Reviewerly/exHarmony huggingface dataset:
Tracing a Black Hole: Probing Cosmic Darkness in Anthropocenic Times

Abstract In April 2019, the Event Horizon Telescope (EHT) project released an unprecedented image of a supermassive black hole at the centre of galaxy Messier 87. The image, which shows a dark disc outlined by swirling hot gas circling the black hole’s event horizon, exhibits a 55 million-year-old cosmic event in the Virgo galaxy cluster—a void of stellar mass measuring some 6.5 billion times that of our sun. Situated within today’s (Good) Anthropocene scenario, characterized as it is by both the rise of an inhospitable planet but also a range of good vibes and affirmative mantras, this tracing explores this newly “discovered” black hole in terms of the unthinkable questions and speculative trajectories it raises for education and its futures. Through a series of forays into astrophysics, historical examples of cosmic imaging, and further exploration of the image created by EHT, this tracing outlines the black hole and its apparent horizons in order to propose a strange vantage point from which pedagogical problem-posing might be interrupted, mutated, and relaunched. By turning to that which lies outside of the traditional science classroom—beyond the school, beyond curriculum, indeed, beyond the planet itself—this tracing seeks to probe this black hole event in terms of its weird and weirding pedagogical trajectories so as to speculate on unthought possibilities for resituating (science) education in the age of the Anthropocene.
Actor-Critic Traction Control Based on Reinforcement Learning with Open-Loop Training

The use of actor-critic algorithms can improve the controllers currently implemented in automotive applications. This method combines reinforcement learning (RL) and neural networks to achieve the possibility of controlling nonlinear systems with real-time capabilities. Actor-critic algorithms were already applied with success in different controllers including autonomous driving, antilock braking system (ABS), and electronic stability control (ESC). However, in the current researches, virtual environments are implemented for the training process instead of using real plants to obtain the datasets. This limitation is given by trial and error methods implemented for the training process, which generates considerable risks in case the controller directly acts on the real plant. In this way, the present research proposes and evaluates an open-loop training process, which permits the data acquisition without the control interaction and an open-loop training of the neural networks. The performance of the trained controllers is evaluated by a design of experiments (DOE) to understand how it is affected by the generated dataset. The results present a successful application of open-loop training architecture. The controller can maintain the slip ratio under adequate levels during maneuvers on different floors, including grounds that are not applied during the training process. The actor neural network is also able to identify the different floors and change the acceleration profile according to the characteristics of each ground.
Path Planning for UAV Reconnoitring in Complex Environment Based on Improved Visibility Graph and Genetic Algorithm

In this paper, a path planning method for unmanned aerial vehicle (UAV) reconnoitering in complex environment has been proposed. Firstly, visibility graph (VGRAPH), which can be solve by Dijkstra Algorithm, is utilized to build the model of the mission environment with several obstacles and multiple reconnaissance points. Then, VGRAPH will be improved based on geometric principles, in order to simplified the complexity of the problem. In addition, Genetic Algorithm (GA) will be used to solve the reconnaissance sequence problem. Finally, two schemes utilizing improved VGRAPH and GA are put forward, one of which is using VGRAPH to get the real distance based on global optimization, the other is using VGRAPH as a path correction base on local optimization. The simulation results show that the improved VGRAPH and the second scheme can solve the problem of path planning in complex environment quickly.
Grounding Linguistic Commands to Navigable Regions

Humans have a natural ability to effortlessly comprehend linguistic commands such as "park next to the yellow sedan" and instinctively know which region of the road the vehicle should navigate. Extending this ability to autonomous vehicles is the next step towards creating fully autonomous agents that respond and act according to human commands. To this end, we propose the novel task of Referring Navigable Regions (RNR), i.e., grounding regions of interest for navigation based on the linguistic command. RNR is different from Referring Image Segmentation (RIS), which focuses on grounding an object referred to by the natural language expression instead of grounding a navigable region. For example, for a command "park next to the yellow sedan," RIS will aim to segment the referred sedan, and RNR aims to segment the suggested parking region on the road. We introduce a new dataset, Talk2Car-RegSeg, which extends the existing Talk2car dataset with segmentation masks for the regions described by the linguistic commands. A separate test split with concise manoeuvre-oriented commands is provided to assess the practicality of our dataset. We benchmark the proposed dataset using a novel transformer-based architecture. We present extensive ablations and show superior performance over baselines on multiple evaluation metrics. A downstream path planner generating trajectories based on RNR outputs confirms the efficacy of the proposed framework.
Land Subsidence Study Using Geodetic GPS and GAMIT/GLOBK Software (Case Study : Banjarasri Village and Kedungbanteng Village, Tanggulangin District, Sidoarjo Regency)

