Reviewerly/exHarmony huggingface dataset:
Fast calculations of Jackknife covariance matrix estimator

We consider data in which each observed subject belongs to one of different subpopulations (components). The true number of component which a subject belongs to is unknown, but the researcher knows the probabilities that a subject belongs to a given component (concentration of the component in the mixture). The concentrations are different for different observations. So the distribution of the observed data is a mixture of components’ distributions with varying concentrations. A set of variables is observed for each subject. Dependence between these variables is described by a nonlinear regression model. The coefficients of this model are different for different components. Normality of estimator for nonlinear regression parameters is demonstrated under general assumptions. A mixture of logistic regression models with continuous response is considered as an example. In the paper we construct confidence ellipsoids for the regression parameters based on the modified least squares estimators. The covariances of these estimators are estimated by the multiple modifications of jackknife technique. Performance of the obtained confidence ellipsoids is assessed by simulations.
Making touch visible with the suture of fantasy with virtual aesthetician in “The Best Facial Clinic” – The glitchy-score of tele-synaesthesia performance in the age of global pandemic

In the wake of the COVID-19 pandemic, we have been adapting as fugitives of this accidental encounter with an “untouchable” virus, while being rendered into the shared virtual arena, where our discursive bodies are situated within, embedded in and tele-commuted to. Our increasing dependence on online interaction and video conferencing during the pandemic is not only facilitating social connectedness, but also contemplating the question: How to elongate our somatosensation and echo the embodied experience of touching through the incorporeal virtual connectivity? This essay focuses on the embodied nature of tele-synaesthesia performance, its potential effect of forging a rhythmic connection from one sensuous modality to another, and the concurrent emergences of glitch and internet latency as non-verbal cues of internet-situated communication. In reference to Laura Mark’s theory of haptic visuality, where vision triggers a tactile experience in the body; Naomi Bennett’s concept of virtual touch, in which an affective sensory response of touch can be elicited through non-tactile senses, Paul Sermon’s artistic production of Telematic Quarantine (2020) and Pandemic Encounter (2020), that telepresents the stories of self (isolation), and in relation to Michel Foucault’s concept Heterotopia in the context of internet-situated performance, I examine the performance work I have been developing during lockdown since March 2020: The Best Facial (2021), supported by Centre for Digital Media Cultures Research and School of Art Postgraduate Research at University of Brighton, a number of sessions of 25-minute 1-to-1 participatory tele-synaesthesia performances that take place on Zoom, where I become a virtual aesthetician and use telepresence to perform meditative virtual facial tactics, and to make tele-contact improvisation upon the surface of the participant’s face to trigger tactile experiences through haptic visuality, virtual touching, and auditory fantasization.
Implementing TEL in VET 4.0 and Future Possibilities

This final chapter draws the various concepts and strategies presented and discussed from the previous chapters, into the proposal of a model for supporting practice-based ‘learning by doing’ programmes. Recommendations are proposed to assist with the planning, development, implementation, and review of TEL for VET 4.0. As much of digitally enabled learning necessitates access to and competency with digital devices, platforms, and apps, these recommendations include being prepared with aspects of digital literacy/fluency, digital equity, and the creation or archiving of learning resources, before they are required. Future possibilities, including technologies enabling AR/VR, to support simulation and situated learning in VET and practice-based learning environments are also presented and discussed in this chapter.
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy uses predictive modeling techniques to “mine” variables of interest from available data and then includes those variables into an econometric framework to estimate causal effects. However, because the predictions from machine learning models are inevitably imperfect, econometric analyses based on the predicted variables likely suffer from bias due to measurement error. We propose a novel approach to mitigate these biases, leveraging the random forest technique. We propose using random forest not just for prediction but also for generating instrumental variables for bias correction. The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make “different” mistakes, that is, have weakly correlated prediction errors. A key observation is that these properties are closely related to the relevance and exclusion requirements of valid instrumental variables. We design a data-driven procedure to select tuples of individual trees from a random forest, in which one tree serves as the endogenous covariate and the others serve as its instruments. Simulation experiments demonstrate its efficacy in mitigating estimation biases and its superior performance over alternative methods. History: David Martens served as the senior editor for this article. Data Ethics &amp; Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/7039927/tree/v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2022.0019 ).
SENTIMENT ANALYSIS OF PERFORMANCE EFFECTIVENESS OF MALIOBORO PEDESTRIAN USING SENTISTRENGTH METHOD ON TWITTER

