Reviewerly/exHarmony huggingface dataset:
Aesthetic Trends and Semantic Web Adoption of Media Outlets Identified through Automated Archival Data Extraction

The last decade has been a time of great progress in the World Wide Web and this progress has manifested in multiple ways, including both the diffusion and expansion of Semantic Web technologies and the advancement of the aesthetics and usability of Web user interfaces. Online media outlets have often been popular Web destinations and so they are expected to be at the forefront of innovation, both in terms of the integration of new technologies and in terms of the evolution of their interfaces. In this study, various Web data extraction techniques were employed to collect current and archival data from news websites that are popular in Greece, in order to monitor and record their progress through time. This collected information, which took the form of a website’s source code and an impression of their homepage in different time instances of the last decade, has been used to identify trends concerning Semantic Web integration, DOM structure complexity, number of graphics, color usage, and more. The identified trends were analyzed and discussed with the purpose of gaining a better understanding of the ever-changing presence of the media industry on the Web. The study concluded that the introduction of Semantic Web technologies in online media outlets was rapid and extensive and that website structural and visual complexity presented a steady and significant positive trend, accompanied by increased adherence to color harmony.
Joint waveform selection and power allocation algorithm in manned/unmanned aerial vehicle hybrid swarm based on chance-constraint programming

In this paper, we propose a joint waveform selection and power allocation (JWSPA) strategy based on chance-constraint programming (CCP) for manned/unmanned aerial vehicle hybrid swarm (M/UAVHS) tracking a single target.Accordingly, the low probability of intercept (LPI) performance of system can be improved by collaboratively optimizing transmit power and waveform.For target radar cross section (RCS) prediction, we design a random RCS prediction model based on electromagnetic simulation (ES) of target.For waveform selection, we build a waveform library to adaptively manage the frequency modulation slope and pulse width of radar waveform.For power allocation, the CCP is employed to balance tracking accuracy and power resource.The Bayesian Cramér-Rao lower bound (BCRLB) is adopted as a criterion to measure target tracking accuracy.The hybrid intelli gent algorithms, in which the stochastic simulation is integrated into the genetic algorithm (GA), are used to solve the stochastic optimization problem.Simulation results demonstrate that the proposed JWSPA strategy can save more transmit power than the traditional fixed waveform scheme under the same target tracking accuracy.
Vietnamese Sentiment Analysis under Limited Training Data Based on Deep Neural Networks

The annotated dataset is an essential requirement to develop an artificial intelligence (AI) system effectively and expect the generalization of the predictive models and to avoid overfitting. Lack of the training data is a big barrier so that AI systems can broaden in several domains which have no or missing training data. Building these datasets is a tedious and expensive task and depends on the domains and languages. This is especially a big challenge for low-resource languages. In this paper, we experiment and evaluate many various approaches on sentiment analysis problems so that they can still obtain high performances under limited training data. This paper uses the preprocessing techniques to clean and normalize the data and generate the new samples from the limited training dataset based on many text augmentation techniques such as lexicon substitution, sentence shuffling, back translation, syntax-tree transformation, and embedding mixup. Several experiments have been performed for both well-known machine learning-based classifiers and deep learning models. We compare, analyze, and evaluate the results to indicate the advantage and disadvantage points of the techniques for each approach. The experimental results show that the data augmentation techniques enhance the accuracy of the predictive models; this promises that smart systems can be applied widely in several domains under limited training data.
Why Patient Data Cannot Be Easily Forgotten?

Rights provisioned within data protection regulations, permit patients to request that knowledge about their information be eliminated by data holders. With the advent of AI learned on data, one can imagine that such rights can extent to requests for forgetting knowledge of patient's data within AI models. However, forgetting patients' imaging data from AI models, is still an under-explored problem. In this paper, we study the influence of patient data on model performance and formulate two hypotheses for a patient's data: either they are common and similar to other patients or form edge cases, i.e. unique and rare cases. We show that it is not possible to easily forget patient data. We propose a targeted forgetting approach to perform patient-wise forgetting. Extensive experiments on the benchmark Automated Cardiac Diagnosis Challenge dataset showcase the improved performance of the proposed targeted forgetting approach as opposed to a state-of-the-art method.
Virtual object sizes for efficient and convenient mid-air manipulation

