Reviewerly/exHarmony huggingface dataset:
Mirror Matching: Document Matching Approach in Seed-driven Document Ranking for Medical Systematic Reviews

When medical researchers conduct a systematic review (SR), screening studies is the most time-consuming process: researchers read several thousands of medical literature and manually label them relevant or irrelevant. Screening prioritization (ie., document ranking) is an approach for assisting researchers by providing document rankings where relevant documents are ranked higher than irrelevant ones. Seed-driven document ranking (SDR) uses a known relevant document (ie., seed) as a query and generates such rankings. Previous work on SDR seeks ways to identify different term weights in a query document and utilizes them in a retrieval model to compute ranking scores. Alternatively, we formulate the SDR task as finding similar documents to a query document and produce rankings based on similarity scores. We propose a document matching measure named Mirror Matching, which calculates matching scores between medical abstract texts by incorporating common writing patterns, such as background, method, result, and conclusion in order. We conduct experiments on CLEF 2019 eHealth Task 2 TAR dataset, and the empirical results show this simple approach achieves the higher performance than traditional and neural retrieval models on Average Precision and Precision-focused metrics.
Distortion-Aware Self-Supervised 360° Depth Estimation from A Single Equirectangular Projection Image

360{\deg} images are widely available over the last few years. This paper proposes a new technique for single 360{\deg} image depth prediction under open environments. Depth prediction from a 360{\deg} single image is not easy for two reasons. One is the limitation of supervision datasets - the currently available dataset is limited to indoor scenes. The other is the problems caused by Equirectangular Projection Format (ERP), commonly used for 360{\deg} images, that are coordinate and distortion. There is only one method existing that uses cube map projection to produce six perspective images and apply self-supervised learning using motion pictures for perspective depth prediction to deal with these problems. Different from the existing method, we directly use the ERP format. We propose a framework of direct use of ERP with coordinate conversion of correspondences and distortion-aware upsampling module to deal with the ERP related problems and extend a self-supervised learning method for open environments. For the experiments, we firstly built a dataset for the evaluation, and quantitatively evaluate the depth prediction in outdoor scenes. We show that it outperforms the state-of-the-art technique
ESSAY AS A PRODUCT OF THE EDUCATIONAL ACTIVITY OF A FUTURE SPECIALIST IN INFORMATION SECURITY

The research provides evidence for the importance of essay as a productive form of educational activity of university students. The contradictions between the demand of society for specialists with a high intrinsic motivation, sufficient skills to create new educational and information products, the ability to form professional expert opinions and the unrealized potential of universities in the formation of these skills were revealed. The specific features of the professional activity of future specialists in information security are substantiated that require the use of the essay genre in the educational process. The purpose of the article is to define the features and pedagogical conditions for the use of essays in educational activities of future specialists in information security. The informational approach and the theory of self-determination to productive learning activities were used. The specific factors of choosing an essay as a product of the educational activity of students based on the characteristics of their future professional activities were determined. The author offers pedagogical conditions for the inclusion of essays in the educational process as a result of systematic and internally motivated educational and informational activities for the development of professional disciplines on information security. The implementation of these conditions in the educational process at the university proves the practical significance of the study.
Differentiable N-gram Objective on Abstractive Summarization

ROUGE is a standard automatic evaluation metric based on n-grams for sequence-to-sequence tasks, while cross-entropy loss is an essential objective of neural network language model that optimizes at a unigram level. We present differentiable n-gram objectives, attempting to alleviate the discrepancy between training criterion and evaluating criterion. The objective maximizes the probabilistic weight of matched sub-sequences, and the novelty of our work is the objective weights the matched sub-sequences equally and does not ceil the number of matched sub-sequences by the ground truth count of n-grams in reference sequence. We jointly optimize cross-entropy loss and the proposed objective, providing decent ROUGE score enhancement over abstractive summarization dataset CNN/DM and XSum, outperforming alternative n-gram objectives.
Devanagari to Urdu Transliteration System with and Without AIRAAB

The paper presents Rule-based Devanagari to Urdu transliteration system with and without AIRAAB. Though, Hindi and Urdu are same at structural level and most of the words are common in both the languages, their scripts are different i.e. Devanagari and Urdu scripts respectively. These scripts are not mutually intelligible to the speakers of Hindi and Urdu. And at the same time, there is a trend in Urdu script not to use AIRAAB diacritics which represent vowel sounds. Absence of these diacritics and different conventions of writing makes Urdu complex. To bridge these gaps, we have developed a Rule-based Devanagari to Urdu machine transliteration system efficient in converting Hindi text to Urdu text in both the forms—with AIRAAB and without AIRAAB. In Devanagari, use of Nuqta diacritic to represent Urdu sounds is not in good practice; the absence of which causes errors. Tool is programmed to standardized Devanagari script first before starting any transliteration process. Experiments are based on the sample corpus taken from five domains i.e. Literature, Cricket, Education, Entertainment and Politics.
Conceptually Diverse Base Model Selection for Meta-Learners in Concept  Drifting Data Streams

