Reviewerly/exHarmony huggingface dataset:
Visual Kerogen Analysis for Source Rocks Assessment: A Case Study of Some Subsurface Cretaceous (Valanginian-Cenomanian) Strata from Shushan Basin, Northern Western Desert, Egypt

In the Shushan Basin, we examined the organic matter under transmitted light microscopy for spore coloration index (SCI) and kerogen type assessment. We used this index to estimate tentative values of the Thermal Alteration Index (TAI) and the Vitrinite Reflectance (Ro) that are equivalent to these SCI values on available standard color charts. Based on that, we inferred the degree of the thermal maturity of the investigated Early (to earliest Late) Cretaceous rock units. We confirmed their previously described high potential as sources of hydrocarbons. However, the Alam El-Bueib, Alamein, Dahab, Kharita, and Bahariya formations contain abundant AOM, frequent opaque phytoclasts, and common non-opaques, indicating oil- and gas-prone kerogen types II and III and reflect mature to overmature hydrocarbon generation potentiality.
Decentralized Unified Position-Attitude Control of Nonlinear UAVs

In this paper, we propose a fully decentralized, nonlinear feedback control law to maneuver multiple nonlinear quadrotor UAVs with interagent collision avoidance and natural deadlock resolution. Most existing work on this problem adopts a cascaded position-attitude control scheme and utilizes linearized dynamics in controller synthesis. In this work, each UAV controls its position and attitude simultaneously in one unified step, with no dynamics linearization involved at any stage. The proposed scheme is based on a generalization of Gauss’s principle of least constraint that allows constrained systems of any order and any type and that identifies, differentiates, stabilizes, partitions, and incorporates the active constraints at each time instant. The control actions result from asymptotically stabilizing the active constraints by user-specified natural frequencies and damping ratios according to a generalized constraint stabilization. Two numerical examples are used to demonstrate the effectiveness of the present method, whose performance on collision avoidance and deadlock resolution is sufficiently close to that of a centralized method.
Integrating Palm Hand-held Technology into the Webbased Inquiry Science Environment (WISE)

We describe a program of research to explore how Palm Pilot technology can facilitate inquiry activities in K-12 science and mathematics curriculum. This research was conducted within the context of the Web-based Inquiry Science Environment (WISE) project, which addresses fundamental questions concerning the role of inquiry and technology in science education. Working in close collaboration with two large school districts, we developed new approaches for integrating Palm applications into existing WISE curriculum. We developed a sophisticated and generalized solution to enable hand held survey and observation forms that can be uploaded into a class data set.
Mapping Alternative Discourse Structures onto Computer Conferences

In this paper, we describe a constraint-based discussion board that we have developed. A number of discourse systems have sought to constrain the ways that learners converse by requiring them to classify the nature of the comments and replies to others’ comments. Those systems imposed a single constraint system on learners. In our system, we enable users to adjust the structure and content of the system in order to support a variety of discourses, including argumentation, problem-solution, literary analysis, and any other kind of activity. We describe the rationale for the system and will demonstrate the results of two discussions during the conference.
La competencia digital del profesorado canario para atender a la diversidad funcional

Background: teachers have been identified as the cornerstone in the development of digital competences as a guarantee for reducing the digital divide among pupils. Objective: to analyse the level of digital competence of teachers in the Canary Islands with regard to the application of ICT to cater for diversity. Method: a descriptive cross-sectional study was designed with a sample of 382 teachers (100 males and 282 females). The questionnaire used was called Diagnosis and teacher training for the incorporation of ICT in students with functional diversity. Results: Global ICT knowledge scored substantially below the average of the questionnaire in all the Canary Islands. According to the initial training of the teachers, significant differences were found in all dimensions in favour of Special Education teachers (p &lt; .05). Those with 1-3 years of experience have more ICT training (p &lt; .05). Conclusion: there is a low level of training among all teachers in the Canary Islands with regard to the application of ICT with pupils with functional diversity. However, teachers with a specialisation in Special Education and those with less professional experience show greater ICT training. Future lines of research should shed more light on how digital training courses for teachers can have an impact on the holistic development of schoolchildren.
The Effects of Natural Sounds and Proxemic Distances on the Perception of a Noisy Domestic Flying Robot

