Reviewerly/exHarmony huggingface dataset:
Integrative Multi-Modal Computing for Personal Health Navigation

An individual’s health trajectory is most influenced by personal lifestyle choices made regularly and frequently. It is now possible to measure, store, and analyze both multimodal lifestyle signals as well as multimodal physiological and behavioral health signals perpetually. Moreover, it is possible to model the effects of lifestyle precisely on health signals by collecting a variety of longitudinal data. These actions provide the inputs that affect changes in the health state of an individual based on their lifestyle decisions. With the advent of modern relatively inexpensive and common multi-modal data streams and sensing technologies, individuals now have large amounts of data and information about themselves that have the potential to transform decision-making in day-to-day life for health improvement. Multimodal analytics and contextual prediction and retrieval allow this data to make the best decisions to keep the health state optimal while making good lifestyle choices. This critical problem requires making a large variety of data constantly useful, contextually relevant, and most importantly useful in personalized decision-making.
Deep Learning-based Sentiment Analysis of Trip Advisor Reviews

In language processing, sentiment analysis is an essential task that involves analyzing and understanding the opinions, feelings, and emotions expressed in a text by users. In other words, it is a way of analyzing and understanding people's feelings. Since a large amount of data is generated by customers on a variety of online platforms, it has become increasingly important for businesses to analyze this data to better understand their customers' opinions and improve their products and services according to these opinions. One of the most well-known venues for opinion sharing is TripAdvisor, where customers discuss their experiences and reviews of hotels. This proposed work offers a method for the analysis of hotel reviews on TripAdvisor based on sentiment analysis using a deep learning-based approach. The study employs Bidirectional Encoder Representations from Transformers to classify the reviews by their sentiments, after learning the characteristics of the text data. Experimental results demonstrate the comparison of a few deep learning models and provide recommendation of the suitable model for customer feedback analysis. Hotels can utilize the suggested method to examine visitor comments.
Equivalent of Character Design for Design Thinking in Personal Character Design Process

Personal character has gained its popularity in digital platforms. Picking and editing one from a list in a package software, the user can simply create a character for general use. To be more masterly, guided by the specific needs of the user, a character design process helps a professional designer customize the most appropriate character for a precise job. Design thinking is a first - line process in design work, which is based on effective critical thinking, and its framework is user - centered method to solve problems and other activities, while character design process is based on character design methodology, which is employed in customizing a specific character. This study aims to survey common practices in design thinking and character design processes in creating a unique personal character to comprehend the relevance of their methods. The expected result is process of design thinking for effective idea development and productive character design process.
Maintaining an Established Embedded Librarian Program for 15 Years before, during, and Post Pandemic

This article is an update to a previous article on the online embedded librarian program at Athens State University. It examines the nature of embedded librarianship at Athens State University and how it has grown and evolved over the past fifteen years. The authors review the literature and provide definitions and best practices associated with embedded librarianship as well as various levels of embedded librarian involvement. They also provide suggestions for those involved in embedded librarianship.
Perceived Effects of Mixed Reality in Distance Learning for the Mining Education Sector

Mixed reality as a tool for teaching has made only limited use of its possibilities so far. However, it brings a plethora of new opportunities, with benefits ranging from interactivity to more vividness. These factors could improve numerous areas of teaching. The mining sector would benefit from new methods combined with mixed reality especially. Therefore, the MiReBooks project was launched: Various applications have been developed that can vividly present content using 3D models, virtual field trips and other methods. To verify and further improve these tools, an evaluation phase was conducted. During two test lectures in distance learning, a total of 23 participants answered a posttest questionnaire. The results showed that the teaching quality could be maintained well by the mixed reality application even in distance learning. Students were satisfied with the methods used, attributed good usability to the tool, and felt integrated into the classroom. At the same time, the team realized that the quality of the lesson depends heavily on the quality of the materials and the expertise of the lecturer. It also became clear that other factors, such as the technical infrastructure and support, are particularly important in this format.
State Regularized Policy Optimization on Data with Dynamics Shift

