Reviewerly/exHarmony huggingface dataset:
A digital forensics framework for smart settings

Users utilize IoT devices and sensors in a co-operative manner to enable the concept of a smart environment. This integration generate data with high forensic value. Nonetheless, current smart app programming platforms do not provide any digital forensics capability to identify, trace, store, and analyze the data produced in these settings. To overcome these limitations, in this poster, we present our ongoing work to introduce a novel digital forensic framework for a smart environment.
Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets.

The ImageNet dataset ushered in a flood of academic and industry interest in deep learning for computer vision applications. Despite its significant impact, there has not been a comprehensive investigation into the demographic attributes of images contained within the dataset. Such a study could lead to new insights on inherent biases within ImageNet, particularly important given it is frequently used to pretrain models for a wide variety of computer vision tasks. In this work, we introduce a model-driven framework for the automatic annotation of apparent age and gender attributes in large-scale image datasets. Using this framework, we conduct the first demographic audit of the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) subset of ImageNet and the person hierarchical category of ImageNet. We find that 41.62% of faces in ILSVRC appear as female, 1.71% appear as individuals above the age of 60, and males aged 15 to 29 account for the largest subgroup with 27.11%. We note that the presented model-driven framework is not fair for all intersectional groups, so annotation are subject to bias. We present this work as the starting point for future development of unbiased annotation models and for the study of downstream effects of imbalances in the demographics of ImageNet. Code and annotations are available at: this http URL
Collaboratively enhanced consistency checking in a cloud-based engineering environment

Software systems engineering involves many engineers, often from different engineering disciplines. Efficient collaboration among these engineers is a vital necessity. Tool support for such collaboration is often lacking, especially with regards to consistency between different engineering artifacts (e.g., between model and code or requirements and specifications). Current collaboration tools, such as version control systems, are not able to address these cross-artifact consistency concerns. The consequence is unnecessarily complex consistency maintenance during engineering. This paper explores consistent handling of engineering artifacts during collaborative engineering. This work presumes that all engineers collaborate using a joint, cloud-based engineering environment and engineering artifacts are continuously synchronized with this environment. The artifacts can be read and modified by both engineers and analysis mechanisms such as a consistency checker. The paper enumerates different consistency checking scenarios that arise during such collaboration.
Deep Inverse Tone Mapping for Compressed Images

Converting a single low dynamic range (LDR) image into a high dynamic range (HDR) image, which is the so-called inverse tone mapping (ITM), is a challenging ill-posed problem since a lot of information is lost during compression and storage. Traditional ITM techniques mainly focus on high-quality LDR images without compression artifacts. However, in practice, LDR images are usually stored as a lossy compression format for the convenience of transmission, which will cause artifacts, i.e., blocking and ringing. Hence, these ITM methods suffer from severe performance drop for some real-world applications. In this paper, we propose a novel decomposition-based network to reconstruct HDR images from compressed LDR images; in other words, the proposed network can simultaneously remove blocking/ringing artifacts and recover high-quality HDR information. Considering compression artifacts mainly embedded in high frequency part, we thus decompose the input image into the high-frequency component (also known as detail layer) and low-frequency component (also known as a base layer). The detail layer mainly contains the information of texture, noise, and artifacts, while the base layer contains the information of structure and large object. Based on this, we design two subnetworks, i.e., detail layer recovery subnetwork and base layer recovery subnetwork, to restore the two parts separately. The detail layer recovery subnetwork is responsible for the artifacts removal with texture preserving, while the base layer recovery subnetwork is designed for tone expansion and overexposed/underexposed region restoration. Finally, in order to further reconstruct the serious overexposed regions, we adapt a merge subnetwork to fuse the result from the previous two subnetworks. The experimental results on compressed images demonstrate that the proposed method significantly outperforms other state-of-the-art methods.
Medical problem in Asia pacific and ways to solve it: The roles of primary care/family physician (Indonesia Xperience)

WHO reported that Environment contributed significantly as the causal of the disease in Asia Pacific. Indonesia is facing major health challenges, namely triple burden health problems, due to communicable diseases, non-communicable diseases and re-emerging disease. Primary care in Indonesia is conducted by Public Health Centers/Puskesmas and Primary Care Clinic covering around 86% of total health care facilities in Indonesia. Primary Care Physician plays important role as a gatekeeper and care coordinator. Primary care in Indonesia responsible for primary prevention and screening, should also be prepared to deal with various reasons for encounters of patient/community health problems, and conduct comprehensive management including referral management. It is therefore necessary to have teamwork with various health workers and patients/families at all levels of prevention.
Exploring the use of electronic resources by humanities scholars during the research process