Abstract Floods are one of the natural disasters that always occur in various regions in the Republic of Indonesia. Several flood points were recorded in Banjarasri Village and Kedungbanteng Village, Tanggulangin District, Sidoarjo Regency. There is an allegation that one of the factors causing the flooding in the two villages was caused by land subsidence. Therefore, it is necessary to conduct a study to monitor the magnitude of land subsidence in the area. In this study, the Global Positioning System (GPS) observation method was used. The research was carried out by collecting data from six points which were carried out over three periods of time, namely on 22 - 27 October 2020, 24 - 29 November 2020, and 24 - 29 March 2021. Based on the results of processing and analyzing GPS data, the observed values for shift and motion vectors are obtained. There were five points of land subsidence statistically, which included BM02, BM03, BM04, BM05, and BM06 with values of respectively 3.6 cm, 7.3 cm, 5.9 cm, 4.8 cm, and 5.2 cm. The horizontal position vector moves in various ways, where BM01 and BM04 move to the southwest as far as 2.5 cm and 1.9 cm, BM05 moves to the southeast with a shift of 3.3 cm, and BM06 moves to the northeast with a shift of 4.2 cmFloods are one of the natural disasters that always occur in various regions in the Republic of Indonesia. Several flood points were recorded in Banjarasri Village and Kedungbanteng Village, Tanggulangin District, Sidoarjo Regency. There is an allegation that one of the factors causing the flooding in the two villages was caused by land subsidence. Therefore, it is necessary to conduct a study to monitor the magnitude of land subsidence in the area. In this study, the Global Positioning System (GPS) observation method was used. The research was carried out by collecting data from six points which were carried out over three periods of time, namely on 22 - 27 October 2020, 24 - 29 November 2020, and 24 - 29 March 2021. Based on the results of processing and analyzing GPS data, the observed values for shift and motion vectors are obtained. There were five points of land subsidence statistically, which included BM02, BM03, BM04, BM05, and BM06 with values of respectively 3.6 cm, 7.3 cm, 5.9 cm, 4.8 cm, and 5.2 cm. The horizontal position vector moves in various ways, where BM01 and BM04 move to the southwest as far as 2.5 cm and 1.9 cm, BM05 moves to the southeast with a shift of 3.3 cm, and BM06 moves to the northeast with a shift of 4.2 cm.
Deep ST

Abstract Many analyses of notion of metainferences in the non-transitive logic have tackled the question of whether can be identified with classical logic. In this paper, we argue that the primary analyses are overly restrictive of the notion of metainference. We offer a more elegant and tractable semantics for the strict-tolerant hierarchy based on the three-valued function for the material conditional. This semantics can be shown to easily handle the introduction of mixed inferences, i.e. , inferences involving objects belonging to more than one (meta)inferential level and solves several other limitations of the hierarchies introduced by Barrio, Pailos, and Szmuc.
Chaotic synchronization cryptosystems using a Twofish encryption algorithms

Abstract In our work, a methodology of systematic design with chaotic masking‐based secure communications is presented to implement double encryption. Chaotic synchronization has encountered attacks by hackers in which attackers demodulate information through the public channel, and the safety of chaotic synchronization communication has thus been questioned in recent years. To block the attacks by hackers, this study combines chaotic synchronization with a Twofish encryption algorithm, thereby increasing the complexity of the cryptosystem. By doing so, a more secure communication system is achieved to efficaciously protect the ciphertext. Furthermore, the IGA is proposed in this study to effectively overcome the weakness of the traditional GA. The IGA‐based fuzzy controller not only realizes the exponential synchronization, but also minimizes the disturbance attenuation level to achieve optimal H ∞ performance. Finally, we give a numerical example to show the achievements of this study.
Risks to the National Security Generated By the Widespread Use of Cryptocurrency

Abstract The pronounced multi-domain technologicalization specific to the last decades has had a significant impact on all areas of activity, including the financial one. The use of cyberspace to facilitate the actions undertaken in the monetary activity has generated the development of this field to the point where virtual currencies have been created and new technologies have been developed to support their use. Like any emerging domain, the cryptocurrency field and the related technology are in a relatively early stage and exclusively imply operating in cyberspace, thus generating security risks in the event of the involvement of malicious entities in illicit activities. In this context, it is worth analyzing how the improper use of the crypto domain can lead to various risks to national security.
Automated Intermediate Frequency Carrier Estimation by Using Deep Neural Network

