Reviewerly/exHarmony huggingface dataset:
Entity Linking Based on Sentence Representation

Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most existing methods failed to link when a mention appears multiple times in a document, since the conflict of its contexts in different locations may lead to difficult linking. Sentence representation, which has been studied based on deep learning approaches recently, can be used to resolve the above issue. In this paper, an effective entity linking model is proposed to capture the semantic meaning of the sentences and reduce the noise introduced by different contexts of the same mention in a document. This model first uses the symmetry of the Siamese network to learn the sentence similarity. Then, the attention mechanism is added to improve the interaction between input sentences. To show the effectiveness of our sentence representation model combined with attention mechanism, named ELSR, extensive experiments are conducted on two public datasets. Results illustrate that our model outperforms the baselines and achieves the superior performance.
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models

The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way. In this paper, we propose a novel RNN-like deep graph neural network architecture by incorporating AdaBoost into the computation of network; and the proposed graph convolutional network called AdaGCN~(Adaboosting Graph Convolutional Network) has the ability to efficiently extract knowledge from high-order neighbors of current nodes and then integrates knowledge from different hops of neighbors into the network in an Adaboost way. Different from other graph neural networks that directly stack many graph convolution layers, AdaGCN shares the same base neural network architecture among all ``layers'' and is recursively optimized, which is similar to a RNN. Besides, We also theoretically established the connection between AdaGCN and existing graph convolutional methods, presenting the benefits of our proposal. Finally, extensive experiments demonstrate the consistent state-of-the-art prediction performance on graphs across different label rates and the computational advantage of our approach AdaGCN.
Metrics and Continuity in Reinforcement Learning

In most practical applications of reinforcement learning, it is untenable to maintain direct estimates for individual states; in continuous-state systems, it is impossible. Instead, researchers often leverage {\em state similarity} (whether explicitly or implicitly) to build models that can generalize well from a limited set of samples. The notion of state similarity used, and the neighbourhoods and topologies they induce, is thus of crucial importance, as it will directly affect the performance of the algorithms. Indeed, a number of recent works introduce algorithms assuming the existence of "well-behaved" neighbourhoods, but leave the full specification of such topologies for future work. In this paper we introduce a unified formalism for defining these topologies through the lens of metrics. We establish a hierarchy amongst these metrics and demonstrate their theoretical implications on the Markov Decision Process specifying the reinforcement learning problem. We complement our theoretical results with empirical evaluations showcasing the differences between the metrics considered.
“The Second Vice is Lying, the First is Running into Debt.” Antecedents and Mitigating Practices of Social Debt: an Exploratory Study in Distributed Software Development Teams

Although much is known about the concept of technical debt in software development, less is known about its social counterpart, also known as social debt.Social debt refers to future consequences of decisions related to people and their interactions.Omissions in social interactions or reduction of communication can foster social debt -and in turn result in negative outcomes in the long run.In this paper, we explore what factors drive and mitigate social debt in distributed agile software development teams.Utilizing an exploratory case study approach, we derive insights from two case organizations.We present antecedents and mitigating factors of social debt related to communication, collaboration, and coordination.
Large Scale Deep Reinforcement Learning in War-games

War-game is a type of multi-agent real-time strategy game, with challenges of the large-scale decision-making space and the flexible and changeable battlefield situation. In addition to the military field, it has played a role in fields including epidemic prevention and pest control. In recent years, more and more learning algorithms have tried to solve this kind of game. However, the existing methods have not yet given a satisfactory solution for the war-game, especially when preparation time is limited. In this background, we try to solve a traditional war-game based on hexagon grids. We propose a hierarchical multi-agent reinforcement learning framework to rapidly training an AI model for the war-game. The higher-level network in our hierarchical framework is used for task decision, it solves the credit assignment problem between agents through cooperative training. The lower-level network is mainly used for route planning, and it can be reused through parameter sharing for all the agents and all the maps. To deal with various opponents, we improve the robustness of the model through a grouped self-play approach. In experiments, we get encouraging results which show that the hierarchical structure allows agents to learn their strategies effectively. Our final AI model demonstrates that our methods can effectively deal with the challenges in the war-game.
Improving News Popularity Estimation via Weak Supervision and Meta-active Learning