Sentiment analysis is a study to analyze public opinions, sentiments, evaluations, attitudes, and emotions towards services, products, public issues, organizations, general topics, etc. Sentiment analysis is a computational research of various opinions and emotionsexpressed textually and opinions in the form of text can be obtained through social media such as Twitter. The Malioboro area as one of the famous tourist destinations in Yogyakarta has pedestrian facilities for visitors. In the area, there are many pedestrian facilities including pedestrian paths, sidewalks, zebra crossings and parking. This study aims to measure the effectiveness of the use of the pedestrian area in Malioboro based on opinions on Twitter. This study uses the Sentistrength method. The results shows that from 3,572 Tweet data from 2016 to 2020, the results of Positive sentiment are 55.81%, the results of Neutral sentiment are 36.18% and the results of negative sentiment are 8.01%.
Task Scheduling in Cloud Using Improved Genetic Algorithm

Cloud computing usually needs to process a large number of computing tasks,Pabboju, Shyam Sunder and task scheduling strategies play a key role in determining the efficiency of cloud computing.Adilakshmi, T How to allocate computing resources reasonably and schedule task operations effectively so that the time and cost required to complete all tasks are shorter is an important issue. This paper proposes an Improved Genetic Algorithm (I-GA) that considers time and cost constraints. The result of scheduling by this algorithm can not only make the task completion time shorter, but also cost less. Through experiments, I-GA is compared with Genetic Algorithm (T-GA) considering time constraints and Genetic Algorithm (C-GA) considering cost constraints. The experimental results show that this algorithm is an effective task scheduling algorithm in cloud computing.
Privacy Preserving Semantic-Aware Multi-keyword Ranked Dynamic Search Over Encrypted Data

Searchable encryption (SE) is a promising primitive which enables data owners to privately query and retrieve required documents from encrypted outsourced data collection in cloud servers. Most of the SE schemes in literature strive to achieve a balanced trade-off between efficiency, privacy and expressiveness of query. However, a vast majority of efficient and secure schemes lack searching capabilities beyond exact match of queries and ignore the semantic relationship between keywords and documents. Therefore, a new trend in SE research is to address the question: How to make search semantic-aware and context-based? Majority of the existing semantic search schemes are static, do not support dynamic update operations, suffer from leakage of significant information to server, and employ huge size vectors to represent documents, keywords and topics/features. We propose a privacy preserving, relevance ranking-based dynamic search scheme using a sublinear search time hash-based indexing mechanism and semantic-aware search supporting semantic expansion of user queries. Our scheme employs Word2Vec model to quantitatively evaluate semantic relationships between query and other keywords to expand queries to fulfill user’s search intentions. It also supports dynamic operations like insert and delete of documents on outsourced collection with less efforts to update relevance information which is calculated partially at client and server. Experiments on RFC dataset have demonstrated a time and space efficient non-interactive semantic search over encrypted data.
Camdar-Adv: Method for Generating Adversarial Patches on 3D Object

DNN model is the core technology for sensors of the autonomous driving platform to perceive the external environment. However, it has a certain vulnerability, and the artificial designed adversarial examples can make the DNN model output the wrong results. These adversarial examples not only exist in the digital world, but also in the physical world. At present, researches on autonomous driving platform mainly focus on attacking a single sensor. In this paper, we presnet a method called Camdar-adv for generating adversarial examples, which can attack the optical image sensor based on any 3D object. Specifically, based on a 3D object that can attack LiDAR sensors, a geometric transformation can be used to project it onto the 2D plane. Perturbation can be added on the 2D plane to generate 2D adversarial examples, which can attack the optical image sensor in the black-box setting, without changing the object’s geometry.
Effective highlighting modes of graphical user interfaces in visual information search

This study discusses the preferred highlighting mode for graphical user interface (GUI) design. The study investigated the effects of different highlighting modes on the search time and accuracy of the element on the screen. The experimental factors were the type of highlighting (color, bold, or color and bold), the target highlighting (highlighted or not highlighted), and the number of elements on GUI (few or many). The participants had to search for a predetermined target on the screen. The evaluation measures were the reaction time and percentage correctly identified. The statistical analysis result shows that bold highlighting had the shortest search time, and color highlighting had the worst accuracy. Under the conditions of this experiment, bold highlighting should contribute to the design of the screen for an easier search of information in terms of both search speed and accuracy.
Flexible optical bookmark for open-page recognition in augmented paper