Meta-learners and ensembles aim to combine a set of relevant yet diverse base models to improve predictive performance. However, determining an appropriate set of base models is challenging, especially in online environments where the underlying distribution of data can change over time. In this paper, we present a novel approach for estimating the conceptual similarity of base models, which is calculated using the Principal Angles (PAs) between their underlying subspaces. We propose two methods that use conceptual similarity as a metric to obtain a relevant yet diverse subset of base models: (i) parameterised threshold culling and (ii) parameterless conceptual clustering. We evaluate these methods against thresholding using common ensemble pruning metrics, namely predictive performance and Mutual Information (MI), in the context of online Transfer Learning (TL), using both synthetic and real-world data. Our results show that conceptual similarity thresholding has a reduced computational overhead, and yet yields comparable predictive performance to thresholding using predictive performance and MI. Furthermore, conceptual clustering achieves similar predictive performances without requiring parameterisation, and achieves this with lower computational overhead than thresholding using predictive performance and MI when the number of base models becomes large.
On measures strongly log-concave on a subspace

In this work we study the concentration properties of log-concave measures that are curved only on a subspace of directions. Proofs uses an adapted version of the stochastic localization process.
An improved raw image enhancement algorithm using a statistical model for pixel value error

When an image is captured using an electronic sensor, statistical variations introduced by photon shot and other noise introduce errors in the raw value reported for each pixel sample.Earlier work found that modest improvements in raw image data quality reliably could be obtained by using empirically-determined pixel value error bounds to constrain texture synthesis.However, the prototype software implementation, KREMY (Ken-tuckY Raw Error Modeler, pronounced "creamy"), was not effective in processing very noisy images.In comparison, the current work has reimplemented KREMY to make it capable of credibly improving far noisier raw DNG images.The key is a new approach that uses a simpler, but statistical, model for pixel value errors rather than simple bounds constraints.
AI Research Associate for Early-Stage Scientific Discovery

Artificial intelligence (AI) has been increasingly applied in scientific activities for decades; however, it is still far from an insightful and trustworthy collaborator in the scientific process. Most existing AI methods are either too simplistic to be useful in real problems faced by scientists or too domain-specialized (even dogmatized), stifling transformative discoveries or paradigm shifts. We present an AI research associate for early-stage scientific discovery based on (a) a novel minimally-biased ontology for physics-based modeling that is context-aware, interpretable, and generalizable across classical and relativistic physics; (b) automatic search for viable and parsimonious hypotheses, represented at a high-level (via domain-agnostic constructs) with built-in invariants, e.g., postulated forms of conservation principles implied by a presupposed spacetime topology; and (c) automatic compilation of the enumerated hypotheses to domain-specific, interpretable, and trainable/testable tensor-based computation graphs to learn phenomenological relations, e.g., constitutive or material laws, from sparse (and possibly noisy) data sets.
Monotonic Safety for Scalable and Data-Efficient Probabilistic Safety Analysis

Autonomous systems with machine learning-based perception can exhibit unpredictable behaviors that are difficult to quantify, let alone verify. Such behaviors are convenient to capture in probabilistic models, but probabilistic model checking of such models is difficult to scale -- largely due to the non-determinism added to models as a prerequisite for provable conservatism. Statistical model checking (SMC) has been proposed to address the scalability issue. However it requires large amounts of data to account for the aforementioned non-determinism, which in turn limits its scalability. This work introduces a general technique for reduction of non-determinism based on assumptions of "monotonic safety'", which define a partial order between system states in terms of their probabilities of being safe. We exploit these assumptions to remove non-determinism from controller/plant models to drastically speed up probabilistic model checking and statistical model checking while providing provably conservative estimates as long as the safety is indeed monotonic. Our experiments demonstrate model-checking speed-ups of an order of magnitude while maintaining acceptable accuracy and require much less data for accurate estimates when running SMC -- even when monotonic safety does not perfectly hold and provable conservatism is not achieved.
Reducing Flipping Errors in Deep Neural Networks

