Reviewerly/exHarmony huggingface dataset:
How Do I Embed Cyber Risk Management in All Aspects of the Organization?

The question “How do I embed cyber risk management in all aspects of the organization?” addresses how to adopt an Embedded Endurance cyber risk strategy in your day-to-day work as a cyber leader. The chapter begins with a case study about the NotPetya cyberattack, which highlights ongoing challenges in cyber insurance and illuminates the need for embedding cyber mitigation measures across all prioritized critical systems, networks, and data. The chapter describes how to develop an Embedded Endurance cyber risk strategy that is customized for your organization. This chapter walks readers through the key elements of a cyber strategy, from start to finish. This includes defining a risk framework, setting strategic goals, identifying metrics, and establishing strong leadership. The chapter concludes with experiences highlighting the real-world importance of an Embedded Endurance cyber risk strategy from Rosenbach and Falco.
Virtual Reality (VR) for the Support of the Analysis and Operation of a Solar Thermal Tower Power Plant

Abstract A substantial part of the global energy mix depends upon fossil fuels that needed to be reduced to overcome the pollution and environment-related challenges. This has directed the world to shift the energy mix towards renewable energy technologies. Among the development in renewable energy technologies, the development of solar tower power plant is an active research topic. Over the past decade, advances in computers and simulation software systems have greatly expanded their use in design and development, which can facilitate the engineering activities of solar tower power plants. However, an important limitation is the visualization of three-dimensional geometrical design data onto two-dimensional computer screens. VR technologies are a great means in the visualization of 3D data. Therefore, this article attempts to illustrate a concept for the application of VR technologies in the development of solar tower power plant and lists down relevant support scenarios. The main focus of the paper is on analyzing the efficiency of the VR technology used in the design of solar tower power plants and learning from the experience gained in this process. A discussion about further scenarios ranging from on-site visualization of solar tower power plant infrastructure, installation and repair, cleaning and maintenance, etc. is included as well as future directions are pointed out. The demonstrator part consists of an Android Smartphone-based VR application and an HMD based VR application. Furthermore, a brief comparison of both the applications as well as of HMD and sVR is also provided.
“Friends don’t let friends skip rat day”

Building on literature detailing the use of internet memes online, this chapter analyzes the repetition of memes offline in the everyday face-to-face conversations of Millennial friends in their late twenties, who appropriate texts from memes to serve particular functions in their talk. When these speakers encounter interactional dilemmas due to epistemic (knowledge) imbalances, they make references to internet memes, which allow the epistemic territory of talk to shift to a topic to which at least most of the speakers have epistemic access. These epistemic shifts underlie the construction of play frame laminations, allowing for different structures of participation and conversational involvement around shared knowledge of the memes, which serve for group identity construction. At the same time, this chapter highlights how the references to internet memes in particular invoke various cultural stereotypes. This chapter contributes to understanding how intertextual references to different forms of media can resolve interactional dilemmas in conversation by shifting epistemics and laminating frames, ultimately reinforcing a group identity based on shared knowledge.
How Well do Feature Visualizations Support Causal Understanding of CNN Activations

One widely used approach towards the inner workings of deep convolutional neural networks is to visualize unit responses via activation maximization. Feature visualizations via activation maximization are thought to provide humans with precise information about the image features that cause a unit to be activated. If this is indeed true, these synthetic images should enable humans to predict the effect of an intervention, such as whether occluding a certain patch of the image (say, a dog's head) changes a unit's activation. Here, we test this hypothesis by asking humans to predict which of two square occlusions causes a larger change to a unit's activation. Both a large-scale crowdsourced experiment and measurements with experts show that on average, the extremely activating feature visualizations by Olah et al. (2017) indeed help humans on this task ($67 \pm 4\%$ accuracy; baseline performance without any visualizations is $60 \pm 3\%$). However, they do not provide any significant advantage over other visualizations (such as e.g. dataset samples), which yield similar performance ($66 \pm 3\%$ to $67 \pm 3\%$ accuracy). Taken together, we propose an objective psychophysical task to quantify the benefit of unit-level interpretability methods for humans, and find no evidence that feature visualizations provide humans with better causal understanding than simple alternative visualizations.
Combining Low-Level Image Features with Features from A Simple Convolutional Neural Network

