Reviewerly/exHarmony huggingface dataset:
In Memoriam: John C. Medici (1938–2022)

"In Memoriam: John C. Medici (1938–2022)." Rocks & Minerals, 98(2), pp. 198–199 Additional informationNotes on contributorsJanet H. CliffordJanet H. Clifford, a geologist by training but not by profession, has been a mineral collector for about fifty years. She enjoys mineral shows, a little field collecting for micros, and occasionally editing or writing for the hobby.
Split Your Overwhelmed Teams

Two teams of five is not the same as one team of 10.
G-Fuzz: A Directed Fuzzing Framework for gVisor

gVisor is a Google-published application-level kernel for containers. As gVisor is lightweight and has sound isolation, it has been widely used in many IT enterprises [1],[2],[3]. When a new vulnerability of the upstream gVisor is found, it is important for the downstream developers to test the corresponding code to maintain the security. To achieve this aim, directed fuzzing is promising. Nevertheless, there are many challenges in applying existing directed fuzzing methods for gVisor. The core reason is that existing directed fuzzers are mainly for general C/C++ applications, while gVisor is an OS kernel written in the Go language. To address the above challenges, we propose G-Fuzz, a directed fuzzing framework for gVisor. There are three core methods in G-Fuzz, including lightweight and fine-grained distance calculation, target related syscall inference and utilization, and exploration and exploitation dynamic switch. Note that the methods of G-Fuzz are general and can be transferred to other OS kernels. We conduct extensive experiments to evaluate the performance of G-Fuzz. Compared to Syzkaller, the state-of-the-art kernel fuzzer, G-Fuzz outperforms it significantly. Furthermore, we have rigorously evaluated the importance for each core method of G-Fuzz. G-Fuzz has been deployed in industry and has detected multiple serious vulnerabilities.
Introduction

Technology science is about generating knowledge in the form of new artifacts, meaning they did not exist before the research was initiated. On the other hand, explanation science provides new knowledge about the world, including the already existing technology and artifacts.
Relation Preference Oriented High-order Sampling for Recommendation

The introduction of knowledge graphs (KG) into recommendation systems (RS) has been proven to be effective because KG introduces a variety of relations between items. In fact, users have different relation preferences depending on the relationship in KG. Existing GNN-based models largely adopt random neighbor sampling strategies to process propagation; however, these models cannot aggregate biased relation preference local information for a specific user, and thus cannot effectively reveal the internal relationship between users' preferences. This will reduce the accuracy of recommendations, while also limiting the interpretability of the results.
Incorporating Fairness in Large Scale NLU Systems

NLU models power several user facing experiences such as conversations agents and chat bots. Building NLU models typically consist of 3 stages: a) building or finetuning a pre-trained model b) distilling or fine-tuning the pre-trained model to build task specific models and, c) deploying the task-specific model to production. In this presentation, we will identify fairness considerations that can be incorporated in the aforementioned three stages in the life-cycle of NLU model building: (i) selection/building of a large scale language model, (ii) distillation/fine-tuning the large model into task specific model and, (iii) deployment of the task specific model. We will present select metrics that can be used to quantify fairness in NLU models and fairness enhancement techniques that can be deployed in each of these stages. Finally, we will share some recommendations to successfully implement fairness considerations when building an industrial scale NLU system.
Orientation to the Pentatonic Scale

Abstract In this unit and the next, students will learn about the l-s-m subset of the pentatonic scale. This trichord forms the basis of many simple folk songs, some of which are presented as exercises in the chapter. The music theory sections introduce students to the placement of pitches on the staff as well as dynamic markings found in scores.
Simple Meter

Abstract This unit shows how the sounds on the beat are grouped into three uneven sounds: a long sound followed by two short sounds and two short sounds followed by a long sound. Various meters and keys are used for the musical examples, and assessment tools are provided.
Memory aids

Abstract Memory aids can help you remember many things that are important to your daily life. Our recommendation is to always consider using a memory aid to remember information if it is important and/or easy to do so. There are many important general principles to use memory aids effectively: Be organized. Be ready. Don’t delay. Keep it simple. Develop routines. A place for everything and everything in its place. Include the 5 W’s in your calendar or planner: Who, What is it for, What do you need, When, and Where. Use simple lists for simple tasks. Use the four-quadrant system for projects: important/urgent, important/not urgent, not important/urgent, not important/not urgent. Use reminders. Use a pillbox.
Law and Policy: Infrastructures of Interface