It has been taken for granted that the sizes of virtual objects affect the efficiency and convenience of mid-air manipulation in immersive virtual environments. If a virtual object is too small or too large, for example, manipulating it becomes a difficult task. Nevertheless, the virtual object sizes that are optimal and convenient have rarely been studied. In this paper, we select a virtual object with many distinct geometric features and conduct user studies via docking tasks. Through the user studies, the optimal and convenient sizes for mid-air manipulation are estimated. In order to verify the results, a proxy-based manipulation method is designed and implemented, where the proxy is created with the estimated optimal size. The test based on the method shows that the optimal-size proxy enables users to manipulate efficiently virtual objects and the estimated range of convenient sizes is also preferred by the users.The online version contains supplementary material available at 10.1007/s00371-022-02555-6.
Enhancing UML Connectors with Behavioral Specifications

In this study, we propose enhancing UML connectors with additional behaviors. Associated behaviors add capability to specify exogenous coordination and capability to resolve ambiguities when n-ary connectors are used to coordinate more than two entities. We identify the challenges specific to specification of behaviors for connectors and we propose solutions for them. We argue with examples that this kind of behavioral specification suits well for reusability of both connected elements and the connectors.
Statistical Inference for Method of Moments Estimators of a Semi-Supervised Two-Component Mixture Model

A mixture of a distribution of responses from untreated patients and a shift of that distribution is a useful model for the responses from a group of treated patients. The mixture model accounts for the fact that not all the patients in the treated group will respond to the treatment and consequently their responses follow the same distribution as the responses from untreated patients. The treatment effect in this context consists of both the fraction of the treated patients that are responders and the magnitude of the shift in the distribution for the responders. In this article, we investigate asymptotic properties of method of moment estimators for the treatment effect based on a semi-supervised two-component mixture model. From these properties, we develop asymptotic confidence intervals and demonstrate their superior statistical inference performance compared to the computationally intensive bootstrap intervals and their Bias-Corrected versions.
Using Source Parameter Imaging Technique to the Aeromagnetic Data to Estimate the Basement Depth of Tharthar Lake and Surrounding Area in Central Iraq

This research aims to determine the depth of basement rock at Tharthar Lake and its surroundings. The magnetic data were utilized to determine basement depth using the Source Parameter Imaging and the Half-width methods. Using the GeoSoft-Oasis Montaj software version 8.4 application, the total magnetic intensity of the area was converted to the Reduction to Pole, then the Source Parameter Imaging was calculated. Three grids (dx, dy, and dz) were obtained. These grids then are used as input grids for Source Parameter Imaging computations. The resulting Source Parameter Imaging depth map reveals a minimum basement depth of 6 km and a maximum basement depth of 12 km. The basement depth map shows the main basin trending NW-SE from northwest Tharthar Lake to south Falluja City. The basement depth results were compared with the previous information about basement depth in the studied area, and a good result was obtained with the C.G.G. 1974 basement depth map.
Potential Technical Debt and Its Resolution in Code Reviews: An Exploratory Study of the OpenStack and Qt Communities

Background: Technical Debt (TD) refers to the situation where developers make trade-offs to achieve short-term goals at the expense of long-term code quality, which can have a negative impact on the quality of software systems. In the context of code review, such sub-optimal implementations have chances to be timely resolved during the review process before the code is merged. Therefore, we could consider them as Potential Technical Debt (PTD) since PTD will evolve into TD when it is injected into software systems without being resolved. Aim: To date, little is known about the extent to which PTD is identified in code reviews. Many tools have been provided to detect TD, but these tools lack consensus and a large amount of PTD are undetectable by tools while code review could help verify the quality of code that has been committed by identifying issues, such as PTD. To this end, we conducted an exploratory study in an attempt to understand the nature of PTD in code reviews and track down the resolution of PTD after being identified. Method: We randomly collected 2,030 review comments from the Nova project of OpenStack and the Qt Base project of Qt. We then manually checked these review comments, and obtained 163 PTD-related review comments for further analysis. Results: Our results show that: (1) PTD can be identified in code reviews but is not prevalent. (2) Design, defect, documentation, requirement, test, and code PTD are identified in code reviews, in which code and documentation PTD are the dominant. (3) 81.0% of the PTD identified in code reviews has been resolved by developers, and 78.0% of the resolved TD was resolved by developers within a week. (4) Code refactoring is the main practice used by developers to resolve the PTD identified in code reviews. Conclusions: Our findings indicate that: (1) review-based detection of PTD is seen as one of the trustworthy mechanisms in development, and (2) there is still a significant proportion of PTD (19.0%) remaining unresolved when injected into the software systems. Practitioners and researchers should establish effective strategies to manage and resolve PTD in development.
Strong Equivalence of Logic Programs with Ordered Disjunction: A Logical Perspective

