Reviewerly/exHarmony huggingface dataset:
The public response and educational outreach through social media after the Zagreb earthquake of 22 March 2020

Following the 22 March 2020 ML 5.5 earthquake near Zagreb, Croatia, the citizens became increasingly interested in earthquakes and the multitude of simultaneous visitors caused the webpage with Croatian Seismological Survey reports on earthquakes to crash. To remedy the situation, seismologists used social network accounts to provide information, using the opportunity to educate the citizens on basic concepts of seismology, earthquake preparedness and the occurring seismic sequence. Citizens’ feedback was useful to improve the communication, but required extensive moderation. In July 2020, three seismologists from the Department of Geophysics, Faculty of Science, University of Zagreb, one from the Slovenian Environment Agency, and a psychologist from the Croatian Catholic University conducted a poll to find out which sources of information citizens mostly use, their knowledge about earthquakes, and the level of fear they were experiencing due to the earthquakes. Most respondents relied on institutional sources of information and their knowledge on different aspects of earthquake preparedness increased relatively compared to the time before the earthquake. The majority of respondents was extremely worried on the day of the mainshock, predominantly because they were concerned of a possible stronger event, their safety and the safety of their close ones.
El Salvador revamps bitcoin system

The country adopted the cryptocurrency as legal tender but its virtual wallet has problems
A Performance Comparison of Different Cloud-Based Natural Language Understanding Services for an Italian e-Learning Platform

During the COVID-19 pandemic, the corporate online training sector has increased exponentially and online course providers had to implement innovative solutions to be more efficient and provide a satisfactory service. This paper considers a real case study in implementing a chatbot, which answers frequently asked questions from learners on an Italian e-learning platform that provides workplace safety courses to several business customers. Having to respond quickly to the increase in the courses activated, the company decided to develop a chatbot using a cloud-based service currently available on the market. These services are based on Natural Language Understanding (NLU) engines, which deal with identifying information such as entities and intentions from the sentences provided as input. To integrate a chatbot in an e-learning platform, we studied the performance of the intent recognition task of the major NLU platforms available on the market with an in-depth comparison, using an Italian dataset provided by the owner of the e-learning platform. We focused on intent recognition, carried out several experiments and evaluated performance in terms of F-score, error rate, response time, and robustness of all the services selected. The chatbot is currently in production, therefore we present a description of the system implemented and its results on the original users’ requests.
Convolutional Neural Network Based on Diverse Gabor Filters for Deepfake Recognition

Media synthesis and manipulation has reached unprecedented levels of realism owing to the proliferation of deep learning. Deepfake has been the de-facto tool for media manipulation. Although this technology has potential in the entertainment industry, its threats include political manipulation and bypassing biometric security systems. As a result, deepfake detection has garnered widespread attention among research communities. The intuition is to use deep learning to fix the problems created by deep learning. Although convolutional neural networks have shown their dominance in the filed of pattern recognition, the receptive field-model size dilemma still persists along with the lack of interpretation for such models. While the traditional Gabor function was proposed to fix these problems, it can only generate limited linear Gabor filters which makes it optimal for limited data and applications. The contribution of this paper is quadruple: (i) proposing a unified Gabor function capable of generating linear, elliptical, and circular Gabor filters. (ii) leveraging the back-propagation learning framework to incorporate the proposed function in convolutional neural networks and generate adaptive Gabor filters. (iii) presenting a dual scale large receptive field network for deepfake image recognition. (iv) demonstrating where the proposed model stands in terms of performance and architecture size compared to state-of-the-art models. The proposed model is evaluated on four benchmark datasets: Celeb-DF (v2), DeepFake Detection Challenge Preview, FaceForensics++ and Wilddeepfake. Experimental results show that the proposed adaptive Gabor filters reduce the model size by 64.9% compared to adaptive weighted filters without performance reduction.
Drones as Sound Sensors for Energy-Based Acoustic Tracking on Wildfire Environments

The present work proposes a method for continuous localization of a moving acoustic source based on energy measurements in the application of detecting/preventing wildfires. The main focus is on the case in which the acoustic sensors are located on a drone, i.e., when they are also in motion. This localization setting is more challenging than the classical one in which the sensors are typically static (anchors) or have slow dynamics compared with the source. The proposed solution consists of an extended Kalman filter (EKF) that deals with non-linearities and singularities in the acoustic observation model, as well as the dynamic trajectory of the sensors. Performed simulations corroborate its feasibility in different noisy environments and illustrate its superiority over techniques that do not consider process states’ prior knowledge.
A Bayesian Approach to German Personal and Demonstrative Pronouns

