Reviewerly/exHarmony huggingface dataset:
Evaluation of Empirical Daily Solar Radiation Models for the Northeast Coast of the Iberian Peninsula

The ability to accurately predict daily solar radiation reaching the earth’s surface is essential in applications such as solar power generation. Given their ease of use, many empirical models have been proposed based on different dependent variables such as cloud cover, daily temperature range, etc. In this study we evaluate 23 of these models for the prediction of daily solar radiation in the northern coastal zone of the Iberian Peninsula. Daily measurements during the period 2000–2018 from 16 meteorological stations spread over this area are used to adjust the parameters of each model, whose predictive capacity is then evaluated using measurements made between 2019 and 2022. Using different statistical metrics to assess their predictive performance, it was found that models based on hours of sunshine and level of cloudiness are significantly more accurate than those based on maximum and minimum daily temperature and day of the year. Specifically, the sunshine-based model by SBM3 obtained the highest Global Performance Indicator at 5.05. The results offer insight on the ability of each type of empirical model to accurately predict daily solar radiation in the Mediterranean region.
The basic assembly of skeletal models in the fall detection problem

The paper considers the appliance of the featureless approach to the human activity recognition problem, which exclude the direct anthropomorphic and visual characteristics of human figure from further analysis and thus increase the privacy of the monitoring system. A generalized pairwise comparison function of two human skeletal models, invariant to the sensor type, is used to project the object of interest to the secondary feature space, formed by the basic assembly of skeletons. A sequence of such projections in time forms an activity map, which allows an application of deep learning methods based on convolution neural networks for activity recognition. The proper ordering of skeletal models in a basic assembly plays an important role in secondary space design. The study of ordering of the basic assembly by the shortest unclosed path algorithm and correspondent activity maps for video streams from the TST Fall Detection v2 database are presented.
Deep Learning Prediction of TERT Promoter Mutation Status in Thyroid Cancer Using Histologic Images

Background and objectives: Telomerase reverse transcriptase (TERT) promoter mutation, found in a subset of patients with thyroid cancer, is strongly associated with aggressive biologic behavior. Predicting TERT promoter mutation is thus necessary for the prognostic stratification of thyroid cancer patients. Materials and Methods: In this study, we evaluate TERT promoter mutation status in thyroid cancer through the deep learning approach using histologic images. Our analysis included 13 consecutive surgically resected thyroid cancers with TERT promoter mutations (either C228T or C250T) and 12 randomly selected surgically resected thyroid cancers with a wild-type TERT promoter. Our deep learning model was created using a two-step cascade approach. First, tumor areas were identified using convolutional neural networks (CNNs), and then TERT promoter mutations within tumor areas were predicted using the CNN–recurrent neural network (CRNN) model. Results: Using the hue–saturation–value (HSV)-strong color transformation scheme, the overall experiment results show 99.9% sensitivity and 60% specificity (improvements of approximately 25% and 37%, respectively, compared to image normalization as a baseline model) in predicting TERT mutations. Conclusions: Highly sensitive screening for TERT promoter mutations is possible using histologic image analysis based on deep learning. This approach will help improve the classification of thyroid cancer patients according to the biologic behavior of tumors.
An Online Distance Tracker for Verification of Robotic Systems’ Safety

This paper presents an efficient method for minimum distance calculation between a robot and its environment and the implementation framework as a tool for the verification of robotic systems’ safety. Collision is the most fundamental safety problem in robotic systems. Therefore, robotic system software must be verified to ensure that there are no risks of collision during development and implementation. The online distance tracker (ODT) is intended to provide minimum distances between the robots and their environments for verification of system software to inspect whether it causes a collision risk. The proposed method employs the representations of the robot and its environment with cylinders and an occupancy map. Furthermore, the bounding box approach improves the performance of the minimum distance calculation regarding computational cost. Finally, the method is applied to a realistically simulated twin of the ROKOS, which is an automated robotic inspection cell for quality control of automotive body-in-white and is actively used in the bus manufacturing industry. The simulation results demonstrate the feasibility and effectiveness of the proposed method.
Robots in Real Life

This talk will be focused on the unique challenges in deploying a mobile manipulation robot into an environment where the robot is working closely with people on a daily basis. Diligent Robotics' first product, Moxi, is a mobile manipulation service robot that is at work in hospitals today assisting nurses and other front line staff with materials management tasks. This talk will dive into the computational complexity of developing a mobile manipulator with social intelligence. Dr. Thomaz will focus on how human-robot interaction theories and algorithms translate into the real-world and the impact on functionality and perception of robots that perform delivery tasks in a busy human environment. The talk will include many examples and data from the field, with commentary and discussion around both the expected and unexpected hard problems in building robots operating 24/7 as reliable teammates.
Dampak Pembelajaran Daring di Era Pandemi Covid-19 terhadap Kompetensi Numerasi Siswa Sekolah Dasar

