Reviewerly/exHarmony huggingface dataset:
One bad apple can spoil your IPv6 privacy

IPv6 is being more and more adopted, in part to facilitate the millions of smart devices that have already been installed at home. Unfortunately, we find that the privacy of a substantial fraction of end-users is still at risk, despite the efforts by ISPs and electronic vendors to improve end-user security, e.g., by adopting prefix rotation and IPv6 privacy extensions. By analyzing passive data from a large ISP, we find that around 19% of end-users' privacy can be at risk. When we investigate the root causes, we notice that a single device at home that encodes its MAC address into the IPv6 address can be utilized as a tracking identifier for the entire end-user prefix---even if other devices use IPv6 privacy extensions. Our results show that IoT devices contribute the most to this privacy leakage and, to a lesser extent, personal computers and mobile devices. To our surprise, some of the most popular IoT manufacturers have not yet adopted privacy extensions that could otherwise mitigate this privacy risk. Finally, we show that third-party providers, e.g., hypergiants, can track up to 17% of subscriber lines in our study.
A general algorithm for the optimization of photovoltaic modules layout on irregular rooftop shapes

Although the installation of photovoltaic systems on roofs is a successful investment, rooftop space availability has been identified as a significant limiting factor in achieving zero-energy buildings, especially by building components such as chimneys, elevator machine rooms, fans, plumbing vents, etc. This study presents a general algorithm for the optimal deployment of photovoltaic module rows installed on irregular flat roof shapes. The presented algorithm takes into account the irregular rooftop shape, the self-shading of photovoltaic modules, the inclusion of building components, commercial photovoltaic modules with different sizes, mounting systems with different configurations, distances required for maintenance, and the technical reports to minimize shading effects. The proposed algorithm allowed to increase in the amount of solar energy received by the photovoltaic modules. The optimization process takes into account the weather conditions at the specific location. The optimization algorithm was implemented using a specific Mathematica™ code in which the commands used in the development of the code were introduced to facilitate its replication. The optimization algorithm output provides the essential parameters for the optimal photovoltaic system design such as: the optimum number of mounting systems and their configuration, the optimum tilt angle of the mounting system and its dimensions, the photovoltaic module model, the maximum total area of the photovoltaic field and the maximum annual energy captured by the photovoltaic modules. We compared the mounting system layout obtained with the proposed algorithm with the tilt angle photovoltaic module layout recommended by three technical papers (IDAE Technical Report, Lorenzo’s equation and Jacobson’s equation) with respect to photovoltaic field area gain, energy gain and levelized cost of energy.The optimal photovoltaic module layout obtains the maximum photovoltaic field area gain of 35.52% with respect to the Jacobson’s equation and the minimum of 32.29% with respect to the IDAE Technical Report. The optimal photovoltaic module layout obtains the maximum energy gain of 27.83% with respect to the Jacobson’s equation and the minimum of 24.84% with respect to the IDAE Technical Report. The levelized cost of energy of the optimal PV module layout is lower than that of the other arrangements studied. The algorithm presented may be useful for decision-makers or policymakers in determining the optimal distribution of photovoltaic modules on irregular rooftop shapes.
Government by Code? Blockchain Applications to Public Sector Governance

Studies of blockchain governance can be divided into analyses of the governance of blockchains (such as rules and power dynamics within a given network) and governance by blockchains (such as how blockchains can be implemented to improve self-governance of community-based peer production networks). Less emphasis has been placed on applications of distributed ledgers to public sector governance. Our review clarifies that the decentralization and distributive features that enable blockchains to link up loosely connected private organizations and public agencies to improve efficiency and transparency of government transactions. However, most blockchain applications lack clear advantages over the conventional digital recording of information. In addition, our review highlights that blockchain applications in public sector governance are potentially vast, though in most instances, the existing applications have not extended much beyond limited-scale pilots. We conclude with a call for the construction of indexes of public sector implementations of blockchains, as none yet exist, as well as for additional research to understand why governments have not deployed blockchains more widely.
The Need for Interpretable Features