Abstract This chapter investigates how the digital interfaces depicted throughout this book tend to be produced and reproduced in the way that they do, even as they remain ever open to customization and improvement. It focuses on one dimension of the forces or factors that help to sustain this reproduction: international legal and policy infrastructures. As well as suggesting their heterogeneous provenance and supports, this analysis suggests digital interfaces’ openness to reassembly (more thoroughgoing than customization) and multiple possible sites of entry for that purpose. For this and other purposes, this chapter argues that there is value in investigating international law and policy surrounding humanitarian digital interfaces infrastructurally building on structuralist and post-structuralist practices of reading. The chapter’s revisitation of those practices helps to situate this book in relation to prior work in the international legal field.
GestureSurface: VR Sketching through Assembling Scaffold Surface with Non-Dominant Hand

3D sketching in virtual reality (VR) provides an immersive drawing experience for designs. However, due to the lack of depth perception cues in VR, scaffolding surfaces that constrain strokes to 2D are usually used as visual guides to reduce the difficulty of drawing accurate strokes. When the dominant hand is occupied by the pen tool, the efficiency of scaffolding-based sketching can be improved by using gesture input to reduce the idleness of the non-dominant hand. This paper presents GestureSurface, a bi-manual interface that uses non-dominant hand performing gestures to operate scaffolding and the other hand drawing with controller. We designed a set of non-dominant gestures to create and manipulate scaffolding surfaces, which are assembled by automatic combination based on five predefined primitive surfaces. We evaluated GestureSurface through a 20-person user study and found that the method of scaffolding-based sketching using non-dominant hand has the advantages of high efficiency and low fatigue.
Weather Classification

Weather conditions are essential to know what is about to happen. They often disrupt day-to-day activities. In the field of computer vision, it is a difficult task due to the high diversity and the lack of distinguishable features that can be extracted from them. In this chapter, we propose an Image Embedding based Stacked Generalization (IESG) method to determine the best image embeddings that can be generated from a given image. A convolutional autoencoder is an unsupervised technique for feature extraction. Multiple machine learning models are trained to generate a stacked ensemble model using StackNet architecture. Also, the method proposed in the chapter improves predictions by 5% when compared to the original paper which used this data set.
Exploring Planetary Geomorphology with NASA’s Solar System Treks

NASA's Solar System Treks Project (SSTP) online portals provide web-based suites of interactive visualization and analysis tools to enable planetary scientists, mission planners, students, and the general public to access mapped data products from past and current missions for a growing number of planetary bodies.&amp;#160;The Solar System Treks portals provide advanced data visualization and analysis capabilities for data returned from a vast number of instruments aboard many past and current missions to a growing number of planetary bodies throughout Solar System. Multiple map projections as well as interactive 3D views are available to optimize visualization of different landforms. A detailed set of analysis tools helps users find and interpret morphological features across diverse landscapes on the surfaces of planets, moons, and asteroids. In some cases, these tools make use of machine learning and artificial intelligence to help users locate, identify, and understand landforms drawn from very large datasets. Having an integrated suite of portals presenting geomorphology across a range of planetary bodies within the Solar System greatly facilitates studies of comparative planetology. The portals are currently being used for site selection and analysis by NASA and its international and commercial partners supporting upcoming missions.&amp;#160;Today, 11 web portals in the program are available to the public. This list includes portals for the Moon; the planets Mercury, Venus, and Mars; the asteroids Bennu, Ryugu, Vesta, and Ceres; and the outer moons Titan and Europa. The Icy Moons Trek portal features seven of Saturn&amp;#8217;s smaller icy moons. All of the portals are unified under a project home site with supporting content. These web-based portals are free resources and publicly available.&amp;#160;This presentation for EGU will detail and share examples of the how the portals can be applied to research in planetary geomorphology.
Terraferma: an interactive tool for insurance seismic risk awareness in Italy

&amp;lt;p&amp;gt;Despite its high susceptibility to earthquakes, Italy&amp;#039;s protection gap for seismic risk is one of the largest in the world: only a small percentage of economic losses caused by earthquakes come under an insurance policy. One of the reasons is a lack of public awareness of seismic risk.&amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Terraferma attempts to respond to this problem.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Terraferma is an interactive tool developed by Leith&amp;amp;#224;, the competence center of the Unipol Group for Data Science and Computer Science, and adopted by different insurance companies of the Unipol Group.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;The main objective of this tool is to raise customers&amp;#039; awareness about the frequency and intensity of earthquake events in Italy, especially while underwriting a home insurance policy or getting a mortgage loan.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Starting from the user&amp;#039;s geolocation, Terraferma shows an interactive map of the earthquakes recorded by the seismic network of the Italian National Institute of Geophysics and Volcanology (INGV), together with information such as the magnitude, intensity, and depth of the quakes and the estimated area of propagation.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;From a technological point of view, Terraferma is an application developed in the cloud using the serverless computing paradigm.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Terraferma is accessible openly at www.incontra-terraferma.it.&amp;lt;/p&amp;gt;
UAV path planning algorithm based on improved RRT