In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of current methods address such issue by training context encoders to identify environment parameters. Data with dynamics shift are separated according to their environment parameters to train the corresponding policy. However, these methods can be sample inefficient as data are used \textit{ad hoc}, and policies trained for one dynamics cannot benefit from data collected in all other environments with different dynamics. In this paper, we find that in many environments with similar structures and different dynamics, optimal policies have similar stationary state distributions. We exploit such property and learn the stationary state distribution from data with dynamics shift for efficient data reuse. Such distribution is used to regularize the policy trained in a new environment, leading to the SRPO (\textbf{S}tate \textbf{R}egularized \textbf{P}olicy \textbf{O}ptimization) algorithm. To conduct theoretical analyses, the intuition of similar environment structures is characterized by the notion of homomorphous MDPs. We then demonstrate a lower-bound performance guarantee on policies regularized by the stationary state distribution. In practice, SRPO can be an add-on module to context-based algorithms in both online and offline RL settings. Experimental results show that SRPO can make several context-based algorithms far more data efficient and significantly improve their overall performance.
Computation with Sequences in a Model of the Brain

Even as machine learning exceeds human-level performance on many applications, the generality, robustness, and rapidity of the brain's learning capabilities remain unmatched. How cognition arises from neural activity is a central open question in neuroscience, inextricable from the study of intelligence itself. A simple formal model of neural activity was proposed in Papadimitriou [2020] and has been subsequently shown, through both mathematical proofs and simulations, to be capable of implementing certain simple cognitive operations via the creation and manipulation of assemblies of neurons. However, many intelligent behaviors rely on the ability to recognize, store, and manipulate temporal sequences of stimuli (planning, language, navigation, to list a few). Here we show that, in the same model, time can be captured naturally as precedence through synaptic weights and plasticity, and, as a result, a range of computations on sequences of assemblies can be carried out. In particular, repeated presentation of a sequence of stimuli leads to the memorization of the sequence through corresponding neural assemblies: upon future presentation of any stimulus in the sequence, the corresponding assembly and its subsequent ones will be activated, one after the other, until the end of the sequence. Finally, we show that any finite state machine can be learned in a similar way, through the presentation of appropriate patterns of sequences. Through an extension of this mechanism, the model can be shown to be capable of universal computation. We support our analysis with a number of experiments to probe the limits of learning in this model in key ways. Taken together, these results provide a concrete hypothesis for the basis of the brain's remarkable abilities to compute and learn, with sequences playing a vital role.
Human Pose Estimation using Deep Learning Techniques

Human Pose Estimation is a method of detecting and classifying all the key human body joints. A 2D Human Pose Estimation method predicts the two-dimensional coordinates from an input image. Classical methods used deformable parts models for detecting body joints. This paper discusses the state-of-the-art Deep Learning based approaches for estimating human poses. It can be implemented by either using the top-down or bottom-up technique for pose estimation. Here, the authors have studied the Convolutional Pose Machines, Hourglass Network and MoveNet architectures for Pose Estimation. These architectures are compared against each other in terms of their efficiency by measuring the time taken by each of them and their accuracy by evaluating them using the Percentage of Detected Joints metric on the MPII dataset. The architectures were implemented by using TensorFlow and Open-Source libraries built using PyTorch. The inference results for each joint were compared individually for all the body joints. The Hourglass Network showed the best accuracy but is really slow. The MoveNet architecture is the second best according to accuracy but is very fast and can be used in real-time systems.
Enabling Gesture-based Application Interaction on Head Mounted VR Display