Through extensive experience developing and explaining machine learning (ML) applications for real-world domains, we have learned that ML models are only as interpretable as their features. Even simple, highly interpretable model types such as regression models can be difficult or impossible to understand if they use uninterpretable features. Different users, especially those using ML models for decision-making in their domains, may require different levels and types of feature interpretability. Furthermore, based on our experiences, we claim that the term "interpretable feature" is not specific nor detailed enough to capture the full extent to which features impact the usefulness of ML explanations. In this paper, we motivate and discuss three key lessons: 1) more attention should be given to what we refer to as the interpretable feature space, or the state of features that are useful to domain experts taking real-world actions, 2) a formal taxonomy is needed of the feature properties that may be required by these domain experts (we propose a partial taxonomy in this paper), and 3) transforms that take data from the model-ready state to an interpretable form are just as essential as traditional ML transforms that prepare features for the model.
Online site visits using virtual collaborative spaces: A plan-reading activity on a digital building site

Site visits or field trips have been a tool utilized by construction educators to engage students in active learning, assist traditional lessons, and attain stronger and deeper student learning experiences. Nevertheless, site visits present major logistical and accessibility challenges for educational institutions and instructors, reducing the number of students that have access to the benefits of such a technique. The limitations for site visits have further broadened recently, as the reality of the COVID-19 public health concerns has forced educators to move to online course delivery quickly and the majority of site visits have been canceled. The research goal of this paper is to present construction students with opportunities to enable online location-independent site visits where contextualized learning is dangerous, unsafe, or impossible to achieve. In this project, a virtual online learning environment was created to offer the affordances that provide an in-depth learning experience through collaborative communication for a plan-reading activity in a virtual space that resembles a real-world site visit to a building facility. This virtual online learning environment helped students to experience the physical and social aspects of the site visit while getting a collaborative opportunity to practice their plan-reading skills. A comparative study with a business-as-usual condition (online delivery through Zoom®) was conducted and the students’ plan-reading performance and their feedback on the sense of presence, social presence, fatigue, and system usability was reported. The outcome of the study shows that such virtual collaborative site visits present unique opportunities to enable online delivery of spatiotemporal contexts of sites and offer an effective remote alternative when these learning opportunities are not available.
Moneyball

Microsoft Azure SQL Database is among the leading relational database service providers in the cloud. Serverless compute automatically scales resources based on workload demand. When a database becomes idle its resources are reclaimed. When activity returns, resources are resumed. Customers pay only for resources they used. However, scaling is currently merely reactive, not proactive, according to customers' workloads. Therefore, resources may not be immediately available when a customer comes back online after a prolonged idle period. In this work, we focus on reducing this delay in resource availability by predicting the pause/resume patterns and proactively resuming resources for each database. Furthermore, we avoid taking away resources for short idle periods to relieve the back-end from ineffective pause/resume workflows. Results of this study are currently being used worldwide to find the middle ground between quality of service and cost of operation.
Genetic Evolution-Based Feature Selection for Software Defect Prediction Using SVMs

Software Defect Prediction (SDP) involves the early detection of fault-prone modules and reduces the testing efforts and cost. Support Vector Machine (SVM)-based SDP classifiers use large amount of high-dimensional data, and hence feature selection (FS) is applied for better accuracy. Search-based feature selection is found effective to improve the efficiency of predictors. This paper proposes a genetic evolution (GeEv) technique to select features. The GeEv technique involves introduction of diversity at intermediate level by the genetic evolution of random offspring with better survival capability. GeEv searches the feature space for an optimal feature subset using the performance of classification and number of features selected as the fitness function. The FS is modeled as an optimization problem and optimal solution is sought using GeEv. The performance is compared with baseline technique. From the experimental results, it is shown GeEv outperforms the competing FS approach and can achieve better accuracy than others statistically.
LifeSeeker 4.0: An Interactive Lifelog Search Engine for LSC'22

In this paper, we introduce LifeSeeker 4.0 - an interactive lifelog retrieval system developed for the fifth annual Lifelog Search Challenge (LSC'22). In LifeSeeker 4.0, we focus on enhancing our previous system to allow users who have little to no knowledge of underlying system functioning and lifelog data to use it with ease by employing a Contrastive Language-Image Pre-training (CLIP) model. Furthermore, we have exploited the music metadata to facilitate searches that may incorporate emotion. Event clustering is also improved in this version to increase user experience by reducing the occurrence of repeated images, and hence decreasing the search time.
Augmented Reality to Engage Visitors of Science Museums through Interactive Experiences

