Reviewerly/exHarmony huggingface dataset:
Gaze behavior data in the vitrine of human movement science: considerations on eye-tracking technique

Background: The eyes are the main gateway of visual information input. Moving the eyes is essential to extract visual information from scenes while performing motor actions. This has helped to explain motor behavior, especially in relation to visual attention mechanisms, gaze training and learning, and the relevance of visual information in controlling actions. Thus, collecting data on gaze behavior has become important for explaining motor behavior. Aim: We present the main video-based eye-tracking techniques, briefly describing the anatomy of the eyes, explaining the operation of the eye-tracker (eye capture techniques, calibration, and data analysis), and proposing interpretations of the main variables extracted by the technique. In this way, we carry out considerations (limitations and advantages) on the eye-tracking technique that placed gaze behavior data in the vitrine of human movement science. Interpretation: Eye-tracking has become an excellent tool to assist in the analysis of human movement through gaze behavior. Mainly by combining sensory information, such as visual information, with performance during motor tasks, it is possible to infer about perception, cognition, and human behavior during the most diverse day-to-day activities. Eye-tracker systems have been employed in different majors related to motor behavior, such as medicine, commerce, and game development.
OPPORTUNITIES AND CHALLENGES OF AUGMENTED AND VIRTUAL REALITY IN EDUCATION

The development of information technology allows the integration of new innovative approaches into the modern learning environment. In the article „Opportunities and Challenges of Augmented and Virtual Reality in Education“ the author, as a longtime teacher of information technologies in lower secondary school, reveals the opportunities for using augmented and virtual reality in the school class. The applications Popar Toys, Augment Education, Solar System Scope, Breakfast launch, Atlas of Human Anatomy are described. Mozaik 3D, Google Arts &amp; Culture and working with them. The author shares good practices from the implementation of augmented and virtual reality in the learning process and lists the opportunities and challenges of innovative and interactive teaching with their help.
PLANNING A STRATEGY FOR THE DIGITALIZATION OF SCHOOL

The digitalization of school management is an important process that can facilitate and improved many aspects of school management. The digital transformation of school anagement involves multiple processes, such as implementation of digital technologies for information management, automation of routine tasks, building electronic systems for communication and collaboration, and many others. For the implementation of this process, it is necessary to observe a certain sequence of actions related to the construction of an overall strategy and concept for its implementation.
Cognitive Offloading: Systematic Review of a Decade

Cognitive offloading refers to the use of physical action to alter information processing requirement of the given task. Offloading can be done either internally or externally. We have been included cognitive offloading through external tools related papers. Cognitive offloading is applied aspect of Extended Mind Thesis (EMT). EMT is applicable to human beings, technological &amp; cognitive artefacts and self &amp; group. Deliberation points on EMT has been found: what is cognitive and how to differentiate causal coupling from constitutive. Necessary deliberations either acceptance or rejections till date has been included (from 2009 onwards) in this paper. EMT can also be applied to the web, known as web based cognitive offloading where web is collection of external representations. Interaction of mind with artefact is also included in this paper till date to enrich knowledge as mind artefact interaction is one of the forms of cognitive offloading. Empirical studies of cognitive offloading found in literature which revolve around the questions of why one engages in cognitive offloading despite of good memory. For poor memory or brain damage, external tools are good compensatory tool. This paper specifically deals with advances in technological &amp; cognitive artifacts and at the end, future directions in cognitive offloading has been suggested.
Unsupervised and Few-Shot Parsing from Pretrained Language Models (Extended Abstract)

This paper proposes two Unsupervised constituent Parsing models (UPOA and UPIO) that calculate inside and outside association scores solely based on the self-attention weight matrix learned in a pretrained language model. The proposed unsupervised parsing models are further extended to few-shot parsing models (FPOA, FPIO) that use a few annotated trees to fine-tune the linear projection matrices in self-attention. Experiments on PTB and SPRML show that both unsupervised and few-shot parsing methods are better than or comparable to the previous methods.
A False Sense of Security (Extended Abstract)

The growing literature on confidentiality in knowledge representation and reasoning sometimes may cause a false sense of security, due to lack of details about the attacker, and some misconceptions about security-related concepts. This paper analyzes the vulnerabilities of some recent knowledge protection methods to increase the awareness about their actual effectiveness and their mutual differences.
Matting Moments: A Unified Data-Driven Matting Engine for Mobile AIGC in Photo Gallery

