Reviewerly/exHarmony huggingface dataset:
Learning Prediction Intervals for Regression: Generalization and Calibration

We study the generation of prediction intervals in regression for uncertainty quantification. This task can be formalized as an empirical constrained optimization problem that minimizes the average interval width while maintaining the coverage accuracy across data. We strengthen the existing literature by studying two aspects of this empirical optimization. First is a general learning theory to characterize the optimality-feasibility tradeoff that encompasses Lipschitz continuity and VC-subgraph classes, which are exemplified in regression trees and neural networks. Second is a calibration machinery and the corresponding statistical theory to optimally select the regularization parameter that manages this tradeoff, which bypasses the overfitting issues in previous approaches in coverage attainment. We empirically demonstrate the strengths of our interval generation and calibration algorithms in terms of testing performances compared to existing benchmarks.
A Framework for Generating Annotated Social Media Corpora with Demographics, Stance, Civility, and Topicality

In this paper we introduce a framework for annotating a social media text corpora for various categories. Since, social media data is generated via individuals, it is important to annotate the text for the individuals demographic attributes to enable a socio-technical analysis of the corpora. Furthermore, when analyzing a large data-set we can often annotate a small sample of data and then train a prediction model using this sample to annotate the full data for the relevant categories. We use a case study of a Facebook comment corpora on student loan discussion which was annotated for gender, military affiliation, age-group, political leaning, race, stance, topicalilty, neoliberlistic views and civility of the comment.
Concise, type-safe, and efficient structural diffing

A structural diffing algorithm compares two pieces of tree-shaped data and computes their difference. Existing structural diffing algorithms either produce concise patches or ensure type safety, but never both. We present a new structural diffing algorithm called truediff that achieves both properties by treating subtrees as mutable, yet linearly typed resources. Mutation is required to derive concise patches that only mention changed nodes, but, in contrast to prior work, truediff guarantees all intermediate trees are well-typed. We formalize type safety, prove truediff has linear run time, and evaluate its performance and the conciseness of the derived patches empirically for real-world Python documents. While truediff ensures type safety, the size of its patches is on par with Gumtree, a popular untyped diffing implementation. Regardless, truediff outperforms Gumtree and a typed diffing implementation by an order of magnitude.
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs.

As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings). However, the existing one-size-fits-all GNN implementations are insufficient to catch up with the evolving GNN architectures, the ever-increasing graph sizes, and the diverse node embedding dimensionalities. To this end, we propose \textbf{GNNAdvisor}, an adaptive and efficient runtime system to accelerate various GNN workloads on GPU platforms. First, GNNAdvisor explores and identifies several performance-relevant features from both the GNN model and the input graph, and uses them as a new driving force for GNN acceleration. Second, GNNAdvisor implements a novel and highly-efficient 2D workload management, tailored for GNN computation to improve GPU utilization and performance under different application settings. Third, GNNAdvisor capitalizes on the GPU memory hierarchy for acceleration by gracefully coordinating the execution of GNNs according to the characteristics of the GPU memory structure and GNN workloads. Furthermore, to enable automatic runtime optimization, GNNAdvisor incorporates a lightweight analytical model for an effective design parameter search. Extensive experiments show that GNNAdvisor outperforms the state-of-the-art GNN computing frameworks, such as Deep Graph Library ($3.02\times$ faster on average) and NeuGraph (up to $4.10\times$ faster), on mainstream GNN architectures across various datasets.
Book Recommendation Model Based on Wide and Deep Model

[Purpose] Personalized recommendation is one of the hottest research areas in recent years Recommendation systems developed by Google, Amazon, Alibaba and other companies have brought them huge benefits, which are recommendations based on big data analysis. However,For data sets with large data sparseness, traditional recommendation algorithms for historical records cannot obtain satisfactory recommendation results, and traditional recommendation algorithms often cannot discover the potential interests of users. In this paper, we managed to extend the personalized recommendation system to the University Library Lending system [Methodology]Firstly, to meet the challenge of data sparsity, we collected the information of readers and books in the borrowing records of Qinghai University Library in recent 20 years. Secondly, through the analysis and research of the Wide and Deep model, the recommendation model is obtained by joint training of LR (Logistic Regression) and DNN (Deep Neural Network) networks. Moreover, we improved the double-label of the Wide and Deep model into multiple labels and got the final model after extensive training. [Findings]The experimental results show that the accuracy of our book recommendation model is significantly better than traditional recommendation algorithms and hybrid recommendation algorithms. [Originality]Firstly we set up a large Qinghai University book data set for training and testing and verification.Secondly, completed the model migration and improvement of W & D. Through a large number of experiments for comparative research, it is concluded that the improved W & D model is suitable for book recommendation systems.The value of the AUC index of the traditional collaborative filtering model is the lowest. The AUC value of the weighted bipartite graph model is greater than the collaborative filtering model. The AUC value of the hybrid model is basically the same as that of the weighted bipartite graph model. The Wide & Deep model has the highest AUC value. Reached 0.75. Therefore, the Wide & Deep model is suitable for a book personalized recommendation system with sparse characteristics of big data.
The labor behind the tools