In the last years, interactive exhibitions based on digital technologies have become widely common, thanks to their flexibility and effectiveness in engaging visitors and creating memorable experiences. One of the topics in which digital technologies can be particularly effective is the communication of abstract concepts that are difficult for the human mind to imagine. An emblematic example is the astronomy discipline, which requires us to imagine and understand phenomena far away from our everyday life. In this paper, the authors present a research project, MARSS, in which digital technologies are used effectively to enhance the Users’ Experience of the Museo Astronomico di Brera located in Milan. Specifically, the MARSS project aims at designing and developing a new digital journey inside the museum to allow different categories of visitors to enjoy the exhibition in an engaging and interactive way. The paper presents the design and development phases of the experience and its evaluation with users. The results of the evaluation indicate that the digital interactive experience is appreciated by users and is successful in translating the content of high scientific value into more engaging and easily understandable elements.
Multimodal Interactive Lifelog Retrieval with vitrivr-VR

The multimodal nature of lifelog data poses unique challenges for analysis, indexing and interactive retrieval. To address these challenges, the Lifelog Search Challenge (LSC) is an annual evaluation campaign allowing interactive retrieval systems to explore new ideas and measure their performance against each other.
Data Fusion of Total Solar Irradiance Composite Time Series Using 41 Years of Satellite Measurements

Since the late 1970s, successive satellite missions have been monitoring the sun's activity and recording the total solar irradiance (TSI). Some of these measurements have lasted for more than a decade. In order to obtain a seamless record whose duration exceeds that of the individual instruments, the time series have to be merged. Climate models can be better validated using such long TSI time series which can also help to provide stronger constraints on past climate reconstructions (e.g., back to the Maunder minimum). We propose a 3-step method based on data fusion, including a stochastic noise model to take into account short and long-term correlations. Compared with previous products scaled at the nominal TSI value of 1361 W/m2, the difference is below 0.2 W/m2 in terms of solar minima. Next, we model the frequency spectrum of this 41-year TSI composite time series with a Generalized Gauss-Markov model to help describe an observed flattening at high frequencies. It allows us to fit a linear trend into these TSI time series by joint inversion with the stochastic noise model via a maximum-likelihood estimator. Our results show that the amplitude of such trend is $\sim$ -0.004 +/- 0.004 W/(m2yr) for the period 1980 - 2021. These results are compared with the difference of irradiance values estimated from two consecutive solar minima. We conclude that the trend in these composite time series is mostly an artifact due to the colored noise.
Predicting the Hate: A GSTM Model based on COVID-19 Hate Speech Datasets

COVID-19 crisis has been accompanied by copious hate speeches widespread on social media. It reinforces the fragmentation of the world, resulting in more significant racial discrimination and distrust between people, leading to crimes, and injuring individuals spiritually or physically. Hate speech is hard to crack for a global recovery in the post-epidemic era. Conducting with Twitter datasets, this paper aims to find the key indicators that influence the trend of hate speech, then builds a Gaussian Spatio-Temporal Mixture (GSTM) model for trends prediction based on the pre-analysis. Findings show that in the early period, the participation of influential users is closely related to the emergence of sentiment peaks, and the interval time is around one week. After hate speech waves up, the indicator of total exposure becomes more critical, suggesting that grass-root release influences at this stage. Compared with three classical time-series predicting models, the GSTM model shows better peak prediction ability and lower residual mean. This work enriches the approaches of predicting unknown but foreseeable hate speeches accompanied by future pandemics.
Hybrid particle swarm optimizer with fitness-distance balance and individual self-exploitation strategies for numerical optimization problems

Due to the simplicity of the learning strategy, the original particle swarm optimization (PSO) has various deficiencies, such as entrapment in local optima, rapid loss of diversity and a poor balance between exploration and exploitation, especially for many complex optimization problems. To overcome these shortcomings, this paper proposes a hybrid particle swarm optimizer with fitness-distance balance and individual self-exploitation strategies, namely, HPSO-FDB-ISE. First, to reduce the probability of becoming trapped in a local optimum for the population, fitness-distance balance is employed to construct an alternative learning exemplar to the global best position. Second, individual self-exploitation is introduced to achieve intelligent exploitation by learning from individual current information for particles. Finally, a nonlinear time-varying inertia weight is used to efficiently balance the exploitation and exploration in the search process. The proposed HPSO-FDB-ISE is evaluated on the CEC 2017 test suite against six state-of-the-art meta-heuristics and seven state-of-the-art PSO variants. Experimental results and statistical analysis reveal that the proposed HPSO-FDB-ISE algorithm yields excellent performances compared to other algorithms that are considered in this paper in the majority of cases.
Blockchain Platform to Resolve Security Issues in IoT and Smart Networks