With the integration of smartphones, VR head-mounted displays that are affordable (HMDs) have made VR more accessible to the general public and increased its widespread use. However, due to their lack of interaction, these systems frequently have limitations. GestOnHMD, a technique for a pipeline for classifying gestures and gesture-based interaction, is presented in this study. It makes use of the gesture technique in smartphones to detect hand gestures, detecting 21 points on hand on VR headset. Initially, gesture-elicitation research is carried out using specific headset the Google Cardboard to detect the 21 hand points which detects the hand gestures and display the meaning of the hand gesture. Based on signal detectability and user choices, different kinds of gestures are selected to input the dataset. It uses the neural networks to map the hand points and the cam detects the hand gestures and recognizes it and the displays the meaning of each On the VR device’s screen. Each gesture can be made specifically for the commands and its meaning by showing the hand gesture in front of the cam and storing it in the dataset. It can be used to read American Signal Language, etc. which makes people to understand hand gestures and might help in future exploring. The output is displayed on the Google Cardboard Virtual Reality device screen which is connected to the laptop.
Utilization of Heuristic Approaches in Cryptography to Enhance Security

Genetic algorithms are a class of optimization algorithms that simplify genetic processes to solve a variety of problems. Utilizing a genetic algorithm increases the security of the key. The application of the genetic algorithm improves the key, and consequently the algorithm as a whole. This paper proposed a method which encrypts data using multiple distinct procedures. The procedure begins with the generation of a key using genetic processes and a random number generator. The data is then dispersed using genetic operators and encrypted using logical operations involving the dispersed data and the key. In conventional systems, data is encrypted with a key, but there is still the possibility that the key could be intercepted and the text is altered. As a result, a natural selection-based strategy has been presented. Keys must be robust and unpredictable. Transferring information securely is a major challenge in the field of information technology. For high levels of security, neither symmetric nor asymmetric crypto systems are suitable. Traditional systems are outperformed by hash function-based systems, but developing invertible functions is a time-consuming process involving complex procedures. Despite the fact that the proposed model is less computationally efficient than the other two, it has been determined that it yields results with greater key strengths.
A Blockchain-Based, Distributed, Self Hosted And End To End Encrypted Cloud Storage System

Cloud computing is fast taking over due to its convenience and greater safety. You may access your files anytime you need them by using the cloud. Having a large following makes things simpler for consumers. Computing makes phase, programming, and system structure all possible. With the aid of these components, increased cloud-based information and profits might be obtained. It is great to plan and arrange work based on data. The most difficult issue for people with a background in logical thinking is planning work procedures to achieve customer service goals while keeping expenses in control. Although leveraging cloud storage to reduce expenses has been suggested, doing so can be difficult. Although leveraging cloud storage to reduce expenses has been suggested, doing so can be difficult. To handle cloud resources efficiently, a modular infrastructure is needed. Utilizing standards and calculations, parallel resource and service management is maximized in the cloud. Using different work flows to structure work processes is the most entertaining activity in cloud computing. Timing and price have an influence on service quality (tasks). Workflow-based relocation of virtual machines is more effective. NP-hard algorithms for subset and choice issues. Making a choice allows the server to save time and money. PSO and GWO interactions that are advantageous. In this undertaking, both time and money are considerations. The new approach should be used. The study affects the validity of process parameters. intuition with a convex shape. utilizing the PEFT technique. GWO analyses the time and money spent on cloud processes. It is suggested that VMs be optimized as hybrid, both locally and globally. heuristic algorithm based on PEFT. Optimization reduces the likelihood of making a mistake right away. The Grey Wolf Optimization and Floral pollination algorithm outperforms genetic and flower pollination techniques. Biomimicry is compared with swarm intelligence. For our analysis, we use LIGO, GENOME, CYBER SHAKE, and SIPHT. The difficulty and quantity of the jobs have an impact on workflow. A bio-inspired GA, GWO, and FPA are used in the optimization process. In an experimental arrangement, time and cost analysis for two to twenty VMs and workflows may be done. Compared to FPA with PEFT, GWO requires less time and money. In hybrid optimization, GWO and FPA are combined. In this project, efficiency and speed are highly valued. GWO optimizes VM globally, whereas FPA concentrates on local enhancements. FPA GA uses the collective wisdom of the group to identify correlations. As labor prices grow, more virtual machines (VMs) are employed for processing and tasks. Wait times drop and costs rise. Local and global optimization have an impact on virtual machine (VM) and compute time. ACO and PSO are used to accomplish local and global optimization, however employing them requires more time.
Analysis of Peak Flood Discharge in Small-Scale River Flow Area

