Reviewerly/exHarmony huggingface dataset:
Distancing interfaces—improvisational media architectures for place-based discourse under lockdown

The paper presents the case study of BERLIN_LOKAL_ZEIT, a collaborative artistic media project aiming to document and reflect individual experiences of the COVID-19 lockdowns in Berlin, Germany. What began as an observational effort in spring 2020 became a year-long archive, exhibition, and broadcasting platform that generated various hyper-local interfaces. The paper articulates an improvisational approach to media architecture in the form of self-reflection by the project initiators. Necessitated by the limitations imposed by the lockdowns on cultural production and public discourse, the paper presents an alternative conceptual approach to media architecture that is not based on a fully-specified technological infrastructure for discourse and interaction but instead on improvisational practices that manifest themselves in different technical interfaces. This improvisational approach is not merely a mode of production, but raises questions of about the discourse of media architecture and its underlying assumptions of methodological rigor. In traditional HCI, systems and artifacts are often presented as stable and fully specified, while their users are considered interchangeable. In contrast, we consider urban media interfaces as improvisation-driven infrastructures (or improstructures) in which the actors and their relationships are stable, while the technologies they create are provisional and in flux. As a consequence, the defining property of media architecture is no longer the structural integration of media into architecture but the embodiment of mediated communication in objects and performances at concrete places. In contrast to familiar concepts such as probes or prototypes [10, 18], which may also involve bricolage and invite improvisation, improstructure emphasizes the infrastructural processes of maintaining, tweaking, and extending rather than the artifact and its use. In an environment where many physical spaces are longer accessible and human contact among strangers diminished, the project explores the restorative function and capacities of media architecture for shared reflection and discourse in physical public space.
A Classifier of Popular Music Online Reviews: Joy Emotion Analysis

With the development of the web, a large amount of product reviews might accumulate within short time, and the problem of information overload is getting serious. Previous opinion analysis studies on online reviews have focused on finding out the positive and negative opinions on the function of commodities. But the comments, which indicate particular emotion of the reviewer, were rarely discussed. For hedonic products such as music that could invoke users’ feelings, the emotional rules including such as “happiness” and “sadness” mining from the comments can further complement the traditional rules, such as “singer is good” and “cannot sing high pitch.” Taking the example of joy emotion analysis, this study proposed a system structure to classify the emotional feeling of pop music online reviews. A prototype system was built. The experiment reports the satisfactory result, with F1 73.45%.
Defining Beneficiaries of Emerging Data Infrastructures Towards Effective Data Appropriation

The increasing collection and usage of data and data analytics has prompted development of Data Labs. These labs are (ideally) a way for multiple beneficiaries to make use of the same data in ways that are value-generating for all. However, establishing data labs requires the mobilization of various infrastructural elements, such as beneficiaries, offerings and needed analytics talent, all of which are ambiguous and uncertain. The aim of this paper is to examine how such beneficiaries can be identified and understood for the nascent Swedish space data lab. The paper reports on the development of persona descriptions that aim to support and represent the needs of key beneficiaries of earth observation data. Our main results include three thorough persona descriptions that represent the lab’s respective beneficiaries and their distinct characteristics. We discuss the implications of the personas on addressing the infrastructural challenges, as well as the lab’s design. We conclude that personas provide emerging data labs with relatively stable beneficiary archetypes that supports the further development of the other infrastructure components. More research is needed to better understand how these persona descriptions may evolve, as well as how they may influence the continuous development process of the space data lab.
Protective Policy Transfer

Being able to transfer existing skills to new situations is a key capability when training robots to operate in unpredictable real-world environments. A successful transfer algorithm should not only minimize the number of samples that the robot needs to collect in the new environment, but also prevent the robot from damaging itself or the surrounding environment during the transfer process. In this work, we introduce a policy transfer algorithm for adapting robot locomotion skills to novel scenarios while minimizing serious failures. Our algorithm trains two control policies in the training environment: a task policy that is optimized to complete the task of interest, and a protective policy that is dedicated to keep the robot from unsafe events (e.g. falling to the ground). To decide which policy to use during execution, we learn a safety estimator model in the training environment that estimates a continuous safety level of the robot. When used with a set of thresholds, the safety estimator becomes a classifier for switching between the protective policy and the task policy. We evaluate our approach on four simulated robot locomotion problems and show that our method can achieve successful transfer to notably different environments while taking the robot’s safety into consideration.
Publisher-Subscriber Based Context-Aware Application Development Platform