Social news has fundamentally changed the mechanisms of public perception, education, and even dis-information.Apprising the popularity of social news articles can have significant impact through a diversity of information redistribution techniques.In this article, an improved prediction algorithm is proposed to predict the long-time popularity of social news articles without the need for ground-truth observations.The proposed framework applies a novel active learning selection policy to obtain the optimal volume of observations and achieve superior predictive performance.To assess the proposed framework, a large set of experiments are undertaken; these indicate that the new solution can improve prediction performance by 28% (precision) while reducing the volume of required ground truth by 32%.
Elastic Downsampling: An Adaptive Downsampling Technique to Preserve Image Quality

This paper presents a new adaptive downsampling technique called elastic downsampling, which enables high compression rates while preserving the image quality. Adaptive downsampling techniques are based on the idea that image tiles can use different sampling rates depending on the amount of information conveyed by each block. However, current approaches suffer from blocking effects and artifacts that hinder the user experience. To bridge this gap, elastic downsampling relies on a Perceptual Relevance analysis that assigns sampling rates to the corners of blocks. The novel metric used for this analysis is based on the luminance fluctuations of an image region. This allows a gradual transition of the sampling rate within tiles, both horizontally and vertically. As a result, the block artifacts are removed and fine details are preserved. Experimental results (using the Kodak and USC Miscelanea image datasets) show a PSNR improvement of up to 15 dB and a superior SSIM (Structural Similarity) when compared with other techniques. More importantly, the algorithms involved are computationally cheap, so it is feasible to implement them in low-cost devices. The proposed technique has been successfully implemented using graphics processors (GPU) and low-power embedded systems (Raspberry Pi) as target platforms.
Preparation and control of intelligent automation systems

In the automation systems of tomorrow, it is likely that the devices included have various degrees of autonomy, and include advanced algorithms for perception and control. Human operators will be expected to work together with collaborative robots as well as with roaming robots for material handling. The volatile nature of the environment of such intelligent automation systems lead to an enormous amount of possible situations that can arise and which need to be suitably handled. This complexity makes development of control systems for intelligent automation systems difficult using traditional methods. As an alternative, this thesis presents a model-based control framework, which uses a combination of formal specification and automated planning. The proposed framework allows for defining the intentions of the automation system on a high level, which enables decisions that influence when things should occur to be modeled using logical constraints, rather than programming. To achieve a modular framework, low level, reusable, resource models are composed by 1) formal specification to ensure safety and 2) applying an abstraction called an operation, which couples the reusable resources to the intentions of the system. By planning also the resources' detailed actions, the operations can, when possible, be completed regardless of the resources' current state. This eases error-recovery, as resources do not have to be reset when an error occurs. Additionally, the thesis proposes an iterative and interactive workflow for integrating the proposed model-based control framework into a virtual preparation process, using computer-based simulation as a tool for validating formal specifications. The control framework allows for adding new constraints to a running system, enabling an efficient and interactive preparation process. The framework has been applied to a use case from final assembly, which features human-robot collaboration. Experimental results on the ability to handle unforeseen errors and planning performance are presented.
Hello World! I am Charlie, an Artificially Intelligent Conference Panelist

In recent years, advances in artificial intelligence (AI) have far outpaced our ability to understand and leverage them. In no domain has this been more true than in conversational agents (CAs). Transformer-based generative language models, such as GPT-2, significantly advance CAs' ability to generate creative and relevant content. It is critical to start exploring collaboration with these CAs. In this paper, we focus on an initial step by enabling a human-augmented, AI-driven CA to contribute to a panel discussion. Key questions include training a transformer-based AI to talk like a panelist, effectively embodying the CA to interact with panel participants, and defining the operational requirements and challenges to a CA gaining acceptance from its peers. Our results highlight the benefits that varied training, equal and dynamic representation, and fluid operation can have for AI applications. While acknowledging limitations, we present a path forward to richer, more natural human-AI collaboration.
The Role of New York Times Picks in Comment Quality and Engagement

While various methods can be used to maintain online discussion quality, one aspect that is underexplored is the role of highlights from professional moderators. In this work, we look at the impact of New York Times (NYT) Picks. We present an analysis of more than 13 million NYT comments, examining the quality and frequency of commenting on the site in response to NYT Picks. The findings offer evidence that NYT Picks are associated with an increase in the quality of first-time receivers’ next approved comment, as well as the commenting frequency during commenters’ early tenure on the site. The quality boost associated with receiving a Pick attenuates after subsequent picks and diminishes over time as the user continues commenting, but is still higher than commenters who don’t receive Picks. The findings also indicate that exposure to Pick badges is positively correlated with subsequent higher quality approved comments, albeit to a lesser extent.
Density Matrices with metric for Derivational Ambiguity