The purpose of this research was to find out the impact of online learning in students' numeracy skills in addition and subtraction of fractions in grade 5 elementary school. This research used a quantitative approach with a pre-experimental method, and the treatment is an online learning system. The data collection technique used tests. The test given is the students' numeracy skills in addition and subtraction of fractions in grade 5 elementary school. Data analysis in this research used descriptive statistics (N-Gain) and the mean difference test (Mann-Whitney) used the SPSS Statistics 22 application. The results of this research indicate that the student's N-Gain score is 0.39. This indicates that there is an increase in numeracy scores in the moderate category, and based on the average difference test, sig. which is 0.002 smaller than 0.05 (α). Its means there is a significant increase in students' numeracy skills using online learning. Keywords: Online Learning, Numeracy, Addition and Subtraction of Fractions
Pertinent Concepts of Virtual Art

The paper studies the virtual art phenomena emerging as a result of active interplay of digital technologies and art that started in the mid-20th century. This interplay resulted in significant changes in the techniques of the creative art, as well as in the appearance of new trends and genres of art. In addition, the audience’s perception of the work of virtual art has undergone transformations also. The latter is related to interactivity as an integral component of virtual art. At the same time, artworks have different levels of interactivity—from the traditional passive perception, e.g. in the format of virtual museum tours, to the active participation of the viewer in the creation of the work of art. The aim of the paper isto determine the current concepts of virtual art based on the analysis of iconic art projects that represent different methods of working with virtual reality technology. The paper examines the phenomenon of virtual art from several viewpoints: the revival of the artist’s work methods, the peculiarities of the perception of the work of art in the virtual space, and the formats and issues of archiving virtual art projects.
SEAM: An Integrated Activation-Coupled Model of Sentence Processing and Eye Movements in Reading

Models of eye-movement control during reading, developed largely within psychology, usually focus on visual, attentional, lexical, and motor processes but neglect post-lexical language processing; by contrast, models of sentence comprehension processes, developed largely within psycholinguistics, generally focus only on post-lexical language processes. We present a model that combines these two research threads, by integrating eye-movement control and sentence processing. Developing such an integrated model is extremely challenging and computationally demanding, but such an integration is an important step toward complete mathematical models of natural language comprehension in reading. We combine the SWIFT model of eye-movement control (Seelig et al., 2020, doi:10.1016/j.jmp.2019.102313) with key components of the Lewis and Vasishth sentence processing model (Lewis & Vasishth, 2005, doi:10.1207/s15516709cog0000_25). This integration becomes possible, for the first time, due in part to recent advances in successful parameter identification in dynamical models, which allows us to investigate profile log-likelihoods for individual model parameters. We present a fully implemented proof-of-concept model demonstrating how such an integrated model can be achieved; our approach includes Bayesian model inference with Markov Chain Monte Carlo (MCMC) sampling as a key computational tool. The integrated Sentence-Processing and Eye-Movement Activation-Coupled Model (SEAM) can successfully reproduce eye movement patterns that arise due to similarity-based interference in reading. To our knowledge, this is the first-ever integration of a complete process model of eye-movement control with linguistic dependency completion processes in sentence comprehension. In future work, this proof of concept model will need to be evaluated using a comprehensive set of benchmark data.
ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?

ChatGPT embarks on a new era of artificial intelligence and will revolutionize the way we approach intelligent traffic safety systems. This paper begins with a brief introduction about the development of large language models (LLMs). Next, we exemplify using ChatGPT to address key traffic safety issues. Furthermore, we discuss the controversies surrounding LLMs, raise critical questions for their deployment, and provide our solutions. Moreover, we propose an idea of multi-modality representation learning for smarter traffic safety decision-making and open more questions for application improvement. We believe that LLM will both shape and potentially facilitate components of traffic safety research.
Gathering Information and Engaging the User ComBot: A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions

We focus on dialog models in the context of clinical studies where the goal is to help gather, in addition to the closed set of information collected based on a questionnaire, serendipitous information that is medically relevant.To promote user engagement and address this dual goal (collecting both a predefined set of data points and more informal information about the state of the patients), we introduce an ensemble model made of three bots: a task-based, a follow-up and a social bot.We introduce a generic method for developing follow-up bots.We compare different ensemble configurations and we show that the combination of the three bots (i) provides a better basis for collecting information than just the information seeking bot and (ii) collects information in a more efficient manner that an ensemble model combining the information seeking and the social bot.
Operationalizing Cybersecurity Research Ethics Review: From Principles and Guidelines to Practice

