Reviewerly/exHarmony huggingface dataset:
Discovering Latent Knowledge in Language Models Without Supervision

Existing techniques for training language models can be misaligned with the truth: if we train models with imitation learning, they may reproduce errors that humans make; if we train them to generate text that humans rate highly, they may output errors that human evaluators can't detect. We propose circumventing this issue by directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way. Specifically, we introduce a method for accurately answering yes-no questions given only unlabeled model activations. It works by finding a direction in activation space that satisfies logical consistency properties, such as that a statement and its negation have opposite truth values. We show that despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models: across 6 models and 10 question-answering datasets, it outperforms zero-shot accuracy by 4\% on average. We also find that it cuts prompt sensitivity in half and continues to maintain high accuracy even when models are prompted to generate incorrect answers. Our results provide an initial step toward discovering what language models know, distinct from what they say, even when we don't have access to explicit ground truth labels.
Building’s digital twin model shown as first-person and third-person views

Abstract Construction industry is currently facing challenges such as a high number of occupational injuries and a somewhat weak connection between design and construction phases. One way to amend the situation is to increase data availability to construction professionals. Information about the structure and features of the building to be built should be shared so that various professionals can use the so-called digital twin model of the building for his/her purposes. We have created a concept for delivering the building information model (BIM) that can be viewed in various ways from various devices. We tested the viewing and using of BIM in augmented reality (AR), with high-end eXtented Reality (XR) technology (Microsoft HoloLens 2) and a tablet. Briefly, according to our test results, AR seems to be good when user needs to be perceived him/herself as if inside the building model, and tablet suits for viewing the digital model from the outside of it. Novice users of the AR technology need much more support in using the technology than the experienced ones. All test participants found the concept good and the technology with which to use it as promising.
Rethinking Smart Objects: The International Workshop on Interacting with Smart Objects in Interactive Spaces

The increasing proliferation of smart objects in everyday life has changed how we interact with computers. Instead of concentrating computational capabilities and interaction into one device, everyday objects have naturally integrated parts of interactive features. Although this has led to many practical applications, the possibilities for explicit or implicit interaction with such objects are still limited in interaction spaces. We still often rely on smartphones as interactive hubs for controlling smart objects, hence not fulfilling the vision of truly smart objects. The workshop Rethinking Smart Objects invites practitioners and researchers from both academia and industry to discuss novel interaction paradigms and the integration and societal implications of using smart objects in interactive space. This workshop will include an action plan with leading questions, aiming to move the research field forward.
Learning from undercoded clinical records for automated International Classification of Diseases (ICD) coding

To develop an unbiased objective for learning automatic coding algorithms from clinical records annotated with only partial relevant International Classification of Diseases codes, as annotation noise in undercoded clinical records used as training data can mislead the learning process of deep neural networks.We use Medical Information Mart for Intensive Care III as our dataset. We employ positive-unlabeled learning to achieve unbiased loss estimation, which is free of misleading training signal. We then utilize reweighting mechanism to compensate for the imbalance between positive and negative samples. To further close the performance gap caused by poor quality annotation, we integrate the supervision provided by the automatic annotation tool Medical Concept Annotation Toolkit which can ease the heavy burden of manual validation.Our benchmarking results show that positive-unlabeled learning with reweighting outperforms competitive baseline methods over a range of missing label ratios. Integrating supervision provided by annotation tool further boosted the performance.Considering the annotation noise and severe imbalance, unbiased loss estimation and reweighting mechanism are both important for learning from undercoded clinical records. Unbiased loss requires the estimation of false negative ratios and estimation through trained models is practical and competitive.The combination of positive-unlabeled learning with reweighting and supervision provided by the annotation tool is a promising solution to learn from undercoded clinical records.
EyeT4Empathy: Dataset of foraging for visual information, gaze typing and empathy assessment

We present a dataset of eye-movement recordings collected from 60 participants, along with their empathy levels, towards people with movement impairments. During each round of gaze recording, participants were divided into two groups, each one completing one task. One group performed a task of free exploration of structureless images, and a second group performed a task consisting of gaze typing, i.e. writing sentences using eye-gaze movements on a card board. The eye-tracking data recorded from both tasks is stored in two datasets, which, besides gaze position, also include pupil diameter measurements. The empathy levels of participants towards non-verbal movement-impaired people were assessed twice through a questionnaire, before and after each task. The questionnaire is composed of forty questions, extending a established questionnaire of cognitive and affective empathy. Finally, our dataset presents an opportunity for analysing and evaluating, among other, the statistical features of eye-gaze trajectories in free-viewing as well as how empathy is reflected in eye features.
A critical review of internet of things communication environment: Privacy and security constraints