Recent work on vector-based compositional natural language semantics has proposed the use of density matrices to model lexical ambiguity and (graded) entailment (e.g. Piedeleu et al 2015, Bankova et al 2019, Sadrzadeh et al 2018). Ambiguous word meanings, in this work, are represented as mixed states, and the compositional interpretation of phrases out of their constituent parts takes the form of a strongly monoidal functor sending the derivational morphisms of a pregroup syntax to linear maps in FdHilb. Our aims in this paper are threefold. Firstly, we replace the pregroup front end by a Lambek categorial grammar with directional implications expressing a word's selectional requirements. By the Curry-Howard correspondence, the derivations of the grammar's type logic are associated with terms of the (ordered) linear lambda calculus; these terms can be read as programs for compositional meaning assembly with density matrices as the target semantic spaces. Secondly, we extend on the existing literature and introduce a symmetric, nondegenerate bilinear form called a that defines a canonical isomorphism between a vector space and its dual, allowing us to keep a distinction between left and right implication. Thirdly, we use this metric to define density matrix spaces in a directional form, modeling the ubiquitous derivational ambiguity of natural language syntax, and show how this alows an integrated treatment of lexical and derivational forms of ambiguity controlled at the level of the interpretation.
Prediction-based attenuation as a general property of learning in neural circuits.

One of the mechanisms proposed to underpin perceptual learning is the reduction in salience of predicted stimuli. This reduction is held to affect the representation of (conditioned) stimuli before they have been associated with motivationally meaningful consequences but may also affect (unconditioned) stimuli that automatically elicit responding. The purpose of this article is to review past findings and present new evidence of phenomena across a range of domains that are consistent with the idea that responses automatically triggered by stimulating events will be reduced by prediction. We argue that prediction-based attenuation may serve several adaptive functions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
The promises and perils of central bank digital currencies

This paper analyzes the proposal that central banks should issue digital currencies (CBDC) to provide a public alternative to private digital accounts and cryptocurrencies. We build on some recent themes in political economy research to give a broader and more balanced perspective than the existing literature, highlighting both the promises and perils of CBDC. We argue that, on the one hand, the present state of the private financial sector is problematic and regulators should seek to tackle the issues of financial power, financial instability and lack of adequate monetary policy options. On the other hand, implementing CBDC comes with risks of its own, such as that of creating a “Frankenstein scenario” where too much power is given to unelected technocrats. Our tentative conclusion is therefore that CBDC should be seen as a second-best option, while the primary focus of policy makers should be on the possibility of financial re-regulation.
Identifying barriers and facilitators in HIV-indicator reporting for different health facility performances: A qualitative case study

Identifying barriers and facilitators in HIV-indicator reporting contributes to strengthening HIV monitoring and evaluation efforts by acknowledging contributors to success, as well as identifying weaknesses within the system that require improvement. Nonetheless, there is paucity in identifying and comparing barriers and facilitators in HIV-indicator data reporting among facilities that perform well and those that perform poorly at meeting reporting completeness and timeliness requirements. Therefore, this study aims to use a qualitative approach in identifying and comparing the current state of barriers and facilitators in routine reporting of HIV-indicators by facilities performing well, and those performing poorly in meeting facility reporting completeness and timeliness requirements to District Health Information Software2 (DHIS2). A multiple qualitative case study design was employed. The criteria for case selection was based on performance in HIV-indicator facility reporting completeness and timeliness. Areas of interest revolved around reporting procedures, organizational, behavioral, and technical factors. Purposive sampling was used to identify key informants in the study. Data was collected using semi-structured in-depth interviews with 13 participants, and included archival records on facility reporting performance, looking into documentation, and informal direct observation at 13 facilities in Kenya. Findings revealed that facilitators and barriers in reporting emerged from the following factors: interrelationship between workload, teamwork and skilled personnel, role of an EMRs system in reporting, time constraints, availability and access-rights to DHIS2, complexity of reports, staff rotation, availability of trainings and mentorship, motivation, availability of standard operating procedures and resources. There was less variation in barriers and facilitators faced by facilities performing well and those performing poorly. Continuous evaluations have been advocated within health information systems literature. Therefore, continuous qualitative assessments are also necessary in order to determine improvements and recurring of similar issues. These assessments have also complemented other quantitative analyses related to this study.
Supportive and Self Attentions for Image Caption