In the traditional image processing approaches, first low-level image features are extracted and then they are sent to a classifier or a recognizer for further processing. While the traditional image processing techniques employ this step-by-step approach, majority of the recent studies prefer layered architectures which both extract features and do the classification or recognition tasks. These architectures are referred as deep learning techniques and they are applicable if sufficient amount of labeled data is available and the minimum system requirements are met. Nevertheless, most of the time either the data is insufficient or the system sources are not enough. In this study, we experimented how it is still possible to obtain an effective visual representation by combining low-level visual features with features from a simple deep learning model. As a result, combinational features gave rise to 0.80 accuracy on the image data set while the performance of low-level features and deep learning features were 0.70 and 0.74 respectively.
Avoiding static and dynamic obstacles and various other challenges in path planning process of robot navigation

Purpose of the research: The most difficult aspect of robot navigation issues is considered path planning. Scientists have been studying this field for years. This domain is undergoing massive transformations. For effective path planning, a variety of approaches are used. As a result, establishing a secure robotic path in a populated environment is a critical need for most robot project’s achievements.Recent Findings: Numerous updates and novel artificial intelligence algorithms are being abused, and they are now available to the public.Summary: This method is developed primarily to increase the quality and efficacy of globalized path planning for an autonomous mobile robot in an environment depending on the grid with the avoidance of uncertain static obstacle characteristics. The behavior of an autonomous robot can be influenced by the global path quality with respect to path consistency, smoothness, and security. The effectiveness of the Ant Colony Optimization (ACO) method has been enhanced in this work the multi-direction support.Result: Curvature, longitudinal, and lateral coordinate restrictions are all included in the overall cost. Furthermore, for collision identification, the collection of optimum local trajectories is examined for every unpredictable obstacle at each step movement. Simulations are being used to contrast the findings to prior globalized path planning algorithms in order to distinguish the quality and efficacy of the developed technique in diverse constraint settings.
초등 한국어(KSL) 교육을 위한 ‘학습 한국어’ 영역의 서술적 표현 활동 구성 연구

The purpose of this study is to construct predication activity of ‘academic Korean’ in KSL education for elementary learners. To this end, the concept of academic Korean is first examined from a theoretical perspective and a curriculum perspective, and the activities of elementary academic Korean textbooks are analyzed. Academic language skills require clear and specific predicative representation skills, but the activities of the textbook are limited or ambiguous in terms of speech output. Therefore, based on the psycholinguistic language processing model, the framework of predication activity composition was established and the direction of predication activity operation was established at the same time. As a result, predication activity for primary learners could be presented in accordance with the academic meaning and predicative sentence representation procedures. (Seoul National University of Education)
Teachers’ Readiness To Create the Inclusive and Computer-Orientated Environment at Primary School

The article deals with peculiarities of training specialists to create the inclusive and computer-oriented educational environment. A number of structural components that take a crucial system-forming role are highlighted in the article, namely a system of basic and professional future teachers’ values in inclusive education, practical potential, resource support for correctional and development activities and professional skills in the field of information and communication technologies and willingness to participate in e-education.Research methods: psychological and pedagogical experiment, providing the qualitative and quantitative analysis of results, observations, questionnaires, interviews, modeling method and mathematical statistics.The aim of the research work is to substantiate the structure and content of the inclusive and computer-orientated educational environment that will identify priority areas and activities in educational establishments and trace the range of professional competencies as a basis for working out a special course on syllabus.The research work was carried out. It dealt with process of training future teachers to use electronic educational resources in the inclusive environment (to work in a team, implementing a comprehensive approach to educate a child with special educational needs, as well as to carry out diagnostic-advisory and pedagogical support). All mentioned above involved the students in studying the educational discipline.Research findings on training future teachers to work in the inclusive educational and computer-orientated environment indicate that the competencies that are formed are rather satisfying, but there are certain aspects that teachers need to focus on, these are information and communication technology opportunities, they can use at work.
How Do I Assess Our Cyber Risk?