When faced with an ambiguous pronoun, an addressee must interpret it by identifying a suitable referent. It has been proposed that the interpretation of pronouns can be captured using Bayes’ Rule: P(referent|pronoun) ∝ P(pronoun|referent)P(referent). This approach has been successful in English and Mandarin Chinese. In this study, we further the cross-linguistic evidence for the Bayesian model by applying it to German personal and demonstrative pronouns, and provide novel quantitative support for the model by assessing model performance in a Bayesian statistical framework that allows implementation of a fully hierarchical structure, providing the most conservative estimates of uncertainty. Data from two story-continuation experiments showed that the Bayesian model overall made more accurate predictions for pronoun interpretation than production and next-mention biases separately. Furthermore, the model accounts for the demonstrative pronoun dieser as well as the personal pronoun, despite the demonstrative having different, and more rigid, resolution preferences.
Improvement of Student Learning Motivation through Word-Wall-based Digital Game Media

The development of technology has a lot of influence in the field of education, one of which is in terms of learning media. Digital-based learning media are now being developed to create more fun learning, one of which is digital games. It is also known that digital games themselves positively influence increasing student motivation in the learning process. For this reason, the purpose of this study is to prove the effectiveness of digital game-based media in increasing motivation to learn science. The population group consisted of 129 students from Cluster I, North Sukabumi Village schools. Research sampling was carried out by simple random sampling to calculate the number of samples. The Slovin formula was used, which resulted in 98 students as the sample. This study used experimental research methods with a single group pretest-posttest, as for the instrument in a questionnaire using a Likert scale. The type of data analysis used in this research is an inferential analysis using a t-test. The results of this study indicate that digital game media positively influences students' learning motivation. The literature review comprises 40 studies. This study also shows that students experience increased learning motivation by using this digital game media. Because with this digital game media, students become more motivated to follow and understand the learning material being taught.
Real-time face detection in digital video-based on Viola-Jones supported by convolutional neural networks

&lt;span&gt;Face detection is a critical function of security (secure witness face in the video) who appear in a scene and are frequently captured by the camera. Recognition of people from their faces in images has recently piqued the scientific community, partly due to application concerns, but also for the difficulty this characterizes for the algorithms of artificial vision. The idea for this research stems from a broad interest in courtroom witness face detection. The goal of this work is to detect and track the face of a witness in court. In this work, a Viola-Jones method is used to extract human faces and then a particular transformation is applied to crop the image. Witness and non-witness images are classified using convolutional neural networks (CNN). The Kanade-Lucas-Tomasi (KLT) algorithm was utilized to track the witness face using trained features. In this model, the two methods were combined in one model to take the advantage of each method in terms of speed and reduce the amount of space required to implement CNN and detection accuracy. After the test, the results of the proposed model showed that it was 99.5% percent accurate when executed in real-time and with adequate lighting.&lt;/span&gt;
Using Virtual Reality to Design and Evaluate a Lunar Lander: The EL3 Case Study

The European Large Logistics Lander (EL3) is being designed to carry out cargo delivery missions in support of future lunar ground crews. The capacity of virtual reality (VR) to visualize and interactively simulate the unique lunar environment makes it a potentially powerful design tool during the early development stages of EL3, as well as other relevant technologies. Based on input from the EL3 development team, we have produced a VR-based operational scenario featuring a hypothetical configuration of the lander. Relying on HCI research methods, we have subsequently evaluated this scenario with relevant experts (n=10). Qualitative findings from this initial pilot study have demonstrated the usefulness of VR as a design tool in this context, but likewise surfaced a number of limitations in the form of potentially impaired validity and generalizability. We conclude by outlining our future research plan and reflect on the potential use of physical stimuli to improve the validity of VR-based simulations in forthcoming design activities.
Experimental Demonstration of Gaussian Boson Sampling with Displacement

Gaussian boson sampling (GBS) is quantum sampling task in which one has to draw samples from the photon-number distribution of a large-dimensional nonclassical squeezed state of light. In an effort to make this task intractable for a classical computer, experiments building GBS machines have mainly focused on increasing the dimensionality and squeezing strength of the nonclassical light. However, no experiment has yet demonstrated the ability to displace the squeezed state in phase-space, which is generally required for practical applications of GBS. In this work, we build a GBS machine which achieves the displacement by injecting a laser beam alongside a two-mode squeezed vacuum state into a 15-mode interferometer. We focus on two new capabilities. Firstly, we use the displacement to reconstruct the multimode Gaussian state at the output of the interferometer. Our reconstruction technique is in situ and requires only three measurements settings regardless of the state dimension. Secondly, we study how the addition of classical laser light in our GBS machine affects the complexity of sampling its output photon statistics. We introduce and validate approximate semi-classical models which reduce the computational cost when a significant fraction of the detected light is classical.
Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs

Question answering over temporal knowledge graphs (KGs) efficiently uses facts contained in a temporal KG, which records entity relations and when they occur in time, to answer natural language questions (e.g., "Who was the president of the US before Obama?").These questions often involve three time-related challenges that previous work fail to adequately address: 1) questions often do not specify exact timestamps of interest (e.g., "Obama" instead of 2000); 2) subtle lexical differences in time relations (e.g., "before" vs "after"); 3) off-the-shelf temporal KG embeddings that previous work builds on ignore the temporal order of timestamps, which is crucial for answering temporal-order related questions.In this paper, we propose a time-sensitive question answering (TSQA) framework to tackle these problems.TSQA features a timestamp estimation module to infer the unwritten timestamp from the question.We also employ a time-sensitive KG encoder to inject ordering information into the temporal KG embeddings that TSQA is based on.With the help of techniques to reduce the search space for potential answers, TSQA significantly outperforms the previous state of the art on a new benchmark for question answering over temporal KGs, especially achieving a 32% (absolute) error reduction on complex questions that require multiple steps of reasoning over facts in the temporal KG.
Polarization based discrete variables quantum key distribution via conjugated homodyne detection

Optical homodyne detection is widely adopted in continuous-variable quantum key distribution for high-rate field measurement quadratures. Besides that, those detection schemes have been being implemented for single-photon statistics characterization in the field of quantum tomography. In this work, we propose a discrete-variable quantum key distribution (DV-QKD) implementation that combines the use of phase modulators for high-speed state of polarization (SOP) generation, with a conjugate homodyne detection scheme which enables the deployment of high speed QKD systems. The channel discretization relies on the application of a detection threshold that allows to map the measured voltages as a click or no-click. Our scheme relies also on the use of a time-multiplexed pilot tone-quantum signal architecture which enables the use of a Bob locally generated local oscillator and opens the door to an effective polarization drift compensation scheme. Besides that, our results shows that for higher detection threshold values we obtain a very low quantum bit error rate (QBER) on the sifted key. Nevertheless, due to huge number of discarded qubits the obtained secure key length abruptly decreases. From our results, we observe that optimizing the detection threshold and considering a system operating at 500 MHz symbol generation clock, a secure key rate of approximately 46.9 Mbps, with a sifted QBER of [Formula: see text] over 40 km of optical fiber. This considering the error correction and privacy amplification steps necessary to obtain a final secure key.
Time-reversal-symmetry breaking and chiral quantum state manipulation in plasmonic nanorings

The chiral transfer of quantum information in a metal nanoring network is presented. A system of three metal nanorings coupled to a two-level quantum dot enables the chiral transport of quantum states by breaking the time-reversal symmetry, which provides a platform for chiral quantum information processing in nanorings. The direction of the chirality can be controlled by preparing the quantum dot in its ground or excited state. We show that the system dynamics can be controlled such that one can achieve a perfect or a partial chiral transfer of the excitation. The synthetic magnetic field that enables the chirality in the system makes the generation of specifically tailored entangled nanoring states possible. As particular examples, we show that in this platform the generation of a two-excitation NOON state and entangled coherent states among nanorings can be realized.
Variances and covariances of linear summary statistics of segregating sites

Each mutation in a population sample of DNA sequences can be classified by the number of sequences that inherit the mutant nucleotide, the resulting frequencies are known as mutations of different sizes or site frequency spectrum. Many summary statistics can be defined as a linear function of these frequencies. A flexible class of such linear summary statistics is explored analytically in this paper which include several well-known quantities, such as the number of segregating sizes and the mean number of nucleotide differences between two sequences. Some asymptotic variances and covariances are obtained while the analytical formulas for the variances and covariances of nine such linear summary statistics are derived, most of which are unknown to date. This study not only provides some theoretical foundations for exploring linear summary statistics, but also provides some newlinear summary statistics that may be utilized for analyzing sample polymorphism. Furthermore it is showed that a newly developed linear summary statistics has a smaller variance almost uniformly than Watterson’s estimator, and that a class of linear summary statistics given too heavy weights on mutations of smaller sizes result in asymptotically non-zero variance.
A Client-Centered Information Security and Cybersecurity Auditing Framework