Image matting is a fundamental technique in visual understanding and has become one of the most significant capabilities in mobile phones. Despite the development of mobile storage and computing power, achieving diverse mobile Artificial Intelligence Generated Content (AIGC) applications remains a great challenge. To address this issue, we present an innovative demonstration of an automatic system called "Matting Moments" that enables automatic image editing based on matting models in different scenarios. Coupled with accurate and refined matting subjects, our system provides visual element editing abilities and backend services for distribution and recommendation that respond to emotional expressions. Our system comprises three components: 1) photo content structuring, 2) data-driven matting engine, and 3) AIGC functions for generation, which automatically achieve diverse photo beautification in the gallery. This system offers a unified framework that guides consumers to obtain intelligent recommendations with beautifully generated contents, helping them enjoy the moments and memories of their present life.
Sample Efficient Paradigms for Personalized Assessment of Taskable AI Systems

The vast diversity of internal designs of taskable black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. The focus of my dissertation is to develop paradigms that would enable a user to assess and understand the limits of an AI system's safe operability. We develop a personalized AI assessment module that lets an AI system execute instruction sequences in simulators and answer queries about these executions. Our results show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable model of the system's capabilities in fully observable settings.
Survey on Online Streaming Continual Learning

Stream Learning (SL) attempts to learn from a data stream efficiently. A data stream learning algorithm should adapt to input data distribution shifts without sacrificing accuracy. These distribution shifts are known as ”concept drifts” in the literature. SL provides many supervised, semi-supervised, and unsupervised methods for detecting and adjusting to concept drift. On the other hand, Continual Learning (CL) attempts to preserve previous knowledge while performing well on the current concept when confronted with concept drift. In Online Continual Learning (OCL), this learning happens online. This survey explores the intersection of those two online learning paradigms to find synergies. We identify this intersection as Online Streaming Continual Learning (OSCL). The study starts with a gentle introduction to SL and then explores CL. Next, it explores OSCL from SL and OCL perspectives to point out new research trends and give directions for future research.
Sorting and Hypergraph Orientation under Uncertainty with Predictions

Learning-augmented algorithms have been attracting increasing interest, but have only recently been considered in the setting of explorable uncertainty where precise values of uncertain input elements can be obtained by a query and the goal is to minimize the number of queries needed to solve a problem. We study learning-augmented algorithms for sorting and hypergraph orientation under uncertainty, assuming access to untrusted predictions for the uncertain values. Our algorithms provide improved performance guarantees for accurate predictions while maintaining worst-case guarantees that are best possible without predictions. For sorting, our algorithm uses the optimal number of queries for accurate predictions and at most twice the optimal number for arbitrarily wrong predictions. For hypergraph orientation, for any γ≥2, we give an algorithm that uses at most 1+1/γ times the optimal number of queries for accurate predictions and at most γ times the optimal number for arbitrarily wrong predictions. These tradeoffs are the best possible. We also consider different error metrics and show that the performance of our algorithms degrades smoothly with the prediction error in all the cases where this is possible.
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features (Extended Abstract)

Time-series data arises in many real-world applications (e.g., mobile health) and deep neural networks (DNNs) have shown great success in solving them. Despite their success, little is known about their robustness to adversarial attacks. In this paper, we propose a novel adversarial framework referred to as Time-Series Attacks via STATistical Features (TSA-STAT). To address the unique challenges of time-series domain, TSA-STAT employs constraints on statistical features of the time-series data to construct adversarial examples. Optimized polynomial transformations are used to create attacks that are more effective (in terms of successfully fooling DNNs) than those based on additive perturbations. We also provide certified bounds on the norm of the statistical features for constructing adversarial examples. Our experiments on diverse real-world benchmark datasets show the effectiveness of TSA-STAT in fooling DNNs for time-series domain and in improving their robustness.
Evaluating Human-AI Interaction via Usability, User Experience and Acceptance Measures for MMM-C: A Creative AI System for Music Composition

With the rise of artificial intelligence (AI), there has been increasing interest in human-AI co-creation in a variety of artistic domains including music as AI-driven systems are frequently able to generate human-competitive artifacts. Now, the implications of such systems for the musical practice are being investigated. This paper reports on a thorough evaluation of the user adoption of the Multi-Track Music Machine (MMM) as a minimal co-creative AI tool for music composers. To do this, we integrate MMM into Cubase, a popular Digital Audio Workstation (DAW), by producing a "1-parameter" plugin interface named MMM-Cubase, which enables human-AI co-composition. We conduct a 3-part mixed method study measuring usability, user experience and technology acceptance of the system across two groups of expert-level composers: hobbyists and professionals. Results show positive usability and acceptance scores. Users report experiences of novelty, surprise and ease of use from using the system, and limitations on controllability and predictability of the interface when generating music. Findings indicate no significant difference between the two user groups.
Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding (Extended Abstract)