In this study we propose an architecture and toolkit that eases development of context aware applications. Our motivation for building the platform is to provide a reference implementation to the software community for further enhancements, additions, and improvements. The platform is called “A Platform for Context Aware Application Development-PCAD”. The platform is service based and provides its functions via seven services: namely, Context Modeling and Reasoning, Security, Rule, Data Management, Alarm and Notification, Transformation, and Reporting Service. MQTT is used as the context transfer protocol between requestor, providers, and the platform itself. Token and password-based scheme is used for authentication, Role and Attribute based access control is used to enforce authorization. Document database is chosen as the database model.
LITERATURE SURVEY ON THE DAMAGE DUE TO THE OCTOBER 16, 1970 SOUTHEASTERN AKITA EARTHQUAKE

Akita Prefecture, northern Japan, have more than ten large damaging earthquakes during the recent 120 years and the authors made literature surveys on major events. In this paper, the 1970 Southeastern Akita earthquake (M=6.2) was investigated. This local earthquake occurred at the boundary between Iwate prefecture and caused 6 injuries and damages to houses and infrastructures were reported. Through the literature surveys on reconnaissance reports and newspapers in both prefectures, detailed damages were clarified. We believe that the experience of this event can be utilized to future disaster prevention for not only this area but also the other areas.
Comparison and Analysis of Data and Transaction Structure of Bitcoin and Ethereum

With the increasing maturity of encryption algorithms, digital currency emerges as the times require. Blockchain is a highly attractive candidate for developing digital currency. A better understanding of the mechanisms for Blockchain will benefit the comprehensive future development of Blockchain. In the current study, we observed the data and transaction structure of Bitcoin and Ethereum. Therefore, Our study compared the existing structure of Bitcoin and Ethereum and analyzed the reasons for the change of the Blockchain, providing insights for the future development of the Blockchain. The results of this study indicate that he future development of Blockchain will increasingly meet the increasing number of users, lower the threshold for users, meet increasingly cumbersome functional requirements, expand the scope of blockchain applications, and improve security.
Synchronized Information Acquisition Method for Virtual Geographic Scene Image Synthesis in Cities Based on Wireless Network Technology

At present, in the process of image synthesis information acquisition of urban virtual geographic scene, there is information complexity. The existing acquisition technology is easy to disturb in the process of information positioning and transmission, resulting in large acquisition delay and affecting the final synthesis quality of the image. In response to the above problems, the method of synchronous information acquisition for urban virtual geographic scene image synthesis based on wireless network technology is studied. Based on the construction of an urban virtual geographic environment, the spatial localization of the sign target of geographic scene image synthesis is carried out, and the optical flow method is used to register the urban geographic scene images. Based on the greedy algorithm of the beacon to design the synchronous wireless network route for urban virtual scene image synthesis, and after the grid division of the wireless network in the synthesis information acquisition area, the packets of each acquisition node are acquired and transmitted according to the designed wireless network route to realize the synchronous acquisition of image synthesis information. The comparison experimental data show that the acquisition delay of the studied information synchronization acquisition method is less than 0.5 s, the acquisition synchronization rate is significantly improved, and the quality of the synthesized images is better by applying the information acquired by the method, and the practical use is better.
Anomaly-Aware Federated Learning with Heterogeneous Data

Anomaly detection plays a critical role in ensuring the robustness and reliability of federated learning (FL) systems involving distributed implementation of stochastic gradient descent (SGD). Existing methods in the literature usually apply norm-based gradient filters in each iteration and eliminate possible outliers, which can be ineffective in a setting with heterogeneous and unbalanced training data. We propose a heuristic yet novel scheme for adjusting the weights in the gradient aggregation step that accounts for two anomaly metrics, namely the relative distance and the convergence measure. Simulation results show that our proposed scheme brings notable performance gain compared to norm-based policies when the agents have distinct data distributions.
A conceptual framework for effective management of conflict risk within Agile software development environments