The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
Research on face recognition algorithm based on multi task deep learning

With the vigorous development of the new generation of information technology, deep learning technology based on big data has gradually become one of the mainstream technologies in the field of artificial intelligence. Face recognition is an important topic in the field of artificial intelligence and biometrics.It is widely used in business, security, identity authentication and many other aspects, and has become a dynamic research field.With the continuous improvement of application requirements, face recognition technology is no longer only for face identification, face attribute recognition is becoming more and more important.Firstly, a simplified multi task face recognition model is proposed and designed to speed up the operation;Secondly, the correlation among multiple learning tasks is used to improve the recognition accuracy of the model;After model training and selection, an end-to-end multi task face recognition model is obtained.The multi task face recognition algorithm can be fast and accurate in a short time, which can be widely used in intelligent driving behavior analysis, intelligent navigation and other fields.
Comparative Study of Different Approaches for Modeling and Analysis of Activities of Daily Living

In recent years, automatic human activity recognition has drawn much attention. On one hand, this is due to the rapid proliferation and cost degradation of a wide variety of sensing hardware, which resulted in the tremendous explosion of activity data. On the other hand there are urgent growing and pressing demands from many application areas such as: in-home health monitoring especially for the elderly, smart cities, safe driving by monitoring and predicting driver’s behavior, healthcare applications, entertainment, assessment of therapy, performance evaluation in sports, etc. In this paper we focus on activities of daily living (ADL), which are routine activities that people tend to do every day without needing assistance. We have used a public dataset of acceleration data collected with a wrist-worn accelerometer for 14 different ADL activities. Our objective is to perform an extensive comparative study of the predictive power of several paradigms to model and classify ADL activities. To the best of our knowledge, almost all techniques for activity recognition are based on methods from the machine learning literature (particularly, supervised learning). Our comparative study widens the scope of techniques that can be used for automatic analysis of human activities and provides a valuation of the relative effectiveness and efficiency of a potentially myriad pool of techniques. We apply four different paradigms for the analysis and classification of daily living activities: (1) techniques based on supervised machine learning, (2) techniques based on estimating the empirical distribution of the time series data and use measure-theoretic techniques to estimate the dissimilarity between two distributions, (3) techniques based on statistical analysis using goodness-of-fit tests, and (4) techniques based on measuring the dissimilarity between two time signals. We used several evaluation metrics including confusion matrices, overall accuracy, sensitivity and specificity for each activity, and relative computational performance. One of the main conclusions from our analysis is that the simplest techniques, for example, using empirical discrete distributions as models, have almost the best performance in terms of both accuracy and computational efficiency.
Lower Bounds on Stabilizer Rank.

The stabilizer rank of a quantum state $\psi$ is the minimal $r$ such that $\left| \psi \right \rangle = \sum_{j=1}^r c_j \left|\varphi_j \right\rangle$ for $c_j \in \mathbb{C}$ and stabilizer states $\varphi_j$. The running time of several classical simulation methods for quantum circuits is determined by the stabilizer rank of the $n$-th tensor power of single-qubit magic states. We prove a lower bound of $\Omega(n)$ on the stabilizer rank of such states, improving a previous lower bound of $\Omega(\sqrt{n})$ of Bravyi, Smith and Smolin (arXiv:1506.01396). Further, we prove that for a sufficiently small constant $\delta$, the stabilizer rank of any state which is $\delta$-close to those states is $\Omega(\sqrt{n}/\log n)$. This is the first non-trivial lower bound for approximate stabilizer rank. Our techniques rely on the representation of stabilizer states as quadratic functions over affine subspaces of $\mathbb{F}_2^n$, and we use tools from analysis of boolean functions and complexity theory. The proof of the first result involves a careful analysis of directional derivatives of quadratic polynomials, whereas the proof of the second result uses Razborov-Smolensky low degree polynomial approximations and correlation bounds against the majority function.
Building a Social Machine for Graduate Mobility

