Reviewerly/exHarmony huggingface dataset:
Introduction and Deep Roots

This textbook presents an innovative new perspective on the economics of development, including insights from a broad range of disciplines. It starts with the current state of affairs, a discussion of data availability, reliability, and analysis, and an historic overview of the deep influence of fundamental factors on human prosperity. Next, it focuses on the role of human interaction in terms of trade, capital, and knowledge flows, as well as the associated implications for institutions, contracts, and finance. The book also highlights differences in the development paths of emerging countries in order to provide a better understanding of the concepts of development and the Millennium Development Goals. Insights from other disciplines are used help to understand human development with regard to other issues, such as inequalities, health, demography, education, and poverty. The book concludes by emphasizing the importance of connections, location, and human interaction in determining future prosperity.
References

This textbook presents an innovative new perspective on the economics of development, including insights from a broad range of disciplines. It starts with the current state of affairs, a discussion of data availability, reliability, and analysis, and an historic overview of the deep influence of fundamental factors on human prosperity. Next, it focuses on the role of human interaction in terms of trade, capital, and knowledge flows, as well as the associated implications for institutions, contracts, and finance. The book also highlights differences in the development paths of emerging countries in order to provide a better understanding of the concepts of development and the Millennium Development Goals. Insights from other disciplines are used help to understand human development with regard to other issues, such as inequalities, health, demography, education, and poverty. The book concludes by emphasizing the importance of connections, location, and human interaction in determining future prosperity.
Uneven Playing Field

This textbook presents an innovative new perspective on the economics of development, including insights from a broad range of disciplines. It starts with the current state of affairs, a discussion of data availability, reliability, and analysis, and an historic overview of the deep influence of fundamental factors on human prosperity. Next, it focuses on the role of human interaction in terms of trade, capital, and knowledge flows, as well as the associated implications for institutions, contracts, and finance. The book also highlights differences in the development paths of emerging countries in order to provide a better understanding of the concepts of development and the Millennium Development Goals. Insights from other disciplines are used help to understand human development with regard to other issues, such as inequalities, health, demography, education, and poverty. The book concludes by emphasizing the importance of connections, location, and human interaction in determining future prosperity.
Institutions and Contracts

This textbook presents an innovative new perspective on the economics of development, including insights from a broad range of disciplines. It starts with the current state of affairs, a discussion of data availability, reliability, and analysis, and an historic overview of the deep influence of fundamental factors on human prosperity. Next, it focuses on the role of human interaction in terms of trade, capital, and knowledge flows, as well as the associated implications for institutions, contracts, and finance. The book also highlights differences in the development paths of emerging countries in order to provide a better understanding of the concepts of development and the Millennium Development Goals. Insights from other disciplines are used help to understand human development with regard to other issues, such as inequalities, health, demography, education, and poverty. The book concludes by emphasizing the importance of connections, location, and human interaction in determining future prosperity.
Site-To-Site Tunnels Authenticated by Quantum Keys

We describe a scheme that utilizes pre-shared keys from a quantum key distribution (QKD) system to authenticate site-to-site IPsec tunnels. Testbed measurements indicate sustained TCP connections between the site networks, incurring a throughput reduction of about 30% for approximately a minute during key updates.
Recovery of vivid 3D percepts from 125 years of historical motion film and animation clips, part 2 - 3D movies

Stereoscopy involves presenting two differentially-offset images separately to left and right eyes. This 2D image information is combined binocularly in the brain to generate 3D depth perception. Last year, static image pairs were used to introduce and demonstrate the ability to recover and perceive dynamic 3D structure from 2D movies. This year, 3D movies generated from 2D movies demonstrate the same phenomenon, created by introducing a delay between identical tracking shots presented to the left and right eyes. As before, the stereoscopic displays were generated from a variety of lateral and arc tracking movies, including: 1) dolly shots, 2) lateral shots taken while driving, flying, boating, traveling by rail, and orbiting planets, 3) ‘bullet time'/Matrix time sequence shots, and 4) animations based on 3D models. These were scenes from classic motion pictures, or archival footage of significant historical and strategic events. This year's demonstrations include new 3D versions of the first known tracking shots (Venice, 1896), the Hindenburg’s last moments (1937), 'bullet time' animation from Speed Racer/MachGoGogo (1966), The Beatles rehearsals (1969), and scenes generated while orbiting the moon and distant planets. The method enables viewers to (1) see historically or strategically important scenes in 3D; (2) infer depth structure and estimate distance when static monocular cues to depth are sparse or non-existent; and (3) break static forms of camouflage. The method holds the potential to quantify real and perceived depth from motion parallax in historical and contemporary popular movie sequences. Moreover, we demonstrate that binocular disparity sequences derived from dolly-arc tracking shots rotating around a subject can generate robust 3D perception, despite the common practice of avoiding such binocular convergence in the stereoscopy field (e.g., Gao et al. 2018, PLoS One).
Reflection on future directions: a systematic review of reported limitations and solutions in interactive information retrieval user studies

