Reviewerly/exHarmony huggingface dataset:
Lossy Compression of Multispectral Satellite Images with Application to Crop Thematic Mapping: A HEVC Comparative Study

Remote sensing applications have gained in popularity in recent years, which has resulted in vast amounts of data being produced on a daily basis. Managing and delivering large sets of data becomes extremely difficult and resource demanding for the data vendors, but even more for individual users and third party stakeholders. Hence, research in the field of efficient remote sensing data handling and manipulation has become a very active research topic (from both storage and communication perspectives). Driven by the rapid growth in the volume of optical satellite measurements, in this work we explore the lossy compression technique for multispectral satellite images. We give a comprehensive analysis of the High Efficiency Video Coding (HEVC) still-image intra coding part applied to the multispectral image data. Thereafter, we analyze the impact of the distortions introduced by the HEVC’s intra compression in the general case, as well as in the specific context of crop classification application. Results show that HEVC’s intra coding achieves better trade-off between compression gain and image quality, as compared to standard JPEG 2000 solution. On the other hand, this also reflects in the better performance of the designed pixel-based classifier in the analyzed crop classification task. We show that HEVC can obtain up to 150:1 compression ratio, when observing compression in the context of specific application, without significantly losing on classification performance compared to classifier trained and applied on raw data. In comparison, in order to maintain the same performance, JPEG 2000 allows compression ratio up to 70:1.
The Limits to Blockchain? Scaling vs. Decentralization

This discussion paper examines a possible limitation to the advancement of blockchain: the intrinsic tradeoff between scaling to a larger size, and the need to maintain a decentralized and distributed architecture. The scaling vs. decentralization trade-off, the paper argues, may impose a long-term limitation on the growth of blockchain-based technologies including cryptocurrencies.
GIS-Based Irrigation Dams Potential Assessment of Floating Solar PV System

The majority of the Ethiopian population lives in rural areas and uses wood for domestic energy consumption. Using wood and fuel for domestic uses accounts for deforestation and health problems, which is also dangerous for the environment. The Ethiopian government has been planning to generate power from available renewable resources around the community. Therefore, determining the water surface potential of energy harvesting with floating solar photovoltaic system by using geographic information system is used to support decision-makers to use high potential areas. To identify useable areas for floating solar photovoltaic, factors that affect the usability were identified and weighted by using Analytical Hierarchy Processes. Thus, weighted values and reclassified values were multiplied to do the final usability map of floating solar photovoltaic with ArcGIS software. Due to the improper location of floating solar photovoltaic, efficiency is dropped. Therefore, the objective of this study was to identify the most usable surface of water bodies in Amhara regional, state irrigation dams for generating electrical power. The usability of the water surface for floating solar photovoltaic power plant was 63.83%, 61.09%, and 57.20% of Angereb, Rib, and Koga irrigation dams, respectively. The majority of the usable areas were found in the middle of the water surface. Nature water surface is a key factor in generating solar energy; it affects the floating solar photovoltaic and irradiance coming to the solar photovoltaic panel surface.
Stochastic weather generator for the design and reliability evaluation of desalination systems with Renewable Energy Sources

The operation of Renewable Energy Sources (RES) systems is highly affected by the continuously changing meteorological conditions and the design of a RES system has to be robust to the unknown weather conditions that it will encounter during its lifetime. In this paper, the use of Stochastic Weather Generators (SWGENs) is introduced for the optimal design and reliability evaluation of hybrid Photovoltaic/Wind-Generator systems providing energy to desalination plants. A SWGEN is proposed, which is based on parametric Markov-Switching Auto-Regressive (MSAR) models and is capable to simulate realistic hourly multivariate time series of solar irradiance, temperature and wind speed of the target installation site. Numerical results are presented, demonstrating that: (i) SWGENs enable to evaluate the reliability of RES-based desalination plants during their operation over a 20 years lifetime period and (ii) using an appropriate time series simulated with a SWGEN as input to the design optimization process results in a RES-based desalination plant configuration with higher reliability compared to the configurations derived when the other types of meteorological datasets are used as input to the design optimization process.
Artificial Intelligence in the Criminal Justice System: Leading Trends and Possibilities

This paper focuses on the transformation of criminal proceedings in the framework of the development of digital technologies in the international space, in which a significant place belongs to artificial intelligence.We demonstrate that a variety of digital tools in legal proceedings is used by the United States where legal start-ups specializing in Artificial Intelligence (AI) are capable determine the content of a future court decision with a high degree of probability.One can see that most of the European Union (EU) Member States already apply advanced technologies in their legal systems and for legal support.Nevertheless, an issue of using the AI in legal proceedings calls for a deeper dialogue on its affinity with the principles of the European Convention on Human Rights (ECHR).With regard to the above, it might be useful to employ the European Ethical Charter on the Use of Artificial Intelligence that provides five basic principles that define the role and place of AI in legal proceedings.This paper focuses on the leading trends and possibilities for using AI in the criminal justice system.
TurkEyes: A Web-Based Toolbox for Crowdsourcing Attention Data