Background: Whenever teams obtain together to work towards a common goal, the lingering of an inherent conflict risk is always a possibility. This applies to software development teams as well. Researchers even argue this inherent conflict existence as necessary. Agile development environment is no exception. The main purpose for adopting Agile is to improve the technology adequacy to facilitate organisation competitiveness. Agile manages the achievement of this through its advocacy for change, adaptability and constant satisfaction of market demands. This is because Agile enables organisations to create software of quality standard that allows for quicker response to the continuously changing market needs of todays’ world. Objectives: Henceforth, this article investigates this topic to develop a conceptual framework for effectively managing conflict risk in Agile teams, to improve the organisation technology adequacy. Method: This article employed quantitative methods for data collection and analysis. A structured online questionnaire was used for data collection and Statistical Package for the Social Sciences was used for data analysis. Pearson’s correlation was used to test the relationships among variables. Results: The findings show affirming results on the existence of conflict risk among Agile software development teams. The results indicate that there is a strong positive significant correlation between effective interaction, discussions and quality software application. Moreover, the findings demonstrate a strong relation between effective conflict management and achievement of set agile project goals. Conclusion: Agile allows companies to address the market requirements while still being adaptive to constant change. However, the literature identifies conflict risk as one of the hindrances to realising this purpose.
Exploiting Aerial Imagery for Supervised Learning of SAR Despeckling Neural Networks

Many applications utilizing SAR data, such as change detection, segmentation and classification, are impaired by the multiplicative speckle interference inherent in the imagery. Thus despeckling of SAR imagery is a often the key to developing robust algorithms for scene understanding. In recent years numerous deep learning-based approaches to speckle reduction have been proposed. However, the performance of these methods has largely failed to meet the expectations of researchers and industry alike. A key reason for this is due to the lack of accurate SAR-based ground truth training data. In this paper we propose the use of very high-resolution (VHR), low speckle aerial imagery and an accurate speckle model, as a ground truth signal for training a despeckling network based on the DnCNN architecture. Furthermore, we investigate modifications to the training formulation and finally demostrate the approach on Capella-2 VHR X-band imagery.
Addressing the Severity and Intensity of Poverty in Sub-Saharan Africa: How Relevant is the ICT and Financial Development Pathway?

The study examines the effectiveness of financial development, financial access, and ICT diffusion in reducing the severity and intensity of poverty in Sub-Saharan Africa (SSA). Using data from the World Bank’s World Development Indicators, and the Global Consumption and Income Project (1980–2019), we provide evidence robust to several specifications from the dynamic system GMM and the panel corrected standard errors estimation techniques to show that, compared to financial access, ICT usage, and ICT access, ICT skills is remarkable in reducing both the severity and intensity of poverty. The results further unveil that, though ICT skills reduce the intensity and severity of poverty in SSA, the effect is more pronounced in the presence of enhanced financial development and financial access. Policy recommendations are provided in line with the region’s green growth agenda and the rise in technological hubs of the region.
Prophet-Based Research on the Medium and Long-Term Forecast Method of the F10.7 Flux of the Sun

The F10.7 flux of the sun is an important parameter that characterizes the level of solar activity. However, due to the long-term periodicity and short-term randomness of solar activity, it is difficult to obtain accurate prediction results for F10.7 using statistical methods. The Prophet algorithm is based on time series decomposition and machine learning fitting. It can deal with the situation where there are some outliers in the time series, and it can also deal with the problem of partial missing values. F10.7 is a typical time series data, composed of two parts: time and observations, and has a history of nearly one hundred years of observation. It is inevitable that there will be some outliers and missing values in the observation process. Prophet's data processing characteristics make it suitable for the requirements of the solar F10.7 observation data. Through reasonable selection of change points, it can realize the forecast of the future and the forecast of the seasonal trend, and finally realize the model fitting. The experimental results show that using the Prophet model to predict the consistency of F10.7 data and real data can reach more than 90%.
Multi-stage Multi-modal Pre-training for Video Representation

Multi-modal networks are usually challenging to train because of their complexity. On the one hand, multi-modal networks are often prone to underfitting due to their heterogeneous data formats of different modalities. On the other hand, data from different domains have different distributions and domain differences could be difficult to eliminate in joint training. This paper presents a Multi-Stage Multi-Modal pre-training strategy (MSMM) to train multi-modal joint representation effectively. To eliminate the difficulty of multi-modal end-to-end training, MSMM trains different Uni-modal network separately and then jointly trains multi-modal. After multi-stage pre-training, we can get a better multi-modal joint representation and better uni-modal representations. Meanwhile, we design a multi-modal network and multi-task loss to train the whole network in an end-to-end style. Extensive empirical results show that MSMM can significantly improve the multi-modal model's performance on the video classification task.
A Deep Learning Method for 2D Image Stippling