Purpose: Flood discharge is a critical component in reducing the impact of flooding in a given location. An analytical technique based on a watershed's morphometric properties is a method for those without a gauge, particularly in small-scale watersheds.&#x0D; Design/methodology/approach: The synthetic unit hydrograph (HSS) approach is used in this study to analyze flood output. This study was conducted in the Akelaka watershed, a sub-watershed of Tayawi. This analysis relies on spatial and attributes data. A spatial study of watershed morphometric properties was obtained by integrating digital elevation model (DEM) data into geographic information system-based applications (GIS). Meanwhile, using rainfall data from 2019 to 2021, a statistical approach is used to calculate the design rainfall intensity, which is then used as an input variable in the HSS analysis.&#x0D; Findings: The findings of hydrographic analysis using Nakayasu HSS demonstrate that the peak flood time (Tp) is 2.8 hours with a maximum flood discharge (Qp) of 56.636 m3/s, 76, 987 m3/s, 88,091 m3/s, and 101,279 m3/s for return periods of two years, five years, ten years, and 25 years.&#x0D; Paper type: Research paper
A Collaborative Action Research for Curriculum Integration of Extracurricular Music Activities in H Kindergarten

본 연구는 유아교육기관에서 방과 후 특별활동 운영에 대한 문제점을 파악하여 기본교육과정과 방과 후 특별활동 과정을 통합적으로 운영하기 위한 방안을 협력적으로 살펴보고 실행해 봄으로써 교육의 주체자들에게 나타난 변화과정과 의미를 분석해보는 것을 목적으로 하였다. 이를 위해 D시에 위치한 H유치원의 만3세 씨앗반 담임교사와 14명의 유아들을 대상으로 2019년 3월부터 7월까지 총2차(16주)의 협력적 실행과정을 실시하였다. 본 연구에 앞서 3주간의 예비연구를 통해 기관 및 교실의 상황적 맥락과 문제점을 파악하고, 한 회 차당 8주씩 유아교사와 연구자와의 상호 협력적 실행과정이 이루어졌다. 1차 실행과정은 ‘생활주제와 음악 특별활동과의 통합적 연계 활동을 위한 시도과정’으로 음악 특별활동 시 활용했던 악기와 음률영역간의 통합 시도가 관찰되어졌다. 2차 실행은 악기 뿐 아니라 동화, 신체 활동 등의 “정규교육과정과 음악 특별활동의 적극적 통합과정”으로 진행되었다. 이러한 연구과정에서 연구 참여자 면담내용, 유아수업 영상, 자유놀이 사진, SNS에서 주고받은 문자 등 다양한 자료가 수집되었으며 수집된 자료는 코딩범주를 개발하여 데이터를 분류하고 분석하였다. 그 결과 유아들은 음악적 요소를 경험하며 음악을 즐기게 되었으며, 유아 교사들은 음악교육에 대한 인식이 긍정적으로 변화되었다. 이에 본 연구는 기본교육과정과 방과 후 음악특별활동의 통합적 운영을 위해 음악 강사와 유아 교사가 서로의 전문성을 공유하고 함께 협력해 나갈 수 있는 협력체제로서 지원방안과 체계가 구축되어야 함을 제안한다. The purpose of this study was to find problems with the integrated operation of the basic curriculum and extracurricular activities curriculum and to explore the integrated operation. For research, the second time collaborative action researches were conducted from March to July 2019 for early childhood teacher in Seed class, extracurricular music instructors, and 14 children at H kindergarten located in D City. The first collaborative action research was conducted “To keep musical instruments and materials used when music activities time in music area” for integrated activities with topics of the basic curriculum and topics of extracurricular music. It was conducted as an “active integration process of the regular curriculum and special music activities” such as fairy tales and physical activities. In the process of this research, It were collected various data such as interviews with research participants, videos of early childhood classes, photos of free play, and text messages exchanged on SNS. The collected data were analyzed by dividing them into upper and lower concepts. As a result of the analysis, children experienced musical elements and enjoyed music. And early childhood teachers have changed their perception of music education. Based on these result, this study suggests that it should be established support measures and cooperation systems to share their expertise with early childhood teachers and extracurricular activities instructors.
A New Cryptanalysis Model Based on Random and Quantum Walks