Eye movements provide insight into what parts of an image a viewer finds most salient, interesting, or relevant to the task at hand. Unfortunately, eye tracking data, a commonly-used proxy for attention, is cumbersome to collect. Here we explore an alternative: a comprehensive web-based toolbox for crowdsourcing visual attention. We draw from four main classes of attention-capturing methodologies in the literature. ZoomMaps is a novel zoom-based interface that captures viewing on a mobile phone. CodeCharts is a self-reporting methodology that records points of interest at precise viewing durations. ImportAnnots is an "annotation" tool for selecting important image regions, and cursor-based BubbleView lets viewers click to deblur a small area. We compare these methodologies using a common analysis framework in order to develop appropriate use cases for each interface. This toolbox and our analyses provide a blueprint for how to gather attention data at scale without an eye tracker.
Entity Extraction from Wikipedia List Pages

When it comes to factual knowledge about a wide range of domains, Wikipedia is often the prime source of information on the web. DBpedia and YAGO, as large cross-domain knowledge graphs, encode a subset of that knowledge by creating an entity for each page in Wikipedia, and connecting them through edges. It is well known, however, that Wikipedia-based knowledge graphs are far from complete. Especially, as Wikipedia’s policies permit pages about subjects only if they have a certain popularity, such graphs tend to lack information about less well-known entities. Information about these entities is oftentimes available in the encyclopedia, but not represented as an individual page. In this paper, we present a two-phased approach for the extraction of entities from Wikipedia’s list pages, which have proven to serve as a valuable source of information. In the first phase, we build a large taxonomy from categories and list pages with DBpedia as a backbone. With distant supervision, we extract training data for the identification of new entities in list pages that we use in the second phase to train a classification model. With this approach we extract over 700k new entities and extend DBpedia with 7.5M new type statements and 3.8M new facts of high precision.
Mobile Learning Usage of LIS Students in Mainland China

This study aims to explore the usage behaviors of smartphones for learning purposes by Library and Information Science (LIS) students of mainland China. A quantitative questionnaire was used for collecting the subjects' usage behavior, their perceptions on learning experience through using smartphones, and their needs of accessing library services using smartphones. The authors analyzed the results from two major universities in mainland China. This research discovered that LIS students in mainland China used smartphones for their daily-life matters more readily than for learning purposes. They were also interested in accessing library services with smartphones, but not many of them were already using these services. The undergraduate and postgraduate respondents had some differences in their usage preferences and behaviors. This paper suspected those library services are already available but the promotion has been inadequate. There are obviously opportunities for educators, librarians, and technology providers to get some insights to improve mobile learning (m-learning) in universities and for students to learn better with mobile technology and apps. This study provides insights into the users' needs and the application of m-learning in mainland China, where scant similar studies have been conducted before.
vChain: A Blockchain System Ensuring Query Integrity

This demonstration presents vChain, a blockchain system that ensures query integrity. With the proliferation of blockchain applications and services, there has been an increasing demand for querying the data stored in a blockchain database. However, existing solutions either are at the risk of losing query integrity, or require users to maintain a full copy of the blockchain database. In comparison, by employing a novel verifiable query processing framework, vChain enables a lightweight user to authenticate the query results returned from a potentially untrusted service provider. We demonstrate its verifiable query operations, usability, and performance with visualization for better insights. We also showcase how users can detect falsified results in the case that the service provider is compromised.
Noisy Receivers for Quantum Illumination

Quantum illumination (QI) promises unprecedented performances in target detection but there are various problems surrounding its implementation. Where target ranging is a concern, signal and idler recombination forms a crucial barrier to the protocol's success. This could potentially be mitigated if performing a measurement on the idler mode could still yield a quantum advantage. In this paper we investigate the QI protocol for a generically correlated Gaussian source and study the phase-conjugating (PC) receiver, deriving the associated SNR in terms of the signal and idler energies, and their cross-correlations, which may be readily adapted to incorporate added noise due to Gaussian measurements. We confirm that a heterodyne measurement performed on the idler mode leads to a performance which asymptotically approaches that of a coherent state with homodyne detection. However, if the signal mode is affected by heterodyne but the idler mode is maintained clean, the performance asymptotically approaches that of the PC receiver without any added noise.
Classification of official letters using TF-IDF method