When flying robots are used in close-range interaction with humans, the noise they generate, also called consequential sound, is a critical parameter for user acceptance. We conjecture that there is a benefit in adding natural sounds to noisy domestic drones. To test our hypothesis experimentally, we carried out a mixed-methods research study (N = 56) on reported user perception of a sonified domestic flying robot with three sound conditions at three distances. The natural sounds studied were, respectively, added to the robot’s inherent noises during flying; namely, a bird song and a rain sound, plus a control condition of no added sound. The distances studied were set according to proxemics; namely, near , middle , and far . Our results show that adding bird song or rain sound affects the participants’ perceptions, and the proxemic distances play a nonnegligible role. For instance, we found that participants liked the bird condition the most when the drone was at far , while they disliked the same sound the most when at near . We also found that participants’ perceptions strongly depended on their associations and interpretations deriving from previous experience. We derived six concrete design recommendations.
Fast Enumeration of Superspecial Hyperelliptic Curves of Genus 4 with Automorphism Group $$V_4$$

In arithmetic and algebraic geometry, superspecial curves have been studied as one of the most important objects, with practical applications to cryptography and coding theory. The enumeration of those curves is a central problem, but if $$g \ge 4$$ it is not even known whether a superspecial curve of genus g exists in general characteristic $$p>0$$ . In this paper, we propose an algorithm with complexity $$O(p^3)$$ to enumerate superspecial hyperelliptic curves of genus 4 with automorphism group $$V_4$$ , where $$V_4$$ is the non-cyclic group of order 4. By executing the algorithm over Magma, we enumerate those curves over $$\overline{\mathbb {F}_p}$$ for p up to 200. We also succeeded in finding a superspecial hyperelliptic curve of genus 4 in every characteristic p with $$19 \le p \le 6691$$ .
Symphony-Q: A Support System for Learning Music through Collaboration

This paper describes the Symphony-Q support system for learning music. This system integrates a sensing board and a computer, and is used for collaborative learning in a face-to-face setting. One of the aims of Symphony-Q is to enhance music experiences: children who do not have music skills can easily participate in music learning, enjoy making sounds, and play rhythmically to music in collaboration with others. The paper discusses comments and feedback from school children and their teachers, which were collected during experiments with Symphony-Q carried out in a Japanese public elementary school.
An Extreme Learning Machine-Based AutoEncoder (ELM-AE) for Denoising Knee X-ray Images and Grading Knee Osteoarthritis Severity

Osteoarthritis (OA) is the most usual form of arthritis. Radiologists assess the OA severity by observing the pieces of evidence on both sides of knee bones, hinged on the Kellgren–Lawrence (KL) grading system. Computer-assisted diagnosis has been a prime field of research for the past few decades as it tends to provide highly accurate performance. In this work, we propose the Knee Osteoarthritis (KOA) classification problem to segregate the severity into five grades. The proposed work can be framed into two-stage, using X-ray images. Stage one deals with preprocessing and denoising, while stage two deals with classification. This work considers, a standard OAI dataset as well as locally collected images as input, and are fed to an Extreme Learning Machine-based AutoEncoder (ELM-AE) to get the denoised images, which are then used for training the Dense Neural Network model DenseNet201and are later classified, based on KL grades. In experimentation, evaluation of performance is carried out for the model with and without using autoencoders. It is observed that with autoencoders the overall performance is enhanced significantly for standard as well as the local dataset.
A Deep Learning-Based Regression Scheme for Angle Estimation in Image Dataset

A machine needs to recognize orientation in an image to address various rotation related problems. To calculate this rotation, one must require the information about different objects that present into the image. Hence this becomes a pattern recognition task. By using Deep Learning this issue of calculation of image rotation can be addressed as deep learning possess excellent ability of feature extraction. This paper proposes a novel deep learning-based approach to estimate the angle of rotation very efficiently. Kaggle dataset (Rotated Coins) and Caltech-256 has been used for this research, but the data available was limited hence this research utilize data augmentation by rotating the given dataset at random angles. Initially the unlabeled image has been rotated at different angles and store the values to be used as training dataset. Finally at the output a regression layer has been used to identify the angle of rotation for input image. The proposed deep learning approach provides a better result in terms of validation parameters like R-square, MSE, MAE. With proposed approach the value of R-square, MSE, and MAE for Kaggle dataset (Rotated Coins) obtained is 0.9846, 0.0013 and 0.0127 respectively. While for Caltech-256 Dataset proposed approach reported R-square, MSE, and MAE of 0.9503, 0.0039 and 0.0240 respectively. The proposed approach also helps in finding the position of an object by calculating the angle of rotation in an image.
The Uber Driver