We demonstrate a flexible electronic bookmark designed to facilitate the augmentation of printed content with media-rich, dynamic digital content, thereby extending the utility of ordinary books. The system, produced by the hybrid integration of photosensors, detects the currently open page within a book via gestures that are natural to the book reading experience. Physical cut-outs within the page or high contrast patterns printed in graphical ink define a unique code, which is optically read by the functional bookmark.
Ability-oriented Performance Evaluation of General Intelligent Decision-making Engines Based on Knowledge Graphs

The rapid development of artificial intelligence technology has changed modern warfare, in which intelligent decision-making based on knowledge graph technology is one of the most important applications. A general intelligent decision-making engine is expected to realize intelligent semantic search, intelligent question answering, and knowledge reasoning by building knowledge graphs from big data to improve the effectiveness of decision-making in military scenarios. However, there is still no answer on how to evaluate the performance of knowledge graph techniques serving for decision-making. For this reason, an ability-oriented performance evaluation framework for general decision-making engines used in the military field is proposed in this paper. We summarize five basic abilities of general intelligent decision-making engines from its construction and application with military knowledge graphs, and then discuss quantitative evaluation methods for each specific ability in detail.
Boosting IoT data valorization through the adoption of DLT

During the last decade Internet of Things has become one of the key technologies in supporting digital transformation of several ecosystems such urban or industry ones. The huge amount of data generated in such contexts as well as the imperative requirements in terms of trustworthiness, authenticity and integrity make compulsory the adoption of the proper solutions fitting those requirements. This paper presents the design, implementation and validation of a distributed ledger technology architecture emphasizing services linked to data valorization.
A Terminal Classification Scheme with Imbalanced Dataset Based on Low-Complexity Time-LSTM

With the rapid development of wireless communication, various kinds of mobile terminals may communicate with each other. To provide sufficient security and privacy, classification of the source terminals is generally critical. In this paper, we set up several scattered sensors collecting the received field strength and capture time for classification. To deal with the unknown propagation environment, imbalanced dataset and irregular sampling time, we propose a terminal classification scheme based on Long Short-Term Memory (LSTM) network. First, the problem of imbalanced dataset is solved by designing a preprocessing method called random interval sampling method, where the samples for class with less terminals are resampled. Then, the information of the irregular sampling time is incorporated into the classification to obtain extra benefit. Experimental results based on real-world data demonstrate that when compared with the exsiting LSTM schemes, the proposed classification model effectively utilizes the irregular time intervals and achieves excellent classification perfomance with imbalanced dataset.
Image Processing Based Smart Violation Detection in College

According to statistics received via way of mans of the World Health Organization, the worldwide pandemic of COVID-19 has significantly impacted the arena and has now inflamed extra than 8 million humans worldwide. Wearing face mask and following secure social distancing are of the improved protection protocols want to be observed in public locations so one can save you the unfold of the virus. To create secure surroundings that contributes to public protection, we advocate a green laptop imaginative and prescient primarily based totally technique centered at the real-time automatic tracking of humans to locate each secure social distancing and face mask in public locations via way of means of imposing the version on raspberry pi4 to display hobby and locate violations via camera. After detection of breach, the raspberry pi4 sends alert sign to govern middle at kingdom police headquarters and additionally deliver alarm to public. In this proposed machine current deep gaining knowledge of set of rules had been combined with geometric strategies for constructing a strong modal which covers 3 factors of detection, tracking, and validation. Thus, the proposed machine favors the society via way of means of saving time and allows in reducing unfold of corona virus. It may be applied successfully in modern state of affairs while lockdown is eased to check out people in public gatherings, buying malls, etc. Automated inspection reduces manpower to check out the general public and additionally may be utilized in any place.
Estimation of Space Depth Intervals in a Stereoscopic Image

In this paper, the main attention is paid to one of the most important characteristics of the quality of stereoscopic images - the sensation of the depth of space in a stereoscopic image. Based on the calculations performed, it is shown what depth of space can be provided when choosing specific angular values of positive and negative screen parallaxes at given observation distances. The obtained data are compared with the results of experimental studies carried out by various authors.
A Visual Analytics Approach To Explore Potential Anomalous Behavior in Corporate Communication

In this paper, we present a visual analytics approach to analyze temporal patterns of human communication from a vast corporate communication dataset. Our approach mainly relies on visualization and mapping techniques to discover the patterns, which then support feature model development for a machine learning method. In contrast to previous work, our technique targets communication data presenting only temporal and interaction information, and focuses on the pattern searches of anomaly behaviors. The new visual analytics platform can be effectively used to analyze the differences between normal and suspicious procurement behaviors in corporation using email, phone call, and personal meeting records. By using the platform, we successfully found other potentially illegal activity based on suggested suspicious behaviors.
Comparison of petrological and geochemical characteristics of three different types of Eocene copper-gold mineralization in eastern Iran

