to increase their immunity. One of the sports activities that can be done to maintain their immunity is cycling. Cycling itself is a light activity that can be practiced by all ages. This occasion is certainly a good marketing target for bicycle selling companies, but the company sometimes experiences problems regarding bicycle stocks that do not match with the consumer market target. The purpose of this study is to find out what types of bicycles are on demand by predicting bicycle sales and looking at the desired interests of the community. This study uses the K-Means Clustering algorithm. The results of the K-Means Clustering research are divided into three clusters; Cluster 1 with 209 members with the most interest in mountain bikes, Cluster 2 with 787 members with the most interest in folding bicycles, and Cluster 3 with 540 members with bicycle interests. Most of them are city bicycles, from the clustering process above, the Dunn Index validation (Dunn Index) can be obtained with a value of 0.1324532.
SAR: Generalization of Physiological Dexterity via Synergistic Action Representation

Task -agnosti c playSynergy acqui si ti on
Detection of genuine multipartite entanglement in arbitrary multipartite systems

We study the genuine multipartite entanglement of arbitrary n-partite quantum states by representing the density matrices in terms of the generalized Pauli operators. We introduce a general framework for detecting genuine multipartite entanglement and non full-separability of multipartite quantum states with arbitrary dimensions based on correlation tensors. Effective criterion is derived to verify the genuine multipartite entanglement. Detailed examples are given to show that the criterion detects more genuine multipartite entanglement than the existing criteria.
Scientific Bibliography in the State Public Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences: Stages of Development, Modern Realities and Prospects

The article objective is to show the bibliographic activity development on information research support in the State Public Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences (SPSTL SB RAS). Namely, changes in the technology of creating information resources of own generation due to computers and software; to represent the product types created by employees during 1958–2021; to highlight topics of traditional bibliographic current and retrospective indexes, bibliographic and full-text databases. The paper presents quantitative characteristics of the generated electronic resources; shows the forms of bibliographic services: information selective dissemination, differentiated services for managers, electronic thematic exhibitions of literature based on the databases; gives a brief description of bibliometric research. The authors determine prospects to develop the number of SPSTL SB RAS information and bibliographic complex. They are growing the authoritative data files in bibliographic databases, expanding the repertoire of Open Access sources to select information for regional bibliographic resources, developing interactive forms of information services for scientists and specialists; participating the semantic web through linked data technology based on authoritative files as an object (predicate), improving the service capabilities of the information search system (access to full texts).
Mounting media used in Histopathology and Laboratory : Roles and Functions : A Short Review

Mounting is considered as very important step. Various mounting media are there like aqueous mounting media and resinous mounting media. Various researches are done to check which mounting media is best for mounting the histopathological tissues and suitable for different types of microscopy. The mounting media helps in holding the tissue sample in between the slide and the cover slip. The refractive index of the mounting media is also an important factor. In this review article, we are trying to review the various types of the mounting media with special emphasis on their .The data was acquired from earlier published literature and also from searching the different database like Pubmed and Google Scholar.
SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable capability for both comprehension and generation. In this work, we address the evaluation of generative comprehension in MLLMs as a preliminary step towards a comprehensive assessment of generative models, by introducing a benchmark named SEED-Bench. SEED-Bench consists of 19K multiple choice questions with accurate human annotations (x 6 larger than existing benchmarks), which spans 12 evaluation dimensions including the comprehension of both the image and video modality. We develop an advanced pipeline for generating multiple-choice questions that target specific evaluation dimensions, integrating both automatic filtering and manual verification processes. Multiple-choice questions with groundtruth options derived from human annotation enables an objective and efficient assessment of model performance, eliminating the need for human or GPT intervention during evaluation. We further evaluate the performance of 18 models across all 12 dimensions, covering both the spatial and temporal understanding. By revealing the limitations of existing MLLMs through evaluation results, we aim for SEED-Bench to provide insights for motivating future research. We will launch and consistently maintain a leaderboard to provide a platform for the community to assess and investigate model capability.
Bi-Level Image-Guided Ergodic Exploration with Applications to Planetary Rovers

We present a method for image-guided exploration for mobile robotic systems. Our approach extends ergodic exploration methods, a recent exploration approach that prioritizes complete coverage of a space, with the use of a learned image classifier that automatically detects objects and updates an information map to guide further exploration and localization of objects. Additionally, to improve outcomes of the information collected by our robot's visual sensor, we present a decomposition of the ergodic optimization problem as bi-level coarse and fine solvers, which act respectively on the robot's body and the robot's visual sensor. Our approach is applied to geological survey and localization of rock formations for Mars rovers, with real images from Mars rovers used to train the image classifier. Results demonstrate 1) improved localization of rock formations compared to naive approaches while 2) minimizing the path length of the exploration through the bi-level exploration.
Retracted: Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

[This retracts the article DOI: 10.1155/2021/3669204.].
Dynamic Scheduling For Federated Edge Learning With Streaming Data

In this work, we consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration. We formulate a stochastic network optimization problem for designing a dynamic scheduling policy that maximizes the time-average data importance from scheduled user sets subject to energy consumption and latency constraints. Our proposed algorithm based on the Lyapunov optimization framework outperforms alternative methods without considering time-varying data importance, especially when the generation of training data shows strong temporal correlation.