Abstract This study developed an application for official letter management in Sukasada 1 Public High School. The development of the application aimed to contribute to government programs in improving public services, especially in correspondence. In the letter classification process, the researcher implemented the TF-IDF method whose working procedures included conversion, Optical Character Recognition (OCR), filtering, tokenizing, and classification. Conversion is to convert official mail to digital form (jpg). OCR is to get the text of the letter then carries out the official letter in the form of an image and then filtering and tokenizing are carried out. Classification is the process of grouping a letter into its category by calculating the cosine similarity value between the letter being tested and the letter in the system. To test the accuracy of the classification results, a confusion matrix is used. The results of the study are in the form of web-based and mobile-based applications. Operators and admins used the web application to manage official letters at SMAN 1 Sukasada. The mobile application served to facilitate the principal in accessing mail data from a smartphone. The system was able to group letters with 78% accuracy (good), precision 77% and recall 77%.
Unsupervised Depth Learning in Challenging Indoor Video: Weak Rectification to Rescue.

Single-view depth estimation using CNNs trained from unlabelled videos has shown significant promise. However, the excellent results have mostly been obtained in street-scene driving scenarios, and such methods often fail in other settings, particularly indoor videos taken by handheld devices, in which case the ego-motion is often degenerate, i.e., the rotation dominates the translation. In this work, we establish that the degenerate camera motions exhibited in handheld settings are a critical obstacle for unsupervised depth learning. A main contribution of our work is fundamental analysis which shows that the rotation behaves as noise during training, as opposed to the translation (baseline) which provides supervision signals. To capitalise on our findings, we propose a novel data pre-processing method for effective training, i.e., we search for image pairs with modest translation and remove their rotation via the proposed weak image rectification. With our pre-processing, existing unsupervised models can be trained well in challenging scenarios (e.g., NYUv2 dataset), and the results outperform the unsupervised SOTA by a large margin (0.147 vs. 0.189 in the AbsRel error).
FairyTED: A Fair Rating Predictor for TED Talk Data

With the recent trend of applying machine learning in every aspect of human life, it is important to incorporate fairness into the core of the predictive algorithms. We address the problem of predicting the quality of public speeches while being fair with respect to sensitive attributes of the speakers, e.g. gender and race. We use the TED talks as an input repository of public speeches because it consists of speakers from a diverse community and has a wide outreach. Utilizing the theories of Causal Models, Counterfactual Fairness and state-of-the-art neural language models, we propose a mathematical framework for fair prediction of the public speaking quality. We employ grounded assumptions to construct a causal model capturing how different attributes affect public speaking quality. This causal model contributes in generating counterfactual data to train a fair predictive model. Our framework is general enough to utilize any assumption within the causal model. Experimental results show that while prediction accuracy is comparable to recent work on this dataset, our predictions are counterfactually fair with respect to a novel metric when compared to true data labels. The FairyTED setup not only allows organizers to make informed and diverse selection of speakers from the unobserved counterfactual possibilities but it also ensures that viewers and new users are not influenced by unfair and unbalanced ratings from arbitrary visitors to the ted.com website when deciding to view a talk.
Quantum entanglement and the Born-Markov approximation for an open quantum system

We revisit the Born-Markov approximation for an open quantum system by considering a microscopic model of the bath, namely, the Bose-Hubbard chain in the parameter region where it is chaotic in the sense of quantum chaos. It is shown that strong ergodic properties of the bath justify all approximations required for deriving the Markovian master equation from the first principles.
Learning to Branch for Multi-Task Learning

Training multiple tasks jointly in one deep network yields reduced latency during inference and better performance over the single-task counterpart by sharing certain layers of a network. However, over-sharing a network could erroneously enforce over-generalization, causing negative knowledge transfer across tasks. Prior works rely on human intuition or pre-computed task relatedness scores for ad hoc branching structures. They provide sub-optimal end results and often require huge efforts for the trial-and-error process. In this work, we present an automated multi-task learning algorithm that learns where to share or branch within a network, designing an effective network topology that is directly optimized for multiple objectives across tasks. Specifically, we propose a novel tree-structured design space that casts a tree branching operation as a gumbel-softmax sampling procedure. This enables differentiable network splitting that is end-to-end trainable. We validate the proposed method on controlled synthetic data, CelebA, and Taskonomy.
Towards Perceptually-Optimized Compression Of User Generated Content (UGC): Prediction Of UGC Rate-Distortion Category