The Maherabad, Qaleh Zari, and Hired deposits in eastern Iran are porphyry (Cu-Au)-, Iron oxide copper–gold (IOCG)-, and reduced intrusion-related gold system (RIRGS)-type mineralization, respectively. They are located within the Lut block magmatic assemblage, which is the main metallogenic province in eastern Iran. The mineral assemblages and paragenesis of the deposits indicate that highest log fO2 and log fS2 contents belong to the Qaleh Zari deposit. In contrast, the lowest values of oxygen and sulphur fugacity are related to the RIRGS system in the Hired deposit. The Maherabad mineralization has moderate log fO2 and log fS2 contents relative to the Qaleh Zari and Hired deposits. Geochemically, these three deposits are related to different types of granitoids, which occurred within the short time interval from 37 to 41 Ma. The Maherabad and Qaleh Zari intrusions are related to oxidized magnetite-series granitoids, whereas the mineralization at Hired deposit is related to the reduced ilmenite-series granitoids. Magnetite-series intrusive rocks at Hired are barren. Intrusive bodies in the three studied sites are metaluminous, high-K calc-alkaline to shoshonitic, and formed in a volcanic arc setting. However, there are distinct differences in the radiogenic isotopic composition, content of some elements and elemental ratios reflecting the effect of different sources in the formation of intrusions. The reduced granite of the Hired deposit has the lowest Eu/Eu* of <0.7, P + Sr < 1000 ppm, and Sr/Y < 20 associated with (La /Yb)N < 12. Crustal contamination played an important role in magma evolution (87Sr/86Sri = 0.70627 and −εNd(t) = -2.60). P + Sr and Sr/Y contents in the productive oxidant magnetite-series intrusive rocks, such as the Maherabad and Qaleh Zari deposits, are more than 1500 ppm and 35, respectively. On this basis, petrological characteristic, geochemical indicator elements and mineral assemblage and paragenesis can be used for discrimination of productive and barren intrusions in the three different deposit types studied in this research. This can be used as an exploration criteria for the hydrothermal deposits associated with different magmatic intrusions in some areas.
Enhancing OAuth With Blockchain Technologies for Data Portability

To satisfy the requirement of data portability, current service providers (or resource servers) usually provide OAuth-based schemes for third party applications (or clients) to access user data with the user's consent. To shoulder the costs of maintaining relationships with potential third party applications, a service provider may adopt delegate the task of authentication and authorization to an authorization server. However, current OAuth specification does not specify the interactions between an authorization server and a resource server. To address this limitation, this study proposes the MyDataChain framework to enhance the existing OAuth specification with blockchain technology. The proposed framework utilizes smart contracts to establish the standard interface to support the processes of authorization requesting, granting, and revocation. As blockchain technologies can ensure data integrity, the framework can use the data stored in the blockchain to resolve disputes among different parities. Moreover, as the proposed framework uses the Non-Interactive Zero-Knowledge (NIZK) scheme, the proposed framework can achieve its purpose without storing any personal identifiable or traceable data in the blockchain. Therefore, people cannot utilize information stored in the blockchain to compromise user privacy. Furthermore, this study implements a prototype system using Quorum blockchain technology. The experimental results show that the framework can be realized with existing blockchain technologies. Therefore, this study can provide a feasible privacy preserving means of achieving data portability and providing individuals the rights to be forgotten considering dispute resolution.
Integrating Metaheuristic Algorithms and Minimum Cross Entropy for Image Segmentation in Mist Conditions

Presently, there are several problems with the artificial vision of the autonomous cars, one of these is the detection of objects in adverse conditions, a typical example of this problem is rain where visibility is limited or non-existent, in the same way, autonomous driving under sand or snow storms or in mist conditions, just to mention a few. The core of this chapter deals with the problem in mist conditions by improving image segmentation. These types of images represent a challenge that requires the application of efficient and robust computer techniques that enhance; the segmentation, contrast and can be clearly interpreted. This work proposes the use of metaheuristic algorithms (MA) combined with threshold technique such as minimum cross entropy (MCE) to applied to images under mist conditions. The results of the work are analyzed under statistical tests and to guarantee proper segmentation several state-of-the-art metrics were used.