Reviewerly/exHarmony huggingface dataset:
Biometric-Based Electronic Voting System

Abstract: This project presents the design and implementation of an electronic voting machine (EVM) that uses fingerprint verification to enhance the security and accuracy of the voting process. The EVM is integrated with an SQLite database to store voters' fingerprint data and personal details. The system allows voters to cast their votes by verifying their identity through fingerprint recognition and pressing a button on the EVM. Once a vote is cast, the voter receives a message on their mobile device to confirm that their vote has been recorded. The project aims to address the challenges of traditional voting systems and offer a more secure and reliable method for election management.
Overview of Multimedia Cloud

Abstract: Cloud computing is the following era computing stage that gives numerous applications to the clients in seamless way and get to the assets over the organize without any boundary. Mixed media content management and preparing is among the imperative viewpoint of the cloud and it brings the preferences of processing and conveying of Interactive media within the conveyed situations. Cloud clients are having interest to get to and share the media inside the community by utilizing the gadgets with restricted capability
Mobile Keyword Prediction using Federated Learning

Abstract: Federated learning is a decentralized form of Machine Learning in which data subsets are trained on several edge devices aggregated and brought to the centralized server. Here, federated applications store the local copy on all the edge devices such as smartphones where users can use it accordingly. The model gradually learns and trains itself from inputs by the user’s virtual keyboard and becomes smarter iteratively. Devices transfer the results in the form of parameters to the centralized server where these results are aggregated with the help of federated algorithms. For the purpose of next word prediction using federated learning, we implemented different algorithms such as FedAvg, FedProx, FedSgd. In reference to the traditional ML models, data is collected in a centralized location and is used to train the model. However, unlike ML techniques the Federated Learning techniques give more control over user’s data to themselves since the data is not shared with the central server or other devices. Thus, federated learning adheres to the data privacy and feasibility for its users
The Relationship Between Negative Ties and Blue-collar Employees' Performance and Ethical Voice: The Mediating Effects of Personal Reputation

The purpose of this study is to determine the effect of two types of negative ties, namely perceived negative ties and intended negative ties, on the two types of employee work outcomes, namely job performance and ethical voice. In addition, the chapter discusses the role of personal reputation as a mediator in explaining the relationship between negative ties and work outcomes. The study sample comprised of 103 blue-collar workers and their direct supervisors in a large cement factory in southern Iraq. The study used survey method as a mean to collect the data. Data analysis showed that perceived negative ties and intended negative ties influence participants’ job performance and ethical voice. The result also indicates that personal reputation mediates the relationship between negative ties and work outcomes. In light of the results, theoretical and practical implications are presented.
Evidence Gap Maps as Critical Information Communication Devices for Evidence-based Public Policy

The public policy cycle requires increasingly the use of evidence by policy makers. Evidence Gap Maps (EGMs) are a relatively new methodology that helps identify, process, and visualize the vast amounts of studies representing a rich source of evidence for better policy making. This document performs a methodological review of EGMs and presents the development of a working integrated system that automates several critical steps of EGM creation by means of applied computational and statistical methods. Above all, the proposed system encompasses all major steps of EGM creation in one place, namely inclusion criteria determination, processing of information, analysis, and user-friendly communication of synthesized relevant evidence. This tool represents a critical milestone in the efforts of implementing cutting-edge computational methods in usable systems. The contribution of the document is two-fold. First, it presents the critical importance of EGMs in the public policy cycle; second, it justifies and explains the development of a usable tool that encompasses the methodological phases of creation of EGMs, while automating most time-consuming stages of the process. The overarching goal is the better and faster information communication to relevant actors like policy makers, thus promoting well-being through better and more efficient interventions based on more evidence-driven policy making.
PANLILOK: TERVENTIONAL MATERIAL ON READING AND LEARNING OF LITERARY WORKS