The intermediate frequency (IF) carrier is at most important parameter in an adaptive sixth-generation (6G) communication system for automated and reliable detection. In the adaptive communication system, the wide range of antennas is unable to provide accurate IF carrier. In addition to this, due to the Doppler effect or oscillator mismatch, the desired IF carrier gets changed and unable to convert the passband signal to an error-free baseband signal. In this paper, we have estimated IF carrier using a deep neural network (DNN). In statistical-based IF carrier estimation methods, signal bandwidth must be known prior to the IF carrier estimation, but the proposed scheme does not require knowledge of it. The proposed scheme also does not require any information on modulation schemes, channel state information (CSI), and synchronization parameters. The performance of the proposed scheme is evaluated in the presence of Rayleigh and Rician fading environment for five different modulation classes and compared with a statistical method by normalized mean square error (NMSE) performance metric. Simulation results show that the proposed method outperforms the statistical method at a low signal-to-noise ratio (SNR).
Applying image enhancement technology to polymer-dispersed liquid crystals in transparent displays

This work focuses on image enhancement technology for Polymer-Dispersed Liquid Crystals (PDLC) in a transparent display. The visible image in transparent displays is constituted by the output image and backgrounds, so there are some disadvantages, such as the unsaturated colors and low resolution. Generally, simple images can directly enhance contrast adjustment, but complex images will be distorted and abnormal. In order to conquer these challenges, dark channel prior is employed to sharpen the image, and is combined with Guided Filtering to improve the quality of the parameter that is estimated from the dark channel prior. However, these processes will lead to a reduced total grayscale value, which will decrease the image resolution in low grayscale. We therefore introduce an algorithm that is able to the low grayscale parts independently to resolve the problem. We keep the other higher grayscale parts unchanged so that we can get the sharpened image. The image enhancement technology can adapt to the perturbation in the transparent display, making the result more like the original image.
Implementation and Evaluation of an Identity-Based Encryption with Security Against the KGC

In identity-based encryption (IBE), a key generation center (KGC) issues a secret key for an identity. Although any value can be used as a public key, the KGC has the potential to decrypt all ciphertexts even if it is not the actual destination. To solve this key escrow problem, Emura, Katsumata, and Watanabe (EKW) proposed an IBE scheme with security against KGC (ESORICS 2019) and provided a pairing-based construction by extending the Boneh-Franklin IBE scheme (CRYPTO 2001). Briefly, they used the Chow framework (PKC 2009), which introduced a new type of authority known as an identity-certifying authority (ICA). Each user obtains an identity certificate from the ICA and can anonymously obtain a secret key from the KGC through an interactive protocol. Though the KGC can issue a secret key without knowing the user’s identity, an additional communication (between the user and the ICA) and computation by the KGC are required compared to the conventional IBE scheme. In this paper, we implement the pairing-based EKW IBE scheme and show that the additional costs are insignificant compared to the Boneh-Franklin IBE scheme.
A Semantic Navigation Framework for Multi-Floor Building Environment

Autonomous mobile robot navigation in a multi-floor building is a complex task requiring various components: planning, recognition, and localization. Despite the significant progress, an essential issue in a multi-floor environment is to endow the mobile robot with autonomous navigation inside the building via the elevator. Our proposed neuro-inspired cognitive framework provides an efficient solution to this problem based on semantic navigation. In this paper, we utilize three components of our proposed framework, which are the semantic modeling framework (SMF), semantic information processing (SIP) module, and semantic autonomous navigation (SAN) module. The SMF uses Triplet Ontological Semantic Model (TOSM) to build the semantic models of the environment. The SIP module includes active environment perception components, and the SAN module contains a behavior planner and behavior database. The development, integration, and interaction among these components based on semantic understanding is the major contribution of our proposed framework. The experimental results demonstrate that our framework effectively enables the mobile robot to move to different floors using the elevator autonomously.
Digital Literacy in Austrian Lower Secondary Education - A Synthesis and Evaluation of Experiences in the First Two Years

It does not happen very often that there is a chance to analyze the introduction of a brand-new subject. When Austria implemented the mandatory curriculum “Digital Education” in September 2018 for all students in lower secondary education, digital literacy finally found its official way into additional grades. The curriculum covers digital competences, media competences, as well as civic education. Schools can decide if they offer stand-alone subjects or if they implement the curriculum in an integrative way in several other subjects. This paper reports on first experiences with the new curriculum by evaluating a survey conducted approximately two years after the implementation. Aiming to do so, the qualitative data were interpreted, summarized, and discussed, while the quantitative data were analyzed in former papers. The study was sent to 133 local public schools, whereas 117 teachers were willing to complete the questionnaire and 39 filled out the comment section. The following research questions lay out the basis for the evaluation of the qualitative data: 1) How do teachers manage the implementation of the new curriculum? 2) What is the teachers' opinion on the current situation? The results we present in this paper suggest that there are still people who fight with the new subject and that there are various problems that have to be tackled. Still, the attitude towards the current situation of the new curriculum is very positive.
Parallel Extreme Learning Machines Based on Frequency Multiplexing