Deep neural networks (DNNs) have been widely applied in various domains in artificial intelligence including computer vision and natural language processing. A DNN is typically trained for many epochs and then a validation dataset is used to select the DNN in an epoch (we simply call this epoch "the last epoch") as the final model for making predictions on unseen samples, while it usually cannot achieve a perfect accuracy on unseen samples. An interesting question is "how many test (unseen) samples that a DNN misclassifies in the last epoch were ever correctly classified by the DNN before the last epoch?". In this paper, we empirically study this question and find on several benchmark datasets that the vast majority of the misclassified samples in the last epoch were ever classified correctly before the last epoch, which means that the predictions for these samples were flipped from "correct" to "wrong". Motivated by this observation, we propose to restrict the behavior changes of a DNN on the correctly-classified samples so that the correct local boundaries can be maintained and the flipping error on unseen samples can be largely reduced. Extensive experiments on different benchmark datasets with different modern network architectures demonstrate that the proposed flipping error reduction (FER) approach can substantially improve the generalization, the robustness, and the transferability of DNNs without introducing any additional network parameters or inference cost, only with a negligible training overhead.
Task and Model Agnostic Adversarial Attack on Graph Neural Networks

Adversarial attacks on Graph Neural Networks (GNNs) reveal their security vulnerabilities, limiting their adoption in safety-critical applications. However, existing attack strategies rely on the knowledge of either the GNN model being used or the predictive task being attacked. Is this knowledge necessary? For example, a graph may be used for multiple downstream tasks unknown to a practical attacker. It is thus important to test the vulnerability of GNNs to adversarial perturbations in a model and task agnostic setting. In this work, we study this problem and show that GNNs remain vulnerable even when the downstream task and model are unknown. The proposed algorithm, TANDIS (Targeted Attack via Neighborhood DIStortion) shows that distortion of node neighborhoods is effective in drastically compromising prediction performance. Although neighborhood distortion is an NP-hard problem, TANDIS designs an effective heuristic through a novel combination of Graph Isomorphism Network with deep Q-learning. Extensive experiments on real datasets and state-of-the-art models show that, on average, TANDIS is up to 50% more effective than state-of-the-art techniques, while being more than 1000 times faster.
GuaranTEE: Introducing Control-Flow Attestation for Trusted Execution Environments

The majority of cloud providers offers users the possibility to deploy Trusted Execution Environments (TEEs) to protect their data and processes from high privileged adversaries. This offer is intended to address concerns of users when moving critical tasks into the cloud. However, TEEs only allow to attest the integrity of the environment at launch-time. To also enable the attestation of a TEE's integrity at run-time, we present GuaranTEE. GuaranTEE uses control-flow attestation to ensure the integrity of a service running within a TEE. By additionally placing all components of GuaranTEE in TEEs, we are able to not only detect a compromised target, but are also able to protect ourselves from malicious administrators. We show the practicability of GuaranTEE by providing a detailed performance and security evaluation of our prototype based on Intel SGX in Microsoft Azure. Our evaluation shows that the need to transfer information between TEEs and the additional verification process add considerable overhead under high CPU load. Yet, we are able to reduce this overhead by securely caching collected information and by performing the analysis in parallel to executing the application. In summary, our results show that GuaranTEE provides a practical solution for cloud users focused on protecting the integrity of their data and processes at run-time.
Does Interacting Help Users Better Understand the Structure of Probabilistic Models?

Despite growing interest in probabilistic modeling approaches and availability of learning tools, people with no or less statistical background feel hesitant to use them. There is need for tools for communicating probabilistic models to less experienced users more intuitively to help them build, validate, use effectively or trust probabilistic models. Users' comprehension of probabilistic models is vital in these cases and interactive visualizations could enhance it. Although there are various studies evaluating interactivity in Bayesian reasoning and available tools for visualizing the sample-based distributions, we focus specifically on evaluating the effect of interaction on users' comprehension of probabilistic models' structure. We conducted a user study based on our Interactive Pair Plot for visualizing models' distribution and conditioning the sample space graphically. Our results suggest that improvements in the understanding of the interaction group are most pronounced for more exotic structures, such as hierarchical models or unfamiliar parameterizations in comparison to the static group. As the detail of the inferred information increases, interaction does not lead to considerably longer response times. Finally, interaction improves users' confidence.
Selection Strategies for Commonsense Knowledge