Aiming at the problems of traditional Rapidly Exploring Random Tree (RRT) algorithm in route planning, such as slow speed, poor route quality and low flightability, a route planning algorithm based on integrated improvement of RRT was proposed. Firstly, in the selection of nodes to be expanded, the minimum sum of the distance between nodes and the target and the random sampling point is taken as the selection basis instead of the original method of determining nodes only according to random sampling points, so as to increase the probability of nodes near the target in the random tree being selected as nodes to be expanded. Secondly, in the process of node expansion, the reachable region of the next waypoint was determined according to the UAV dynamic constraints, and then multiple alternative nodes were randomly generated in this region. Then the route cost function is designed and the comprehensive generation value of the route formed by the alternative nodes is taken as the judgment criterion for node addition. Finally, B-spline curve smoothing is carried out to further improve the route quality. The simulation results show that the improved algorithm has obvious advantages in improving the planning speed and air route quality, and the obtained air route satisfies the UAV dynamic constraints and has high flightability.
A lightweight model for encrypted traffic classification through sequence modeling

With the increasing awareness of privacy protection in recent years, various encryption techniques are gradually applied to network traffic, which makes encrypted traffic classification an indispensable part of network management. Recent studies show that the approaches based on deep learning are compelling for the traffic classification task. However, most of them take the encrypted payload as input, which not only requires high computational overhead to make classification, but also limits the performance improvement due to the unavailability of the plaintext. In this paper, we treat the encrypted traffic as sequences and solve the classification task from the perspective of sequence modeling, which only depends on several sequence fields obtained from the traffic header. We properly design a lightweight model and name it TGA by its structure, which consists of a temporal convolutional network (TCN), a gated recurrent unit (GRU) and the attention mechanism. TGA first extracts short-term features from sequences by applying the TCN, and then captures the long-term dependencies by exploiting the GRU, and finally focuses on valuable features through dynamic assignment of attention weights. Through these three steps, TGA is expected to obtain the most effective but lightest temporal features. Experimental results on the public dataset demonstrate that TGA shows superiority in terms of classification accuracy and time efficiency, while the number of parameters is reduced to at most 30% of the state-of-the-art models.
Multi-scale feature fusion network with spatial-temporal alignment for video denoising

Most existing video denoising methods based on the PatchMatch algorithm and optical flow estimation often lead to artifacts blurring and poor denoising effect on scale-varying data. To tackle these issues, we propose a multi-scale feature fusion network based on different pyramid blocks and adaptive spatial-channel attention, which enables to effectively extract multi-scale feature information from noisy video data. Furthermore, we develop a spatial-temporal alignment module with deformable convolution to align the implicit features and reduce blurring artifacts. The results show that the proposed method outperforms the state-of-the-art algorithms in visual and objective quality metrics on the public datasets DAVIS and Set8.
Geomorphica, the new diamond open access journal in the field of geomorphology

Today we introduce you to Geomorphica, the new diamond open access journal in geomorphology. Next to Volcanica, Tektonika, Seismica and Sedimentologika, it adds to the growing family of Diamond Open Access Journals in the Geosciences. What exactly is diamond open access publishing? It describes a bottom-up publishing structure that reduces barriers in accessing scientific literature because there are no journal subscription fees and no article processing charges. As such, it allows the reader to access and the author to publish the article for free. Diamond open access publishing promotes inclusivity, equal access to information and provides the newest findings to a large and diverse audience.Geomorphica has been community-built over the last year. Our structure comprises six commissions &amp;#8211; format, website, communications, inclusivity, ethics, and editorial &amp;#8211; and a steering committee formed of the chairs of each commission. We determined our name by poll in January and our logo by friendly competition in April. We started cooperating with the International Association of Geomorphology (IAG) in July and signed our hosting agreement with Penn State University in December 2022. At EGU 2023 we are excited to celebrate the achievements of the previous year as well as to exchange ideas on our future together.&amp;#160;We welcome feedback, comments, ideas and a stimulating discussion from all sectors of the geoscientific community to help us continually improve the initiative. We are always looking for volunteers and we encourage everyone to start brainstorming on potential submissions to Geomorphica, as we plan to open a call for manuscript submissions in spring 2023.
IoMT-Based Smart Health Monitoring