Attention over an observed image or natural sentence is run by spotting or locating the region or position of interest for pattern classification. The attention parameter is seen as a latent variable, which was indirectly calculated by minimizing the classification loss. Using such an attention mechanism, the target information may not be correctly identified. Therefore, in addition to minimizing the classification error, we can directly attend the region of interest by minimizing the reconstruction error due to supporting data. Our idea is to learn how to attend through the so-called supportive attention when the supporting information is available. A new attention mechanism is developed to conduct the attentive learning for translation invariance which is applied for image caption. The derived information is helpful for generating caption from input image. Moreover, this paper presents an association network which does not only implement the word-to-image attention, but also carry out the image-to-image attention via self attention. The relations between image and text are sufficiently represented. Experiments on MS-COCO task show the benefit of the proposed supportive and self attentions for image caption with the keyvalue memory network.
Providing Consistent State to Distributed Storage System

In cloud storage systems, users must be able to shut down the application when not in use and restart it from the last consistent state when required. BlobSeer is a data storage application, specially designed for distributed systems, that was built as an alternative solution for the existing popular open-source storage system-Hadoop Distributed File System (HDFS). In a cloud model, all the components need to stop and restart from a consistent state when the user requires it. One of the limitations of BlobSeer DFS is the possibility of data loss when the system restarts. As such, it is important to provide a consistent start and stop state to BlobSeer components when used in a Cloud environment to prevent any data loss. In this paper, we investigate the possibility of BlobSeer providing a consistent state distributed data storage system with the integration of checkpointing restart functionality. To demonstrate the availability of a consistent state, we set up a cluster with multiple machines and deploy BlobSeer entities with checkpointing functionality on various machines. We consider uncoordinated checkpoint algorithms for their associated benefits over other alternatives while integrating the functionality to various BlobSeer components such as the Version Manager (VM) and the Data Provider. The experimental results show that with the integration of the checkpointing functionality, a consistent state can be ensured for a distributed storage system even when the system restarts, preventing any possible data loss after the system has encountered various system errors and failures.
An Affirmative Learning Techniques to Analyse the Crime Scene in Jewel Theft Murder

Jewel Theft murder has become a serious issue in today’s society as crime rates are increasing rapidly. Police Officials find it difficult to identify things that can accurately and efficiently analyze the growing volume of data due to longer duration of investigation process. Our main aim is to analyze the jewel theft murder occurred over the years 2014-2019 and find crime patterns to reduce the further occurrences. The outcome of our project is to predict the jewel theft murder at a much faster rate and thus reduces the crime rate.
Estimating Arterial Wall Deformations from Automatic Key-Point Detection and Matching

Assessing arterial-wall motion and deformations may reveal pathologic alterations in biomechanical properties of the parietal tissues and, thus, contribute to the detection of vascular disease onset. Ultrasound image sequences allow the observation of this motion and many methods have been developed to estimate temporal changes in artery diameter and wall thickness and to track 2-D displacements of selected points. Some methods enable the assessment of shearing or stretching within the wall, but none of them can estimate all these deformations simultaneously. The method herein proposed was devised to simultaneously estimate translation, compression, stretching and shearing of the arterial wall in ultrasound B-mode image sequences representing the carotid artery longitudinal section. Salient blob-like patterns, called key points, are automatically detected in each frame and matched between successive frames. A robust estimator based on an affine transformation model is then used to assess frame-to-frame motion explaining at best the key-point matches and to reject outliers. Realistic simulated image sequences were used to evaluate the accuracy and robustness of the method against ground truth. The method was also visually assessed on clinical image sequences, for which true deformations are unknown.
DeepWatermark: Embedding Watermark into DNN Model

For the protection of trained deep neural network(DNN) model, it has been studied to embed a watermark into the weights of DNN. However, the amount of changes in the weights is large in the conventional methods. In addition, it is reported that the presence of hidden watermark can be detected from the analysis of weight variance, and that the watermark can be modified by effectively adding noise to the weight. In this paper, we focus on the fully-connected layers and apply a quantization-based watermarking method to the weights sampled from the layers. The advantage of the proposed method is that the changes caused by embedding watermark is much smaller and measurable. This is effective against the problems of previous works. The validity of the proposed method is quantitatively evaluated by changing the conditions during the training of DNN model. The results include the impact of training for DNN model, effective embedding method, and high robustness.