The paper discusses the construction of a social machine to solve a complex problem faced by doctoral students: “What Comes Next?” especially important during this pandemic when many traditional career paths have been compromised. This system utilizes TimeBank, SkillBank and Knowledge Graphs to provide users with an OpportunityBank: an opportunity to connect with other doctorates, to understand different career paths after graduation, alternative use of acquired skills, and to network sharing available positions, research work, and data. Users can collaborate while keeping an eye on what is happening in other social platforms. The system uses ‘prosopography narrative’ and provides a platform for ‘shadow institutes’, while maintaining provenance, trust, and accessibility to required information through knowledge graphs.
Maximum Physically Consistent Trajectories

Trajectories are usually collected with physical sensors, which are prone to errors and cause outliers in the data. We aim to identify such outliers via the physical properties of the tracked entity, that is, we consider its physical possibility to visit combinations of measurements. We describe optimal algorithms to compute maximum subsequences of measurements that are consistent with (simplified) physics models. Our results are output-sensitive with respect to the number k of outliers in a trajectory of n measurements. Specifically, we describe an O ( n log n log 2 k )-time algorithm for 2D trajectories using a model with unbounded acceleration but bounded velocity, and an O(nk) -time algorithm for any model where consistency is “concatenable”: a consistent subsequence that ends where another begins together form a consistent sequence. We also consider acceleration-bounded models that are not concatenable. We show how to compute the maximum subsequence for such models in O ( n k 2 log k ) time, under appropriate realism conditions. Finally, we experimentally explore the performance of our algorithms on several large real-world sets of trajectories. Our experiments show that we are generally able to retain larger fractions of noisy trajectories than previous work and simpler greedy approaches. We also observe that the speed-bounded model may in practice approximate the acceleration-bounded model quite well, though we observed some variation between datasets.
Context-Guided Adaptive Network for Efficient Human Pose Estimation

Although recent work has achieved great progress in human pose estimation (HPE), most methods show limitations in either inference speed or accuracy. In this paper, we propose a fast and accurate end-to-end HPE method, which is specifically designed to overcome the commonly encountered jitter box, defective box and ambiguous box problems of box-based methods, e.g. Mask R-CNN. Concretely, 1) we propose the ROIGuider to aggregate box instance features from all feature levels under the guidance of global context instance information. Further, 2) the proposed Center Line Branch is equipped with a Dichotomy Extended Area algorithm to adaptively expand each instance box area, and Ambiguity Alleviation strategy to eliminate duplicated keypoints. Finally, 3) to achieve efficient multi-scale feature fusion and real-time inference, we design a novel Trapezoidal Network (TNet) backbone. Experimenting on the COCO dataset, our method achieves 68.1 AP at 25.4 fps, and outperforms Mask-RCNN by 8.9 AP at a similar speed. The competitive performance on the HPE and person instance segmentation tasks over the state-of-the-art models show the promise of the proposed method. The source code will be made available at https://github.com/zlcnup/CGANet.
Validation and Verification of Data Marketplaces

This paper presents a Validation and Verification (V&V) model of Data Marketplaces. Data is extracted from the sensors embedded within the Smart city, infrastructure, or building via Application Programming Interfaces (APIs) and inserted into a Data Marketplace. The technology is based on smart contracts deployed on a private ethereum blockchain. Current issues with data in Smart cities, infrastructure, buildings, or any real estate, are the difficulty of its access and retrieval, therefore integration; its quality in terms of meaningful information; its large quantity with a reduced coverage in terms of systems and finally its authenticity, as data can be manipulated for economic advantage. In order to address these issues, this paper proposes a Data Marketplace model with a hierarchical process for data validation and verification where each stage adds a layer of data abstraction, value-added services and authenticity based on Artificial Intelligence. By using a blockchain, this presented approach is based on a decentralised method where each stakeholder stores the data. The proposed model is validated in a real application with live data: Newcastle urban observatory smart city project.
Unsupervised Class-Specific Abstractive Summarization of Customer Reviews

Large-scale unsupervised abstractive summarization is sorely needed to automatically scan millions of customer reviews in today's fastpaced e-commerce landscape.We address a key challenge in unsupervised abstractive summarization -reducing generic and uninformative content and producing useful information that relates to specific product aspects.To do so, we propose to model reviews in the context of some topical classes of interest.In particular, for any arbitrary set of topical classes of interest, the proposed model can learn to generate a set of class-specific summaries from multiple reviews of each product without ground-truth summaries, and the only required signal is class probabilities or class label for each review.The model combines a generative variational autoencoder, with an integrated class-correlation gating mechanism and a hierarchical structure capturing dependence among products, reviews and classes.Human evaluation shows that generated summaries are highly relevant, fluent, and representative.Evaluation using a reference dataset shows that our model outperforms state-of-theart abstractive and extractive baselines.
Flexible Coinduction in Agda.