Randomness plays a key role in the design of attacks on cryptographic systems and cyber security algorithms in general. Random walks and quantum walks are powerful tools for mastering random phenomena. In this article, I propose a probabilistic attack model of a cryptographic system. This model is based on a random or quantum walk with the space of states being a space containing the secret to be revealed. This space can be a subspace of keys, plain texts or cipher texts.
Incremental schema integration for data wrangling via knowledge graphs

Virtual data integration is the current approach to go for data wrangling in data-driven decision-making. In this paper, we focus on automating schema integration, which extracts a homogenised representation of the data source schemata and integrates them into a global schema to enable virtual data integration. Schema integration requires a set of well-known constructs: the data source schemata and wrappers, a global integrated schema and the mappings between them. Based on them, virtual data integration systems enable fast and on-demand data exploration via query rewriting. Unfortunately, the generation of such constructs is currently performed in a largely manual manner, hindering its feasibility in real scenarios. This becomes aggravated when dealing with heterogeneous and evolving data sources. To overcome these issues, we propose a fully-fledged semi-automatic and incremental approach grounded on knowledge graphs to generate the required schema integration constructs in four main steps: bootstrapping, schema matching, schema integration, and generation of system-specific constructs. We also present Nextia DI , a tool implementing our approach. Finally, a comprehensive evaluation is presented to scrutinize our approach.
When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming

AI powered code-recommendation systems, such as Copilot and CodeWhisperer, provide code suggestions inside a programmer's environment (e.g., an IDE) with the aim to improve their productivity. Since, in these scenarios, programmers accept and reject suggestions, ideally, such a system should use this feedback in furtherance of this goal. In this work, we leverage prior data of programmers interacting with GitHub Copilot, a system used by millions of programmers, to develop interventions that can save programmer time. We propose a utility theory framework, which models this interaction with programmers and decides which suggestions to display. Our framework Conditional suggestion Display from Human Feedback (CDHF), relies on a cascade of models that predict suggestion acceptance to selectively hide suggestions reducing both latency and programmer verification time. Using data from 535 programmers, we perform a retrospective evaluation of CDHF and show that we can avoid displaying a significant fraction of suggestions that would have been rejected doing so without total knowledge of the suggestions themselves. We further demonstrate the importance of incorporating the programmer's latent unobserved state in deciding when to display suggestions through ablations on user study data. Finally, we showcase that using suggestion acceptance as a reward signal to know which suggestions to display leads to reduced quality suggestions indicating an unexpected pitfall.
Enhancing Robustness of AI Offensive Code Generators via Data Augmentation

In this work, we present a method to add perturbations to the code descriptions to create new inputs in natural language (NL) from well-intentioned developers that diverge from the original ones due to the use of new words or because they miss part of them. The goal is to analyze how and to what extent perturbations affect the performance of AI code generators in the context of security-oriented code. First, we show that perturbed descriptions preserve the semantics of the original, non-perturbed ones. Then, we use the method to assess the robustness of three state-of-the-art code generators against the newly perturbed inputs, showing that the performance of these AI-based solutions is highly affected by perturbations in the NL descriptions. To enhance their robustness, we use the method to perform data augmentation, i.e., to increase the variability and diversity of the NL descriptions in the training data, proving its effectiveness against both perturbed and non-perturbed code descriptions.