The recent past has experienced a steady increase in the adoption of Internet of Things (IoT) in a number of areas such as smart cities, smart homes, smart transportation and smart health. Due to the message exchanges among IoT devices over the public internet, the transmitted data is vulnerable to many security and privacy attacks. Therefore, many schemes have been presented over the recent past to address these challenges. However, many security holes still exist in most of the current techniques. It was also noted that the resource limited nature of most IoT devices renders traditional authentication schemes for main-powered systems with high processing power and large memory infeasible and inapplicable. To address this performance issue, numerous lightweight authentication schemes have been put forward. However, this paper discovered numerous security and privacy gaps in these schemes. Based on these shortcomings, recommendations are given towards the end of this paper which are very critical for security enhancements in this pervasive computing environment.
Anonymous Account Detection in Social Media Using Machine Learning and Natural Language Processing

Spammers have transformed significant person to person communication destinations into a stage for the spread of a tremendous measure of inadequate and maybe hazardous substance and data. Interpersonal interaction administrations are utilized by a great many people from one side of the planet to the other. The cooperations that people have with web-based media destinations, for example, Twitter and Facebook significantly affect their everyday lives, for certain terrible repercussions now and again, also. For instance, Facebook has developed to get quite possibly the most lavishly utilized foundation ever, empowering an unsuitably immense measure of spam to be sent out of the site. Client accounts made by counterfeit clients send spontaneous tweets to different clients to advance organizations or sites, which influence genuine clients as well as motivation asset utilization to ascend too. The chance of spreading off base data to clients by means of the utilization of phony personalities has additionally expanded, possibly prompting the appropriation of unsafe things. Thus, thediscovery of spammers and the ID of phony Twitter clients have as of late emerged as an unmistakable examination subject in the space of contemporary online interpersonal organizations (OSNs). All through this article,we will take a gander at the methods that are presently being used to distinguish spammers on the web-based media stage Twitter. Besides,a scientific categorization of Twitter spam location strategies is introduced, what Separates the strategies into four classifications dependent on their capacity to distinguish I counterfeit material, (ii) spamdependent on URL,(iii) spam in hot subjects,and (iv) fake clients on the person to person communication site. Just as a scope of models like client qualities, content attributes, diagram properties and different components, the provided procedures are additionally evaluated and thought about. There are three kinds of attributes: singular attributes, underlying qualities,and transient characteristics.Eventually, we accept that the exploration we've given will be an important asset for researchers looking for the features of ongoing progressions in Facebook spam identification in a one area.
Does Learners’ School Security and Safety Measures Impact on Learners Academic Performance? Tales with focus on Selected Primary Schools in Majanji Sub-County, Busia District

The study was intended to establish the effect of school security and safety on learners’ academic performance in selected primary schools in Majanji Sub County, Busia District. With intent to find out the factors influencing learners’ academic performance as well as identifying the strategies in place to cater for school security and also determine the effect of school security strategies on learners’ academic performance. Findings showed cases of early pregnancies or early marriages, failure to consider children’s rights and presence of learners with emotional problems. Conclusively, an unsafe school is more likely to result in students who have emotional problems, particularly direct victims of violence or bullying. Consider setting up a hotline to allow victims and witnesses to report occurrences in confidence. Some public schools are increasingly enforcing uniforms for all pupils, just like private institutions. Thus, the study recommends that schools work with the parents together with development partners and government to put up security systems in schools for learners’ safety.
Analysis of Media Reports on Child Abuse Using Big Data Technology

The purpose of this study is to analyze domestic media reports related to 'child abuse' using big data.In BIG KINDS, a news analysis system that combines big data analysis technology, 3,360 articles were extracted by searching for articles with 'child abuse' as the main keyword, and then keyword trends, word clouds, and relationship analysis were conducted.As a result of analyzing the trend of articles related to 'child abuse' by year through keyword trends analysis, the theme of 'child abuse' began to appear in media reports for the first time in 1997, and the number of reports exploded from 2013, 2015 and 2020.As a result of word cloud analysis, it was found that keywords such as daycare centers and the Ministry of Health and Welfare appeared a lot in reports related to 'child abuse'.In the relationship analysis, keywords with a high connection relationship and high semantic similarity were extracted and presented, and it was found that the Ministry of Health and Welfare had a large connection with other keywords, and it can be seen that it forms a network with keywords such as 'early childhood teachers', 'parent,' and 'infant care law'.The results of this study are expected to be used as basic data to help comprehensively understand the trend of media reports related to 'child abuse', solve related problems, and prepare countermeasures.
Development of the Process-oriented Elementary School Mathematics Diagnostic Evaluation Materials - From the Point of View of Online Learning Platform Application -