The main purpose of this study is to create material that includes the literature found in the town of Santa Cruz, Laguna.This will be the material as a reading intervention along with the learning of local literature.PANLILOK as a material was developed and used by the researcher on two hundred students of Linga National High School (Students in every grade with low level of performance in reading.)The outcome of this study is presented.The PanLiLok level: Interventional material based on Parts according to; Objectives; Content; Task; and Importance, all are acceptable with the respondents' answer to the totality of each part with a comment that strongly agree and a literal explanation of "extremely high."The PanLiLok level: Interventional material based on the Trait according to Personal Views and Knowledge, all are acceptable that the answer of the respondents in the totality of each trait has a comment that strongly agrees and a literal explanation that is "very high."The level of Literary Reading and Learning based on Vocabulary is acceptable that the answer of the respondents throughout each question has a descriptive equivalent of Good and a literal explanation of Somewhat Satisfactory.The level of Reading and Learning Literary Work based on Comprehension is acceptable with respondents' answers to the entirety of each question having a descriptive equivalent of Very Good and a literal explanation of Very Satisfactory.In sum, PanLiLok as an Interventional Material has a "No significant" effect on students'Reading and Learning of Literary Works.Therefore, the conclusion presented is acceptable based on the results of each problem presented.After the study and analysis of the findings, the researcher suggests to the teachers enjoy and use the local literature in their area so that the students have enough knowledge about the culture and beliefs of the people living there.
Pervasive computing technology to support education during adverse situations in Bangladesh

&lt;p&gt;Education is a humanitarian right. The alarming increase in armed conflict, forceful displacements, health, and climatic disasters, along with other crises dramatically impact the education system in long term. Pervasive computing technology can provide a smooth interaction to support education during a crisis. The paper, therefore, aims to discuss the support that pervasive computing technologies can provide during a crisis for the Least Developed Countries like Bangladesh with the least possible cost. The paper highlights different real-life cases in Bangladesh and then uses secondary data from 2006 to 2020 to understand the relation at the times when education was hampered for the adverse situation. Based on the findings the importance of the application of pervasive computing and necessary recommendations are given which may help the state and locals in responsibility to support education at such times.&lt;/p&gt;
ASSESSMENT ON THE REVISED IMPLEMENTATION OF THE HOMEROOM GUIDANCE PROGRAM IN ZONE 2, DIVISION OF ZAMBALES: BASIS FOR FORMULATION OF ACTION PLANS

This study aimed to assessed the revised implementation of the Homeroom Guidance Program in Zone 2, Division of Zambales during SY 2021-2022: Basis for the formulation of Action Plans.The study utilized the descriptive research design with questionnaire as the main instrument in gathering data from one hundred forty-two (142) randomly selected elementary teacher-respondents in Palauig, Iba and Botolan districts respectively.Based on the statistical data analysis revealed that there is significant difference when grouped according to sex towards Academic and Personal and Social development; significant on age towards Academic and Career development; and significant on the number of years in the services towards Academic and Career Development.There is no significant difference on the domains towards the level of implementation of homeroom guidance program.The researcher recommended that the teachers are encouraged to be creative and skillful in motivating learners in engage actively in all class undertakings and activities; that the school administration is encouraged to consider Homeroom Guidance as one of the priority programs that foster positivity of perspective, attainment of good academic achievement, and wise choice of future career' to have communication with parents on appropriate mechanism to help improve study habits of children for better academic achievement; that the school guidance program shall consider to include and address the challenges confronting among learners as to issues like academic failures, school dropouts, bullying, unhealthy sexual behavior, teenage pregnancy, drug addiction, online and social media addiction, and confusion in and/or poor career choice.
Parental Involvement and Reading Performance of Pupils

This study focused on the extent of parental involvement and reading performance of the Grade VI pupils in Villanueva South District for the School Year 2022-2023.This study answered the following questions: the extent of parental involvement in terms of Parenting, Communication, and Learning at Home; the level of pupils' reading performance when categorized as Independent, Instructional, Frustration, and Non-reader; the relationship between parental involvement and the level of pupils' reading performance.This study used an adapted and modified 4-Point Likert questionnaire of Epstein (1993) as cited by Yuliante et al. ( 2018) and the components of Phil-IRI that served as the second instrument because this was the dependent variable in this study.The researcher utilized the descriptivecorrelational research design to justify and realize the study's objectives.Results revealed that the pupils agreed that parents must be involved in their reading practices specifically in parenting, communication, and learning at home.Additionally, results showed that more pupils belonged to the Instructional Level of Reading Performance.The study conveyed the significant relationship between the extent of parental involvement and the reading performance of the pupils.Therefore, parents should understand that parenting, communication with the teachers, and learning at home have significant impacts on how well school-aged children read.It is recommended that the school must introduce various school programs and projects that involve the parents to foster a strong relationship between the teachers and the parents and parents and pupils.
Invariant Scattering Transform for Medical Imaging