The robustness of deep neural networks in safety-critical systems has received significant interest recently, which measures how sensitive the model output is under input perturbations. While most previous works focused on the local robustness property, the studies of the global robustness property, i.e., the robustness in the entire input space, are still lacking. In this work, we formulate the global robustness certification problem for ReLU neural networks and present an efficient approach to address it. Our approach includes a novel interleaving twin-network encoding scheme and an over-approximation algorithm leveraging relaxation and refinement techniques. Its timing efficiency and effectiveness are evaluated and compared with other state-of-the-art global robustness certification methods, and demonstrated via case studies on practical applications.
A Framework for Participatory Budgeting with Resource Pooling

Participatory budgeting (PB) is an implementation of direct democracy that allows members of a community to make collective budgeting decisions. However, existing PB processes rely upon a pre-defined central budget. We introduce a framework for pooling resources in addition to selecting projects, which we call PB with Resource Pooling. We motivate the key characteristics of this model and the basic properties we would like a mechanism to satisfy. We summarize results and discuss interesting questions related to our framework.
Latent Inspector: An Interactive Tool for Probing Neural Network Behaviors Through Arbitrary Latent Activation

This work presents an active software instrument allowing deep learning architects to interactively inspect neural network models' output behavior from user-manipulated values in any latent layer. Latent Inspector offers multiple dimension reduction techniques to visualize the model's high dimensional latent layer output in human-perceptible, two-dimensional plots. The system is implemented with Node.js front end for interactive user input and Python back end for interacting with the model. By utilizing a general and modular architecture, our proposed solution dynamically adapts to a versatile range of models and data structures. Compared to already existing tools, our asynchronous approach of separating the training process from the inspection offers additional possibilities, such as interactive data generation, by actively working with the model instead of visualizing training logs. Overall, Latent Inspector demonstrates the possibilities as well as the appearing limits for providing a generalized, tool-based concept for enhancing model insight in terms of explainable and transparent AI.
Translating Images into Maps (Extended Abstract)

We approach instantaneous mapping, converting images to a top-down view of the world, as a translation problem. We show how a novel form of transformer network can be used to map from images and video directly to an overhead map or bird's-eye-view (BEV) of the world, in a single end-to-end network. We assume a 1-1 correspondence between a vertical scanline in the image, and rays passing through the camera location in an overhead map. This lets us formulate map generation from an image as a set of sequence-to-sequence translations. This constrained formulation, based upon a strong physical grounding of the problem, leads to a restricted transformer network that is convolutional in the horizontal direction only. The structure allows us to make efficient use of data when training, and obtains state-of-the-art results for instantaneous mapping of three large-scale datasets, including a 15\% and 30\% relative gain against existing best performing methods on the nuScenes and Argoverse datasets, respectively.
Incremental and Decremental Optimal Margin Distribution Learning

Incremental and decremental learning (IDL) deals with the tasks where new data arrives sequentially as a stream or old data turns unavailable continually due to the privacy protection. Existing IDL methods mainly focus on support vector machine and its variants with linear-type loss. There are few studies about the quadratic-type loss, whose Lagrange multipliers are unbounded and much more difficult to track. In this paper, we take the latest statistical learning framework optimal margin distribution machine (ODM) which involves a quadratic-type loss due to the optimization of margin variance, for example, and equip it with the ability to handle IDL tasks. Our proposed ID-ODM can avoid updating the Lagrange multipliers in an infinite range by determining their optimal values beforehand so as to enjoy much more efficiency. Moreover, ID-ODM is also applicable when multiple instances come and leave simultaneously. Extensive empirical studies show that ID-ODM can achieve 9.1x speedup on average with almost no generalization lost compared to retraining ODM on new data set from scratch.
CROP: Towards Distributional-Shift Robust Reinforcement Learning Using Compact Reshaped Observation Processing

The safe application of reinforcement learning (RL) requires generalization from limited training data to unseen scenarios. Yet, fulfilling tasks under changing circumstances is a key challenge in RL. Current state-of-the-art approaches for generalization apply data augmentation techniques to increase the diversity of training data. Even though this prevents overfitting to the training environment(s), it hinders policy optimization. Crafting a suitable observation, only containing crucial information, has been shown to be a challenging task itself. To improve data efficiency and generalization capabilities, we propose Compact Reshaped Observation Processing (CROP) to reduce the state information used for policy optimization. By providing only relevant information, overfitting to a specific training layout is precluded and generalization to unseen environments is improved. We formulate three CROPs that can be applied to fully observable observation- and action-spaces and provide methodical foundation. We empirically show the improvements of CROP in a distributionally shifted safety gridworld. We furthermore provide benchmark comparisons to full observability and data-augmentation in two different-sized procedurally generated mazes.
Differentially Private Partial Set Cover with Applications to Facility Location