We provide an Agda library for inference systems, also supporting their recent generalization allowing flexible coinduction, that is, interpretations which are neither inductive, nor purely coinductive. A specific inference system can be obtained as an instance by writing a set of meta-rules, in an Agda format which closely resembles the usual one. In this way, the user gets for free the related properties, notably the inductive and coinductive intepretation and the corresponding proof principles. Moreover, a significant modularity is achieved. Indeed, rather than being defined from scratch and with a built-in interpretation, an inference system can also be obtained by composition operators, such as union and restriction to a smaller universe, and its semantics can be modularly chosen as well. In particular, flexible coinduction is obtained by composing in a certain way the interpretations of two inference systems. We illustrate the use of the library by several examples. The most significant one is a big-step semantics for the λ-calculus, where flexible coinduction allows to obtain a special result (∞) for all and only the diverging computations, and the proof of equivalence with small-step semantics is carried out by relying on the proof principles offered by the library.
A Combinatorial Optimization Approach to Determining Optimal Data in Cluster

Clustering is one of the data analysis activities for grouping data into several categories with the same characteristics based on certain criteria. The problem that often arises in the clustering process is getting optimal clustering results. So far there is no fixed provision to regulate the number of clusters and the type of data that must be placed in each cluster and also there is no optimal size for data grouping. By using a combinatorial optimization approach, a model that is able to group data optimally was developed. The solution was presented as a decision in the form of 0 and 1. The cluster data model was linearized to obtain cluster optimization. To obtain accurate information from a group of data, the results of this study can be used as an alternative solution for cluster optimization problems.
DUCE: Distributed Usage Control Enforcement for Private Data Sharing in Internet of Things

The emerging Cloud-Enabled Internet of Things (CEIoT) is becoming increasingly popular since it enables end users to remotely interact with the connected devices, which collect real-world data and share with diverse cloud services. The shared data will often be sensitive as well as private. According to the General Data Protection Regulation (GDPR), the privacy issue should be addressed by the cloud services and subsequent data custodians. In this paper, we propose DUCE, an enforcement model for distributed usage control for data sharing in CEIoT. DUCE leverages both blockchain and Trusted Execution Environment (TEE) technologies to achieve reliable and continuous life-cycle enforcement for cross-domain data sharing scenarios. The core components of DUCE are distributed Policy Decision Points (PDPs) and Policy Enforcement Points (PEPs) to enable reliable execution of usage control policies without a centralized trusted authority. Policy administration is also distributed and controlled by the data owner, who can modify the rules anywhere anytime. The policy rules expressed in eXtensible Access Control Markup Language (XACML) are parsed into smart contracts to be executed on the blockchain service. A detailed explanation of the enforcement process is given for an example “delete-after-use” rule. A prototype system is implemented with an open-source permissioned blockchain system and evaluated on an experimental deployment. The results show reasonable performance and scalability overhead in comparison to OAuth 2.0. We believe additional cross-domain data usage control issues can also be addressed by DUCE.
Automated cell header generator for Jupyter notebooks

Jupyter notebooks are now widely adopted by data analysts as they provide a convenient environment for presenting computational results in a literate-programming document that combines code snippets, rich text, and inline visualizations. Literate-programming documents are intended to be computational narratives that are supplemented with self-explanatory text, but, recent studies have shown that this is lacking in practice. Efforts in the software engineering community to increase code comprehension in literate programming are limited. To address this, as a first step, this paper presents a prototype Jupyter notebook annotator, HeaderGen, that automatically creates a narrative structure in notebooks by classifying and annotating code cells based on the machine learning workflow. HeaderGen generates a markdown cell header for each code cell by statically analyzing the notebook, and in addition, associates these cell headers with a clickable table of contents for easier navigation. Further, we discuss our vision and opportunities based on this prototype.
Cyber Security in Cloud: Risk Assessment Models

The present paper shows a proposal of the characteristics Cloud Risk Assessment Models should have and presents the review of the literature considering those characteristics in order to identify current gaps. This work shows a ranking of Cloud RA models and their degree of compliance with the theoretical reference Cloud Risk Assessment model. The review of literature shows that RA approaches leveraging CSA (Cloud Security Alliance) STAR Registry that have into account organization’s security requirements present higher degree of compliance but they still lack risk economic quantification. The myriad of conceptual models, methodologies and frameworks although based on current NIST SP 800:30, ISO 27001, ISO 27005, ISO 30001, ENISA standards could be enhanced by the use of techno-economic models like UTEM, created by the author, in order to conceive more simplified models for effective Risk Assessment and Mitigation closer to the theoretical reference model for Cloud Risk Assessment, available for all cloud models (IaaS, PaaS, SaaS) and easy to use for all stakeholders.