Cybersecurity research involves ethics risks such as accidental privacy breaches, corruption of production services, and discovery of weaknesses in networked systems.Although literature describes these and other issues in some depth, reflection on these issues is not yet well embedded in typical Ethics Review Board procedures.In this paper, we operationalize existing guidance on cybersecurity research ethics into a proposal that can be directly implemented in an Ethics Review Board.We provide a set of self-assessment questions to effectively and efficiently probe the ethics of proposed cybersecurity research, a Coordinated Vulnerability Disclosure procedure for discoveries made in the course of research, and an outline of a university policy to institutionally embed this procedure, which could be adapted and adopted by research institutes.With this paper, we hope to contribute to more Ethics Review Boards taking up the challenge of addressing cybersecurity research ethics in an adequate and productive manner.
Evasion Attacks and Defenses on Smart Home Physical Event Verification

In smart homes, when an actuator's state changes, it sends an event notification to the IoT hub to report this change (e.g., the door is unlocked).Prior works have shown that event notifications are vulnerable to spoofing and masking attacks.In event spoofing, an adversary reports to the IoT hub a fake event notification that did not physically occur.In event masking, an adversary suppresses the notification of an event that physically occurred.These attacks create inconsistencies between physical and cyber states of actuators, enabling an adversary to indirectly gain control over safety-critical devices by triggering IoT apps.To mitigate these attacks, event verification systems (EVS), or broadly IoT anomaly detection systems, leverage physical event fingerprints that describe the relations between events and their influence on sensor readings.However, smart homes have complex physical interactions between events and sensors that characterize the event fingerprints.Our study of the recent EVS, unfortunately, has revealed that they widely ignore such interactions, which enables an adversary to evade these systems and launch successful event spoofing and masking attacks without getting detected.In this paper, we first explore the evadable physical event fingerprints and show that an adversary can realize them to bypass the EVS given the same threat model.We develop two defenses, EVS software patching and sensor placement, with the interplay of physical modeling and formal analysis, to generate robust physical event fingerprints and demonstrate how they can be integrated into the EVS.We evaluate the effectiveness of our approach in two smart home settings that contain 12 actuators and 16 sensors when two different state-of-the-art EVS are deployed.Our experiments demonstrate that 71% of their physical fingerprints are vulnerable to evasion.By incorporating our approach, they build robust physical event fingerprints, and thus, properly mitigate realistic attack vectors.
REDsec: Running Encrypted Discretized Neural Networks in Seconds

Machine learning as a service (MLaaS) has risen to become a prominent technology due to the large development time, amount of data, hardware costs, and level of expertise required to develop a machine learning model.However, privacy concerns prevent the adoption of MLaaS for applications with sensitive data.A promising privacy preserving solution is to use fully homomorphic encryption (FHE) to perform the ML computations.Recent advancements have lowered computational costs by several orders of magnitude, opening doors for secure practical applications to be developed.In this work, we introduce the REDsec framework that optimizes FHE-based private machine learning inference by leveraging ternary neural networks.Such neural networks, whose weights are constrained to {-1,0,1}, have special properties that we exploit to operate efficiently in the homomorphic domain.REDsec introduces novel features, including a new data re-use scheme that enables bidirectional bridging between the integer and binary domains for the first time in FHE.This enables us to implement very efficient binary operations for multiplication and activations, as well as efficient integer domain additions.Our approach is complemented by a new GPU acceleration library, dubbed (RED)cuFHE, which supports both binary and integer operations on multiple GPUs.REDsec brings unique benefits by supporting user-defined models as input (bring-yourown-network), automation of plaintext training, and efficient evaluation of private inference leveraging TFHE.In our analysis, we perform inference experiments with the MNIST, CIFAR-10, and ImageNet datasets and report performance improvements compared to related works.
Accuracy of Malaysia Public Response to Economic Factors During the Covid-19 Pandemic Using Vader and Random Forest

This study conducted a sentiment analysis of the impact of the Covid-19 pandemic in the economic sector on people's lives through social media Twitter. The analysis was carried out on 23777 tweet data collected from 13 states in Malaysia from 1 December 2019 to 17 June 2020. The research process went through 3 stages, namely pre-processing, labeling, and modeling. The pre-processing stage is collecting and cleaning data. Labeling in this study uses Vader sentiment polarity detection to provide an assessment of the sentiment of tweet data which is used as training data. The modeling stage means to test the sentiment data using the random forest algorithm plus the extraction count vectorizer and TF-IDF features as well as the N-gram selection feature. The test results show that the polarity of public sentiment in Malaysia is predominantly positive, which is 11,323 positive, 4105 neutral, and 8349 negative based on Vader labeling. The accuracy rate from the random forest modeling results was obtained 93.5 percent with TF-IDF and 1 gram.
To breach or not? Profiling students’ likelihood of breaching university ICT Codes of Conduct