The question “How do I assess our cyber risk?” addresses how to identify and characterize cyber risk unique to an organization’s critical systems, networks, and data. The chapter begins with a case study about a cyberattack on Ukraine’s electric grid. It details risk assessment for three types of critical systems: mission-critical systems, business-critical systems, and safety-critical systems. It explains the three types of networks critical to many organizations: business and administrative networks, operational and service delivery networks, and communication networks. In outlining the “CIA triad,” it shows how cyber risk can be characterized as a confidentiality, integrity, or availability issue relating to digital assets. Further, it describes how to assess the importance of different digital assets and how to prioritize them using a business impact analysis (BIA). The chapter concludes with real-world Embedded Endurance strategy lessons Rosenbach gained in Saudi Arabia in the wake of one of the world’s most destructive cyberattacks.
Running Horizon in the Cloud

So far in this book, we have focused on installing, configuring, and deploying the Horizon solution for delivering apps and desktops on-premises. That is on internal infrastructure hosted within the confines of your datacenter.
Multi-Vector Attention Models for Deep Re-ranking

Large-scale document retrieval systems often utilize two styles of neural network models which live at two different ends of the joint computation vs. accuracy spectrum. The first style is dual encoder (or two-tower) models, where the query and document representations are computed completely independently and combined with a simple dot product operation. The second style is cross-attention models, where the query and document features are concatenated in the input layer and all computation is based on the joint query-document representation. Dual encoder models are typically used for retrieval and deep re-ranking, while cross-attention models are typically used for shallow re-ranking. In this paper, we present a lightweight architecture that explores this joint cost vs. accuracy trade-off based on multi-vector attention (MVA). We thoroughly evaluate our method on the MS-MARCO passage retrieval dataset and show how to efficiently trade off retrieval accuracy with joint computation and offline document storage cost. We show that a highly compressed document representation and inexpensive joint computation can be achieved through a combination of learned pooling tokens and aggressive downprojection. Our code and model checkpoints are open-source and available on GitHub.
Development of a Modern Personality Through Listening to and Perceiving Music

The article indicates the importance and value of music that is present in our lives.It is said that in fact, music and the sounds around us affect the perception of the world, our mood, the quality of our work, the effectiveness of rest ... Musical perception is based on the analysis of sounds and their sequence.In this case, an important role is played by the activity of the listener himself, his focus on the perception of differences in sounds.What a person hears in music depends on what he can expect in it, on what we are able to imagine.The musical image is enriched with associations.With the help of the methods of listening to music and observation, it is possible to reveal not only the music that sounds everywhere, be it in the forest, in the field, on the farm, in the city, in the underpass ... Through listening to music, you can also reveal your personal potential.Hearing music means distinguishing its character, following the development of an image: a change in intonation, moods.Music will be beneficial only when it starts to please listeners, passing itself through thoughts and consciousness.Music should sound only for each specific person.The article reflects the importance, significance and relevance of the influence of music on the development and formation of personality.Describes how a person develops through listening to and perceiving music.The importance of such concepts as "listening" and "listening" to music is shown.
To what extent do human explanations of model behavior align with actual model behavior

Given the increasingly prominent role NLP models (will) play in our lives, it is important for human expectations of model behavior to align with actual model behavior. Using Natural Language Inference (NLI) as a case study, we investigate the extent to which human-generated explanations of models’ inference decisions align with how models actually make these decisions. More specifically, we define three alignment metrics that quantify how well natural language explanations align with model sensitivity to input words, as measured by integrated gradients. Then, we evaluate eight different models (the base and large versions of BERT,RoBERTa and ELECTRA, as well as anRNN and bag-of-words model), and find that the BERT-base model has the highest alignment with human-generated explanations, for all alignment metrics. Focusing in on transformers, we find that the base versions tend to have higher alignment with human-generated explanations than their larger counterparts, suggesting that increasing the number of model parameters leads, in some cases, to worse alignment with human explanations. Finally, we find that a model’s alignment with human explanations is not predicted by the model’s accuracy, suggesting that accuracy and alignment are complementary ways to evaluate models.
SPACE, TIME and MATTER

From Kai Sørlander’s Philosophy we can, by logical reasoning, infer space and time as facts. We do not have to presume them, as did Immanuel Kant.
Automatic Job Ads Classification, Based on Unstructured Text Analysis

Machine learning models have been tested on countless classification problems in the past.However, there is little information available on how well they perform when the task is learning abstract concepts, that are difficult to understand, even for humans.The object of this research was to find the best model for capturing the concepts of whitecollar and blue-collar jobs, based on unstructured job ads, in Serbian.These concepts have become very difficult to define in the modern job market, since there are now many factors besides the required level of formal education, that determines the category of a job.
Influence of Participation for Living Sports Program of Middle-aged Men on Health-Related Fitness and Male-Related Hormone in Blood