How to best evaluate the perceptual quality, and efficiently optimize the compression of User Generated Content (UGC) within an adaptive streaming system is becoming one of the most intractable challenges in the community. Rate-Distortion (R-D) characteristic based content analyses, which could be applied on the non-pristine originals, is inevitable to provide guidance in developing quality metrics and efficient compression system. To this end, we present a novel complete R-D category prediction system through the identification of discriminate features. To better understand the Rate-Distortion (R-D) behaviors of UGC, we first propose a Bjontegaard Delta (BD)-Rate, BD-Quality-based algorithm to categorize UGC. By using the predicted R-D related categories as ground-truth labels, we further identify features that characterize the R-D behaviors of UGC via a hierarchical feature selection framework. Finally, selected features are employed to predict the R-D category of under-test UGC. Comprehensive observations and results are summarized through extensive experiments.
Efficient Continuous Pareto Exploration in Multi-Task Learning

Tasks in multi-task learning often correlate, conflict, or even compete with each other. As a result, a single solution that is optimal for all tasks rarely exists. Recent papers introduced the concept of Pareto optimality to this field and directly cast multi-task learning as multi-objective optimization problems, but solutions returned by existing methods are typically finite, sparse, and discrete. We present a novel, efficient method that generates locally continuous Pareto sets and Pareto fronts, which opens up the possibility of continuous analysis of Pareto optimal solutions in machine learning problems. We scale up theoretical results in multi-objective optimization to modern machine learning problems by proposing a sample-based sparse linear system, for which standard Hessian-free solvers in machine learning can be applied. We compare our method to the state-of-the-art algorithms and demonstrate its usage of analyzing local Pareto sets on various multi-task classification and regression problems. The experimental results confirm that our algorithm reveals the primary directions in local Pareto sets for trade-off balancing, finds more solutions with different trade-offs efficiently, and scales well to tasks with millions of parameters.
Climatology of the Linke and Unsworth–Monteith Turbidity Parameters for Greece: Introduction to the Notion of a Typical Atmospheric Turbidity Year

Solar rays are attenuated by the Earth’s atmosphere. This attenuation can be expressed by the turbidity parameters; two of them are the Linke turbidity factor (TL) and the Unsworth–Monteith turbidity coefficient (TUM). In this sudy, both parameters are estimated for 33 sites across Greece, and the notion of a Typical Atmospheric Turbidity Year (TATY) is also introduced. Use of the modified clearness index (k’t) is made, while a suggestion for a modified diffuse fraction (k’d) is given. The adoption of the four climatic zones in Greece for energy purposes is made, where the variation of TL and TUM is studied during a TATY under all and clear-sky conditions. The analysis shows maximum levels in both parameters in late winter–early spring in morning and evening hours, with minimum values at midday. The intra-annual variation of the parameters shows maximum values around March and August and minimum values in summertime and late winter. Maps of annual mean TL and TUM values over Greece show persistent minimum values over Peloponnese and maximum values over South Ionian Sea. Linear expressions of TUM vs. TL are derived for all sites under all and clear-sky conditions. Finally, linear expressions for k’d vs. k’t are given for all sites and sky conditions.
Joint Aspect-Sentiment Analysis with Minimal User Guidance

Aspect-based sentiment analysis is a substantial step towards text understanding which benefits numerous applications. Since most existing algorithms require a large amount of labeled data or substantial external language resources, applying them on a new domain or a new language is usually expensive and time-consuming. We aim to build an aspect-based sentiment analysis model from an unlabeled corpus with minimal guidance from users, i.e., only a small set of seed words for each aspect class and each sentiment class. We employ an autoencoder structure with attention to learn two dictionary matrices for aspect and sentiment respectively where each row of the dictionary serves as an embedding vector for an aspect or a sentiment class. We propose to utilize the user-given seed words to regularize the dictionary learning. In addition, we improve the model by joining the aspect and sentiment encoder in the reconstruction of sentiment in sentences. The joint structure enables sentiment embeddings in the dictionary to be tuned towards the aspect-specific sentiment words for each aspect, which benefits the classification performance. We conduct experiments on two real data sets to verify the effectiveness of our models.
Neural Abstractive Summarization with Structural Attention

Attentional, RNN-based encoder-decoder architectures have obtained impressive performance on abstractive summarization of news articles. However, these methods fail to account for long term dependencies within the sentences of a document. This problem is exacerbated in multi-document summarization tasks such as summarizing the popular opinion in threads present in community question answering (CQA) websites such as Yahoo! Answers and Quora. These threads contain answers which often overlap or contradict each other. In this work, we present a hierarchical encoder based on structural attention to model such inter-sentence and inter-document dependencies. We set the popular pointer-generator architecture and some of the architectures derived from it as our baselines and show that they fail to generate good summaries in a multi-document setting. We further illustrate that our proposed model achieves significant improvement over the baseline in both single and multi-document summarization settings -- in the former setting, it beats the baseline by 1.31 and 7.8 ROUGE-1 points on CNN and CQA datasets, respectively; in the latter setting, the performance is further improved by 1.6 ROUGE-1 points on the CQA dataset.