Abstract Logic Programs with Ordered Disjunction (LPODs) extend classical logic programs with the capability of expressing preferential disjunctions in the heads of program rules. The initial semantics of LPODs, although simple and quite intuitive, is not purely model-theoretic. As a result, certain properties of programs appear non-trivial to formalize in purely logical terms. For example, the current characterization of strong equivalence for LPODs, does not coincide with logical equivalence in some specific logic. This comes in sharp contrast with the well-known characterization of strong equivalence for classical logic programs, which coincides with logical equivalence in the logic of here-and-there. In this paper we obtain a purely logical characterization of strong equivalence for LPODs as logical equivalence in a four-valued logic. Moreover, we provide a new proof of the coNP-completeness of strong equivalence for LPODs, which has an interest in its own right since it relies on the special structure of such programs. Our results are based on the recent logical semantics of LPODs, a fact which we believe indicates that this new semantics may prove to be a useful tool in the further study of LPODs.
A Deep Learning Based Image Steganalysis Using Gray Level Co-Occurrence Matrix

Image steganalysis is the technique to identify steganography in images and if possible try to predict the quantity of hidden data. Targeted steganalysis need the knowledge of the steganographic algorithm used to embed the data, whereas blind steganalysis is independent of the embedding process. Its objective is to find patterns in the stego image that are generated due to steganographic process. This work proposed an elegant technique of blind steganalysis which takes input clean image from a benchmark dataset and find the co-occurrence matrix of grayscale image(GLCM) for four pixel pair direction and produces average GLCM. After that PCA and Haralick features are generated from average GLCM. Next, steganographic embedding is applied to the clean images with Steghide application with different payload. Each of this stego images are applied similar feature extraction process as clean images in the dataset. Now a deep neural network based model is trained on the prepared dataset with proper label. The proposed method gives 90.93% and 84.63% accuracy for LFW and BOSS dataset respectively and outperform many similar algorithms.
Phase-space dynamics of a single-atom laser: Nonclassicality and bistability

We investigate the dynamics of a single-qubit single-mode laser with continuous incoherent pumping of a qubit, the field mode being initially prepared in a coherent state. Analysis of partial differential equations for quasiprobability distributions helps us distinguish two stages of evolution (coherent and incoherent ones) with their characteristic features. The system can exhibit bistabilitylike behavior, similar to that reported for a single-atom laser with continuous coherent pumping, and is capable of generating Schr\"odinger cat states. Quantitative analysis of the field state nonclassicality shows that its maximum is reached at intermediate stages of evolution.
Transaction confirmation in proof-of-work blockchains: auctions, delays and droppings

Blockchains based on Proof-of-Work (PoW) reward miners thanks to an auction among the transactions that ask to be confirmed. Each block contains the transactions offering the highest fee per Byte among those pending in the queue called Mempool. Various types of queueing models have been proposed to study this auction mechanism but most of them have some simplifying assumptions, e.g, fixed block capacity or stochastic stability of the system. In this work, we first propose a queueing model for the Mempool that takes into account the variable block capacity of real systems as well as the priority among the transactions due to the offered fees. We compare this more accurate model with those known in the literature. Then, we investigate a problem that received little attention from the literature: the transaction dropping. Indeed, when the workload intensity is higher than the maximum throughput (a very frequent situation in bitcoin network), miners need to evict some transactions from the Mempool. In this context, we investigate the relation between the dropping probability and the offered fee.
A Novel Machine-Learning Framework With a Moving Platform for Maritime Drift Calculations

A novel data-driven conceptual framework using a moving platform was developed to accurately estimate the drift of objects in the marine environment in real time using a combination of a perception-based sensing technology and deep-learning algorithms. The framework for conducting field experiments to establish the drift properties of moving objects is described. The objective of this study was to develop and test the integrated technology and determine the leeway drift characteristics of a full-scale three-dimensional mannequin resembling a person in water (PIW) and of a rectangular pelican box to accurately forecast the trajectory and the drift characteristics of the moving objects in real time. The wind and ocean current speeds were measured locally for the entire duration of the tests. A sensor hardware platform with a light detector and ranging sensor (LiDAR), stereoscopic depth cameras, a global positioning system, an inertial measurement unit, and operating software was designed and constructed by the team. It was then mounted on a boat (mobile test platform) to collect data. Tests were conducted by deploying the drifting objects from the mobile test platform into Galveston Bay and tracking them in real time. A framework was developed for applying machine learning and localization concepts on the data obtained from the sensors to determine the leeway trajectory, drift velocity, and leeway coefficients of the drifting objects in real time. Consistent trends in the downwind and crosswind leeway drift coefficients were observed for the pelican (significantly influenced by the wind) and PIW (influenced by the winds and currents).
Scalably Using Node Attributes and Graph Structure for Node Classification