IoT provided a unified operating picture across a wide range of modern-day applications. The operating image is accomplished thanks to improvements in wireless sensor network devices that can interact over a network, exchanging data and executing various analyses. The only way to share information and authenticate data in the internet of things is through a central server, which raises security and privacy problems. Device spoofing, incorrect authentication, and a lack of data exchange dependability are all possibilities. A central server idea is abolished, and blockchain technology is used as part of IoT to solve such security and privacy problems. This chapter examines the potential security and privacy concerns associated with component interaction in the internet of things, as well as how the distributed ledger-based blockchain technology might help. Here, in this chapter, the authors discuss the application of BC to certain areas and categories. Various IoT and IoT with BC problems were also highlighted in order to better comprehend blockchain technology's role.
Embodiment of supernumerary robotic limbs in virtual reality

The supernumerary robotic limb system expands the motor function of human users by adding extra artificially designed limbs. It is important for us to embody the system as if it is a part of one's own body and to maintain cognitive transparency in which the cognitive load is suppressed. Embodiment studies have been conducted with an expansion of bodily functions through a "substitution" and "extension". However, there have been few studies on the "addition" of supernumerary body parts. In this study, we developed a supernumerary robotic limb system that operates in a virtual environment, and then evaluated whether the extra limb can be regarded as a part of one's own body using a questionnaire and whether the perception of peripersonal space changes with a visuotactile crossmodal congruency task. We found that the participants can embody the extra-limbs after using the supernumerary robotic limb system. We also found a positive correlation between the perceptual change in the crossmodal congruency task and the subjective feeling that the number of one's arms had increased (supernumerary limb sensation). These results suggest that the addition of an extra body part may cause the participants to feel that they had acquired a new body part that differs from their original body part through a functional expansion.
Web3 &amp; DAOs: an overview of the development and possibilities for the implementation in research and education

Web3 brings an enabling set of technologies that has the potential to completely reshape many different fields, including research and education. Today we are faced with challenges around regulation, data safety and privacy, as well as governance and bureaucracy - to name just a few. As Web3 gains more traction globally it activates new paradigms to engage, govern, create, iterate, and implement around research results in ways that were previously complex and arduous. The Decentralised Autonomous Organisation (DAO) is one such empowering Web3 utility, and within this framework researchers and experts have the opportunity to implement and test their research results promptly on a small or large scale as required. In this paper, we provide an overview of Web3 technologies and their implications for education and research. Our aim is to introduce researchers, educators, and decision-makers to the potential of using Web3
A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

We propose a novel text classification model, which aims to improve the performance of Arabic text classification using machine learning techniques. One of the effective solutions in Arabic text classification is to find the suitable feature selection method with an optimal number of features alongside the classifier. Although several text classification methods have been proposed for the Arabic language using different techniques, such as feature selection methods, an ensemble of classifiers, and discriminative features, choosing the optimal method becomes an NP-hard problem considering the huge search space. Therefore, we propose a method, called Optimal Configuration Determination for Arabic text Classification (OCATC), which utilized the Particle Swarm Optimization (PSO) algorithm to find the optimal solution (configuration) from this space. The proposed OCATC method extracts and converts the features from the textual documents into a numerical vector using the Term Frequency-Inverse Document Frequency (TF–IDF) approach. Finally, the PSO selects the best architecture from a set of classifiers to feature selection methods with an optimal number of features. Extensive experiments were carried out to evaluate the performance of the OCATC method using six datasets, including five publicly available datasets and our proposed dataset. The results obtained demonstrate the superiority of OCATC over individual classifiers and other state-of-the-art methods.
Interrelationship between levels of digital competences and Technological, Pedagogical and Content Knowledge (TPACK): a preliminary study with Polish academic teachers

The purpose of this study was to develop and to validate a study tool for determining the levels of academic teachers’ digital competence regarding the TPACK framework and self-reflection questionnaire of the European Competence Framework for the Digital Competence of Educators in the context of higher education. The classic 7-factor TPACK model was changed to a 2-factor model (Pedagogical and Technological Knowledge). A survey was administered to 103 academic teachers from Polish universities. Exploratory factor analysis and confirmatory factor analysis confirmed the goodness-of-fit for the 2-factor TPACK model for higher education studies and internal consistency of the study survey. Descriptive analysis indicated a significant negative correlation between some demographic variables and domains from both factors. However, some positive associations with certain variables from the professional background were also noticed. Implications for professional development and suggestions regarding teachers’ digital competences and TPACK are discussed.