The purpose of this study is to develop online mathematics diagnostic evaluation materials that help students acquire the tools and resources that are the basis of mathematics learning in terms of student-centered customized mathematics education and preventing students from giving up on mathematics.In this paper, by utilizing the original purpose of process-oriented evaluation, we develop diagnostic evaluation materials that include formative and diagnostic functions, and discuss how to provide personalized feedback in consideration of the learning hierarchy.This study is to provide implications for the application of the online learning platform of mathematical diagnostic evaluation data.The researcher developed diagnostic evaluation data for the 6th grade 'Ratio and Proportion' to present diagnostic evaluation data including formative and diagnostic functions.In order to achieve the purpose of the study, an expert council was held for the development of diagnostic evaluation data, and representative examples among them were also presented.As a result of the study, the process-oriented online diagnostic evaluation should be a subjective question, but it should be possible to provide immediate feedback and to reduce the grading procedure and effort.In the developing mathematical diagnostic evaluation materials including formative and diagnostic functions, the process of learning is evaluated in consideration of the method and order of presenting materials to students and the learning hierarchy.In addition, examples of specific items for diagnosis, correction data, and sample data for recommended items were presented.It is necessary to develop and disseminate process-oriented mathematics diagnostic evaluation materials to be applied to the online learning platform for teachers at each grade level, and it is urgent to build an online learning platform that can utilize it.In order to provide customized feedback to students, it is necessary to accumulate data of the learning process and scoring results by applying the built platform to each schools across the country.It should be possible to improve the recommendation performance of customized data by using this for machine learning.
Digital Literacy and Communicative Competence among Academic Leaders: Post-COVID-19 Study

Communicative competence represents an academic leader's ability to use language effectively. However, to date, communicative competence among academic leaders is not as effective as expected. This research aims to explore the affective factors hindering the academic leaders' communicative competence, identify the cultural factors affecting their communicative competence, and investigate the impact of using technology in developing communicative competence during the Covid-19 pandemic. This qualitative study employed semi-structured interviews with nine academic leaders from different universities in Iraq. It adopts four theories: communicative competence, leadership competence, communication and planned behaviour for the basis and guidance for data collection, data analysis, and interpretation. The qualitative data were transcribed and analysed using thematic analysis. It was found that the following Key factors, such as digital literacy became crucial during lockdown because of COVID-19. All meetings and conferences adopt online mode instead of physical appearance. In such a situation, majority respondents mentioned that communicative competence has proven helpful in problem-solving and developing critical skills. The respondents have proposed a framework for developing academic leaders' communicative competence for digital literacy post-Covid -19. Such as training to use digital tools for language usage, monitoring the electronic examination process more efficient and initiatives for communication competence.
Surgical-VQA: Visual Question Answering in Surgical Scenes using Transformer

Visual question answering (VQA) in surgery is largely unexplored. Expert surgeons are scarce and are often overloaded with clinical and academic workloads. This overload often limits their time answering questionnaires from patients, medical students or junior residents related to surgical procedures. At times, students and junior residents also refrain from asking too many questions during classes to reduce disruption. While computer-aided simulators and recording of past surgical procedures have been made available for them to observe and improve their skills, they still hugely rely on medical experts to answer their questions. Having a Surgical-VQA system as a reliable 'second opinion' could act as a backup and ease the load on the medical experts in answering these questions. The lack of annotated medical data and the presence of domain-specific terms has limited the exploration of VQA for surgical procedures. In this work, we design a Surgical-VQA task that answers questionnaires on surgical procedures based on the surgical scene. Extending the MICCAI endoscopic vision challenge 2018 dataset and workflow recognition dataset further, we introduce two Surgical-VQA datasets with classification and sentence-based answers. To perform Surgical-VQA, we employ vision-text transformers models. We further introduce a residual MLP-based VisualBert encoder model that enforces interaction between visual and text tokens, improving performance in classification-based answering. Furthermore, we study the influence of the number of input image patches and temporal visual features on the model performance in both classification and sentence-based answering.
Use of Digital Platforms to Commit Nefarious Activities Globally. A Critical Literature Review.