Stippling is a fascinating art form, which is widely used in printing industry. In computer graphics, digital color stippling produces colored points with a certain distribution (e.g. blue noise distribution) from an input color image. However, it is challenging as each color channel should be evenly distributed with respect to each other channel. Deep learning approaches have shown great advantage on many image stylization applications and have not been utilized for stippling yet. The main reason is that stippling has strict constrains, which requires an even and random distribution of the points. In this paper, we propose the first deep learning approach for stippling, which is able to produce point distribution visually similar to stippling. We regard the stippling results as a 3D point cloud structure where the third channel represents for colors. Then we propose a deep network to transform images to points distribution, consisting of a feature extracting encoder to extract features from the input image and a point generating decoder to translate the features into stippling form. We exploit a spectrum loss to achieve the even distribution. As a result, our method can produce color stippling with reasonable cost. Experiments show that our method can produce stippling with a reasonable balance between the quality of the results and the computational efficiency.
Establishing Software Root of Trust Unconditionally (CMU-CyLab-18-003)

Root-of-Trust (RoT) establishment ensures either that the state of an untrusted system contains all and only content chosen by a trusted local verifier and the system code begins execution in that state, or that the verifier discovers the existence of unaccounted for content. This ensures program booting into system states that are free of persistent malware. An adversary can no longer retain undetected control of one’s local system. We establish RoT unconditionally; i.e., without secrets, trusted hardware modules and instructions, or bounds on the adversary’s computational power. The specification of a system’s chipset and device controllers, and an external source of true random numbers, such as a commercially available quantum RNG, is all that is needed. Our system specifications are those of a concrete Word Random Access Machine (cWRAM) model – the closest computation model to a real system with a large instruction set. We define the requirements for RoT establishment and explain their differences from past attestation protocols. Then we introduce a RoT establishment protocol based on a new computation primitive with concrete (non-asymptotic) optimal space-time bounds in adversarial evaluation on the cWRAM. The new primitive is a randomized polynomial, which has k- independent uniform coefficients in a prime order field. Its collision properties are stronger than those of a k-independent (almost) universal hash function in cWRAM evaluations, and are sufficient to prove existence of malware-free states before RoT is established. Preliminary measurements show that randomized polynomial performance is practical on commodity hardware even for very large k. To prove the concrete optimality of randomized polynomials, we present a result of independent complexity interest: a Horner rule program is uniquely optimal whenever the cWRAM execution space and time are simultaneously minimized.
Fast Performance Modeling across Different Database Versions Using Partitioned Co-Kriging

Database systems have a large number of configuration parameters that control functional and non-functional properties (e.g., performance and cost). Different configurations may lead to different performance values. To understand and predict the effect of configuration parameters on system performance, several learning-based strategies have been recently proposed. However, existing approaches usually assume a fixed database version such that learning has to be repeated once the database version changes. Repeating measurement and learning for each version is expensive and often practically infeasible. Instead, we propose the Partitioned Co-Kriging (PCK) approach that transfers knowledge from an older database version (source domain) to learn a reliable performance prediction model fast for a newer database version (target domain). Our method is based on the key observations that performance responses typically exhibit similarities across different database versions. We conducted extensive experiments under 5 different database systems with different versions to demonstrate the superiority of PCK. Experimental results show that PCK outperforms six state-of-the-art baseline algorithms in terms of prediction accuracy and measurement effort.
Choice-Driven Counterfactuals

Abstract In this paper, we investigate the semantics and logic of choice-driven counterfactuals , that is, of counterfactuals whose evaluation relies on auxiliary premises about how agents are expected to act, i.e. , about their default choice behavior . To do this, we merge one of the most prominent logics of agency in the philosophical literature, namely stit logic (Belnap et al. 2001; Horty 2001), with the well-known logic of counterfactuals due to Stalnaker (1968) and Lewis (1973). A key component of our semantics for counterfactuals is to distinguish between deviant and non-deviant actions at a moment, where an action available to an agent at a moment is deviant when its performance does not agree with the agent’s default choice behavior at that moment. After developing and axiomatizing a stit logic with action types, instants, and deviant actions, we study the philosophical implications and logical properties of two candidate semantics for choice-driven counterfactuals, one called rewind models inspired by Lewis ( Nous 13 (4), 455–476 1979) and the other called independence models motivated by well-known counterexamples to Lewis’s proposal Slote ( Philos. Rev. 87 (1), 3–27 1978). In the last part of the paper we consider how to evaluate choice-driven counterfactuals at moments arrived at by some agents performing a deviant action.