Set Cover is a fundamental problem in combinatorial optimization which has been studied for many decades due to its various applications across multiple domains. In many of these domains, the input data consists of locations, relationships, and other sensitive information of individuals which may leaked due to the set cover output. Attempts have been made to design privacy-preserving algorithms to solve the Set Cover under privacy constraints. Under differential privacy, it has been proved that the Set Cover problem has strong impossibility results and no explicit forms of the output can be released to the public. In this work, we observe that these hardness results dissolve when we turn to the Partial Set Cover problem, where we only need to cover a ρ ∈ (0,1) fraction of the elements. We show that this relaxation enables us to avoid the impossibility results, and give the first algorithm which outputs an explicit form of set cover with non-trivial utility guarantees under differential privacy. Using our algorithm as a subroutine, we design a differentially private bicriteria algorithm to solve a recently proposed facility location problem for vaccine distribution which generalizes the k-supplier with outliers. Our analysis shows that relaxing the covering requirement to serve only a ρ ∈ (0,1) fraction of the population/universe also allows us to circumvent the inherent hardness of k-supplier and give the first non-trivial guarantees.
Robust Steganography without Embedding Based on Secure Container Synthesis and Iterative Message Recovery

Synthesis-based steganography without embedding (SWE) methods transform secret messages to container images synthesised by generative networks, which eliminates distortions of container images and thus can fundamentally resist typical steganalysis tools. However, existing methods suffer from weak message recovery robustness, synthesis fidelity, and the risk of message leakage. To address these problems, we propose a novel robust steganography without embedding method in this paper. In particular, we design a secure weight modulation-based generator by introducing secure factors to hide secret messages in synthesised container images. In this manner, the synthesised results are modulated by secure factors and thus the secret messages are inaccessible when using fake factors, thus reducing the risk of message leakage. Furthermore, we design a difference predictor via the reconstruction of tampered container images together with an adversarial training strategy to iteratively update the estimation of hidden messages. This ensures robustness of recovering hidden messages, while degradation of synthesis fidelity is reduced since the generator is not included in the adversarial training. Extensive experimental results convincingly demonstrate that our proposed method is effective in avoiding message leakage and superior to other existing methods in terms of recovery robustness and synthesis fidelity.
On Building a Semi-Automated Framework for Generating Causal Bayesian Networks from Raw Text

The availability of a large amount of unstructured text has generated interest in utilizing it for future decision-making and developing strategies in various critical domains. Despite some progress, automatically generating accurate reasoning models from the raw text is still an active area of research. Furthermore, most proposed approaches focus on a specific do-main. As such, their suggested transformation methods are usually unreliable when applied to other domains. This research aims to develop a framework, SCANER (Semi-automated CAusal Network Extraction from Raw text), to convert raw text into Causal Bayesian Networks (CBNs). The framework will then be employed in various domains to demonstrate its utilization as a decision-support tool. The preliminary experiments have focused on three domains: political narratives, food insecurity, and medical sciences. The future focus is on developing BNs from political narratives and modifying them through various methods to reduce the level of aggressiveness or extremity in the narratives without causing conflict among the masses or countries.
Analyzing and Combating Attribute Bias for Face Restoration

Face restoration (FR) recovers high resolution (HR) faces from low resolution (LR) faces and is challenging due to its ill-posed nature. With years of development, existing methods can produce quality HR faces with realistic details. However, we observe that key facial attributes (e.g., age and gender) of the restored faces could be dramatically different from the LR faces and call this phenomenon attribute bias, which is fatal when using FR for applications such as surveillance and security. Thus, we argue that FR should consider not only image quality as in existing works but also attribute bias. To this end, we thoroughly analyze attribute bias with extensive experiments and find that two major causes are the lack of attribute information in LR faces and bias in the training data. Moreover, we propose the DebiasFR framework to produce HR faces with high image quality and accurate facial attributes. The key design is to explicitly model the facial attributes, which also allows to adjust facial attributes for the output HR faces. Experiment results show that DebiasFR has comparable image quality but significantly smaller attribute bias when compared with state-of-the-art FR methods.