Selection strategies are broadly used in first-order logic theorem proving to select those parts of a large knowledge base that are necessary to proof a theorem at hand. Usually, these selection strategies do not take the meaning of symbol names into account. In knowledge bases with commonsense knowledge, symbol names are usually chosen to have a meaning and this meaning provides valuable information for selection strategies. We introduce the vector-based selection strategy, a purely statistical selection technique for commonsense knowledge based on word embeddings. We compare different commonsense knowledge selection techniques for the purpose of theorem proving and demonstrate the usefulness of vector-based selection with a case study.
MLProxy: SLA-Aware Reverse Proxy for Machine Learning Inference Serving on Serverless Computing Platforms

Serving machine learning inference workloads on the cloud is still a challenging task on the production level. Optimal configuration of the inference workload to meet SLA requirements while optimizing the infrastructure costs is highly complicated due to the complex interaction between batch configuration, resource configurations, and variable arrival process. Serverless computing has emerged in recent years to automate most infrastructure management tasks. Workload batching has revealed the potential to improve the response time and cost-effectiveness of machine learning serving workloads. However, it has not yet been supported out of the box by serverless computing platforms. Our experiments have shown that for various machine learning workloads, batching can hugely improve the system's efficiency by reducing the processing overhead per request. In this work, we present MLProxy, an adaptive reverse proxy to support efficient machine learning serving workloads on serverless computing systems. MLProxy supports adaptive batching to ensure SLA compliance while optimizing serverless costs. We performed rigorous experiments on Knative to demonstrate the effectiveness of MLProxy. We showed that MLProxy could reduce the cost of serverless deployment by up to 92% while reducing SLA violations by up to 99% that can be generalized across state-of-the-art model serving frameworks.
Meininga i mellomromma: Ei innleiing

This introductory chapter is divided into two main sections. In the first section we set out a theoretical background for a transdisciplinary research embracing multiple artforms, in this particular instance with focus on music and literature. Art and artworks are examined not only as finished products, but as elements of living practices in the meeting of composer, writer, music, text, performer and listener or recipient. This leads on to questions of eudaimonia and of what it means to live a fulfilling and meaningful life, with art as an essential ingredient. The central question for the chapter, and for the book as a whole, concerns the paradoxical relation between voice and silence as fundamental contingents for the emergence of meaning. Our hypothesis is that it is precisely in the spaces in-between, in the artistic-aesthetic and interactive tensions between sound and silence, and between subjects and their Others, that meaning emerges. In the second section, we present the different contributions to this collection of texts, before we gather the various strands in a conclusion.
On The Quality Of Cryptocurrency Markets: Centralized Versus Decentralized Exchanges

We compare the market quality of centralized crypto exchanges (CEXs) such as Binance and Kraken to decentralized blockchain-based venues (DEXs) such as Uniswap v2 and v3. After discussing the microstructure of such exchanges, we analyze two key aspects of market quality: transaction costs and deviations from the no-arbitrage condition. We find that CEXs and DEXs operate on roughly equal footing in terms of transaction costs, particularly in light of recent innovations in DEX protocols. Moreover, while CEXs provide superior price efficiency, DEXs eliminate custodian risk. These complementary advantages may explain why both market structures coexist.
Analysis of Multiscale Wavelet-based Fractional Gradient-Anisotropic  Diffusion Fusion for single hazy and underwater image enhancement

This report presents the results of a multi-scale wavelet based scheme for single image de-hazing and underwater image enhancement. The scheme is fast and highly localized in addition to global enhancement of hazy images. A PDE-based formulation enables additional versatility as the iterative nature allows more flexibility for various types of images. Visual and objective results from experiments indicate that the proposed approach competes favourably or surpasses most of the state-of-the-art approaches.
Comparison of Eloquent ORM with Query Builder in Work Management System (Case Study: Muhammadiyah Lamongan Hospital)

A management system is a set of policies, processes, and procedures used by an organization to ensure that the system can fulfill the tasks required to achieve its objectives. With this system, the company can make it easier for the management of a task/project that is being done by the employee/employee. In the SIRS Unit at Lamongan Muhammadiyah Hospital, the implementation of Work Management is still done manually, namely by holding monthly meetings held at the beginning of the month which can affect the efficiency and effectiveness of employees in terms of collecting/reporting tasks assigned by the project leader. For this reason, a Job Management System is needed that can overcome these problems. Job Management System, created on a Web-based basis using Laravel 8.0 as the framework and MySQL as the database. In this study, researchers will focus on implementing Eloquent ORM which is a special feature in Laravel to make it easier for developers to process data and compare Eloquent ORM with Query builder in its implementation on Job Management System. The result of this research is Query Builder can execute database query more faster and more memory efficient than Eloquent, but Eloquent use simple code than Eloquent.