Purpose The purpose of this paper is to understand how users behave and evaluate how systems with users are essential for interactive information retrieval (IIR) research. User study methodology serves as a primary approach to answering IIR research questions. In addition to designing user study procedures, understanding the limitations of varying study designs and discussing solutions to the limitations is also critical for improving the methods and advancing the knowledge in IIR. Design/methodology/approach Given this unresolved gap, we apply the faceted framework developed by Liu and Shah (2019) in systematically reviewing 131 IIR user studies recently published (2016–2021) on multiple IR and information science venues. Findings Our study achieve three goals: (1) extracting and synthesizing the reported limitations on multiple aspects of user study (e.g. recruitment, tasks, study procedures, system interfaces, data analysis methods) under associated facets; (2) summarizing the reported solutions to the limitations; (3) clarifying the connections between types of limitations and types of solutions. Practical implications The bibliography of user studies can be used by students and junior researchers who are new to user-centered IR studies as references for study design. Our results can facilitate the reflection and improvement on IR research methodology and serve as a checklist for evaluating customized IIR user studies in varying problem spaces. Originality/value To our knowledge, this work is the first study that systematically reviews the study limitations and solutions reported by IIR researchers in articles and empirically examines their connections to different study components.
Predictable object motion is extrapolated to support visual working memory for surface features

Space and time support visual working memory (VWM) by providing reference frames, in which objects are incidentally represented relative to other objects. Whereas this has been shown for stationary items appearing sequentially at different locations, natural scenes also contain moving objects, whose positions change over time, often in a predictable manner. VWM may bridge the gaps when objects temporarily move out of view. Here, we investigated if predictable motion is utilised to facilitate VWM for an object’s surface features – that is, if spatiotemporal reference frames are updated for moving objects. In a dynamic change-detection task, participants memorised the colours of three disks moving at constant speed in different directions before disappearing. After a retention interval, the disks then reappeared (I) at the movement endpoint locations where they had disappeared (II) at positions spatiotemporally congruent with their previous motion direction and speed (i.e., where they would have been had they continued their movement), (III) at positions spatially congruent but with a temporal offset (consistent with a change in speed while the objects were out of view) or (IV) at positions temporally congruent but with a spatial offset (consistent with a change in movement direction). Memory performance decreased with an increasing temporal or spatial offset relative to spatiotemporally congruent positions, indicating that spatiotemporal reference frames incorporated an extrapolation of the memorised objects’ motion to facilitate retrieval. This congruency effect diminished over time, however, in a task context with unreliable motion patterns: When items were likely to reappear at spatiotemporally incongruent positions, performance increased at the movement endpoint locations. Thus, objects’ motion and resulting changes in their configuration can be leveraged to support VWM for surface features, but this updating of spatiotemporal reference frames only occurs if the mental motion extrapolation allows for a reliable prediction of where occluded objects will reappear.
Oculomotor Changes Following Learned use of an Eccentric Retinal Locus

People with bilateral central vision loss affecting the fovea often adopt compensatory viewing strategies that involve the use of the peripheral retina. In some cases, individuals develop a new point of oculomotor reference called a preferred retinal locus (PRL) that is used like a pseudo-fovea for fixating and planning saccadic eye movements. Reduced visual acuity and impaired control of eye movements to peripheral targets make learning to use a PRL for everyday tasks such as reading and locating objects of interest in the environment a challenge. Much of the current research focuses on improving fixation stability at the PRL. How individuals learn to effectively use a PRL is not fully understood, in particular, the relationship between learning to have a stable peripheral fixation and plan saccades using an eccentric target have not been well described. Here we address this question, describing how eye movements change as a person learns to adopt an eccentric retinal locus. Using a gaze contingent eye tracking system and simulated central scotoma, 40 participants were trained to use a PRL for an oculomotor and visual recognition task. After 12 training sessions, measurable improvements were observed in six eye movement metrics. Importantly, we examined the stability of fixation of a target as well as the dispersion of the landing location of saccades. These measures explained a great deal of across-subject variance in accuracy. Fixation stability and saccadic precision were strongly positively correlated. While there was no statistically significant difference in rate of learning, individuals did tend to learn saccadic precision faster than fixation stability. Saccadic precision was also more associated with accuracy for the behavioral task. This suggests that effective intervention strategies should address both fixation stability and saccadic precision.
The Role of Native English-Speaking Teachers in Improving Students' Speaking Skills