Over the years, the Invariant Scattering Transform (IST) technique has become popular for medical image analysis, including using wavelet transform computation using Convolutional Neural Networks (CNN) to capture patterns' scale and orientation in the input signal. IST aims to be invariant to transformations that are common in medical images, such as translation, rotation, scaling, and deformation, used to improve the performance in medical imaging applications such as segmentation, classification, and registration, which can be integrated into machine learning algorithms for disease detection, diagnosis, and treatment planning. Additionally, combining IST with deep learning approaches has the potential to leverage their strengths and enhance medical image analysis outcomes. This study provides an overview of IST in medical imaging by considering the types of IST, their application, limitations, and potential scopes for future researchers and practitioners.
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT

In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various natural language learning tasks, extensions of which have also been applied to study the vision tasks with multi-modal data. However, when it comes to the graph learning tasks, existing LLMs present very serious flaws due to their several inherited weaknesses in performing {multi-step logic reasoning}, {precise mathematical calculation} and {perception about the spatial and temporal factors}. To address such challenges, in this paper, we will investigate the principles, methodologies and algorithms to empower existing LLMs with graph reasoning ability, which will have tremendous impacts on the current research of both LLMs and graph learning. Inspired by the latest ChatGPT and Toolformer models, we propose the Graph-ToolFormer (Graph Reasoning oriented Toolformer) framework to teach LLMs themselves with prompts augmented by ChatGPT to use external graph reasoning API tools. Specifically, we will investigate to teach Graph-ToolFormer to handle various graph data reasoning tasks in this paper, including both (1) very basic graph data loading and graph property reasoning tasks, ranging from simple graph order and size to the graph diameter and periphery, and (2) more advanced reasoning tasks on real-world graph data, such as bibliographic networks, protein molecules, sequential recommender systems, social networks and knowledge graphs.
altiro3D: Scene representation from single image and novel view synthesis

We introduce altiro3D, a free extended library developed to represent reality starting from a given original RGB image or flat video. It allows to generate a light-field (or Native) image or video and get a realistic 3D experience. To synthesize N-number of virtual images and add them sequentially into a Quilt collage, we apply MiDaS models for the monocular depth estimation, simple OpenCV and Telea inpainting techniques to map all pixels, and implement a 'Fast' algorithm to handle 3D projection camera and scene transformations along N-viewpoints. We use the degree of depth to move proportionally the pixels, assuming the original image to be at the center of all the viewpoints. altiro3D can also be used with DIBR algorithm to compute intermediate snapshots from a equivalent 'Real (slower)' camera with N-geometric viewpoints, which requires to calibrate a priori several intrinsic and extrinsic camera parameters. We adopt a pixel- and device-based Lookup Table to optimize computing time. The multiple viewpoints and video generated from a single image or frame can be displayed in a free-view LCD display.
Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK

Public attention towards explainability of artificial intelligence (AI) systems has been rising in recent years to offer methodologies for human oversight. This has translated into the proliferation of research outputs, such as from Explainable AI, to enhance transparency and control for system debugging and monitoring, and intelligibility of system process and output for user services. Yet, such outputs are difficult to adopt on a practical level due to a lack of a common regulatory baseline, and the contextual nature of explanations. Governmental policies are now attempting to tackle such exigence, however it remains unclear to what extent published communications, regulations, and standards adopt an informed perspective to support research, industry, and civil interests. In this study, we perform the first thematic and gap analysis of this plethora of policies and standards on explainability in the EU, US, and UK. Through a rigorous survey of policy documents, we first contribute an overview of governmental regulatory trajectories within AI explainability and its sociotechnical impacts. We find that policies are often informed by coarse notions and requirements for explanations. This might be due to the willingness to conciliate explanations foremost as a risk management tool for AI oversight, but also due to the lack of a consensus on what constitutes a valid algorithmic explanation, and how feasible the implementation and deployment of such explanations are across stakeholders of an organization. Informed by AI explainability research, we conduct a gap analysis of existing policies, leading us to formulate a set of recommendations on how to address explainability in regulations for AI systems, especially discussing the definition, feasibility, and usability of explanations, as well as allocating accountability to explanation providers.
Advances in Deep Concealed Scene Understanding

Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive objects exhibiting camouflage. The current boom in terms of techniques and applications warrants an up-to-date survey. This can help researchers to better understand the global CSU field, including both current achievements and remaining challenges. This paper makes four contributions: (1) For the first time, we present a comprehensive survey of deep learning techniques aimed at CSU, including a taxonomy, task-specific challenges, and ongoing developments. (2) To allow for an authoritative quantification of the state-of-the-art, we offer the largest and latest benchmark for concealed object segmentation (COS). (3) To evaluate the generalizability of deep CSU in practical scenarios, we collect the largest concealed defect segmentation dataset termed CDS2K with the hard cases from diversified industrial scenarios, on which we construct a comprehensive benchmark. (4) We discuss open problems and potential research directions for CSU. Our code and datasets are available at https://github.com/DengPingFan/CSU, which will be updated continuously to watch and summarize the advancements in this rapidly evolving field.
SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation

We present SSS3D, a fast multi-objective NAS framework designed to find computationally efficient 3D semantic scene segmentation networks. It uses RandLA-Net, an off-the-shelf point-based network, as a super-network to enable weight sharing and reduce search time by 99.67% for single-stage searches. SSS3D has a complex search space composed of sampling and architectural parameters that can form 2.88 * 10^17 possible networks. To further reduce search time, SSS3D splits the complete search space and introduces a two-stage search that finds optimal subnetworks in 54% of the time required by single-stage searches.
Effective Neural Network $L_0$ Regularization With BinMask

$L_0$ regularization of neural networks is a fundamental problem. In addition to regularizing models for better generalizability, $L_0$ regularization also applies to selecting input features and training sparse neural networks. There is a large body of research on related topics, some with quite complicated methods. In this paper, we show that a straightforward formulation, BinMask, which multiplies weights with deterministic binary masks and uses the identity straight-through estimator for backpropagation, is an effective $L_0$ regularizer. We evaluate BinMask on three tasks: feature selection, network sparsification, and model regularization. Despite its simplicity, BinMask achieves competitive performance on all the benchmarks without task-specific tuning compared to methods designed for each task. Our results suggest that decoupling weights from mask optimization, which has been widely adopted by previous work, is a key component for effective $L_0$ regularization.
Spatial-Language Attention Policies for Efficient Robot Learning

Despite great strides in language-guided manipulation, existing work has been constrained to table-top settings. Table-tops allow for perfect and consistent camera angles, properties are that do not hold in mobile manipulation. Task plans that involve moving around the environment must be robust to egocentric views and changes in the plane and angle of grasp. A further challenge is ensuring this is all true while still being able to learn skills efficiently from limited data. We propose Spatial-Language Attention Policies (SLAP) as a solution. SLAP uses three-dimensional tokens as the input representation to train a single multi-task, language-conditioned action prediction policy. Our method shows an 80% success rate in the real world across eight tasks with a single model, and a 47.5% success rate when unseen clutter and unseen object configurations are introduced, even with only a handful of examples per task. This represents an improvement of 30% over prior work (20% given unseen distractors and configurations). We see a 4x improvement over baseline in mobile manipulation setting. In addition, we show how SLAPs robustness allows us to execute Task Plans from open-vocabulary instructions using a large language model for multi-step mobile manipulation. For videos, see the website: https://robotslap.github.io
A Group-Specific Approach to NLP for Hate Speech Detection

Automatic hate speech detection is an important yet complex task, requiring knowledge of common sense, stereotypes of protected groups, and histories of discrimination, each of which may constantly evolve. In this paper, we propose a group-specific approach to NLP for online hate speech detection. The approach consists of creating and infusing historical and linguistic knowledge about a particular protected group into hate speech detection models, analyzing historical data about discrimination against a protected group to better predict spikes in hate speech against that group, and critically evaluating hate speech detection models through lenses of intersectionality and ethics. We demonstrate this approach through a case study on NLP for detection of antisemitic hate speech. The case study synthesizes the current English-language literature on NLP for antisemitism detection, introduces a novel knowledge graph of antisemitic history and language from the 20th century to the present, infuses information from the knowledge graph into a set of tweets over Logistic Regression and uncased DistilBERT baselines, and suggests that incorporating context from the knowledge graph can help models pick up subtle stereotypes.
Machine Learning and the Future of Bayesian Computation

Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the posterior distribution. Practical posterior computation is commonly performed via MCMC, which can be computationally infeasible for high dimensional models with many observations. In this article we discuss the potential to improve posterior computation using ideas from machine learning. Concrete future directions are explored in vignettes on normalizing flows, Bayesian coresets, distributed Bayesian inference, and variational inference.