Purpose This study aims to examine the types of documents that humanities scholars use in their publications, how humanities scholars use electronic resources during their research, and their opinions are regarding their library’s acquisition policy for electronic and printed resources. Design/methodology/approach A total of 13 university faculty members from the fields of Chinese literature, history and philosophy participated in this study. The documents cited in their publications were analyzed and in-depth interviews were conducted. Findings The results showed that books were the dominant document type cited by the participants. They frequently used electronic resources during the stages of idea generation; document search and collection; and interpretation and writing. If they used an ancient book in an electronic format, they cross-checked it with its printed version to verify the accuracy of the text. Although the participants anticipated that the university library would favour e-journals instead of printed journals, they expected the university library to continue purchasing printed books. Originality/value Many university libraries encounter difficulties in choosing between materials in electronic and printed formats, particularly in the humanities field. The study findings provide a clear understanding of how humanities scholars use electronic resources in the digital age.
Synthetic QA Corpora Generation with Roundtrip Consistency

We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency. By pretraining on the resulting corpora we obtain significant improvements on SQuAD2 and NQ, establishing a new state-of-the-art on the latter. Our synthetic data generation models, for both question generation and answer extraction, can be fully reproduced by finetuning a publicly available BERT model on the extractive subsets of SQuAD2 and NQ. We also describe a more powerful variant that does full sequence-to-sequence pretraining for question generation, obtaining exact match and F1 at less than 0.1% and 0.4% from human performance on SQuAD2.
Toward Accurate and Efficient Emulation of Public Blockchains in the Cloud

Blockchain is an enabler of many emerging decentralized applications in areas of cryptocurrency, Internet of Things, smart healthcare, among many others. Although various open-source blockchain frameworks are available in the form of virtual machine images or docker images on public clouds, the infrastructure of mainstream blockchains nonetheless exhibits a technical barrier for many users to modify or test out new research ideas in blockchains. To make it worse, many advantages of blockchain systems can be demonstrated only at large scales, e.g., thousands of nodes, which are not always available to researchers. This paper presents an accurate and efficient emulating system to replay the execution of large-scale blockchain systems on tens of thousands of nodes. In contrast to existing work that simulates blockchains with artificial timestamp injection, the proposed system is designed to be executing real proof-of-work workload along with peer-to-peer network communications and hash-based immutability. In addition, the proposed system employs a preprocessing approach to avoid the per-node computation overhead at runtime and thus achieves practical scales. We have evaluated the system for emulating up to 20,000 nodes on Amazon Web Services (AWS), showing both high accuracy and high efficiency with millions of transactions.
High dynamic range three-dimensional shape reconstruction via an auto-exposure-based structured light technique

High dynamic range object has large surface reflectivity variations, which easily produces saturated and low-contrast regions in the captured images, further resulting in large calculation errors of gray value or phase value, then seriously affecting the reconstruction accuracy. An auto-exposure-based structured light technique is proposed. In this method, the relationship between noise-induced phase error and intensity modulation is first analyzed. It is demonstrated that once the intensity modulation of a pixel is larger than a threshold, its phase error can be considered acceptable. Then, the surface reflectivity of the measured target is estimated by projecting a series of uniform gray-level patterns and classified into several groups. The exposure time for each group is automatically determined after establishing its mathematical model based on the modulation threshold. Phase-shifted images are then captured at the computed exposure times, and a set of composite phase-shifted images is acquired by extracting the brightest unsaturated pixels in the raw fringe images. The experiments show that the proposed method can automatically calculate the exposure times and capture the fringe images without human intervention. In addition, it can well tackle saturated and dark areas of the measured target and the reconstructed three-dimensional shape is complete and accurate.
Big Data and HPC Convergence for Smart Infrastructures: A Review and Proposed Architecture