This study was conducted to determine whether (1) native English-speaking teachers can increase students' learning motivation in speaking skills, (2) English-speaking teachers can improve students’ achievement in spoken English, (3) there is a significant difference in learning achievement between students who are taught by English-speaking teachers and those who are taught by non-native-speaking teachers. This study used a mix of quantitative and qualitative research techniques. Quantitative data was obtained from speaking skill tests with predetermined criteria, while qualitative data was obtained from a questionnaire that is adopted from [1]. A total of 80 students from class XI of a public high school in Sukabumi Regency Indonesia participated in this study. The results show that native English-speaking teachers can increase students' learning motivation in speaking skills. They can also improve the students’ achievement in spoken English, and there is a significant difference in learning achievement between students who are taught by English-speaking teachers and those who are taught by non-native-speaking teachers. This implies that English-speaking teachers play a very important role in improving students’ motivation and in developing students’ achivement.
Semantics, not Atypicality Reflect Memorability Across Concrete Objects

Why do we remember some things while forgetting others? Prior work has demonstrated remarkable consistency in what stimuli people will later remember, a quantifiable and robust property known as memorability. To explain memorability of faces and scenes, prior research has used singular image features or a linear combination of image features with limited success. In order to provide a richer account of memorability, we utilize a spatial framework with individual images represented as points in a multidimensional space generated from a broad, general stimulus set. Specifically, we leveraged THINGS, a naturalistic object image database of 26,107 images that representatively samples concrete objects to examine the image features that most strongly influence memorability and whether it is the most prototypical or most atypical items that are most memorable. We focus on the role of semantic and visual features and their distribution in determining which images are best remembered. We collected memory performance data from 13,946 participants and utilized three complementary measures of typicality including human typicality judgments, similarity across object space dimensions, and across deep neural network features to relate stimulus typicality to memorability. Our results suggest that semantic information has a stronger influence on memorability than visual information with a slight bias toward the most prototypical items being the most memorable. These findings run counter to the predominant view that semantic information is not required to explain what is memorable and that the most atypical images are best remembered. Our findings shed new light on the determinants of what makes something memorable that could only be found using a large, representative dataset.
The Cloud Native Philosophy

Kubernetes is a cloud-level operating system which ties together multiple physical machines into a cluster and manages running and networking containers across these different machines. Before we dive into the details of how Kubernetes works, in this chapter, I want to take a step back and talk about the philosophy of cloud native applications. What is it that really makes Kubernetes (and other cloud native systems) tick? How is it that someone building a cloud native cluster thinks that is different from someone who is building a traditional server-side application?
Introduction

There has been a very large shift over the last several years in the way that web applications are built and deployed. The modern approach is termed “cloud native,” and it is an approach that is geared toward maximizing the benefit and ease of development and scalability that modern cloud computing offers. Whether you have spent years building applications “the old way” or you are just starting out and want to start building applications the right way, this book will set you on the right track to become a cloud native master.
Data and Media Elements

In this chapter, we will focus on data that we can see and hear. This type of data is in the form of images, audio, videos, or animations. This is known as multimedia data. An application may contain any one or a combination of these types of data.
Basic Cloud Architectures

In this chapter, I want to walk through several common examples of cloud architecture. The goal is to present you with a starting point for building your own architecture. For basic applications, most cloud architectures are pretty similar to each other, so don’t worry if you wind up using one of these essentially unchanged. That’s actually pretty typical for smaller applications. However, as you grow, you will find certain aspects of your architecture or your application giving you trouble, which should trigger you to think about redesigning your cloud architecture to address those pain points.
Cross-cutting policy tensions and objectives for data governance

This chapter elaborates three fundamental policy tensions and objectives common to data governance policy making across different domains: balancing data openness and control while maximising trust; managing overlapping and potentially conflicting interests and regulations related to data governance; and incentivising investments in data and their effective re-use. For each of these policy tensions and objectives, it outlines underlying issues and presents promising approaches that can help address them. These approaches are based on the OECD Horizontal Project on Data Governance for Growth and Well-being, on OECD Recommendations relating to data governance, and on relevant policy examples.
Meta’s Oversight Board and Beyond – Corporations as Interpreters and Adjudicators of International Human Rights