This study is undertaken for the health-related fitness and male related hormone in blood after implementing exercises on middle-aged men. Subjects for the study were residents in B City with a total of 12 persons (6 persons for exercise group and 6 persons for control group). The frequency and period of exercise was implemented three times a week with 50 minutes (5 minutes of preparatory exercise, 40 minutes of main exercise and 5 minutes of wrap-up exercise) per session for a total of 8 weeks and the exercise intensity was 40~50% HRR in 1~4 weeks and 51~60% HRR in 5~8 weeks. As a result of the examination for each group, the research result indicates that the health-related fitness showed noticeable difference in muscle strength / muscle endurance / flexibility for the exercise group where there was no noticeable difference in the control group. And, as a result of average examination for each group, there was a noticeable difference in muscle strength / muscle endurance, and as a result of examination for each group, there was a noticeable difference in male related hormone for the exercise group.
Scalable Diverse Model Selection for Accessible Transfer Learning

With the preponderance of pretrained deep learning models available off-the-shelf from model banks today, finding the best weights to fine-tune to your use-case can be a daunting task. Several methods have recently been proposed to find good models for transfer learning, but they either don't scale well to large model banks or don't perform well on the diversity of off-the-shelf models. Ideally the question we want to answer is, given some data and a source model, can you quickly predict the model's accuracy after fine-tuning? In this paper, we formalize this setting as Scalable Diverse Model Selection and propose several benchmarks for evaluating on this task. We find that existing model selection and transferability estimation methods perform poorly here and analyze why this is the case. We then introduce simple techniques to improve the performance and speed of these algorithms. Finally, we iterate on existing methods to create PARC, which outperforms all other methods on diverse model selection. We have released the benchmarks and method code in hope to inspire future work in model selection for accessible transfer learning.
Speculative Execution Attacks and Hardware Defenses

Speculative execution attacks like Spectre and Meltdown exploit hardware performance optimization features to illegally access a secret and then leak the secret to an unauthorized recipient. Many variants of speculative execution attacks (also called transient execution attacks) have been proposed in the last few years, and new ones are constantly being discovered. While software mitigations for some attacks have been proposed, they often cause very significant performance degradation. Hardware solutions are also being proposed actively by the research community, especially as these are attacks on hardware microarchitecture. In this talk, we identify the critical steps in a speculative attack, and the root cause of successful attacks. We define the concept of "security dependencies", which should be implemented to prevent data leaks and other security breaches. We propose a taxonomy of defense strategies and show how proposed hardware defenses fall under each defense strategy. We discuss security-performance tradeoffs, which can decrease the performance overhead while still preventing security breaches. We suggest design principles for future security-aware microarchitecture.
Mapmaking for memory's sake

opinion Share on Mapmaking for memory's sake: the Tigray genocide Author: Salem Tewelde the Otherwise School with input from the Otherwise School the Otherwise School with input from the Otherwise SchoolView Profile Authors Info & Claims InteractionsVolume 28Issue 6November - December 2021 pp 18–21https://doi.org/10.1145/3491439Online:10 November 2021Publication History 0citation664DownloadsMetricsTotal Citations0Total Downloads664Last 12 Months664Last 6 weeks34 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Design of Vocational Training of Managers and Pedagogical Personnel

In this article, the methodological problems of designing additional professional education of managers and pedagogical personnel, defining the basic requirements for teachers of different categories, their professional and personal qualities, features of professional models are revealed.
Style Control for Schema-Guided Natural Language Generation

Natural Language Generation (NLG) for task-oriented dialogue systems focuses on communicating specific content accurately, fluently, and coherently. While these attributes are crucial for a successful dialogue, it is also desirable to simultaneously accomplish specific stylistic goals, such as response length, point-of-view, descriptiveness, sentiment, formality, and empathy. In this work, we focus on stylistic control and evaluation for schema-guided NLG, with joint goals of achieving both semantic and stylistic control. We experiment in detail with various controlled generation methods for large pretrained language models: specifically, conditional training, guided fine-tuning, and guided decoding. We discuss their advantages and limitations, and evaluate them with a broad range of automatic and human evaluation metrics. Our results show that while high style accuracy and semantic correctness are easier to achieve for more lexically-defined styles with conditional training, stylistic control is also achievable for more semantically complex styles using discriminator-based guided decoding methods. The results also suggest that methods that are more scalable (with less hyper-parameters tuning) and that disentangle context generation and stylistic variations are more effective at achieving semantic correctness and style accuracy.