The world has seen exponential data growth due to social media, mobility, E-commerce, and other factors. The issues related to avalanche of data being produced are immense and cover variety of challenges that need a careful consideration. The use of HPDA (High Performance Data Analytics) is increasing at brisk speed in many industries and has resulted in expansion of HPC market in many new territories. HPC (High Performance Computing) and big data are different systems, not only at the technical level, but also have different ecosystems. HPC systems are mainly developed for computationally intensive applications but recently data intensive applications are also among the major workload in HPC environment. Big data analytics have grown in different perspectives and have separate developer communities. As we head towards the exascale and smart infrastructure era, the necessary integration of big data and HPC is currently a hot topic of research but still at very infant stages. Both systems have different architecture and their integration brings many challenges. The aim of this work is to identify the driving forces, challenges, current and future trends associated with the integration of HPC and big data. This paper is an extension of our earlier work. We have reviewed programming models and frameworks of big data and HPC. The big data and HPC challenges in the exascale-computing era are discussed. Additional elaborations are provided on HPC and big data convergence research efforts and future directions are provided. The HPC-big data convergence architecture proposed in our earlier paper has been enhanced.
A modelling framework for collaborative network emotions

Recent research in collaborative networks have shown that the development of "human-inspired" friendly systems with cognitive models of factors such as stress, trust, decision-making ability, and emotion can be seen as promising approach to prevent technological failures and, consequently contribute to their sustainability. Accordingly, an human-emotions-based approach is proposed aiming at conceptualizing the idea of collaborative network players "having" and "feeling" emotions, helping in the management of potential collaboration conflicts, decision-making, and collaborative network governance. This paper presents the underlying theoretical background to support emotional processes within collaborative networks and the description of the proposed modelling framework – the Collaborative EMOtion modelling framework (C-EMO).
MultiPLZW: A novel multiple pattern matching search in LZW-compressed data

Searching encrypted or compressed data provides security and privacy without sacrificing efficiency. It has many applications in cloud storage, bioinformatics, IoT, unmanned aerial vehicles and drones. This paper introduces a novel, simple, and efficient algorithm to locate all occurrences of a set of patterns in LZW-compressed data, in a single pass. The algorithm comprises a preprocessing phase and a subsequent search phase. It uses a modified version of the generalized suffix tree, a lookup table, a mapping table, and a history tree. The proposed algorithm is superior in terms of the time complexity, while maintaining a space complexity of the same order as the best of existing algorithms. The time complexity is O(n+m+r), which is proportional to the length of the LZW-compressed data, where n is the length of the compressed data, m is the total size of the patterns, and r is the number of pattern occurrences in the compressed data. The space complexity is O(m2+t+r), where t is the size of the dictionary table that is used during compression. Experimental results show a significant improvement in search time, approximately twice as fast, compared to decompressing and then searching using Aho–Corasick algorithm. Also, results on various dataset sizes, demonstrate the algorithm’s superior scalability, which improves as the size of the dataset increases.
Rate-Accuracy Trade-Off in Video Classification With Deep Convolutional Neural Networks

Advanced video classification systems decode video frames to derive texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs). However, when considering visual Internet-of-Things applications, surveillance systems, and semantic crawlers of large video repositories, the video capture and the CNN-based semantic analysis parts do not tend to be co-located. This necessitates the transport of compressed video over networks and incurs significant overhead in bandwidth and energy consumption, thereby significantly undermining the deployment potential of such systems. In this paper, we investigate the trade-off between the encoding bitrate and the achievable accuracy of CNN-based video classification models that directly ingest AVC/H.264 and HEVC encoded videos. Instead of retaining entire compressed video bitstreams and applying complex optical flow calculations prior to CNN processing, we only retain motion vector and select texture information at significantly reduced bitrates and apply no additional processing prior to CNN ingestion. Based on three CNN architectures and two action recognition datasets, we achieve 11%-94% savings in bitrate with marginal effect on classification accuracy. A model-based selection between multiple CNNs increases these savings further to the point where, if up to 7% loss of accuracy can be tolerated, video classification can take place with as little as 3 kb/s for the transport of the required compressed video information to the system implementing the CNN models.
Gaussian Process Latent Variable Alignment Learning

We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner. Our proposed method casts alignment learning in a framework where both alignment and data are modelled simultaneously. Further, we automatically infer groupings of different types of sequences within the same dataset. We derive a probabilistic model built on non-parametric priors that allows for flexible warps while at the same time providing means to specify interpretable constraints. We demonstrate the efficacy of our approach with superior quantitative performance to the state-of-the-art approaches and provide examples to illustrate the versatility of our model in automatic inference of sequence groupings, absent from previous approaches, as well as easy specification of high level priors for different modalities of data.
The pathway to academic success: Scaling up a text-based analytical writing intervention for Latinos and English learners in secondary school.