Abstract Social media platform corporations such as Meta (Facebook), Twitter, etc. find themselves in a position of having to interpret international human rights norms, in particular Article 19 of the International Covenant on Civil and Political Rights ( ICCPR ) (freedom of expression). Millions or even billions of content moderation decisions need to be taken on the platforms each day that affect users’ human rights interests. Since content moderation is integral to the technical and commercial set-up of these platforms, corporate decision-making vis-à-vis human rights and thus corporate interpretation of international human rights norms is inevitable. Yet, corporations are flawed interpreters. Whereas they act, like a court or tribunal, as triadic decision-makers, they, unlike a court or tribunal, do not share the neutrality, impartiality and independence of the latter. In particular, they are responsible to their shareholders and they pursue commercial interests when moderating content. This article grapples with the theoretical and doctrinal implications of flawed but inevitable corporate human rights interpretation. Taking the early practice of the Oversight Board, a body established by Meta, Inc. (Facebook), in order to tackle the “hard cases” of content moderation as a case study, the pitfalls and challenges of corporate human rights interpretation become apparent. In the end, I submit a few suggestions in order to remedy what seems to be here to stay with us at least for as long as an important part of public discourse is channelled through social media platforms.
Keynote Speaker 1: Random Neural Networks Optimise QoS, Security and Energy Consumption

Random Neural Networks (RNNs) are recurrent network models with strong machine learning capabilities that have been exploited in many applications. We will first recall their theoretical properties, including the product form solution, their capability as approximations for continuous and bounded functions and their efficient deep learning algorithms. Then we will focus on the Cognitive Packet Network (CPN) which uses RNNs to introduce AI into network algorithms to route traffic intelligently with the objective of optimizing Quality of Service (QoS), reducing energy consumption and mitigating security threats and attacks. Then we will provide experimental results showing that CPN can be implemented in various ways: through additional software resident in conventional routers, also through Software Defined Networks (SDN), or also as overlay networks.
Keynote Speaker 4: Introducing Homomorphic Encryption in Blockchain-enabled Applications

Recent advances in blockchain technology are enabling a data-centric revolution in the ICT ecosystem. With the intrinsic need to enforce transparency and distributed storage of the smart contracts in blockchain architectures, a number of challenges and hurdles are often raised about the use of blockchain in contexts where privacy, confidentiality, and secrecy are a concern. This talk discusses recent researches aiming at introducing homomorphic encryption as a solution to cope with this issue based on their ability of encrypted data processing. Such approaches extend the use of blockchain to modern distributed applications where data is collected, processed, and transmitted by disparate devices across multiple physical locations. The contribution brought by homomorphic encryption to blockchain-enabled applications is highlighted and the potential evolution and the underlying open issues are also explored.
Improve Robustness of Latent Feature Learning Using Double-Space

In a high dimensional and incomplete (HDI) matrix, the original data is sparse. Among numerous missing data estimation approaches [1–12], latent feature learning (LFL) is widely studied and adopted because of its high efficiency and scalability.
Robust Latent Feature Learning based on Smooth L1-norm

In this era of information explosion, big data for various industrial applications are surrounding people [1–5], such as recommendation systems (RSs), social networks, wireless sensor networks, and intelligent transportation. In these applications, the relationship between the two types of entities is usually represented by a matrix. For example, it is common to see a user-item rating matrix in RSs [6–9], where each row represents a specific user, each column represents a specific item, and each entry represents the user’s preference for an item.
Segmentation of liver CT images based on the improved UNet network

In clinical medicine, the liver segmentation is indispensable for the diagnosis of liver diseases. The shape and size of the liver varies in CT images and the similar grayscale values with neighboring organ tissues, which cause difficulties for segmentation. For these problems, we propose a network for the segmentation of liver CT images, which based on encoder-decoder structure. The network applies the SE-Res block instead of the original convolution block to optimize the boundary information and apply the spatial-channel attention gate to enhance the features of the liver in the decoder. The proposed algorithm was validated on the LITS-28 dataset, the Mean Intersection over Union (MIOU) and Dice similarity coefficient (Dice) were 93.68% and 96.45%, respectively. Compared with other similar algorithms, the performance of the proposed algorithm is better and the segmented liver results are more accurate.