Requiring users to sign commitments to follow Information and Communication Technology (ICT) Codes of Conduct is a common strategy to protect an organisation’s ICT services and resources from misuse and cybersecurity threats posed by internal users. In a university context, students typically need to sign such an agreement prior to having their enrolment confirmed. Unfortunately, compliance with these codes is imperfect, posing organisational cybersecurity risks. This raises the question: can we predict which students are likely to breach these codes of conduct? To answer this question, we developed profiles to predict which students are likely to commit a code breach by collecting the responses of 260 students to five scenario-pairs (breach/non-breach) based on our university’s ICT policy. The study also captured participants’ demographics, moral stances, values, and cyber hygiene practices, to build predictive models of compliance towards the university’s ICT policy. Our findings reveal that most students could identify breaches and appropriate uses of ICT resources. Data modelling found the strength of students’ commitments to ethical principles and knowledge of their university’s ICT Policy were the main predictors of a breach versus non-breach behaviours and confirmed the importance of training in ethics, increasing commitment to the institution, and improving knowledge of the ICT policy and appropriate usage of ICT resources.
3D Audio: The Next Step in Realism for Gaming: Benefits and pitfalls of 3D audio in games from a creative content and technical implementation point of view

When using 3D audio in games, the goal should be to create an immersive, believable auditory experience. To achieve this on the technical side of things, game developers must combine the appropriate hardware with sophisticated software algorithms and sound propagation techniques. Regarding content, creators need to distinguish between 3D sounds inside the scene and sounds with a narrative function that are non-diegetic. This article looks behind the curtains of the spatial audio hype and lets you know what you're in for and what is coming in the future.
GazePair: Efficient Pairing of Augmented Reality Devices Using Gaze Tracking

As Augmented Reality (AR) devices become more prevalent and commercially viable, the need for quick, efficient, and secure schemes for pairing these devices has become more pressing. Current methods to securely exchange holograms require users to send this information through large data centers, creating security and privacy concerns. Existing techniques to pair these devices on a local network and share information fall short in terms of usability and scalability. These techniques either require hardware not available on AR devices, intricate physical gestures, removal of the device from the head, do not scale to multiple pairing partners, or rely on methods with low entropy to create encryption keys. To that end, we propose a novel pairing system, called GazePair, that improves on all existing local pairing techniques by creating an efficient, effective, and intuitive pairing protocol. GazePair uses eye gaze tracking and a spoken key sequence cue (KSC) to generate identical, independently generated symmetric encryption keys with 64 bits of entropy. GazePair also achieves improvements in pairing success rates and times over current methods. Additionally, we show that GazePair can extend to multiple users. Finally, we assert that GazePair can be used on any Mixed Reality (MR) device equipped with eye gaze tracking.
SparSFA: Towards robust and communication-efficient peer-to-peer federated learning

Federated Learning (FL) has emerged as a powerful paradigm to train collaborative machine learning (ML) models, preserving the privacy of the participants' datasets. However, standard FL approaches present some limitations that can hinder their applicability in some applications. Thus, the need of a server or aggregator to orchestrate the learning process may not be possible in scenarios with limited connectivity, as in some IoT applications, and offer less flexibility to personalize the ML models for the different participants. To sidestep these limitations, peer-to-peer FL (P2PFL) provides more flexibility, allowing participants to train their own models in collaboration with their neighbors. However, given the huge number of parameters of typical Deep Neural Network architectures, the communication burden can also be very high. On the other side, it has been shown that standard aggregation schemes for FL are very brittle against data and model poisoning attacks. In this paper, we propose SparSFA, an algorithm for P2PFL capable of reducing the communication costs. We show that our method outperforms competing sparsification methods in P2P scenarios, speeding the convergence and enhancing the stability during training. SparSFA also includes a mechanism to mitigate poisoning attacks for each participant in any random network topology. Our empirical evaluation on real datasets for intrusion detection in IoT, considering both balanced and imbalanced-dataset scenarios, shows that SparSFA is robust to different indiscriminate poisoning attacks launched by one or multiple adversaries, outperforming other robust aggregation methods whilst reducing the communication costs through sparsification.