Information security and cybersecurity management play a key role in modern enterprises. There is a plethora of standards, frameworks, and tools, ISO 27000 and the NIST Cybersecurity Framework being two relevant families of international Information Security Management Standards (ISMSs). Globally, these standards are implemented by dedicated tools to collect and further analyze the information security auditing that is carried out in an enterprise. The overall goal of the auditing is to evaluate and mitigate the information security risk. The risk assessment is grounded by auditing processes, which examine and assess a list of predefined controls in a wide variety of subjects regarding cybersecurity and information security. For each control, a checklist of actions is applied and a set of corrective measures is proposed, in order to mitigate the flaws and to increase the level of compliance with the standard being used. The auditing process can apply different ISMSs in the same time frame. However, as these processes are time-consuming, involve on-site interventions, and imply specialized consulting teams, the methodology usually adopted by enterprises consists of applying a single ISMS and its existing tools and frameworks. This strategy brings overall less flexibility and diversity to the auditing process and, consequently, to the assessment results of the audited enterprise. In a broad sense, the auditing needs of Small and Medium-sized Enterprises (SMEs) are different from large companies and do not fit with all the existing ISMSs’ frameworks, that is a set of controls of a particular ISMS is not suitable to be applied in an auditing process, in an SME. In this paper, we propose a generic and client-centered web-integrated cybersecurity auditing information system. The proposed system can be widely used in a myriad of auditing processes, as it is flexible and it can load a set of predefined controls’ checklist assessment and their corresponding mitigation tasks’ list. It was designed to meet both SMEs’ and large enterprises’ requirements and stores auditing and intervention-related data in a relational database. The information system was tested within an ISO 27001:2013 information security auditing project, in which fifty SMEs participated. The overall architecture and design are depicted and the global results are detailed in this paper.
Effectiveness of Students' Motivation Factors in the Competency-Based Approach: A Case Study of Universities in Russia and Indonesia

The study aimed to explore the influence of motivation factors on the development of professional competencies using Russian and Indonesian institutions of higher education as case studies. In pursuit of this objective, quantitative survey methodology was incorporated, and surveys were conducted during November and April of the 2018/2019 to 2021/2022 school years. The questionnaire was developed with a 10-point rating scale, aimed at addressing the development of students' professional competencies and the factors that motivate learning and competency development. Using the questionnaire, the level of professional competency development of students in Russian and Indonesian universities has been empirically analysed. The results of the study supported the spiral nature of students' professional competencies development, showing that the development of professional competencies follows a progressive and non-linear nature of component development. These results confirm that the process of professional competency development is structurally divided into separate, relatively independent stages reflecting sequential and gradual progression. The positive character of the influence of the balanced development level of intrinsic and extrinsic motivation factors on the formation of students' professional competencies has been established. The results of the research may prove useful for educational institutions and public administration bodies for the development of effective mechanisms for students' motivation within the framework of competency-based approach implementation in higher education. Doi: 10.28991/ESJ-2022-06-03-012 Full Text: PDF
TimeTables: Embodied Exploration of Immersive Spatio-Temporal Data

We propose TimeTables, a novel prototype system that aims to support data exploration, using embodiment with space-time cubes in virtual reality. TimeTables uses multiple space-time cubes on virtual tabletops, which users can manipulate by extracting time layers or individual buildings to create new tabletop views. The surrounding environment includes a large space for multiple linked tabletops and a storage wall. TimeTables presents information at different time scales by stretching layers to drill down in time. Users can also jump into tabletops to inspect data from an egocentric perspective. We present a use case scenario of energy consumption displayed on a university campus to demonstrate how our system could support data exploration and analysis over space and time. From our experience and analysis we believe the system has a high potential in assisting spatio-temporal data exploration and analysis.
Supply chain information coordination based on blockchain technology: A comparative study with the traditional approach

Blockchain technology has subverted traditional supply chain operational models and transformed information interactions along supply chains. This paper examines the impact of blockchain technology on supply chain information collaboration and operating costs. This paper develops a three-level supply chain model based on blockchain technology that incorporates retailer sensitivity to information. First, the manufacturer’s profit function is developed, and the optimal information-sharing quantity and supply chain pricing decisions are analysed. Then, cost models for both the traditional supply chain and the novel supply chain using blockchain technology are developed and the impact of blockchain technology on supply chain operating costs is determined. The results demonstrate that blockchain technology can effectively reduce supply chain operating costs. In addition, this study has an interesting finding that if blockchain adoption is valuable for the supply chain, the quantity of information-sensitive should be moderate. Too many or too few information-sensitive retailers can reduce the value of the blockchain's use. This is because blockchain is implementation will increase the privacy concerns of supply chain companies.