This study reports findings from a multisite cluster randomized controlled trial designed to validate and scale up an existing successful professional development program that uses a cognitive strategies approach to text-based analytical writing. The Pathway to Academic Success Project worked with partner districts affiliated with 4 National Writing Project (NWP) sites in southern California. Informed by a wide body of research on the efficacy of strategy instruction to enhance students’ academic literacy, the intervention aimed to help secondary school students, particularly Latinos and mainstreamed English learners, to develop the academic writing skills called for in the rigorous Common Core State Standards for English Language Arts. Two hundred thirty teachers from partner districts affiliated with the NWP sites were stratified by school and grade and then randomly assigned to the treatment or control group. Treatment teachers participated in 46 hrs of training and learned how to apply cognitive strategies by using an on-demand writing assessment to help students understand, interpret, and write analytical essays about nonfiction texts. Multilevel models revealed significant effects on a holistic measure of an on-demand writing assessment (d = .32) as well as on 4 analytic attributes: content (d = .31), structure (d = .29), fluency (d = .27), and conventions (d = .32). Four dimensions of scaling up—spread, reform ownership, depth, and sustainability—are also discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved)
Spectral sparsification of hypergraphs

For an undirected/directed hypergraph G = (V, E), its Laplacian LG: RV → RV is defined such that its form x⊤LG(x) captures the cut information of G. In particular, 1⊤SLG(1S) coincides with the cut size of S ⊆ V, where 1S ∈ RV is the characteristic vector of S.A weighted subgraph H of a hypergraph G on a vertex set V is said to be an ϵ-spectral sparsifier of G if (1-ϵ)x⊤Lh(x) ≤ x⊤LG(x) ≤ (1 + ϵ)x⊤Lh(x) holds for every x ∈ RV. In this paper, we present a polynomial-time algorithm that, given an undirected/directed hypergraph G on n vertices, constructs an ϵ-spectral sparsifier of G with O(n3 log n/ϵ2) hyperedges/hyperarcs.The proposed spectral sparsification can be used to improve the time and space complexities of algorithms for solving problems that involve the quadratic form, such as computing the eigenvalues of LG, computing the effective resistance between a pair of vertices in G, semi-supervised learning based on LG, and cut problems on G. In addition, our sparsification result implies that any nonnegative hypernet-work type submodular function can be concisely represented by a directed hypergraph of polynomial size, even if the original representation is of exponential size. Accordingly, we show that, for any distribution, we can properly and agnostically learn nonnegative hypernetwork type submodular functions with O(n4 log(n/ϵ)/ϵ4) samples.
Learning Hyperedge Replacement Grammars for Graph Generation

The discovery and analysis of network patterns are central to the scientific enterprise. In the present work, we developed and evaluated a new approach that learns the building blocks of graphs that can be used to understand and generate new realistic graphs. Our key insight is that a graph's clique tree encodes robust and precise information. We show that a Hyperedge Replacement Grammar (HRG) can be extracted from the clique tree, and we develop a fixed-size graph generation algorithm that can be used to produce new graphs of a specified size. In experiments on large real-world graphs, we show that graphs generated from the HRG approach exhibit a diverse range of properties that are similar to those found in the original networks. In addition to graph properties like degree or eigenvector centrality, what a graph “looks like” ultimately depends on small details in local graph substructures that are difficult to define at a global level. We show that the HRG model can also preserve these local substructures when generating new graphs.
New Tools for Finding Full-Text Articles Faster: Kopernio, Nomad, Unpaywall, and More

Portable document format (PDF) electronic files are by far the most popular format used to publish, store, and share scholarly journal articles. Recently, new tools for quickly finding and linking to journal article PDFs have been developed. These tools integrate into the researcher’s web browser and scan for unique identifiers associated with a journal article. When a unique identifier is found, the tool attempts to locate a PDF copy of the associated article and provide a link. This article will explore how these full-text finders work and discuss the advantages and disadvantages of these tools. Descriptions of several of the most popular tools are provided, and a short discussion of how librarians can facilitate the use of these tools at their institutions is included.