In a recent work, we reported on an Extreme Learning Machine (ELM) implemented in a photonic system based on frequency multiplexing, where each wavelength of the light encodes a different neuron state. In the present work, we experimentally demonstrate the parallelization potentialities of this approach. We show that multiple frequency combs centered on different frequencies can copropagate in the same system, resulting in either multiple independent ELMs executed in parallel on the same substrate or a single ELM with an increased number of neurons. We experimentally tested the performances of both these operation modes on several classification tasks, employing up to three different light sources, each of which generates an independent frequency comb. We also numerically evaluated the performances of the system in configurations containing up to 15 different light sources.
Managing Risk

Abstract The Internet and social media platforms are disrupting societies and politics on a global level. While these technologies have had immense benefit, facilitating individual empowerment and establishing the data economy, they have also helped to mainstream terrorism, hate, and anti-democratic beliefs. These forces will continue to disrupt traditional democratic politics and contribute to the breakdown of societal cohesion. Regulations that ensure the protection of fundamental rights and freedoms are now a necessary element in the establishment of a new social contract for the digital age. Stakeholders must work to reimpose gatekeeper functions that the Internet has swept away, in turn limiting the ability of dangerous marginal views to influence the mainstream. However, in doing so, these same stakeholders must ensure that their efforts do not result in an Orwellian future that leaves citizens less secure and less empowered.
Geotechnical Test and Effective Processing Analysis of Geotechnical Engineering Investigation Based on Geographic Information System Under BP Neural Network

Geotechnical test is an important part of geotechnical engineering investigation, which has a profound impact on geotechnical engineering investigation. The abnormal situation of soil test is related to its own soil properties and the characteristics that the soil is easy to change due to the changes of internal and external environment, and the test procedure is more complex, which is also related to the inaccurate handling of data by field technicians, resulting in abnormal results. Therefore, based on the BP neural network geographic information system of geotechnical engineering investigation geotechnical test and its effective processing analysis and research, in order to ensure the smooth progress of the test, ensure that the data is reliable, provide an important basis for geotechnical engineering investigation.
A Product Recommendation Method by Analyzing Images and Reviews in E-Commerce

This paper proposes a method for the product visual search service in e-commerce to recommend products with high satisfaction by computing image scores and review scores. To compute image scores, we calculate the image similarities between user-inputted images and product representative images, and the text similarities between image labels and product descriptions. To compute review scores, we perform a sentiment analysis on product reviews and combine with user repeated purchase behavior to recommend products with high satisfaction. Finally, we evaluated and discussed our proposed method based on a visual search using real product data.
Geoscience Language Processing for Exploration

Abstract Deep learning has recently been providing step-change capabilities, particularly using transformer models, for natural language processing applications such as question answering, query-based summarization, and language translation for general-purpose context. We have developed a geoscience-specific language processing solution using such models to enable geoscientists to perform rapid, fully-quantitative and automated analysis of large corpuses of data and gain insights. One of the key transformer-based model is BERT (Bidirectional Encoder Representations from Transformers). It is trained with a large amount of general-purpose text (e.g., Common Crawl). Use of such a model for geoscience applications can face a number of challenges. One is due to the insignificant presence of geoscience-specific vocabulary in general-purpose context (e.g. daily language) and the other one is due to the geoscience jargon (domain-specific meaning of words). For example, salt is more likely to be associated with table salt within a daily language but it is used as a subsurface entity within geosciences. To elevate such challenges, we retrained a pre-trained BERT model with our 20M internal geoscientific records. We will refer the retrained model as GeoBERT. We fine-tuned the GeoBERT model for a number of tasks including geoscience question answering and query-based summarization. BERT models are very large in size. For example, BERT-Large has 340M trained parameters. Geoscience language processing with these models, including GeoBERT, could result in a substantial latency when all database is processed at every call of the model. To address this challenge, we developed a retriever-reader engine consisting of an embedding-based similarity search as a context retrieval step, which helps the solution to narrow the context for a given query before processing the context with GeoBERT. We built a solution integrating context-retrieval and GeoBERT models. Benchmarks show that it is effective to help geologists to identify answers and context for given questions. The prototype will also produce a summary to different granularity for a given set of documents. We have also demonstrated that domain-specific GeoBERT outperforms general-purpose BERT for geoscience applications.
Is it Possible to Verify if a Transaction is Spendable?

With the popularity of Bitcoin, there is a growing need to understand the functionality, security, and performance of various mechanisms that comprise it. In this paper, we analyze Bitcoin’s scripting language, Script, that is one of the main building blocks of Bitcoin transactions. We formally define the semantics of Script, and study the problem of determining whether a user-defined script is well-formed; that is, whether it can be unlocked, or whether it contains errors that would prevent this from happening.