Other| November 01 2022 The Uber Driver Vi Khi Nao Vi Khi Nao Vi Khi Nao is the author of six poetry collections, the short story collection A Brief Alphabet of Torture (2017; winner of the 2016 FC2’s Ronald Sukenick Innovative Fiction Prize), and the novel Swimming with Dead Stars (2022). Her work includes poetry, fiction, film, and cross-genre collaboration. Her first play, Waiting for God, appeared January 2022. She was the Fall 2019 fellow at the Black Mountain Institute. www.vikhinao.com. Search for other works by this author on: This Site Google the minnesota review (2022) 2022 (99): 54–56. https://doi.org/10.1215/00265667-9993111 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Vi Khi Nao; The Uber Driver. the minnesota review 1 November 2022; 2022 (99): 54–56. doi: https://doi.org/10.1215/00265667-9993111 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll Journalsthe minnesota review Search Advanced Search The text of this article is only available as a PDF. Copyright © 2022 Vi Khi Nao2022 Article PDF first page preview Close Modal Issue Section: Creative You do not currently have access to this content.
FastVer2: A Provably Correct Monitor for Concurrent, Key-Value Stores

FastVer is a protocol that uses a variety of memory-checking techniques to monitor the integrity of key-value stores with only a modest runtime cost. Arasu et al. formalize the high-level design of FastVer in the F* proof assistant and prove it correct. However, their formalization did not yield a provably correct implementation---FastVer is implemented in unverified C++ code.
GRU Based Time Series Forecast of Oil Temperature in Power Transformer

With the continuous progress of the society, the demand for electrical power is urgent. The transformer plays an important role in the power energy transmission. The oil temperature inside transformer effectively could reflect working condition of the transformer, which makes it necessary to monitor and forecast the oil temperature to monitor the operating status of the power transformer. However, the oil temperature time series data generated by the power transformer has the characteristics of being complex and nonlinear. In recent years, long and short time memory networks (LSTM) are often used to predict transformer oil temperature. Gated recurrent unit (GRU) is a new version for LSTM. In the structure of GRU, there exist two gates, which are updating gate and resetting gate, respectively. Compared with LSTM network, The structure of GRU is simpler and its effect is better. A novel predicting method for transformer oil temperature is proposed based on time series theory and GRU in this paper, which is verified on the dataset of the oil temperature of the transformers in the two regions. The experimental results are compared with traditional time series prediction models to demonstrate that the proposed method is effective and feasible.
Efficient and Trusted Task Offloading in Vehicular Edge Computing Networks

In order to meet the ever-increasing task processing demands of computation-intensive and delay-sensitive applications in the era of autonomous driving, a promising approach is to adopt nearby roadside units (RSUs) or/and vehicles passing by to provide edge computing services, i.e., vehicular edge computing (VEC). However, due to the untrustworthiness of fast-moving vehicles, the vehicles' tasks may face false result attacks or processing timeout. Note that there is little research on the vehicle trust evaluation in VEC networks, especially taking processing delay minimization into consideration. Toward this end, this paper studies the joint optimization problem of vehicle trust evaluation and task processing delay, aiming to ensure the security of the vehicles with tasks and minimize the task offloading delay. To solve this problem, we propose an efficient and trusted VEC offloading scheme based on fuzzy comprehensive strategy (FCS) and adopt the concept of game theory to motivate nearby vehicles to share computing resources. Experimental results corroborate that our proposed scheme can accurately evaluate the trustworthiness of vehicles and improve service security in VEC networks. Moreover, it can significantly reduce task offloading delay.
Socially-aware Concurrent Entanglement Routing with Path Decomposition in Quantum Networks

Quantum teleportation via quantum entanglement enables high-security communications in networks. However, if two quantum nodes are far away, it may be difficult to create an entangled path due to the low success probability. Besides, existing approaches neglect social relations among nodes' owners. In this paper, we propose a new framework SEER to minimize the waiting time of all source-destination (SD) pairs' requests. SEER has two promising features: 1) Social-relation Consideration. SEER makes the first attempt to select trusted owners' nodes via social networks as intermediate nodes for requests to temporarily store data qubits to increase the success probability. 2) Starvation Mitigation. SEER divides long SD pairs and slices resources reasonably to remedy starvation due to their low success probability. To this end, we design RATE and PLAN to find the proper intermediate nodes and reduce the average waiting time. Simulation results manifest SEER outperforms others by 37%.
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference

We present a static analysis for discovering differentiable or more generally smooth parts of a given probabilistic program, and show how the analysis can be used to improve the pathwise gradient estimator, one of the most popular methods for posterior inference and model learning. Our improvement increases the scope of the estimator from differentiable models to non-differentiable ones without requiring manual intervention of the user; the improved estimator automatically identifies differentiable parts of a given probabilistic program using our static analysis, and applies the pathwise gradient estimator to the identified parts while using a more general but less efficient estimator, called score estimator, for the rest of the program. Our analysis has a surprisingly subtle soundness argument, partly due to the misbehaviours of some target smoothness properties when viewed from the perspective of program analysis designers. For instance, some smoothness properties, such as partial differentiability and partial continuity, are not preserved by function composition, and this makes it difficult to analyse sequential composition soundly without heavily sacrificing precision. We formulate five assumptions on a target smoothness property, prove the soundness of our analysis under those assumptions, and show that our leading examples satisfy these assumptions. We also show that by using information from our analysis instantiated for differentiability, our improved gradient estimator satisfies an important differentiability requirement and thus computes the correct estimate on average (i.e., returns an unbiased estimate) under a regularity condition. Our experiments with representative probabilistic programs in the Pyro language show that our static analysis is capable of identifying smooth parts of those programs accurately, and making our improved pathwise gradient estimator exploit all the opportunities for high performance in those programs.
CN: Verifying Systems C Code with Separation-Logic Refinement Types

Despite significant progress in the verification of hypervisors, operating systems, and compilers, and in verification tooling, there exists a wide gap between the approaches used in verification projects and conventional development of systems software. We see two main challenges in bringing these closer together: verification handling the complexity of code and semantics of conventional systems software, and verification usability. We describe an experiment in verification tool design aimed at addressing some aspects of both: we design and implement CN, a separation-logic refinement type system for C systems software, aimed at predictable proof automation, based on a realistic semantics of ISO C. CN reduces refinement typing to decidable propositional logic reasoning, uses first-class resources to support pointer aliasing and pointer arithmetic, features resource inference for iterated separating conjunction, and uses a novel syntactic restriction of ghost variables in specifications to guarantee their successful inference. We implement CN and formalise key aspects of the type system, including a soundness proof of type checking. To demonstrate the usability of CN we use it to verify a substantial component of Google's pKVM hypervisor for Android.
PrISM-Tracker

A user often needs training and guidance while performing several daily life procedures, e.g., cooking, setting up a new appliance, or doing a COVID test. Watch-based human activity recognition (HAR) can track users' actions during these procedures. However, out of the box, state-of-the-art HAR struggles from noisy data and less-expressive actions that are often part of daily life tasks. This paper proposes PrISM-Tracker, a procedure-tracking framework that augments existing HAR models with (1) graph-based procedure representation and (2) a user-interaction module to handle model uncertainty. Specifically, PrISM-Tracker extends a Viterbi algorithm to update state probabilities based on time-series HAR outputs by leveraging the graph representation that embeds time information as prior. Moreover, the model identifies moments or classes of uncertainty and asks the user for guidance to improve tracking accuracy. We tested PrISM-Tracker in two procedures: latte-making in an engineering lab study and wound care for skin cancer patients at a clinic. The results showed the effectiveness of the proposed algorithm utilizing transition graphs in tracking steps and the efficacy of using simulated human input to enhance performance. This work is the first step toward human-in-the-loop intelligent systems for guiding users while performing new and complicated procedural tasks.
SC-FGCL: Self-Adaptive Cluster-Based Federal Graph Contrastive Learning

As a self-supervised learning method, the graph contrastive learning achieve admirable performance in graph pre-training tasks, and can be fine-tuned for multiple downstream tasks such as protein structure prediction, social recommendation, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> One prerequisite for graph contrastive learning is the support of huge graphs in the training procedure. However, the graph data nowadays are distributed in various devices and hold by different owners, like those smart devices in Internet of Things. Considering the non-negligible consumptions on computing, storage, communication, data privacy and other issues, these devices often prefer to keep data locally, which significantly reduces the graph contrastive learning performance. In this paper, we propose a novel federal graph contrastive learning framework. First, it is able to update node embeddings during training by means of a federation method, allowing the local GCL to acquire anchors with richer information. Second, we design a Self-adaptive Cluster-based server strategy to select the optimal embedding update scheme, which maximizes the richness of the embedding information while avoiding the interference of noise. Generally, our method can build anchors with richer information through a federated learning approach, thus alleviating the performance degradation of graph contrastive learning due to distributed storage. Extensive analysis and experimental results demonstrate the superiority of our framework.
Face Morphing Detection Based on a Two-Stream Network with Channel Attention and Residual of Multiple Color Spaces

Aiming at the performance improvement of face morphing detection, a novel method is proposed by using a two-stream network with channel attention and residual of multiple color spaces. This method first obtains H, S, V, Y, Cb, Cr six color channel image, then use the bilateral filter for filtering the six color channel to get the corresponding residual noise image, then the combined six channel image and the residual noise image as input to the designed two-stream network for training, so as to detect the morphed face image. In addition, an efficient channel attention module is proposed to improve the expressiveness of the model. Experiments are conducted on the standard databases, and the performance of the proposed method is compared with that of 9 state-of-the-art morphing attack detection methods. The results show that the proposed method can achieve better detection performance than the existing works.