The task of node classification concerns a network where nodes are associated with labels, but labels are known only for some of the nodes. The task consists of inferring the unknown labels given the known node labels, the structure of the network, and other known node attributes. Common node classification approaches are based on the assumption that adjacent nodes have similar attributes and, therefore, that a node's label can be predicted from the labels of its neighbors. While such an assumption is often valid (e.g., for political affiliation in social networks), it may not hold in some cases. In fact, nodes that share the same label may be adjacent but differ in their attributes, or may not be adjacent but have similar attributes. In this work, we present JANE (Jointly using Attributes and Node Embeddings), a novel and principled approach to node classification that flexibly adapts to a range of settings wherein unknown labels may be predicted from known labels of adjacent nodes in the network, other node attributes, or both. Our experiments on synthetic data highlight the limitations of benchmark algorithms and the versatility of JANE. Further, our experiments on seven real datasets of sizes ranging from 2.5K to 1.5M nodes and edge homophily ranging from 0.86 to 0.29 show that JANE scales well to large networks while also demonstrating an up to 20% improvement in accuracy compared to strong baseline algorithms.
A protocol for anonymous short communications in social networks and its application to proximity-based services

Several innovative applications could be advantageously placed within social networks, to be effective, attractive, and pervasive. Examples of application domains that could benefit from social networks are e-democracy, e-participation, online surveys, crowdsourcing, and proximity-based services. In all the above cases, users’ anonymity could represent a considerable added value or could be even necessary to develop the service. We observe that all the above domains are characterized by the fact that only a few asynchronous messages should be exchanged. Therefore, we do not need the full communication power of anonymous communication networks, in which low-latency and connection-oriented communication should be supported. On the other hand, unlike communication networks, the threat model we have to consider assumes the presence of an adversary (represented by an honest-but-curious social network provider) able to monitor the entire flow of the exchanged messages. In this paper, we propose an anonymous communication protocol for short communications in social networks, based on a collaborative approach. The proposed solution hides from the social network provider not only the content of the messages but also the communication itself, which, per se, can result in considerable privacy leakage (think of the case of proximity testing performed between two users). This enables the implementation, within the social network, of the above-mentioned applications. To give a concrete proof of this statement, we develop a privacy-preserving proximity-based solution which provides both symmetric and asymmetric proximity testing entirely within social networks.
Study of the Coherence and Entanglement of Macroscopic Quantum Interfering Alternatives

An approach has been proposed to calculate the coherence and interference characteristics of macroscopic quantum systems. A general method of the analysis of two-particle quantum systems based on the Schmidt decomposition has been presented to analyze quantum entanglement between the system and environment, as well as the coherence of interfering alternatives. Simple relations have been obtained between the coherence, interference visibility, and Schmidt number. The developed method has been applied to multimode quantum states of Schrödinger’s cat.
Studying the Utilization of a Map-Based Visualization with Vitality Datasets by Domain Experts

With the rapid growth of information technology and geographic information science, many map-based visualization applications for decision-making have been proposed. These applications are used in various contexts. Our study provides empirical evidence of how domain experts utilize map-based data visualization for generating insights into vitality with respect to health-related concepts. We conducted a study to understand domain experts’ knowledge, approach, and experience. Nine domain experts participated in the study, with three experts each from the fields of government, business, and research. The study followed a mixed-methods approach involving an online survey, open-ended tasks, and semi-structured interviews. For this purpose, a map-based data visualization application containing various vitality-related datasets was developed for the open-ended tasks. Our study confirms the importance of maps in this domain but also shows that vitality is strongly geographical. Furthermore, we found that map-based visualizations require multiple data sources and dimensions to enhance the utilization of them in the context of vitality. Therefore, our study suggests the necessity of a combination of multiple datasets as ‘vitality themes’ to efficiently communicate this particular subject to experts. As such, our results provide guidelines for designing map-based data visualizations that support the decision-making process across various domain experts in the field of vitality.