The healthcare sector has received an immense boost in terms of science, technology, and services in the recent past. The next big change that will skyrocket this field in terms of quality, efficiency, and quantity is the introduction of “automation” in these sectors. With the assistance of the Internet of Things (IoT), the target is a large part of the population that will receive a diagnosis easily at cheaper rates even in the absence of a medical professional. In this type of device, the heartbeat and the temperature sensor collects data from the patient and sends it to the cloud via a Wi-Fi module, which then displays the information remotely, helping to ensure care of the patient within the crucial golden hour. In this process, patients themselves can also monitor their health parameters while sitting at home, thus, increasing the efficiency of the healthcare sector. Moreover, if the patient falls, the designed fall-detection system sends an alert to the caregiver for further processing. By implementing this technology in the near future, people can effectively reduce the hassles they face regarding healthcare and, thus, not only unlock, but also boost the true potential of the healthcare system.
Self-Adaptive Application Monitoring for Decentralized Edge Frameworks

In recent years, Edge computing has changed from central to decentralized computing for self-adaptive applications. There are many monitoring approaches available to monitor the cloud and Edge services. The comprehensive monitoring approaches are necessary to ensure better quality of services of self-adaptive applications as well as a better quality of experience for various users. The aim of this chapter is to address three main challenges by focusing on three major things. First, different adaptive applications in Edge framework are yet to be fully resolved. Second, we will discuss the new taxonomy of adaptive application monitoring requirements orchestrated in accordance with the basic Edge framework. Third, we will discuss the use of widely employed monitoring technologies in the cloud and compare network parameters such as reliability, performance, and delay. Research and development are beginning to take place in the applications developed for Edge computing frameworks. Recently, great attention has been given to the monitoring of adaptive applications of the Edge framework, an area still to be examined and improved. A taxonomy of key functional and non-functional needs to be addressed by the cloud systems. In this chapter, we compared a number of widely used cloud monitoring tools, their capabilities and weaknesses, and the monitoring system requirements. This is another paradigm of more container-based virtual technology monitoring systems offering a light mechanism to provide initiation, deployment, scaling, and movement services in the Edge computing infrastructure.
Detection Accuracy Improvement on One-Stage Object Detection Using Ap-Loss-Based Ranking Module and Resnet-152 Backbone

Localization-loss and classification-loss are optimized at the same time to train the one-stage object detectors. Because of the large number of anchors, the severe foreground–background class disproportion causes significant classification-loss. This paper discusses using a ranking module instead of the classification module to mitigate this difficulty and also Average-Precision loss (AP-loss) is utilized on the ranking module. An optimization algorithm is used to make the AP-loss as effective as possible. Optimization algorithm blends the error-driven updating method of perceptron learning and the deep network backpropagation technique. This optimization algorithm handles the foreground–background class disproportion issues. One-stage detector with AP-loss and backbone with ResNet-152 attains improvement in the detection performance compared to the classification-losses-based detectors.
Research on feature extraction algorithm based on meta-learning

Feature extraction is an important research topic in the field of image processing.In autonomous driving, it is of great importance to extract the feature information of the picture obtained by the vehicle camera for the agent to better understand the environment information. In order to improve the quality of feature extraction, this paper combines meta-learning and deep learning-based feature extraction methods, and proposes a Meta-VAE-WGAN-GP (MVWP) feature extraction algorithm, and applies it to automatic driving. Firstly, aiming at the problem of parameter centralization in Wasserstein generative adversarial network (WGAN) and the problem of gradient explosion and gradient disappearance caused by improper manual parameter adjustment, a generative adversarial network based on gradient penalty and Wasserstein distance (WGAN-GP) was proposed, and it was combined with VAE. The VAE-WGAN-GP model is formed. Secondly, aiming at the problem that the feature extraction model needs to be trained from scratch every time it is faced with a new task, and the training time is too long, the MVWP model is formed by combining meta-learning with VAE-WGAN-GP (VWP) mentioned above. Finally, the experimental results show that compared with VAE, VAE-WGAN and VWP, the training speed of MVWP model is increased by about 6 times, the reconstruction loss is reduced by 55.9%, 37.8% and 20.2%, respectively, and the reconstructed images are clearer.
Growing neural networks using orthogonal initialization

In the training of neural networks, the architecture is usually determined first and then the parameters are selected by an optimizer. The choice of architecture and parameters is often independent. Whenever the architecture is modified, an expensive retraining of the parameters is required. In this work, we focus on growing the architecture instead of the expensive retraining. There are two main ways to grow new neurons: splitting and adding. In this paper, we propose orthogonal initialization to mitigate the gradient vanish of the new adding neurons. We use QR decomposition to obtain orthogonal initialization. We performed detailed experiments on two datasets (CIFAR-10, CIFAR-100) and the experimental results show the efficiency of our method.