Purpose: Social media use among youth has been associated with influences on personal security. Factors such as victim predisposition to cyber-attacks, presence of vulnerabilities in network, lack of specific legal framework to deal with cybercrime and inadequate capacity of law enforcement to deal with online crimes has led to cases of breach to personal security. The overall objective of this study was to examine use of digital platforms to commit nefarious activities globally.&#x0D; Methodology: The paper used a desk study review methodology where relevant empirical literature was reviewed to identify main themes and to extract knowledge gaps.&#x0D; Findings: This study concluded that about 84% of youth respondents had opinion that Facebook was most insecure. Fake jobs were listed as the most prevalent crime in social media by youth respondents at 35.90% of all the social media crimes listings in this research. It was followed by online romance scams at 16.24%, hacking at 13.68%, cyber bullying at 12.82%, cyberstalking at 11.11% and identity theft at 10.28%, based on the number of times they were listed by youth respondents. The youth respondents felt that social media platform that had the highest number of crimes was Facebook at 79.49%, followed by Email at 5.98%, Instagram at 5.13% and both WhatsApp and YouTube at 4.27%.&#x0D; Unique Contribution to Theory, Policy and Practice: This study recommended that it would be necessary for tracking of social media criminals and victims to be improved, there should be awareness creation to the youth to come out and identify possible accounts, interactions and persons likely to be suspects. Victims should not fear reporting to law enforcement agencies. Public and private sector partnerships and cooperation in tracking of suspects of social media crimes should be improved.
Interactive Visualization on Large High-Resolution Displays: A Survey

In the past few years, large high-resolution displays (LHRDs) have attracted considerable attention from researchers, industries, and application areas that increasingly rely on data-driven decision-making. An up-to-date survey on the use of LHRDs for interactive data visualization seems warranted to summarize how new solutions meet the characteristics and requirements of LHRDs and take advantage of their unique benefits. In this survey, we start by defining LHRDs and outlining the consequence of LHRD environments on interactive visualizations in terms of more pixels, space, users, and devices. Then, we review related literature along the four axes of visualization, interaction, evaluation studies, and applications. With these four axes, our survey provides a unique perspective and covers a broad range of aspects being relevant when developing interactive visual data analysis solutions for LHRDs. We conclude this survey by reflecting on a number of opportunities for future research to help the community take up the still open challenges of interactive visualization on LHRDs.
General-Purpose In-Context Learning by Meta-Learning Transformers

Modern machine learning requires system designers to specify aspects of the learning pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn, instead aims to learn those aspects, and promises to unlock greater capabilities with less manual effort. One particularly ambitious goal of meta-learning is to train general-purpose in-context learning algorithms from scratch, using only black-box models with minimal inductive bias. Such a model takes in training data, and produces test-set predictions across a wide range of problems, without any explicit definition of an inference model, training loss, or optimization algorithm. In this paper we show that Transformers and other black-box models can be meta-trained to act as general-purpose in-context learners. We characterize transitions between algorithms that generalize, algorithms that memorize, and algorithms that fail to meta-train at all, induced by changes in model size, number of tasks, and meta-optimization. We further show that the capabilities of meta-trained algorithms are bottlenecked by the accessible state size (memory) determining the next prediction, unlike standard models which are thought to be bottlenecked by parameter count. Finally, we propose practical interventions such as biasing the training distribution that improve the meta-training and meta-generalization of general-purpose in-context learning algorithms.
Diffusion Guided Domain Adaptation of Image Generators

Can a text-to-image diffusion model be used as a training objective for adapting a GAN generator to another domain? In this paper, we show that the classifier-free guidance can be leveraged as a critic and enable generators to distill knowledge from large-scale text-to-image diffusion models. Generators can be efficiently shifted into new domains indicated by text prompts without access to groundtruth samples from target domains. We demonstrate the effectiveness and controllability of our method through extensive experiments. Although not trained to minimize CLIP loss, our model achieves equally high CLIP scores and significantly lower FID than prior work on short prompts, and outperforms the baseline qualitatively and quantitatively on long and complicated prompts. To our best knowledge, the proposed method is the first attempt at incorporating large-scale pre-trained diffusion models and distillation sampling for text-driven image generator domain adaptation and gives a quality previously beyond possible. Moreover, we extend our work to 3D-aware style-based generators and DreamBooth guidance.